catalyst journal of the institute for interdisciplinary

CATALYST

Asia-Pacific International University

Volume 12 5 6- 23

24 - 33

JOURNAL OF THE INSTITUTE FOR INTERDISCIPLINARY STUDIES

Number 2

December 2015

Editorial - Damrong Sattayawaksakul Lifelong Learning for Personal and Professional Development in Malaysia

Su-Hie Ting, Siti Halipah Ibrahim, Rohaida Affandi, Azhaili Baharun, Wan Azlan Wan Zainal Abidin, Edmund Ui-Hang Sim

The Correlation Between Student’s Academic Achievement and Ethical and Moral Activities Involvement in a Christian Institution Nakhon Kitjaroonchai

34 - 43

Predicting Student Academic Achievement by Using the Decision Tree and Neural Network Techniques Pimpa Cheewaprakobkit

44 - 55 56 - 62

Risk Factors for Hypertension among a Church-based, Black Population in London

Maxine A Newell, Naomi N Modeste, Helen Hopp Marshak, Colwick Wilson, Sherma J Charlemagne-Badal

Knowledge, Attitude and Practice of Dengue Fever Prevention among the Villagers of Moo 1 Baan Klongsai, Nhongyangsuea Subdistrict, Muaklek District, Saraburi Province, Thailand Supatcharee Makornkan, Pornpan Saminpanya, Ampaiwan Toomsan, Poomarin Intachai, Panipha Saengproa, Daramas Marerngsit

63 - 67

Analysis of Customer Satisfaction by Perceived Leadership Practices and Front-line Staff Performance in Selected Public Sector Agencies in Central Manchester: A Multivariate Approach Sandra Tomlinson and Risper A Awuor

68 - 76

Decreasing Anxiety among Communication Arts EFL students Through Peer Teaching and Activities Jeffrey Dawala Wilang and Atita Satitdee

77 - 84

Integrating English to Science Teacher Training Classroom Supat Sairattanain

85 - 93

A Study of the Learning Strategies Used by Business Students at Asia-Pacific International University, Thailand Esther Hungyo

94 - 102

The Perceived Effects of Internet Usage on Academic Achievement among Southeast Asian College Students Gabby Jed Catane Galgao

CATALYST, Journal of the Institute for Interdisciplinary Studies, Asia-Pacific International University Online ISSN 2408-137X Editor Damrong Sattayawaksakul, Asia-Pacific International University, Thailand Managing Editor Assistant Professor Dr Joy C Kurian, Asia-Pacific International University, Thailand Assistant Editor/Copy Editor: Daron Benjamin Loo, Asia-Pacific International University, Thailand Administrative Board Dr Jarurat Sriratanaprapat, Director of Research Department, Asia-Pacific International University, Thailand Dr Pak T Lee, Director of MBA Program, Asia-Pacific International University, Thailand Yuan Yuan Huo, Research Associate, Asia-Pacific International University, Thailand Design and Layout May Su Thwe Mang, Institue for Interdisciplinary Studies, Asia-Pacific International University, Thailand Editorial Board Professor Dr Beulah Manuel Assistant Professor Dr Joy C Kurian Dr Chayada Thanavisuth Dr Oktavian Mantiri Dr Wayne Hamra Reviewers Professor Dr Elizabeth Role Professor Dr Gilbert Valentine Professor Dr Jimmy Kijai Professor Dr Reuel Almocera Professor Dr Siroj Sorajjakool Associate Professor Dr Bienvenido Mergal Associate Professor Dr Edelweiss Ramal Associate Professor Dr Evelyn V Almocera Associate Professor Dr James Park Associate Professor Dr Richard Apelles Sabuin Associate Professor Dr Safary Wa-Mbaleka Assistant Professor Dr Joy C Kurian Assistant Professor Dr Ragui Paoring L Assistant Professor Thanis Bunsom Dr Darrin Thomas Dr Chayada Thanavisuth Dr Daniel Bedianko Dr Oktavian Mantiri

Washington Adventist University, USA Asia-Pacific International University, Thailand Assumption University, Thailand Asia-Pacific International University, Thailand Asia-Pacific International University, Thailand University of Eastern Africa, Baraton, Kenya La Sierra University, USA Andrews University, USA Adventist International Institute of Advanced Studies, the Philippines Loma Linda University, USA Adventist International Institute of Advanced Studies, the Philippines Loma Linda University, USA Adventist International Institute of Advanced Studies, the Philippines Adventist International Institute of Advanced Studies, the Philippines Adventist International Institute of Advanced Studies, the Philippines Adventist International Institute of Advanced Studies, the Philippines Asia-Pacific International University, Thailand Adventist International Institute of Advanced Studies, the Philippines King Mongkut’s University of Technology Thonburi, Thailand Asia-Pacific International University, Thailand Assumption University, Thailand Valley View University, Ghana Asia-Pacific International University, Thailand

Dr Pak T Lee Dr Gerald Schafer Jariya Sudtho Stuart G Towns Daron Benjamin Loo Veraliza Kirilov Valentino Junior Milton Gumbilai Parinda Jantori

 

Asia-Pacific International University, Thailand Carroll University, USA Sisaket Rajhabat University, Thailand Walden University, USA Asia-Pacific International University, Thailand Asia-Pacific International University, Thailand Kyoto University, Japan King Mongkut’s University of Technology Thonburi, Thailand

Editorial Statement CATALYST is the flagship journal of Asia-Pacific International University (AIU). It is an inter-disciplinary, peer-reviewed journal published by AIU’s Institute for Interdisciplinary Studies through its publishing arm, Institute Press. The journal is published online with a limited number of hard copies available. Scope of CATALYST As an interdisciplinary journal, CATALYST brings together articles in several areas of the humanities and social sciences such as religion, education, arts and humanities, and business, as well as social science research in other disciplines.

Objectives of CATALYST 1. To facilitate scholarly activity among the faculty of AIU 2. To engender scholarly exchanges with other universities within Thailand and with visiting lecturers, pastors and teachers from other parts of the world 3. To encompass scholarly as well as professional articles, seminar/ forum papers, research papers and book reviews

Publishing Schedule CATALYST is published biannually by Institute Press during the months of June and December.

Indexing ASEAN Citation Index (ACI) and Thailand Citation Index (TCI), EBSCO and CAR

Submission Procedure 1. Manuscripts should be in MS Word format and should relate to one of the relevant disciplines listed in “focus and scope”. 2. Manuscripts should adhere to the Catalyst Publishing Guidelines; failure to comply with the guidelines may result in the rejection of a submission. 3. Manuscripts should be submitted through the online submission system found in “submission request” 4. Manuscripts should be submitted by the last day of February for the June issue, or the last day of August for the December issue.

Current and Past Issues Volume 11 June 2015 Volume 10 December 2014 Volume 9 July 2014 Volume 8 December 2013 Volume 7 December 2012 Volume 6 December 2011 Volume 5 December 2010 Volume 4 December 2009 Volume 3 December 2008 Volume 2 December 2007 Volume 1 December 2006

Contact information Damrong Sattayawaksakul Email: [email protected] Tel: +66-36-720777 ext 1239 May Su Thwe Mang Email: [email protected] Tel: +66-36-720777 ext 1504 All opinions, errors, omissions and such expressed in Catalyst are the responsibility of the authors. © Institute Press, Asia-Pacific International University, 2015

Editorial This year marks the 10th year for Catalyst. It has served as a flagship interdisciplinary peer-reviewed journal of Asia-Pacific International University (AIU). It is my privilege to inform that the journal has recently been promoted to Level 1 of the Thai Journal Citation Index (TCI). Moreover, the journal was accepted and approved by the ASEAN Citation Index (ACI) Steering Committee on September 10, 2015, to be included in the ACI database. The ACI database is the first and the only regional citation index database for ASEAN member nations. This means that Catalyst is ranked at the top tier academic journal index in Thailand and in ASEAN nations. We appreciate all the authors and our staff who have contributed to make this achievement a reality. In this issue, there are five qualified articles submitted from various researchers external to the university and there are five qualified articles submitted by the researchers of the university. We are delighted to see reports and findings of various content areas, including education, administration, business, language and psychology. We hope this issue of Catalyst will be a contribution to the academic and professional development of the society and a source of information for various disciplines and researches. We would like to express our sincere gratitude to all authors, reviewers, editorial board members, executive board members, as well as journal staff for their contributions to this issue of Catalyst. Last but not least, we would like to invite our readers to publish your valuable paper with us. You can find more information at our website, http://www.apiu.edu/catalyst-issues. We would also appreciate comments or suggestions from you to help us improve Catalyst. Damrong Sattayawaksakul

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Catalyst ISSN: 2408-137X, Volume 12, No. 2, 2015 Institute Press

Lifelong Learning for Personal and Professional Development in Malaysia Su-Hie Ting, Siti Halipah Ibrahim, Rohaida Affandi, Azhaili Baharun, Wan Azlan Wan Zainal Abidin, Edmund Ui-Hang Sim

Abstract The study examined economic and non-economic benefits of non-formal lifelong learning for participants. A survey of 1,923 participants of non-formal lifelong learning programmes offered by six ministries in Malaysia showed that 50% participated in programmes that are related to the jobs and 50% participated in non job-related programmes. In the category of job-related lifelong learning programmes, participants of technical skills-based programmes are the most likely to enjoy salary increment and promotion. For others, the employment benefits are in the form of additional opportunities for training and increased job responsibilities. Besides bringing about personal development, non job-related lifelong learning programmes also endowed participants with useful skills and knowledge to earn additional income, get a job, and set up small businesses. For lifelong learning programmes to bring about better economic returns, the findings indicate that the programmes need to be structured based on skill levels (basic to advanced) and market surveys need be conducted to determine industry needs. Keywords: non-formal lifelong learning, economic returns, personal development, professional development

Introduction Lifelong learning refers to learning throughout life. Lifelong learning is not restricted to learning in formal education systems and includes vocational learning as well as “learning leading to self-development or self-actualisation” (Cropley, 1980, p. 2). This means that self-directed learning activities are pursued not only for professional development but also for personal development. In this paper, lifelong learning for professional development is defined as learning of new skills and knowledge for the purpose of career advancement whereas personal development encompasses personal growth in self-esteem, knowledge and skills as well as networking. Adult learning, a synonym for lifelong learning more commonly used in Europe, is conceptualised as having six characteristics: (1) voluntary participation; (2) respect for self-worth; (3) collaboration; (4) praxis [practice]; (5) fostering of a spirit of critical reflection; and (6) an aim of nurturing self-directed, empowered adults (Brookfield, 1985). In countries like the United States and Australia, lifelong learning is also referred to as continuing education. For example, the Institute of Continuing and TESOL Education at the University of Queensland offers academic, technical and vocational programmes of two to seven weeks for international students and professionals (The University of Queensland, 2015). Vocation-related training tends to be prioritised by policy makers in allocation of resources (Tight, 1998a, 1998b) and the focus is on the returns from the investment in lifelong learning (Cohn & Addison, 1998; Jenkins, Vignoles, Wolf, & Galindo-Rueda, 2003). However, in recent years, employers in Scotland have changed their priority from supporting lifelong learning to apprenticeship (Lowe & Gayle, 2015). Benefits of lifelong learning Lifelong learning enables individuals to acquire useful skills which increase their employability. This is one of the main findings of Jenkins et al.’s (2003) study. The data for this study were drawn from the National Child Development Study conducted in Great Britain. The 5,127 respondents in Jenkins et al.’s study were tracked 6

from the time they were 7 years old. The data used for analysis were from the 1991 and 2000 surveys when the respondents were 33 and 42 years old respectively. The findings showed that male respondents who left school earlier had a higher likelihood of finding jobs if they participated in lifelong learning programmes but other respondents were hardly rewarded with salary increments despite their participation in lifelong learning programmes. Jenkins et al. were of the view that in the 1990s, Great Britain had not promoted lifelong learning as a means to improve the economic situation of individuals and the respondents might have joined lifelong learning programmes for personal enjoyment or to fulfil requirements by their organisation rather than to obtain work-related benefits. Nevertheless, other researchers have found that lifelong learning brings about work-related benefits. Similar to Jenkins et al. (2003), Rothes, Lemos, and Goncalves (2014) found that lifelong learning is more likely to benefit unemployed male respondents with a lower level of education in Portugal. These respondents were more extrinsically motivated to participate in lifelong learning programmes as they believed that they would improve in their work status and economic situation. However, their dropout rate was high. Rothes et al.’s (2014) study involved 310 adult students registered in three types of courses: short courses (50-175 hours), vocational courses (1-2.5 years) and non-vocational courses (4 years). These findings concur in showing that the skills and knowledge acquired from lifelong learning programmes can enhance employment prospects for the respondents who fall into the category of unskilled workers (see also Daehlen & Ure, 2009; Konrad, 2005). Consistent with this, older adults above 45 years old are more inclined to believe that they would not derive much benefits from participating in lifelong learning programmes and they also receive less support from their organisation to participate in these programmes (Kyndt, Michielsen, Van Nooten, Nijs, & Baert, 2011). Since younger individuals are more likely to benefit from lifelong learning, they make up a larger proportion of the participants of lifelong learning programmes. Most of the programmes are vocation-related and participants are motivated by hopes of using the newly acquired skills and knowledge for employment purposes (Awuor & Parks, 2015; Lowe & Gayle, 2015). Besides lifelong learning for employability, lifelong learning can be beneficial for personal growth. An example of personal growth resulting from lifelong learning is improved self-esteem, and individuals with a higher level of education are more likely to value this than those with a lower level of education (Berker & Horn, 2003). Besides gains in self-esteem, older adults who have worked and return to pursue higher education reported acquisition of new competencies, developing pre-acquired competencies, developing adaptation skills and career progression – all of which are associated with professional benefits (Ambrósio, Sá, & Simões, 2014). However, the 195 adult students pursuing university degrees in Public Administration, Languages and Business Relations, and Information Technology in a Portuguese university also valued their personal development in terms of learning new languages. Benefits of lifelong learning for older adults who have retired from active work takes on a different meaning. Many of the recent studies on lifelong learning in developed countries revealed that lifelong learning is crucial for a successful aging process (e.g., Zunzunegui, Alvarado, Del Ser, & Otero, 2003). Community well-being for older adults is among the benefits of lifelong learning (Borges & Roger, 2014; Merriam & Kee, 2014). Improvement in mental health is another benefit of lifelong learning for the older adults (Hammond, 2004). Hammond interviewed 145 respondents and 12 instructors and group leaders of lifelong learning in Essex, Nottingham and North London on the effects of lifelong learning on the respondents. The study showed that the older adults had better self-esteem, communication and social integration after participating in lifelong learning programmes. They were also more competent and effective and experienced a faster recovery from their mental health problems. In developed countries where the percentage of older adults is larger (World Economic Forum, 2012), lifelong learning is important in ensuring and enhancing the well-being and quality of life of the aging population. The benefits of lifelong learning for professional and personal development are clear from the literature. In fact, Majhanovich and Napier (2014) went as far as saying that lifelong learning is the characteristic of the society in the 21st century. Merriam and Kee (2014) gave Thailand as an example of a country which has succeeded in enculturating lifelong learning for the whole country. In Thailand, lifelong learning is required by the law and the Office of Non-formal and Informal Education (ONIE) plays an important role in promoting lifelong learning and creating a learning society. Access to lifelong learning opportunities Research has indicated that there are some groups who derive greater benefits from their participation 7

in lifelong learning, and scholars such as Cross (1981) and Riddell, Weedon, and Holfod (2014) have raised the question of equity of access of lifelong learning opportunities. In Australia, socio-economic status is apparently a strong predictor of participation in lifelong learning. Gorard, Rees, and Ralph’s (1999) survey of 1,104 adults in New South Wales, Australia showed that socio-economic status has a stronger influence than educational level in determining who participates in lifelong learning (see also Belanger & Valdivielso, 1997; Field, 2000). However, in the United States, socio-economic status influences completion of lifelong learning programmes rather than enrolment in these programmes. For instance, Anderson and Darkenwald (1979a) reported that participation is affected by age and educational level but not by the socio-economic status of the participants In their study involving 79,631 adults, 11.5% had participated in adult education [lifelong learning]. However, Anderson and Darkenwald (1979a) found that the drop-out rate among young Afro-American participants from lower socio-economic groups was four times higher than other groups. In another report, Anderson and Darkenwald (1979b) presented the profile of a typical part-time student based on their analysis of the 1975 National Center for Educational Statistics/Bureau of Census survey data, that is, a woman aged 25-34 who is married but does not have children, has completed a college education, is working full-time and has a family income of US$15,000-20,000 per year. The part-time student is likely to be a teacher rather than one working in the health and finance industries as the percentage of participation in lifelong learning among these occupational groups is lower. Houle’s (1988) study in Chicago confirm that most of the participants of lifelong learning programmes are in the age range of 20s to 50s, and tend to have a lower level of education. In the interviews, Houle learnt that the respondents were more interested in vocational courses but some of them were motivated by the opportunity to meet others and develop social networks. Houle identified the following factors as important in affecting their participation in lifelong learning: job, educational level, family background, influence of peers, teacher, school and the library. Houle’s findings suggest that besides the socio-demographic variables, the publicity on lifelong learning programmes influences participation. In Malaysia, lifelong learners tend to be younger. Since community colleges have been established under the Ministry of Higher Education for the purpose of enculturating lifelong learning, this review will focus on lifelong learning programmes offered by community colleges – and partly because information on lifelong learning programmes offered by other sectors are not readily available. As an example, most of the 152 participants of lifelong learning programmes conducted by Mas Gading Community College in Sarawak are in their twenties (Amdan, Abdullah, & Johan, 2014). Of these 55% are not employed whereas 23% were working government departments, indicating that a large number of the participants were hoping to use the skills and knowledge acquired either to seek employment or to improve their employment prospects. This deducation is supported by information on their earnings. The monthly salary of 66% of the respondents were below RM1000, which is just above the minimum wage of RM800 in West Malaysia and RM900 in East Malaysia (Sabah and Sarawak). The results of another study (Lee & Michael, 2014) also show that the participants of a computer software application course were planning to use their skills to either seek employment or further their studies. However, they also reported gains in personal development such as interaction and communication skills, and problem solving and decision-making skills. Yet another study conducted in Miri Community College in Sarawak showed that most of the lifelong learners were below 40 years old and hoping to use the new skills and knowledge for work and business purposes (Chong & Abdul Rahman, 2014). Their survey involved 375 out of 11,227 participants of lifelong learning programmes offered between 2011 and 2014. Their results showed that 52% were 25-40 years old and 46% were SPM school leavers (equivalent to Year 12). Nevertheless, there are some programmes for retirees of the armed forces to prepare them for a smooth transition to alternative work or entrepreneurship activities (Mohd Zaitun, Mohd Khalil, & Dady, 2014). In fact, based on this review, it can be concluded that where lifelong learning in Malaysia is concerned, most of the participants of lifelong learning programmes are also in the younger age group, have a lower level of education, and tend to be unemployed – not that different from the United States. Background on lifelong learning in Malaysia In the United States, Europe and Australia, lifelong learning has been promoted since the 1980s and 1990s but it is a recent emphasis by the Malaysian government. For Malaysia to become a developed nation by the year 2020, lifelong learning is seen as a necessary investment to move towards a knowledge-based economy in the information communication technology era. To ensure realisation of a knowlege-based economy, the government has invested in education and human capital training, particularly in lifelong learning (Mustapha & Abdullah, 8

2006). The sixth thrust of the National Higher Education Strategic Plan (NHESP) is enculturation of lifelong learning. Lifelong learning enables individuals to reskill and upskill, and in the process gain socio-economic benefits. Based on the blueprint of lifelong learning for Malaysia 2011-2020, lifelong learning is defined as learning undergone by individuals aged 15 and above with the exception of professional students (Blueprint on enculturation of lifelong learning in Malaysia 2011-2012, Ministry of Higher Education Malaysia, 2012a). Professional students are defined as full-time students in school, college, training institutions and universities with the goal of entering the workplace for the first time after the studies. Enculturation of lifelong learning is implemented through various ministries. For example, under the Ministry of Higher Education Malaysia, community colleges are specifically set up for the enculturation of lifelong learning. As of June 2009, 56,056 learners have been enrolled in short-term courses at community colleges (Ministry of Higher Education Malaysia, 2012b, p. 11). The latest report in Utusan Melayu (2014) shows that more than 1.3 million Malaysians have benefitted from the lifelong learning courses offered by the 90 community colleges, and the groups benefitting are the senior citizens, police personnel, women, and the disabled. As projected by Ministry of Higher Education Malaysia (2012), there is an increase in the number of lifelong learners in Malaysia. The main investor in lifelong learning in Malaysia is the government although funding mechanisms have been put in place for the industry to offer lifelong learning programmes (e.g., The Human Resources Development Fund). Over the years, the government has increased investment in lifelong learning programmes through various ministries and government agencies. The socio-economic indicators pooled from various economic reports showed an increase in allocated budget for education and training from 23,058 million in 2005 to 37,668 million in 2009 (Ministry of Higher Education, 2012, p. 11). An example is Skim Latihan 1Malaysia implemented in 2011. The investment of 100 million brought about a spike in the number of participants in lifelong learning programmes. Since the primary role of lifelong learning is “for Malaysia to come out of the middle income trap it is in, its people from the lower education level need to have their qualification upgraded” (Ministry of Higher Education, 2012, p. 12), it is important to find out whether the lower income group has access to upskill and reskill through lifelong learning programmes. While government agencies may collect feedback from participants at the end of lifelong learning programmes, the data are restricted; the data collected may vary from agency to agency. Therefore, there is a need for a large scale study of the impact of lifelong learning programmes on participants. The findings of the study would provide a database on the impact of lifelong learning programmes for reference in policy formulation human capita development in the context of Malaysia’s goal to achieve the status of a high income nation and a knowledge-based economy by the year 2020. Purpose of the Study The study examined the benefits of non-formal lifelong learning for personal and professional development in Malaysia. The specific objectives of the study were to: 1. determine economic and non-economic benefits from job-related lifelong learning programmes; 2. determine economic and social benefits from non job-related lifelong learning programmes; and 3. examine participants’ suggestions for improvement of lifelong learning programmes. In this paper, the term “lifelong learning programmes” will be used to refer to non-formal programmes (Direktori Pembelajaran Sepanjang Hayat Peringkat Nasional 2012/2013, Ministry of Higher Education Malaysia, 2012) which encompass: • Cluster 2 (technical skill-based course less than 6 months), and • Cluster 3 (self-development courses that do not lead to award of formal qualifications) Non-formal lifelong learning programmes do not include Cluster 1 which encompasses part-time study programmes at certificate, diploma, degree and postgraduate levels, including e-learning (e.g., Long distance degree programmes offered by Universiti Sains Malaysia). Non-formal lifelong learning programmes also do not include Cluster 4 which refers to part-time study programmes for adult learners at certificate, diploma, degree and postgraduate levels. For this study, lifelong learning programmes are for participants aged 15 and above. 9

Methodology Research Design For this study, a survey research design was chosen to study large scale patterns of lifelong learning patterns in Malaysia. Surveys can study a big population at relatively low cost and in a shorter time compared to qualitative designs that delve into individual experiences. “In fact, survey research is often the only means available for developing a representative picture of the attitudes and characteristics of a large population” (Check & Schutt, 2012, p. 160). The survey covered the 2013-2014 period and involved participants of lifelong learning programmes offered by six ministries as follows: 1. Kementerian Sumber Manusia (KSM, Ministry of Human Resources), 2. Kementerian Kemajuan Luar Bandar dan Wilayah (KKLW, Ministry of Rural and Regional Development), 3. Kementerian Pembangunan Wanita, Keluarga dan Masyarakat (KPWKM, Ministry of Women, Family and Community Development), Kementerian Pendidikan Tinggi (KPT, Ministry of Higher Education), 4. Kementerian Pertanian dan Industri Asas Tani (KPIAT, Ministry of Agriculture and Agro-based Industry), and 5. Kementerian Belia dan Sukan (KBS, Ministry of Youth and Sports). The headquarters of the six ministries of the federal government are based in Kuala Lumpur and Putrajaya but there are branch offices in each state in Malaysia. The agencies offering the lifelong learning programmes are also found all over Malaysia, and therefore the participants of the study are from all parts of Malaysia. Sample selection The minimum sample size was determined based on the database provided by the six ministries who were requested to supply 10 participant's contact details for each of their lifelong learning programmes offered in 2013-2014 to Planning, Research and Policy Coordination Division, Ministry of Education. Table 1 shows the minimum sample size for the six ministries based on 95% confidence level and response rate of 70% based on a review of existing literature on survey response rates. Social science research traditionally rely on 95% confidence level (Kellstedt & Whitten, 2013). The response rate for telephone surveys in Western settings had decreased over the years. Curtin, Presser, and Singer (2005) reported that the return rate for the General Social Survey in the United States was in the range of 73.5%-82.4% in the years 1975-1998 and 70.1% in the year 2002. Other researchers stated that even with the best effort, it is too difficult and expensive to obtain survey response rates exceeding 70% (Nulty, 2008). For mailed questionnaires, the accepted response rate in social surveys is 50% (Babbie, 1973; Kidder, 1981; Richardson, 2005). Based on the literature review, the response rate for the present study was fixed at 70%, and this was used for the calculation of the minimum sample size to target for each of the six ministries. Table 1. Sample size calculation for survey of lifelong learning participants

KPM KKLW KSM KPIAT KPWKM KBS Jumlah

Given database for survey1 200,000 123 1,267 70 82 773 202,265

Minimum number2 323 90 258 58 66 228 1,023

Targeted sample size3 1,110 90 400 60 70 250 1,980

Actual sample size after survey 1,033 102 403 66 82 237 1,923

Number of participants with complete contact details Minimum number calculated based on 95% dan 99% confidence levels and response rate of 70% 3 Targeted sample size took account of the larger database provided by some ministries to obtain survey data on a larger range of courses offered by these ministries 1 2

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The sample size calculator from National Business Research Institute was used (https://www.nbrii.com/ our-process/sample-size-calculator/) to calculate the targeted sample sizes for the six ministries. This sample size calculator was designed for studies intended for decision-making, which fits the purpose of the present study because findings on social and economic benefits gained by participants of Malaysian lifelong learning programmes can be used for policy making. Some modifications were made to survey a larger sample size for ministries which provided a larger database to obtain data for a larger range of courses. The outcome of the survey was 1,923 participants from lifelong learning programmes offered by the six selected ministries in 2013-2014. Participant description Out of 1,923 participants, 46% were male and 54% were female (Figure 1). KPIAT had a gender balance in the participants attending lifelong learning programmes in 2013-2014 but there were more female participants for KPM and KPWKM and more male participants for KKLW, KSM and KBS programmes. The gender proportion depends on the type of programmes. For example, KPWKM offers programmes on making various kinds of food which cater to a female audience whereas KBS programmes on electrical wiring and power maintenance are mainly attended by men.

Figure 1. Percentage of male and female participants for lifelong learning programmes offered by the six selected ministries Most of the lifelong learning programme participants were 20-29 years old (Figure 2). The participation is the lowest among the 15-19 and more than 60 years old groups. KPIAT programmes attracted participants in the 50-59 age group. Because of the small percentage of retirees, lifelong learning for community well-being for older adults is not relevant (see Borges & Roger, 2014; Merriam & Kee, 2014).

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Figure 2. Percentage of lifelong learning participants classified according to age group for the six selected ministries A majority of the participants of lifelong learning programmes were Malay (93%, Figure 3), and the pattern is similar across ministries. Only 3% were Chinese, 3% Indian, and 3% other Indigenous. The Malay is the majority ethnic group in Malaysia accounting for 50.33% of the 30.26 million population, followed by the Chinese (21.76%), Other Indigenous (11.80%) and Indian (6.52%) (Department of Statistics Malaysia, 2014).

Figure 3. Percentage of participants by ethnic group for lifelong learning programmes offered by the six selected ministries Among the 1,923 participants surveyed, 33.94% held white collar jobs such as managers and teachers whereas 40.37% had blue collar jobs such as technicians, clerks and welders, and 21.30% were unemployed (Figure 4). Going by ministry, KBS and KPM programmes catered to blue collar workers whereas KPWKM programmes catered to white collar workers. In this study, job is used as an indicator of socio-economic status. According to Figueiredo and Elkins (2002), social status can be determined from their job, prestige of the job, income and self-evaluation.

Figure 4. Percentage of blue and white collar jobs held by participants of lifelong learning programmes offered by the six selected ministries

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Instruments A questionnaire was formulated for the study on social and economic benefits of lifelong learning programmes offered by six selected ministries. For the purpose of this paper, the relevant items are: 1. Name of the lifelong learning programme attended 2. Economic returns with financial benefits (e.g., salary increment, additional income, and promotion for job-related lifelong learning programmes; career advancement, setting up business, and advance business for non job-related lifelong learning programmes) 3. Non-economic benefits (e.g., additional opportunities for training, additional job responsibilities, and employer recognition of new competency for job-related lifelong learning programmes; and self-development and new skills and knowledge for information sharing for non job-related lifelong learning programmes) Other items on the source of information and payment of fees are not relevant. The questionnaire and protocol for the telephone survey were pilot tested on two participants of a lifelong learning programme and found to be suitable. Subsequently the questionnaire was improved by adding a question which allowed the participants to say that they did not derive any benefits from attending lifelong learning programmes. Data collection and analysis procedures Representatives of government agencies under the six selected ministries were invited to attend a meeting on 29 September 2014 with the assistance of Planning, Research and Policy Coordination Division, Ministry of Education. The purpose of the meeting was to explain the study and to seek their cooperation in supplying information on lifelong learning programmes offered by their ministry in 2013-2014. The government agencies were requested to submit contact details of 10 participants for each lifelong learning programme offered to Planning, Research and Policy Coordination Division, Ministry of Education. Based on the targeted sample size for the six ministries, enumerators were given a set target and asked to use the telephone survey protocol for the survey. They filled in the questionnaire during the telephone call. The questionnaire data sent by the enumerators were keyed into Excel sheets by a research assistant. Frequencies and percentages of participants reporting various social, non-economic and economic benefits from participating in lifelong learning programmes were computed. Limitations of the study Two limitations of the study arose from incomplete and inaccurate data received. First, a proportion of the data was incomplete because the participant contact details were missing. This reduced the population size of participants to be surveyed because the minimum sample size was calculated based on this. Second, some data were inaccurate. In some cases, in-house training was reported as lifelong learning by some government agencies although the scope of the lifelong learning was clearly specificied in the meeting on 29 September 2014. However, when the enumerators found this out, these participants were not included in the survey. In other instances, some participants who were telephoned claimed that they did not attend the programme. Their names could have been submitted by their employers but they did not eventually attend the lifelong learning programme. Finally, a proportion of the participants could not be reached using the telephone numbers given. Even with a large proportion of participants refusing to participate in the study, not answering the telephone calls and not replying sms sent by enumerators, the study succeeded in surveying the impact of lifelong learning programmes on 1,923 participants. Results and Discussion The results in this section are presented based on the three objectives of the study. Out of 1,923 participants, 50% attended lifelong learning programmes related to the job whereas the other half attended programmes not related to their job. 13

Social and economic benefits from job-related lifelong learning programmes The two types of economic returns from job-related lifelong learning programmes studied were promotion and salary increment. In the telephone survey, they were asked whether they were given promotion and salary increment after participating in the lifelong learning programme but if they did not think that their job promotion or salary increment was due to skills and knowledge acquired from the lifelong learning programme, they would answer “no” to the question. Job promotion is accompanied by a salary increase but lifelong learning participants can be given salary increments without a promotion. Figure 5 shows that participants who attended KBS programmes were more likely to be promoted (27%) and to be given salary increment (30.38%). The percentages for the other ministries were less than 10%. Examples of KBS programmes are electrical wiring (single-phase, three-phrase), electrical generator, and main switchboard. These are clearly job-related skills for technicians which could have helped them to perform better in their jobs. We have information from another part of the questionnaire which is not the focus of this paper, that is, the person who pays for the course fees. For a majority of the KBS participants, the fees were paid by themselves (71.31%) or their family (12.24%). Only 16.03% of the KBS participants had their fees paid by their employers, indicating that generally employers do not provide much additional training for their technicians as they are expected to have the skills already but if the technicians were motivated enough to attend professional development programmes at their own expense, then they were rewarded with salary increment and/or promotion.

Figure 5. Percentages of participants who were promoted and given salary increment after attending job-related lifelong learning programmes Three types of non-economic returns from job-related lifelong learning programmes were examined, namely, additional responsibilities in the workplace, more opportunities for training and praise for new skills. These benefits are classified as non-economic benefits because the participants do not directly obtain monetary gains but recognition of their higher level of competence at their workplace. When employees are entrusted with a bigger scope of job responsibilities, this shows their employers’ recognition of their enhanced skills and knowledge. The bigger scope of job responsibilities may open the way for future job promotion and salary increment. Figure 6 shows that a substantial proportion of participants were given additional responsibilities after they attended job-related lifelong learning programmes: 45.12% of KPWKM participants, 43.94% of KPIAT participants, 33.76% of KBS participants, 30.39% of KKLW participants, and 23.08% of KSM participants. However, the percentage was the lowest for KPM participants (10.16%). Many of the lifelong learning programmes offered by KPM were sewing, grooming and cooking-related but the job-related ones include computer and graphic design skills. In this present era, employees are expected to have computer skills.

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The pattern of results is similar for further opportunities for training. In career path planning, employers often send their employees for on-the-job training to equip them with new skill sets. For example, lecturers in public universities are required to attend a minimum of 42 hours of job-related training per year. Although the employees do not obtain monetary gains from the training, the employers have to allocate funds for their training (course fee, travel expenses). Figure 6 shows that a substantial proportion of participants was given more opportunities for training after attending job-related lifelong learning programmes organised by KPIAT (51.52%), KPWKM (46.34%), KKLW (35.29%) and KSM (28.54%). However, participants who attended job-related lifelong learning programmes offered by KPM (10.84%) and KBS (18.99%) were less likely to be given additional job responsibilities. As mentioned above, computer and graphic design skills may be in the job description, and employers may not see the point in investing in this form of training. In fact, the current trend in Scotland indicates that employers believe in apprenticeship more than training (Lowe & Gayle, 2015).

Figure 6. Percentages of participants reporting non-economic benefits after attending job-related lifelong learning programmes Figure 6 shows that it is not the Malaysian culture for employers to praise their employees for new skills as the percentages are below 25%. Among the ministries, KPWKM (23.17%) participants were the most likely to get employer recognition of enhanced work capabilities. The KPWKM lifelong learning programmes were mainly on health, entrepreneurship and grooming. It is possible that most of the KPWKM participants were women who might have been working in professions which needed knowledge of health and skills in entrepreneurship and grooming, which makes it more likely for them to receive compliments on new skills in these areas. Verbal praise is regarded as a strategy to increase productivity without incurring monetary expense (salary increment, promotion) but it is not popular in this setting. Recognition of better work capability is more often acknowledged in the yearly performance appraisal, which translates to better bonuses and perhaps greater salary increment than the annual increments. An overall comparison of the results show that not all job-related lifelong learning programmes bring economic returns to the participants. KBS participants are the most likely to enjoy both economic and non-economic returns because the lifelong learning programmes were technical-skills based. Because of this, they were willing to pay for the fees themselves instead of relying on their employers. In other words, they were willing to invest in lifelong learning for their professional development. For participants of lifelong learning programmes offered by KKLW, KSM, KPIAT and KPWKM, the additional skills and knowledge acquired may open the way for upskilling in the form of more opportunities for training and additional responsibilities (for them to practise their new skills) but they had not gained monetary rewards at the time of the study. The results also show that it is not beneficial for participants to attend computer and graphic design lifelong learning programmes in terms of gaining economic and non-economic returns; therefore, these programmes are mainly for self-improvement but not to the level of upskilling.

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Figure 7. Percentage of participants not gaining any benefit from job-related lifelong learning programmes Government agencies which offer lifelong learning programmes may assume that lifelong programmes bring benefits to participants, whether economic or non-economic. However, the findings of this study challenges this assumption. For example, 21.09% of 403 KSM participants reporting not gaining any benefit from job-related lifelong learning programmes (Figure 7). Figures 5 and 6 confirm the low relevance of KSM programmes for professional development because the benefits tend to be non-economic rather than monetary. Next, despite rather high percentages of KBS participants reporting economic and non-economic returns from lifelong learning programmes, 13.5% of the 237 participants stated that they did not benefit from the job-related lifelong programme they attended in 2013 or 2014. These results suggest that the types of lifelong programmes offered need to be constantly reviewed so that they focus on more useful skills for professional development. Economic and social benefits from non job-related lifelong learning programmes The economic benefits from participation in non job-related lifelong learning programmes are different from job-related programmes. Three possible forms of economic benefits were examined in this study: earning additional income, setting up business, and getting a job. Figure 8 shows that KPWKM participants have the most opportunity to earn additional income with the new skills acquired from lifelong learning programmes on health, grooming and entrepreneurship (25.61%). The percentages were much less than 16% for the other five ministries.

Figure 8. Percentage of participants reporting economic benefits after attending non-job related lifelong learning programmes 16

Similarly, the KPWKM participants are the most likely to set up business (15.85%), followed by KKLW (7.84%). The percentages are less than 5% for the other ministries. Examples of setting up business as reported by the particpants are taking orders for sewing head scarves and making cakes through online business websites. The percentages of KPWKM participants setting up businesses are lower than those reporting additional income – a larger proportion may earn some extra income on an ad-hoc basis but do not venture into establishing small-scale businesses. The pattern of results for getting a job are different from the other two economic benefits of non job-related lifelong learning programmes. The programmes offered by five ministries are useful in increasing the employability of 9-15% of the participants. The percentage of KPM participants getting a job after attending lifelong learning programmes is lower (5.23%), but the sewing, grooming, cooking and computer and graphic design skills help them to earn some additional income. An overall comparison of the results show that the forte of KBS is job-related lifelong programmes but not non-job related programmes. For non-job related lifelong learning programmes, KPIAT and KPWKM programmes are useful in generating income. Examples of KPIAT programmes are fish farming, bottling and other forms of food processing. These programmes equip participants with skills to venture into small businesses. Next, the results on the social benefits from non job-related lifelong learning programmes are described. The term “non-economic” which was earlier used for job-related programmes is not used here, but the benefits are clearly social benefits and are not attached to monetary gains of any kind: self-development and new skills and knowledge for information sharing.

Figure 9. Percentage of participants reporting social benefits after attending non-job related lifelong learning programmes The large proportion of participants reporting self-development after attending non job-related lifelong learning programmes indicate that this is the main benefit; the percentages range from 23% to 61%. Participants felt more confident of themselves, and they could use the new skills for themselves or their family (e.g., cooking, sewing, vegetable growing). KBS is the only ministry with a relatively lower percentage (13.92%, Figure 9), and this is to be expected because the forte of KBS lifelong learning is technical skills for professional development of technicians and not personal growth. In comparison with self-development, fewer participants reported sharing the new skills and knowledge with others, as indicated by percentages of less than 15% for the six ministries (Figure 9). However, among the six ministries, participants attending non job-related lifelong learning programmes offered by KPWKM (14.63%) were the most likely to share the information with others. Some of the participants were teachers and lecturers in various institutions, and they shared the information with their students. Other participants passed on their newly acquired knowledge and skills to their friends and family. Overall, non job-related lifelong learning programmes offered by the ministries (except KBS) can be assumed 17

to bring about personal development to the participants and some additional income. Self-development encompasses character development, which Kolej Komuniti Kos Lanas (2015) reported as the main social benefit of their lifelong learning programmes (see also Hammond, 2005). An example of personal development course is the Mandarin course offered by community colleges, where the participants are mainly non-Chinese who want to learn the language for basic communication (Soong & Ting, 2014). A small proportion used their newly learnt knowledge and skills to engage in entrepreneurial activities or to get new jobs. This indicates that the non job-related lifelong learning programmes have the potential to enable reskilling to take place, albeit for a small number of participants. When the gains are merely personal growth, policy makers and funders of lifelong learning are likely to regard it as having intangible returns. Even in Australia where the emphasis of lifelong learning is on improving employment prospects, “most workers do not move into a different occupational or skill levels post training” (Clemans, Newton, Guevara, & Thompson, 2012, p. 3). Clemans et al. also found that workers are not motivated by employment and higher wages alone when they pursue lifelong learning; in fact, their motivation for skill-building is their social and personal wellbeing. Based on the Australian case, Clemans et al. argues that lifelong learning that is not for the purpose of employment (entering it, maintaining it, or retraining for it) enhances human capital potential by ensuring “health and personal wellbeing and community cohesiveness” (p. 17). Participants’ suggestions for improvement of lifelong learning programmes Finally, the participants’ suggestions for improvement of the lifelong learning programmes are described. The last question in the survey was whether participants had any suggestions to improve lifelong learning programmes. Indirectly, this would reveal their dissatisfaction with some aspects of the lifelong learning programmes. Some participants had no comments. Altogether 723 responses were collected from 723 participants (Table 2). Table 2. Participants’ suggestions for improvement of lifelong learning programmes Suggestion 1. Offer courses with levels (from basic to advanced) 2. Ensure suitable course duration (many requests a longer time) 3. Offer courses related to industry and current market demands* 4. Improve the course environment (equipment, environment, enough materials) 5. Increase frequency of courses to open up opportunities to public to attend lifelong learning programmes (don’t restrict participation) 6. Increase variety of course content (scope, topic, new information) 7. Increase advertising and promotion of lifelong learning programmes 8. Invite experienced lecturers 9. Increase use of practical and interactive approaches 10. Reduce fee (e.g., allow loans or special schemes) 11. Work towards certification of courses 12. Increase the number of locations in which the courses are offered (especially in rural areas) 13. Organise courses at suitable times (e.g., holiday and weekends, and not at night) 14. Offer financial aid for business 15. Contact agencies that can offer jobs

Frequency 112 112 68 61 58 55 47 45 42 39 24 24 22 3 2

The most frequent requests are for programmes with levels (basic to advanced) because this allows participants to learn skills at a suitable level (112 requests). Some participants felt that the lifelong learning programme they attended was either too basic or too advanced. There were just as many requests for programmes with a suitable duration – 112 requests. Most of the requests were for a longer duration. A lot of the programmes were 1-day courses or 5 to 7 days but KKLW offered a number of 6-month long programmes in 2013 and 2014 like refrigeration and tailoring. The third most frequent request was for the government agencies to offer courses related to industry and current market demands (68 requests). The participants wanted lifelong learning programmes which would bring economic returns, such as jobs and enhanced career opportunities. Examples of courses requested by participants include: 18

a. finance: financial management, business, GST, accounts, b. DIY (do-it-yourself) skills: home repair, landscape, c. technical skills: PDP, environmental protection, hybrid engine EEV, autogear, CVT, electric and solar cars, CIDB NPOSS (OSHA), Microcontroller, d. information technology skills: computer software dan hardware C++ language applications, network, website management, Adobe Photoshop, ICT, e. engineering skills: Red Hat Certified System Administrator (RHCSA), Red Hat Certified System Engineer (RHCE), CISCO Certified Entry Networking Technican (CCENT), CISCO Certified Network Associate Routing and Switching (CCNA), CCNP Routing and Switching Certificate, HSE, OKU, f. culinary skills: traditional cakes, cooking and preserving food, g. farming skills: farming, animal husbandry, and mushroom cultivation, h. others: work life balance, herbal medicine, Mandarin, religion (Al-Quran), face and body massage, decoration, head scarve sewing, funeral management There was also dissatisfaction with the management of the lifelong learning programmes, which led to suggestions to improve the following aspects:



a. Improve the course environment (equipment, environment, materials) b. Increase variety of course content (scope, topic, new information) c. Invite experienced lecturers d. Increase use of practical and interactive approaches as they felt that there was too much one-way communication and inadequate practice e. Increase frequency of courses to open up opportunities to public to attend lifelong learning programmes (do not restrict participation)

Most of these suggestions concerned the quality of the lifelong learning programmes, and only one was on accessibility or opportunity to participate in the programmes. As many as 47 participants also felt that advertising and promotion of lifelong learning programmes should be heightened. From the participants’ perspective, for lifelong learning programmes to be more beneficial, the programmes need to focus on market demands and be pitched at different levels. The programmes should also provide certification of skills so that the participants can use the certificate of attendance for getting new jobs or advancing in their career. This is the idea of APEL (Accreditation of Prior Experiential Learning) mentioned in the Direktori Pembelajaran Sepanjang Hayat Peringkat Nasional 2012/2013 (Ministry of Higher Education Malaysia, 2012). This study shows that the lifelong learning programmes that need recognition are those targeted at enhancing the skills of blue collar workers as this would enable them to upskill. This is in line with Strategy 3 of the National Higher Education Strategic Plan (NHESP), where enculturation of lifelong learning among the blue collar workers would move the country towards a knowledge-based economy. Research has consistently shown that lifelong learning programmes benefitted men who stopped schooling earlier than usual more than any other group because they learnt skills which enabled them to find employment or get a wage increase, whether the studies were conducted in the United Kingdom (Jenkins et al., 2003) or in Portugal (Rothes et al., 2014). Findings have also concurred on the dominant orientation of the lifelong learning programmes towards professional development (Awuor & Parks, 2015; Daehlen & Ure, 2009; Konrad, 2005; Kyndt, Michielsen, Van Nooten, Nijs, & Baert, 2011; Lowe & Gayle, 2015). Because of the vocation-orientation, more young people are attracted to participate in the lifelong programmes (Anderson & Darkenwald, 1979a, 1979b; Houle, 1988) – a similar trend is observed in the present study as well as other studies conducted on community colleges in Malaysia (Amdan, Abdullah, & Johan, 2014; Chong & Abdul Rahman, 2014; Lee & Michael, 2014). Personal development also takes place even though the programmes are targetted at professional development (Ambrósio, Sá, & Simões, 2014; Berker & Horn, 2003), thereby achieving both thrusts of the goal of lifelong learning in Malaysia (Ministry of Education, 2015). In this light, it is more economically worthwhile to support lifelong learning programmes aimed at professional development as it brings both economic returns and personal growth benefits to the participants. Furthermore, studies on lifelong learning in other countries also focus on economic returns, for example, supply and demand (Coffield, 1999; Cohn, & Addison, 1998; Jenkins et al., 2003; Plewis & Preston, 2001).

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Conclusion The survey of 1,923 participants of non-formal lifelong learning programmes offered by six ministries in Malaysia showed that 50% participated in programmes that are related to the jobs and the other 50% participated in programmes that are not related to their jobs. The participants are mainly Malay, aged 20-29, and a majority are either unemployed or holding blue collar jobs. In the category of job-related lifelong learning programmes, technical skills-based programmes are the most likely to equip the participants with noticeably better skills and knowledge which result in their employers giving them salary increment and promotion. For participants who attended programmes which are less technical in nature, the employment benefits are in the form of additional opportunities for training and increased job responsibilities which give them the opportunity to demonstrate their higher level of competence in the workplace. This may lead to future promotion and salary increments. Undoubtedly, non job-related lifelong learning programmes brings about personal development but there are also monetary benefits. On average, about 20% of the participants used their newly acquired knowledge and skills to earn additional income, get a job, and set up small businesses (including online businesses). This shows that even non job-related lifelong learning programmes have good economic returns for participants. Policy makers and funders who invest in lifelong learning programmes would also view this as good returns from the investment. As the bulk of lifelong learning programmes, whether job-related or not, do not bring about much economic returns, we recommend that the programmes be structured based on skill levels (basic to advanced) and market surveys be carried out to determine industry needs so that the skills and knowledge taught are useful for professional development of the participants. Considering the resources put into provision of lifelong learning programmes, the focus should be on professional development rather than personal development because the latter can be assumed to occur whenever there is lifelong learning. There is, however, a place for lifelong learning programmes focusing on specific skills that individuals may be keen to acquire such as culinary or information technology skills for personal development but these programmes should also be structured based on skill levels so that the participants can reap better returns. Future research on lifelong learning in Malaysia should focus on the impact of lifelong learning from the perspective of employers, in particular whether the professional development that is assumed to occur results leads to better productivity for the organisation. References Ambrósio, S., e Sá, M. H. A., & Simões, A. R. (2014). Lifelong learning in higher education: the development of non-traditional adult students' plurilingual repertoires. Procedia-Social and Behavioral Sciences, 116, 3798-3804. Amdan, M. K., Abdullah, S. N. Z., & Johan, M. H. (2014). Persepsi masyarakat setempat terhadap program kursus pendek dalam membantu meningkatkan ekonomi keluarga dan kebolehdapatan pekerjaan di Kolej Komuniti Mas Gading. Prosiding Seminar Penyelidikan Kolej Komuniti Wilayah Sarawak (pp. 125-136). Bau, Sarawak, Malaysia: Kolej Komuniti Mas Gading. Anderson, R. E., & Darkenwald, G. G. (1979a). Participation and persistence in American adult education: Implications for public policy and future research from a multivariate analysis of a national data base. Direction Papers in Lifelong Learning. Anderson, R. E., & Darkenwald, G. G. (1979b). The adult part-time learner in colleges and universities: A clientele analysis. Research in Higher Education, 10(4), 357-370. Awuor, R. A., & Parks, D. (2015). Development of graduate education programs in the age of broken borders. Catalyst, 11(1), 41-45. Babbie, E. R. (1973). Survey research methods. Belmont, CA: Wadsworth. Belanger, P., & Valdivielso, S. (1997). The emergence of learning societies: Who participates in adult learning? Tarrytown, NY: Elsevier Science Inc. Berker, A., & Horn, L. (2003). Work first, study second: Adult undergraduates who combine employment and postsecondary enrollment. (NCES 2003-167). U.S. Department of Education, National Center for Education Statistics. Washington, DC. Borges, B., & Roger, K. (2014). Lifelong learning as a source of well-being and successful aging.  Revista Série-Estudos, 38, 35-46. Brookfield, S. (1985). A critical definition of adult education. Adult Education Quarterly, 36(1), 44-49. http:// dx.doi.org/10.1177/0001848185036001005 20

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About the Authors Associate Professor Dr Su-Hie Ting teaches English at the Centre for Language Studies, Universiti Malaysia Sarawak. She has a Ph.D in Applied Linguistics from the University of Queensland. Her research interests are language use, attitudes and identity, strategic competence, and academic writing. Dr Siti Halipah Ibrahim is a lecturer at the Faculty of Engineering, Universiti Malaysia Sarawak. She received her B.Sc (HBP) majoring in Building Engineering and MSc in Housing from Universiti Sains Malaysia, and Ph.D in Building Services Engineering from University of Leeds. Her research interests are housing design, thermal comfort in buildings, Industrialised Building System (IBS), energy efficiency design and green technology. Rohaida Affandi graduated with Master of Science in Construction Management from Universiti Teknologi Malaysia. She is a lecturer of Civil Engineering at the Faculty of Engineering, and a research fellow at the Centre of Renewable Energy, Universiti Malaysia Sarawak. Her research interests are construction project management and micro hydro projects for the rural community.

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Associate Professor Dr Azhaili Baharun is a lecturer at the Faculty of Engineering, Universiti Malaysia Sarawak. Associate Professor Dr Wan Azlan Bin Wan Zainal Abidin is the Deputy Dean at the Graduate School and a lecturer at the Faculty of Engineering, Universiti Malaysia Sarawak. His research interests are in telecommunication and renewable energy, and he has been actively involved in life-long learning activities. Dr Edmund Ui-Hang Sim is an associate professor at the Department of Molecular Biology, Universiti Malaysia Sarawak. He obtained his PhD in Biochemistry from the University of Queensland. His research specialisation is in cancer genetics but he also publishes in disciplines related to higher education and politics.

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Catalyst ISSN: 2408-137X, Volume 12, No. 2, 2015 Institute Press

The Correlation between Students’ Academic Achievement and Ethical and Moral Activities Involvement in a Christian Institution Nakhon Kitjaroonchai

Abstract This study investigates the correlation between students’ academic achievement and their involvement in ethical and moral activities. The sample of the study was 472 students from Asia-Pacific International University, Thailand, who were enrolled in different programs in the second semester of the academic year 2013-2014. Data were obtained from the internal software created by the IT department at Asia-Pacific International University called System for Administration, Reports, Requests and Academics. Data were analyzed using One-way ANOVA to examine significant difference between students’ academic achievement and their participation in ethical and moral activities. Pearson Product Moment Correlation Coefficient (Pearson’s r) was used to analyze the correlation between students’ academic achievement and their ethical and moral activities involvement. The study revealed that there was a statistically significant difference between students academic achievement and their involvement in ethical and moral activities at a = 0.05. The findings of the study also revealed that there was also a statistically positive correlation between students’ academic achievement and their involvement in ethical and moral activities (r-value = 0.447, p-value = 0.000 at a = 0.01). Keywords: academic achievement, ethical and moral activities, college students

Introduction Off-site school activities have been recognized for promoting ways to enhance students’ school experiences and increase social skills, self-discipline, public interest, and leadership skills (Adeyemo, 2010). Extracurricular activities and activities promoting ethical and moral values have long been integrated into the school syllabus as a means to propel students to a greater path of life, as well as to develop desired graduate traits outlined by various stakeholders. Worldwide educational systems perceive moral values as a significant component to govern behaviors and cultivate a person to be upright and virtuous. These ethical and moral values are consolidated either directly or indirectly in a school curriculum. In Thailand, for example, the Implementation Handbook of the National Qualification Framework for Higher Education designates five domains of learning, which are integrated into courses that students need to take. The five domains are ethical and moral integrity, knowledge, cognitive skills, interpersonal skills and responsibility, and numerical analysis, communication and IT skills. Of the five, ethical and moral development takes precedence over other domains (Handbook of National Qualification Framework for Higher Education in Thailand, 2006). This is also apparent in the manual of the Office for National Education Standards and Quality Assessment for external quality assessment, whereby the first indicator in the ONESQA manual (ONESQA, 2014) on quality of graduate stated the following: “students should live worthwhile and valuable daily lives; they are on good terms with others, willing to serve, and develop various moral virtues such as discipline, gratitude, kindness, patience, honesty, frugality, diligence, unselfishness. They also obtain work experience, join in extracurricular activities, and engage in beneficial service” (p. 1). This criterion requires all learners in all programs of study to be engaged in beneficial service (extracurricular) while pursuing professional skills and knowledge at school. At Asia-Pacific International University (AIU), holistic education is provided in which the principles of education are grounded by Biblical values and students are prepared for lives of joyful and selfless service to others (Academic Bulletin, 2014). The institution is guided by principles and philosophy of education stated 24

over a century ago by a church pioneer, Ellen G. White, in her book entitled ‘Education’ published in 1903. She stated, “True education means more than the perusal of a certain course of study. It has to do with the whole being and with the whole period of existence possible to man. It is the harmonious development of the physical, the mental, and the spiritual powers.” (p. 13). This statement can be explained that education must not put an emphasis in the learner of one particular dimension, but it must deal with the whole being, namely, physical development (curriculum or programs that enhance physical health which includes practical work-study programs, community service projects, or service learning), mental empowerment (all subjects or courses offered in school settings that promote function of mental discipline and empowerment), and spiritual emphasis (extra-curricular activities such as personal devotions, worships, prayers, vespers, chapels, or other religious-related activities that enhance an individual’s faith in the Christian God). In corresponding to its mission, the institution requires all students to attend a Chapel Program every Wednesday morning and this ethical and moral activity is known as Character Development Program (GENL110) and it is obligatory for all students at Asia-Pacific International University. Failure to attend this character development program on a weekly basis or receiving a ‘U’ grade will be noted in students’ academic records. The university enforces this policy to implement its identity to uphold ethical and moral values in building desirable characters in its students. Boarding students at Asia-Pacific International University are strongly encouraged to attend dormitory worships to learn and digest moral lessons from the Christian Bible perspectives to live a worthy and meaningful life. It is not only this character development program and worships that students are required to attend, but also a variety of activities as well as other religious-related programs and community service programs in which students are urged to be engaged in. The institution believes that all these ethical and moral exercises will prepare students to be well-rounded citizens with public-mindedness in serving others selflessly in the community they live in. The Office of Higher Education Commission in Thailand has given a strong emphasis on ethical and moral values to be integrated in all courses in addition to theoretical knowledge and skills imparted to learners. To address these concerns as well as to enrich other researchers’ findings on positive impact of students’ involvement in extracurricular activities, this research intends to examine if extra-curricular programs (ethical and moral activities, community service activities, and other religious-related activities) organized by Asia-Pacific International University have any impact on students’ academic achievement. The findings could serve as a reference point for further development of students’ character. Review of Literature Morality is associated with both a descriptive and a normative sense. In the descriptive sense, morality refers to a code of proper conduct corroborated by individuals or groups (Gert, 2012). In contrast, the normative sense defines ‘morality’ as a code of conduct, given specified conditions and endorsed by particular groups (Luco, 2014). Pornrungroj (2014) stated that morality is a beneficial act which each organization, society, or community prizes its value. Morality is essential and necessary, and it is a desired characteristic which differ individuals to be distinctive. According to the Pornrungroj (2014), the moral values which enrich a person to be virtuous and noble are such as self-discipline, conscience, gratitude, kindness, patience, honesty, austerity, perseverance, and selflessness. Morality refines one’s character and behavior to be well thought of and admired. A person with moral values is able to endure hardship, overcome criticism, and optimistically confront challenges in a tranquil manner. Guseinov asserted that the golden rule of morality is “a rule of mutuality” which means “relationship between people are moral when they are interchangeable as subjects of individually responsible conduct and when they have the ability to put themselves in other’s places.” (Guseinov, 2014, p. 91). In religious perspectives, for example Christianity, morality is based on God’s character and laws outlined in the biblical doctrines, and morality is modeled by Christ. In the Christian perspective, morality should not differ from one person to another for the Bible is the source of morality and so is God. The golden rule in the scripture sums up the core doctrine of Christianity that human beings must love their God and their neighbors. Any individual who complies with this golden rule and perceives that God is the source of morality, that person is anticipated to act and live moral life. In Buddhism, morality has to do with the Five Precepts, namely 1) ahimsa, no killing; 2) no stealing; 3) no lying; 4) no adultery; and 5) no imbibing in intoxicating drinks (Sellmann, 2009). Buddhists believe that moral development can be secured when a person can control his own actions or refrain from exercising thoughts or actions that yield negative effects, and making moral decisions can lead to positive feelings and emotional enhancement (Malti, Keller & Buchmann, 2012). 25

A number of research articles showed that learners who uplift ethical and moral values and actively participate in ethical and moral activities can improve their inner potential as well as their academic performance. In other words, students’ academic achievement may be linked with their involvement in activities that promote ethical and moral values (Marsh & Kleitman, 2002; Broh, 2002; Huang & Change, 2004; Jeynes, 2007; Wang & Shiveley, 2008; Strapp & Farr, 2010; Mooney, 2010; Erickson & Phillips, 2012). Research by Adeyemo (2010) found that students’ involvement in extracurricular activities helped boost their achievement in learning physics, and social activities that they were engaged in positively influenced their academic achievement, while Metsapelto and Pulkkinen’s study (2012) revealed that students’ participation in academic clubs was associated with their higher academic achievement and lower internalizing problems compared to those non-participating students. Extracurricular activities provide students with a sense of personal belonging to their committed group and they often receive moral support from companions to achieve the objectives or common goal of their plan. Chip, Cynthia and Jane (2003) studied the improvement of student achievement through character education with middle school students in Chicago, Illinois, and they found in their post-intervention data that the improvement of moral character such as integrity, honesty, trustworthiness, and respect increased student academic achievement. Character education facilitates students on decision making, which Stiff-William (2010) calls ‘decision filter’ where a decision making process engages both cognitive and affective processes and it supports individuals when they encounter barriers or problems in life. This ‘decision filter’ will enhance learners’ ability to make perceptive and sound decisions in life. Mooney (2010) and Erickson and Phillips (2012) revealed that extra curriculum activities such as religious activities which promote morality and virtue had a positive impact on student success. Mooney found that students who attended religious services once a week or more during their last year of high school had higher grades at college than non-regular religious attendees. In her study, she averred that religious students reported studying more, partying less, and dedicating more time to extracurricular activities which they find beneficial to their academic life, while Erickson and Phillips (2012) revealed in their study a positive relationship between religious participation and educational outcomes. Mooney’s (2010) and Erickson and Phillips’ (2012) studies are congruent with that of Jeynes (2007) whose study showed that students attending religious schools had higher levels of academic achievement than those who are in public schools. Jeynes affirmed that religious schools outperformed nonreligious schools due to differences in school culture such as school atmosphere, racial harmony, level of school discipline, school violence, and amount of homework done. This, according to Jackson and Coursey (1986, cited in Jeynes, 2007, p. 15) is because “religious people are more likely to have internal locus of control and perform well.” Students attending a religious institution with strict regulations and disciplinal procedures under the supervision of caring teachers tend to reduce the academic gap and succeed in school. Jeynes (2007) argued that religious commitment could have a positive impact on academic outcomes. This is echoed by Sumari, et al. (2010) where religiosity is cited as one of the significant factors that contributes to academic achievement. With these in mind, the present study intends to enrich existing ideas by examining the correlation between students’ academic achievement and their involvement in ethical and moral activities regulated by Asia-Pacific International University, as well as investigating if there is significant difference between students with a higher academic achievement and the ones with lower achievement with respect to their participation in ethical and moral activities. The researcher anticipates that the current study would contribute to the understanding of the relation between moral enrichment and academic achievement. It could also serve as a reference for individuals who have invested in a large amount of time in promoting extracurricular activities that are associated with ethical and moral values in order to enhance and nurture character development in students’ lives while pursuing a Christian education. Research Methodology Participants The target population for this study was 472 college students consisting of 204 male and 268 female students enrolled in different majors at Asia-Pacific International University, Thailand, in the second semester of academic year 2013-2014. Of this population, 223 students were from the Thai program while 249 were from the International program. These students resided in the university dormitories so that they could independently participate in the on-going extra-curricular activities provided by the university.

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Instrument The researcher retrieved data of students’ records of extracurricular activities attendances from the learning management system of AIU called SARRA (System for Administration, Reports, Requests and Academics). This software was built on .NET framework version 3.5 and used ASP.NET and runs on Windows Server 2008. SARRA features different capacities and the system generates grade reports, financial report, expense report, course evaluation, and other reports such as worship attendance, citizenship point, learning center attendance, and many more. SARRA also provides different features and facilitates administrative procedures for various departments. For example, the registrar from Admission and Records Office can use this software for student data management, course evaluation management, student academic achievement (GPA). Thus, the researcher obtained prompt data of students’ ethical and moral activities attendances which were recorded in the software and investigated their correlation with student academic achievement (GPA) which was provided by the Admission and Records Office. Students’ ethical and moral activities attendances’ scores were tabulated into percentage by SARRA as the students participated in such activities as dormitory worships, vespers, Sabbath worships, community service on Saturday afternoon, chapel programs, family group, student assemblies, departmental worships, and small groups or clubs. All these ethical and moral activities attendances were recorded by student attendance system which was closely monitored by student residence hall assistants. Results and Discussion The demographic characteristics of the participants are summarized in Table 1 below. Table 1. Frequency of Percentage of subjects divided according to gender, program of study, year of study, and major of study (N= 472).

Gender Male Female Program of study International Thai Year of study Freshman Sophomore Junior Senior Major of study English Business Science Theology Education and psychology

Frequency

Percentage

204 264

43.2 56.8

249 223

52.8 47.2

150 115 101 106

31.8 24.4 21.4 22.5

250 111 26 23 62

53.0 23.5 5.5 4.9 13.1

The majority of the research participants were English majors, which made up 53% of the total sample followed by business (23.5%), while the smallest group was theology students (4.9%). The aims of this study were to examine the correlation between students’ academic achievement and their involvement in ethical and moral activities, and to investigate if there is significant difference between students with higher academic achievement and the ones with lower academic achievement with regards to their involvement in the ethical and moral activities regulated by the university. The researcher assumed that getting involved in ethical and moral activities has a positive impact on students’ academic performance. Table 2 below shows students’ academic achievement and mean scores for involvement in ethical and moral activities. 27

Table 2. Learning achievement and mean scores and standard deviation of involvement in ethical and moral activities Learning Achievement low achievement (0.00-1.99) average achievement (2.00-2.99) high achievement (3.00-4.00) Total

N

Mean

Std. Deviation

34 219 219 472

24.7009 57.9098 80.5990 66.0451

24.36010 37.00061 35.90920 39.04755

As can be seen from Table 2, the analysis shows that students with higher learning achievement demonstrated greater involvement in ethical and moral activities (M= 80.59, SD = 35.90) than those students with average achievement (M=57.90, SD=37.00) and low achievement (24.70, SD = 24.36). Interestingly, as the mean score of academic achievement increases, the mean score of ethical and moral activities involvement is also boosted subsequently as shown in Figure A.

Figure A. Line graph of the mean of ethical and moral activities attendance vs academic achievement (GPA) To determine whether there are significant differences among heterogeneous learners with regard to their involvement in ethical and moral activities, One-way ANOVA and Scheffe test were used to analyze data. The result of statistical analysis shows in Tables 3 and 4 below. Table 3. Analysis of the significant difference between heterogeneous learners in regard to their involvement in ethical and moral activities (I) Learning Achievement low achievement (0.00-1.99)

(J) Learning Achievement

average achievement (2.00-2.99) high achievement (3.00-4.00) average low achievement (0.00achievement (2.00- 1.99) high achievement 2.99) (3.00-4.00)

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Mean Difference (I-J)

Std. Error

Sig.

95% Confidence Interval Lower Upper Bound Bound

-33.20893* 6.58834 .000

-49.3872

-17.0307

-55.89816* 6.58834 .000

-72.0764

-39.7199

33.20893* 6.58834 .000

17.0307

49.3872

-22.68922* 3.41563 .000

-31.0766

-14.3019

high achievement low achievement (0.0055.89816* 6.58834 .000 (3.00-4.00) 1.99) average achievement 22.68922* 3.41563 .000 (2.00-2.99) *.The mean difference is significant at the 0.05 level.

39.7199

72.0764

14.3019

31.0766

Table 4. Analysis of groups classified in different subset (Involvement in ethical and moral activities – Scheffe) Learning Achievement low achievement (0.00-1.99) average achievement (2.00-2.99) high achievement (3.00-4.00) Sig.

Subset for alpha = 0.05 1 2 3

N 34 219 219

24.7009 57.9098 1.000

Means for groups in homogeneous subsets are displayed.

1.000

80.5990 1.000

In Table 3, One-way ANOVA statistical analysis shows that there are significant differences among the three different groups by their involvement in ethical and moral activities at a = .05. Students with high academic achievement showed statistically significant difference from the other two groups with average achievement (p=0.000) and low academic achievement (p= 0.000) by their participation in ethical and moral activities. Likewise, the group with average academic achievement shows statistically significant difference from their peers with low academic achievement (p = 0.000) by their participation in ethical and moral activities. The analysis also shows that the three different groups were classified in different subsets (Subset for alpha = 0.05) as shown in Table 4. To respond to the research objective of examining the correlation between students’ academic achievement and their involvement in ethical and moral activities, Pearson Product Moment Correlation Coefficient (Pearson’s  r)  was used to analyze data and the statistical analysis is shown in Table 5. Table 5. Correlation statistics between student academic achievement and their involvement in ethical and moral activities Involvement in Ethical and Moral Activities Involvement in Ethical and Moral Activities products

Academic Achievement products

Pearson Correlation Sig. (2-tailed) Sum of Squares and CrossCovariance N Pearson Correlation Sig. (2-tailed) Sum of Squares and Cross-

Covariance N **. Correlation is significant at the 0.01 level (2-tailed).

Academic Achievement 1

188.065 .399 472 .477** .000 5194.253 11.028 472

.447** .000 5194.253 11.028 472 1 718138.859 1524.711 472

The statistical analysis shows in Table 5 that there is a significantly positive correlation between students’ academic achievement and their involvement in ethical and moral activities (r-value = 0.447, p < 0.01). In other words, ethical and moral activities organized in the university may play a positive role in students’ 29

academic performance. Such finding is congruent with Mooney’s (2010) and of Sumari, Hussin, and SiraJ’s (2010) who all reported that ethical and moral as well as religious daily practices establish social control and promote positive educational outcomes at school settings. Discussion From the results of this study, it can be assumed that ethical and moral activities organized by AIU may have a positive bearing on the students’ academic performance. The findings of this current study suggested that students with higher academic achievement (GPA) show trends of positive involvement in ethical and moral activities. From this current study it is obvious that participation in ethical and moral activities did not lower students’ academic performance per se, but instead helped them to persist and perform better in their academic life. The finding of the study is congruent with Wang and Shiveley’s (2008) whose study revealed that extracurricular activities have a very positive impact on the academic performance of students at Sacramento State. They discovered that students achieved higher rates of retention and graduation, better GPAs when students are engaged in activities such as board members of associated students, residence hall associates, orientation leaders, or student club leaders. The findings of this current study also corresponded with Jeynes’ (2007), Mooney’s (2010), and Erickson and Phillips’ (2012) who found that attending religious activities increases students’ academic achievement and reduces disciplinary behavioral problems as well as drug problems in school. The results of this study paralleled other researchers’ works on correlation between students’ academic achievement and their participation in extracurricular activities as studied and synthesized in the literature review (Broh, 2002; Huang & Change, 2004; Jeynes, (2007); Wang & Shiveley, 2008; Mooney, 2010; Erickson & Phillips, 2012). This phenomenon can be explained from different angles. From the researcher’s perspectives, the current study results can be interpreted as the following: Ethical and moral activities as well as religious-related activity enhance students’ academic performance. This is because religion plays a vital role in an individual’s life and religious belief and practice as suggested by Mooney (2010) where religion “give(s) believers solace in time of trouble, thereby enabling them to deal better than nonbelievers with stressful events that might otherwise negatively impact their academic achievement.” (p. 199). According to Jeynes (2003: 119) religious practice produces “internal locus of control” in propelling devotees to confront challenges with positive perception. Religious practice can also establish social control and promote positive educational outcomes (Mooney, 2010), and involvement in religious activities during adolescence has long-term effects on a range of life outcomes which result in mental health as well as educational attainment. Religiosity is one of the significant determinants of high academic success and moral standard, so students who uphold religious disciplines and bring them into compliance in daily practice will enhance their mental ability (Sumari, Hussin, & SiraJ, 2010). In the Christian and Biblical perspective, the scripture claims, “the fear of the Lord is the beginning of wisdom and knowledge” and “the Lord gives wisdom; from his mouth come knowledge and understanding (Proverbs 1:7, 2:6, New International Version). Nedley argued that in a Christian school setting, nurturing spiritual faith and reading spiritual materials on a daily basis could enhance the brain function as well as intellectual empowerment, so students’ academic performance can be improved (Nedley, 2010). Students’ active involvement in ethical and moral activities at Asia-Pacific International University could be an essential determinant for them to apply for available educational scholarships procured by the university. As a practice here at the university, an ad hoc committee assigned to select qualified candidates for scholarships would often define a criterion that applicants must exhibit characteristics that depict an earnest involvement in ethical and moral activities regulated by the institution, apart from their outstanding academic performance. Along with this, a number of academic excellence awards initiated by various departments are presented annually during the consecration ceremony on graduation day attended by academia, guardians, patrons, distinguished guests, relatives and friends. This honorable ceremony brings dignity and acknowledgement to distinctive students whose academic achievement as well as life aspect is well-worth praising. In achieving a favorable outcome, learners are often motivated by internal and external factors to stimulate them to reach their goals since both intrinsic and extrinsic motivations have a positive relation with students’ learning achievement (Kitjaroonchai & Kitjaroonchai, 2012). In addition to this, a number of subjects used for this study are sponsored students whom their respective mission, church, or patron anticipates will grow in wholesomeness as defined in the Adventist holistic educational philosophy; the harmonious development 30

of the physical, the mental, and the spiritual powers (White, 1903). The university strongly emphasizes these three aspects of development through ethical and moral activities in order to shape students to become fully prepared to live in a competitive society where ethical and moral values are often neglected. Furthermore, involvement in a committed group or club activity strengthens social bonding among the members. The findings of this study correspond with Wang and Shiveley (2008), whose study revealed that students obtained better GPA and higher good standing when they are engaged in activities such as serving as a board member or serving in a leadership position in student clubs. This might be due to social relations among the members that boost them to support each other to achieve their set goals as well as learning objectives. Their sense of belonging drives them to harmoniously achieve the outcomes. As Hinck and Brandell mentioned (cited in Fujita, 2005, p. 6), “involvement in community and service learning affects learners’ higher level thinking skills, motivation to learn, application of learning, problem solving, as well as basic academic skills.” Students’ involvement in a well-structured community service, club activity, or ethical and moral activities could enhance their potentiality as they learn skills necessary for life, social skills, and time management. Huang and Chang (2004) claimed that students’ co-curricular involvement is highly associated with cognitive development, affective growth, and interpersonal skills growth. Thus, students should be encouraged to participate in such practical activities to maximize their cognitive domain. Ethical and moral activities in particular play significant roles in students’ lives by improving their characters, self-discipline, positive attitudes as well as school performance, as they deal with internal and external conflicts in solemn manner with the application of an internal locus of control mode (Jeynes, 2003). Students who uphold moral values and religious disciplines rigorously may respond well to challenges to their worldview and attempt to conquer those obstacles by learning more about their faith and striving to excel in their field of study in order to show their peers and teachers that religious faith and academic life are not mutually exclusive (Mooney, 2010). Ethical and moral values as well as religious principles have a positive impact on students’ academic achievement and they are inevitably needed in all walks of life in this competitive world where materialism and intellectualism are prized. The knowledge of ethical and moral values and religious principles need to be fostered together with academic knowledge, and demonstrated through an individual’s life for service to people and society at large. Recommendations for Further Study The study provides useful information regarding the issue of whether extracurricular activities such as ethical and moral activities have a positive or negative impact on students’ academic performance. Additional questions pertaining to whether or not ethical and moral activities have a positive impact on the academic performance of students need further investigation. Thus, the researcher would suggest that consideration be given to further studies being made in the following areas: 1. This study should be replicated, using a different population, particularly Buddhist or non-Christian students who enrolled in a Christian school in other regions to investigate if ethical and moral activities or religious-related activities regulated by a Christian-based school have any impact on their academic performance or college life. 2. The data sources used for analyzing correlation between academic achievement and involvement in ethical and moral activities in this current study are quantitative in nature. Therefore, a qualitative study should be conducted to enrich cross-validation of data for in-depth discussion of the findings. 3. A study on the effects of involvement in the university’s ethical and moral activities on students’ future profession should be researched or followed up to investigate whether or not these extracurricular activities have an impact on students’ profession or daily life following graduation

Conclusion

This study aimed to examine the correlation between students’ academic achievement and their involvement in ethical and moral activities as well as to examine if there is any significant difference between students with higher academic achievement and the ones with lower academic achievement with regard to their involvement in ethical and moral activities. The study’s results showed that involvement in ethical and moral activities had a positive correlation with students’ academic achievement. The findings also revealed 31

that there was a statistically significant difference between students with high GPA and those with lower GPA. To sum up, the current study results should be valuable to the Student Administration Office and Chaplaincy Office at Asia-Pacific International University as well as other sister institutions who have invested a large amount of time to regulate these meaningful activities in order to foster students to grow mentally, socially, and spiritually as defined by the Adventist education philosophy.

References Academic Bulletin. (2014). Asia-Pacific International University Academic Bulletin 2013-2015. Bangkok: Darnsutha Press Co., Ltd. Adeyemo, S. A. (2010). The relationship between students’ participation in school extracurricular activities andtheir achievement in physics. International Journal of Science and Technology Education Research, 1(6), 111-117. Broh, B. A. (2002). Linking extracurricular programming to academic achievement: who benefits and why? Sociology of Education. 75, 69-96. Chip, F., Cynthia, H.,& Jane, J. (2003). Improving student achievement through character education. Master of Arts Action Research Project, Saint Xavier University and SkyLight Professional Development Field-Based Master’s Program. Retrieved March 22, 2015, from http://files.eric.ed.gov/fulltext/ ED477142.pdf Erickson, L.D. & Phillips, J. W. (2012). The effect of religious-based mentoring on educational attainment: more than just a spiritual high? Journal for the Scientific Study of Religion, 51(3): 568-587. Fujita, K. (2005). The effects of extracurricular activities on the academic performance of junior high students. Retrieved May 15, 2015, from http://www.kon.org/urc/v5/fujita.html Gert, B. (2012). The definition of morality. Stanford Encyclopedia of Philosophy. Retrieved March 3, 2015, from http://plato.stanford.edu/entries/morality-definition/ Guseinov, A.A. (2014). The golden rule of morality. Russian Social Science Review, 55(6). 84-100. Huang, Y.& Chang, S. (2004). Academic and co-curricular involvement: Their relationship and the best combinations for student growth. Journal of College Student Development, 45, 391-406. Jeynes, W. H. (2003). Religion, education, and academic success. Greenwich: information age. Jeynes, W. H. (2007). Religion, intact families, and the achievement gap. Interdisciplinary Journal of Research on Religion, 3, 1-24. Kitjaroonchai, N. & Kitjaroonchai, T. (2012). Motivation toward English language learning of Thai students majoring in English at Asia-Pacific International University. Catalyst, 7(1), 21-40. Luco, A. (2014). The definition of morality: threading the needle. Social Theory and Practice, 40(3), 361-387. Malti, T., Keller, M., & Buchmann, M. (2012). Do moral choices make us feel good?: the development of adolescent’s emotions following moral decision making. Journal of Research on Adolescence, 23(2), 389-397. Marsh, H.W.,& Kleitman, S. (2002). Extracurricular activities: the good, the bad, and the nonlinear. Harvard Educational Review, 72, 464-512. Metsapelto, R.L.,& Pulkkinen, L. (2012). Socioemotional behavior and school achievement in relation to extracurricular activity participation in middle childhood. Scandinavian Journal of Education Research, 56(2), 167-182. Mooney, M. (2010). Religion, college grades, and satisfaction among students at elite colleges and universities. Sociology of Religion, 71(2), 197-215. Morality. (1995). In Oxford advanced learner’s dictionary (5th edition). UK: Oxford University Press. Morality. (2015). In Online business dictionary.com. Retrieved March 3, 2015, from http://www.businessdictionary.com/definition/morality.html National Qualifications Framework for Higher Education in Thailand. (2006). Bangkok. Nedley, N. (2010). Adventist youth conference: How to enhance your intelligence. [3ABN]. Australia. Office of National Educational Standards and Quality Assessment. (2014). Bangkok. Pornrungroj, C. (2014). The quality of students is a reflection of teacher quality. Bangkok: The office for national education standards and quality assessment. Proverbs 9:10 – New International Version. Retrieved May 10, 2015, from http://biblehub.com/niv/ proverbs/9-10.htm

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Puntachai, S. & Khamawattana, P. (2012). A study on the desirable characteristics of bachelor’s of engineering graduates from the viewpoint of employers. Online Journal of Education, 7(1), 518-528. Retrieved March 4, 2015, from http://www.edu.chula.ac.th/ojed/doc/V71/v71d0038.pdf Sellmann, J.D. (2009). Buddhist morality and trans-morality. The International Journal of the Asian Philosophical Association. 1(2), 62-72. Stiff-Williams, H. R. (2010). Widening the lens to teach character education alongside standard curriculum. The Clearing House, 83: 115–120. Strapp, C.M., & Farr, R. J. (2010). To get involved or not: the relation among extracurricular involvement, satisfaction, and academic achievement. Teaching of Psychology, 37, 50-54. Sumari, M., Hussin, Z., Siraj, S. (2010). Factors contributing to academic achievement and moral development: A qualitative study. The International Journal of Research and Review, 5(2), 18-23. Wang J. & Shively J. (2008). The impact of extracurricular activity on student academic performance. Retrieved April 28, 2015, from http://www.csus.edu/oir/Research%20Projects/Student%20Activity% 20Report%202009.pdf White, E.G. (1903). Education. Mountain View, CA: Pacific Press Publishing Association.

About the Author Nakhon Kitjaroonchai is a lecturer at the Faculty of Arts and Humanities, Asia-Pacific International University, Thailand.

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Catalyst ISSN: 2408-137X, Volume 12, No. 2, 2015 Institute Press

Predicting Student Academic Achievement by Using the Decision Tree and Neural Network Techniques Pimpa Cheewaprakobkit

The following article was first presented as a peer-reviewed paper at International MultiConference of Engineers and Computer Scientists 2013 held at Hong Kong, on 13-15 March, 2013. Abstract The aims of this study are 1) to study the prediction accuracy rate between the two data mining techniques: decision tree and neural network in classifying a group of student academic achievement, 2) to analyze factors affecting academic achievement that contribute to the prediction of students’ academic performance. In this study, the researcher used WEKA open source data mining tool to analyze attributes for predicting undergraduate students’ academic performance in an international program. The data set comprised of 1,600 student records with 22 attributes of students registered between year 2001 and 2011 in a university in Thailand. Preprocessing included attribute importance analysis. The researcher applied the data set to differentiate classifiers (Decision Tree, Neural Network). A cross-validation with 10 folds was used to evaluate the prediction accuracy. An experimental comparison of the performance of the classifiers was also conducted. Results show that the decision tree classifier achieves high accuracy of 85.188%, which is higher than that of neural network classifier by 1.313%. Keywords: Academic Achievement, Data Mining, Decision Tree, Neural Network Introduction Data mining is a tool to analyze data from different perspectives and to summarize it into useful information. It has been recognized by many researchers as a key research topic in database systems. Several emerging applications in information technology also call for various data mining techniques to better understand user behavior, to improve the service provided and to increase business opportunities (Chen, Han & Yu 1996). However, these techniques do not generate the same prediction results. In response to such an issue, this research studies the prediction accuracy rate between the two popular data mining techniques: decision tree and neural network in classifying a group of student academic achievement. Higher education is an important contributor to the development of human resources. However, one of the major problems of students is failure to meet academic requirements to remain in higher education. Many students struggle with a GPA below the required standard. As a result, students may not graduate in a given period of time, and lose potential job opportunities. Each year the number of students who are dropping out of higher education increases. Therefore, this research also aims to investigate the factors that affect the academic achievement of students. This will help faculty advice and assist at-risk students in a timely fashion. The data sample used in this study was a group of undergraduate students in an international program. This analysis uses data mining techniques to classify the data. Literature review Theory: Data mining Data mining (Affendey, Paris, Mustapha, Sulaiman & Muda, 2010) is a process of automatically discovering 34

useful information in large data repositories. It is an integral part of the Knowledge Discovery in Database (KDD), which is the overall process of converting a series of transformation steps, from data preprocessing to the post-processing of data mining result. Data mining tasks are generally divided into 2 major categories, namely, predictive and descriptive tasks. Predictive modeling refers to the task of building a model for target variable as a function of the explanatory variable. The two types of predictive modeling tasks are classification, which is used for predicting discrete attributes and regression, which is used for predicting continuous target attributes. The goal of both tasks is to create a model that minimizes the error between the predicted and true values of the target variable. Classification Classification is the process of data management model building that identifies in-group data to illustrate the differences between groups of data and to predict the data that should be in any class. The model used to classify data into determined groups is based on an analysis of the data set. This data set would lead the system to classify data. The end result is a model of learning which can be represented in many forms such as the Classification (IF-THEN) rules, Decision Tree, or Neural Networks. Then the rest of the data, as the actual data, will be drawn to test and compare with those acquired from the model for the accuracy testing. The model will be updated and tested to have a satisfactory level. Later, when new data comes and is plugged into the model, the data can predict grouping by the model. Decision trees Decision trees present a system using a top-down strategy based on the divide and conquer approach where the major aim is to partition the tree in many subsets mutually exclusive. Each subset of the partition represents a classification sub-problem. A decision tree is a representation of a decision procedure allowing to determine the class of a case. It is composed of three basic elements (Utgoff, 1989): • Decision nodes specifying the test attributes. • Edges corresponding to the possible attribute outcomes. • Leaves named also answer nodes and labeled by a class. The decision tree classifier is used in two different contexts: 1. Building decision trees where the main objective is to find at each decision node of the tree, the best test attribute that diminishes, as much as possible, the mixture of classes with each subset created by the test. 2. Classification where we start by the root of the decision tree, then we test the attribute specified by this node. The result of this test allows to move down the tree branch relative to the attribute value of the given example. This process will be repeated until a leaf is encountered. So, the case is classified by tracing out a path from the root of the decision tree to one of its leaves (Quinlan, 1990). Neural Network Neural networks are being applied to an increasing large number of real world problems. Their primary advantage is that they can solve problems that are too complex for conventional technologies; problems that do not have an algorithmic solution or for which an algorithmic solution is too complex to be defined. In general, neural networks are well-suited to problems that people are good at solving, but for which computers generally are not. These problems include pattern recognition and forecasting, which requires the recognition of trends in data. The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics. The most common neural network model is the multi-layer perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output in order to learn. The goal of this type of network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is 35

unknown. The MLP and many other neural networks learn using an algorithm called backpropagation. With backpropagation, the input data is repeatedly presented to the neural network. With each presentation the output of the neural network is compared to the desired output and an error is computed. This error is then fed back (backpropagated) to the neural network and used to adjust the weights such that the error decreases with each iteration and the neural model gets closer and closer to producing the desired output. This process is known as “training” (Hyndman & Athanasopoulos, 2014). Related Works Work by Thai Nghe, Janecek, and Haddawy (2007) have compared two classifiers, Decision Tree and Bayesian Network, to predict students GPA at the end of the third year of undergraduate studies and at the end of the first year of postgraduate from two different institutes. Each data set has 20,492 and 936 complete student records respectively. The results show that the Decision Tree outperformed Bayesian Network in all classes. The accuracy was further improved by using resampling technique, especially for Decision Tree, in all cases of classes. At the same time, resampling was used to reduce misclassification, especially on minority class of imbalanced datasets, because Decision Tree algorithm tends to focus on local optimum. Ian and Eibe (2005) gave a case study that used educational data mining to identify behavior of failing students to warn students at risk before final exam. Romero, Ventura and Garcia (2008) gave another case study of using educational data mining in Moodle course management system. They used each step in data mining process for mining e-learning data. Also, educational data mining used by Polpinij (2002) to predict students’ final grade using data collected from Web based system. Beikzadeh and Delavari (2005) used educational data mining to identify and then enhance educational process in higher educational system which can improve their decision making process. Finally, Waiyamai (2003) used data mining to assist in the development of new curricula, and to help engineering students to select an appropriate major. In other works, Kotsiantis, Pierrakeas and Pintelas (2003) compared six classification methods (Naive Bayes, Decision Tree, Feed-forward Neural Network, Support Vector Machine, 3-nearest Neighbor and Logistic Regression) to predict drop-outs in the middle of a course. The data set contained demographic data, results of the first writing assignments and participation in group meetings. The data set contained records of 350 students. Their best classifiers, Naive Bayes and Neural Network, were able to predict about 80% of drop-outs. The results also showed that a simple model such as Naïve Bayes is able to generalize well on small data sets compared to other methods such as Decision Tree and Nearest Neighbor that require a much larger size of datasets. Method To investigate the propositions, two classification algorithms were adopted and compared: the neural network and the C4.5 decision tree algorithm. These two techniques are the most widely-used classification techniques, especially in artificial intelligence (Elouedi, Mellouli & Smets, 2000; Fayyad, Piatetsky-Shapiro & Smyth 1996). The classification models were implemented using WEKA 3.7.5 version (Merceron & Yacef, 2005). Series of records of first year undergraduate students who enrolled in the international programs of a private university in the academic years of 2001 to 2011 with 1,600 items and 22 attributes were used for the study. The investigation process consists of three main steps: Data Preprocessing; Attribute Filtering; and Classification Rules (Minaei-Bidgoli, Kashy, Kortemeyer & Punch, 2003). Data Preprocessing The student records were still not in a form that could be used in the data mining testing and analysis; therefore, the data needed to be prepared to be in a proper format before using them. The process was divided into various stages: Data Cleaning; Data Selection; and Data Transformation. The records of samples were drawn from many departments: for example, the study performance samples were taken from the Office of Admissions and Records, the number of hours in the extra curricula activities was taken from the Office of the Student Administration, and the number of hours worked was taken from the Finance Department. These data were in the form of several Microsoft Excel files with some duplicate fields. To make it easier to write programs, the researcher restored the data into a form of table using the Oracle Database version 10g Express Edition as shown in Figure 1. The program was developed by Java and SQL language for selecting the 36

attributes as presented in Figure 2. Then, the researcher recalculated values of the attribute. For example, the researcher recalculated the number of hours worked and the number of hours in the extra curricula activities per semester to per month, and from the hours of study outside classroom per semester to each month for all the studied students. Next, the researcher collected all data and the Export File from the table records format into the .CSV data file format as shown in Figure 3, to be used in the analysis with the WEKA. The attributes used in research were reduced into only the desired attributes. Figure 1. The data import from Excel file into the Oracle database

Figure 2. The development of program through Java language to select desired attributes

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Figure 3. Example of students’ data after the cleaning

Attribute Filtering This is the process of screening for the most needed attributes. The working principle of data classification of this study was based on the selected 20 attributes out of the initial 22 attributes as shown in Table 1. The student Identification Number (ID) and semester Grade Point Average (GPA) were left out from the study through this process. The student Identification Number for all students, were either identical or different from each other and did not affect the analysis. Because the semester GPA of the students, was close to Cumulative Grade Point Average (CGPA), the CGPA was selected as the dependent variable in this analysis. If the GPA attribute was included in this study, it would have the most influence in the study and the relationship of other attributes would not be seen. Classification Rules Classification of data is a technique for data classification from various feature items through the survey of attributes in the database to distinguish categories which have been defined in advance. The techniques used to categorize the data include the Decision Trees and Neural Network techniques. A Decision Tree is a tree-shaped structure that represents sets of decisions (Connolly, Begg & Strachan, 1999). These decisions generate rules for the classification of a dataset. Trees develop arbitrary accuracy and use validation data sets to avoid spurious detail. They are easy to understand and to modify. Moreover, the tree representative is more explicit, and has easy-to-understand rules for each cluster of student’s performance. The classes in the Decision Tree are cluster IDs obtained in the first step of the method. The Decision Tree represents the knowledge in the form of IF-THEN rules. Each rule can be created for each path from the root to a leaf. The leaf node holds the class prediction. The C4.5 is an algorithm used to generate a Decision Tree developed by Ross Quinlan. The C4.5 is an extension of Quinlan’s earlier ID3 algorithm. It employs a “divide and conquer” strategy and uses the concept of information entropy. The general algorithm for building Decision Trees is: (Quinlan, 1992) • If all cases are of the same class, the tree is a leaf and so the leaf is returned labeled with this class; • For each attribute, calculate the potential information provided by a test on the attribute (based on the probabilities of each case having a particular value for the attribute). Also calculate the gain in 38

information that would result from a test on the attribute (based on the probabilities of each case with a particular value for the attribute of a particular class) • Depending on the current selection criterion, find the best attribute to branch on. A multilayer perceptron (Hagan & Menhaj, 1994) is a Feed forward Artificial Neural Network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separable by a hyper plane. MLP networks are general-purpose, flexible, nonlinear models consisting of a number of units organized into multiple layers. The complexity of the MLP network can be changed by varying the number of layers and the number of units in each layer. Given enough hidden units and enough data, it has been shown that MLPs can approximate virtually any function to any desired accuracy. Table 1. All variables in the student database Attribute

Type

ID Gender

binary

Status

nominal

Age

numeric

Continent School_Ed

nominal nominal

Qualification

numeric

Father_Occ

numeric

Mother_Occ

numeric

Scholarship

numeric

Dorm

binary

ESL Dept NativeEng

binary numeric binary

G.P.A

numeric

Description student’s identification student’s gender (female or male) student’s status (single, married, divorced) student’s age (1:< 18 years, 2: 18-25 years, 3: 26-30 years, 4: 31-40 years, 5: > 40 years) Continent (Asia, America, Europe, Africa) Educational background (BA, College, Diploma, High School, MA) Student’s qualification (1-General, 2-Vocational) Father’s occupation (1-Government, 2-Private, 3-Business, 4-other) Mother’s occupation (1- Government, 2- Private, 3-Business, 4- Other) Get a scholarship (25%, 50%, 75%, 100%) On-campus residence (yes or no) Pre-University English ESL (yes or no) Department (from 1 – 10) Native English speaker (yes or no) Grade Point  Average (< 2.00, 2.00-2.49, 2.50- 2.99, 3.00-3.49, > 3.50)

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Attribute C.G.P.A

Type numeric

Major_CGPA

numeric

Credits

numeric

NumCourse

numeric

Learning_ Center

numeric

Work_Hour

numeric

Activity_Hour

numeric

Description Cumulative Grade Point Average (< 2.00, 2.00-2.49, 2.50-2.99, 3.00-3.49, > 3.50) Grade Point  Average of major subject(< 2.00, 2.00-2.49, 2.50-2.99, 3.00-3.49, > 3.50) Number of credits (from 1 – lowest to 3 – highest) Extra-curricular subject (from 1 – lowest to 3 – highest) Extra study hour (from 1 – lowest to 5 – highest) Work hour (from 1 – lowest to 5 – highest) Work activity (from 1 – lowest to 5 – highest)

Model Building For the data classification format determinant for the Training Set and the set of data format to use in testing the validity of the Testing Set, the researcher used the classification technique of Cross-validation Fold: 10 (Romero, Ventura & Garcia, 2008). This method divided the data into 10 sets and at each time of study, one data set was used for testing and the remaining nine sets were used to develop the model. The testing was repeated to cover the 10 series data set. Then, the study was tested several times by adjusting the values, choosing the Correct Value, and comparing the Precision Value and the Recall Value to have the most appropriate value to be used for the model. Model Evaluation To create a rule of Decision Trees to use as a model, the researcher selected the technique of Decision Rules: PART by selecting rules with a clear condition: not too many or too few rules: rules that can be easily understood: and the appropriate Correct Value, Precision Value and Recall Value. However, whenever there were too many rules, the researcher used the pruning method to reduce the classification errors caused by outliers, and then compared the tested results with the Neural Network method from the Correct Value, Precision Value, and Recall Value. Result The results of the analysis show the Decision Tree Model had an accuracy rate of 85.188% while the Neural Network Model had an accuracy rate of 83.875%. The result suggests that the Decision Tree Model is more accurate than the Neural Network Model. Further results reveal the factors that affect academic achievement of students are as follows: 1. The number of hours worked per semester; 2. An additional English course; 3. The number of credits enrolled per semester; 4. Status of students such as single, married, or divorced.

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The results from the test created 13 rules. Following are examples of these rules: WorkHr_5 = High: Risk (320.0/12.0) WorkHr_5 = Low AND Credit = 12-15 credits AND Mother_Occ = Other: Not Risk (65.0/7.0) ActivityHr_5 = Medium: Risk (9.0/2.0) ESL = No AND Credit = 12-15 credits: Not Risk (290.0/50.0) NumCourse = Low AND Continent = Asia AND Age = 18-25 years AND ActivityHr_5 = Low AND WorkHr_5 = Medium: Risk (14.0/3.0) Credit = 12-15 credits AND ESL = No AND ActivityHr_5 = Lowest AND Status = Single AND School_ed = High School AND Dorm = Yes: Not Risk (82.0/21.0) WorkHr_5 = Lowest AND NumCourse = Low: Not Risk (139.0/39.0) Credit = < 12 credits AND Gender = Male AND Status = Single: Risk (95.0/18.0) ESL = No AND Credit = > 15 credits: Not Risk (109.0/9.0) Table 2. Performance comparison between Decision Tree and Neural Network models Performance Measures Correctly Classified Instances (%) Incorrectly Classified Instances (%) Precision Recall

J48 (C4.5) 85.188 14.812 0.852 0.852

MLP 83.875 16.125 0.838 0.838

Table 2 shows performance comparison between the Decision Tree and the Neural Network models. When comparing the Precision Value and the Recall Value of the 2 models, the Decision Tree model generates the Precision Value of 0.852. This represents the number of found class and a prediction accuracy of 85.2% when compared to the number of whole classes from the database. It is in line with the Recall Value of 0.852 which also means the number of found class and a prediction accuracy of 85.2% when compared to the number of whole classes from the database. On the other hand, the Neural Network model generates the Precision Value of 0.838 or 83.8% and the Recall Value of 0.838 or 83.8%. The results reveal that the Decision Tree Model gives more accurate prediction than the Neural Network Model. When comparing the Precision and Recall values ​​of the Decision Tree Model, the results are equal at 0.852 or 85.2%, while the Precision and Recall values of the Neural Network Model are also equal at 0.838 or 41

83.8%. The results show the accuracy of prediction when compared to the found classes and all classes in the database. The percentages of accuracy are equal. Conclusion and future work After classifying the academic achievement of undergraduate students registered in the International Program using the Decision Tree and Neural Network techniques, it can be concluded that the Decision Tree technique has better accuracy of data classification for this data set. The analysis of important factors for grouping students could be concluded as follows: Firstly, most of the students who do not have risk of low academic achievement are the students who have never studied additional English courses. This group of students had a good grasp of English proficiency before entering the university. This is why they did not need to take additional English courses to improve their English skill. They are single, work few hours per semester, and register for 12-15 credits per semester. Secondly, most of the students who have risk of low academic achievement are the students who study additional English courses. The result shows that this group of students did not have good foundation of English proficiency before entering the university. This is why they needed to take additional English courses to improve their English skills. Many of them are either married or divorced. They work at a moderate to high number of hours per semester. They register for either less than 12 credits per semester (students are not allowed to register more than 12 credits if the CGPA is low) or more than 15 credits per semester (students are allowed to register more than 15 credits if the CGPA is high). There were several limitations to the study and any future study should expand to look at some of the following issues. Firstly, the results from the data analysis from the data mining method are only the important factors affecting student achievement. Each factor has a different significant value. Thus, the grouping of students who are at risk or not at risk, and other factors or elements should be considered as well. Secondly, the model can be improved to be applicable to analyze the risk level of students, and find ways to advise and assist the at-risk students. Thirdly, the research analyzed only data from students registered in the international program of the undergraduate level. Future investigation should expand this study in applying this method to other study programs. There are some recommendations to the institution studied. Firstly, there should be a system to record important student information accurately and completely. Lastly, there should be a central database to store the information of all students. References Affendey, L. S., Paris, I.H.M., Mustapha, N., Nasir Sulaiman, Md. & Muda, Z. (2010). Ranking of Influencing Factors in Predicting Students’ Academic Performance. International Technology Journal, 9(6), 832-837. Beikzadeh, M. R. & Delavari N. (2005). A New Analysis Model for Data Mining Processes in Higher Educational Systems: proceedings of the 6th Information Technology Based Higher Education and Training, 7-9 July 2005. Chen, M. S., Han, J. & Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on  Knowledge and Data Engineering, 8 (6), 866 – 883. Connolly, T., Begg, C. & Strachan, A. (1999). Database Systems: A Practical Approach to Design Implementation and Management. Harlow: Addison-Wesley. Elouedi, Z., Mellouli, K., & Smets, P. (2000). Decision trees using the belief function theory. In Proceedings of the international conference on Information Processing and Management of Uncertainty IPMU.1. 141-148. Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17 (3), 37-54. Hagan, M. T. & Menhaj, M. B. (1994). Training Feed-forward Networks with the Marquardt Algorithm. IEEE Trans. on Neural Networks, 5(6), 989-993. Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: principles and practice [OTexts]. Retrieved from https://www.otexts.org/fpp/9/3 Ian, H. W. & Eibe, F. (2005). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. California: Morgan Kaufmann.

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Kotsiantis, S. B., Pierrakeas, C. J. & Pintelas, P. E. (2003). Preventing Student Dropout in Distance Learning Using Machine Learning Techniques. In proceedings of 7th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2003), 267-274. Merceron, A. & Yacef, K. (2005). Educational Data Mining: a Case Study. In Proceedings of the 12th International Conference on Artificial Intelligence in Education AIED 2005, Amsterdam: IOS Press. Minaei-Bidgoli, B., Kashy, D., Kortemeyer, G. & Punch, W. (2003). Predicting Student Performance: An Application of Data Mining Methods with an Educational Web-Based System. In the Processing of 33rd ASEE/IEEE conference of Frontiers in Education. Piatetsky-Shapiro, G. & Frawley, W. J. (1991). Knowledge Discovery in Databases. MIT Press. Polpinij, J. (2002). The Probabilistic Models Approach for Analysis the Factors Affecting of Car Insurance Risk. M.S. thesis, Department of Computer Science, Kasetsart University. Thailand. Quinlan, J. R. (1990). Decision trees and decision-making. Systems, Man and Cybernetics, IEEE Transactions on, 20(2), 339-346. Quinlan, J. R. (1992). C 4. 5: Programs for Machine Learning. Morgan Kaufmann. Romero, C., Ventura, S. & Garcia, E. (2008). Data Mining in Course Management Systems: Moodle Case Study and Tutorial. Computers & Education, 51(1). 368-384. Thai Nghe, N., Janecek, P. & Haddawy, P. (2007). A Comparative Analysis of Techniques for Predicting Academic Performance. ASEE/IEEE Frontiers in Education Conference. Utgoff, P. E. (1989). Incremental induction of decision trees. Machine learning, 4(2), 161-186. Waiyamai, K. (2003). Improving Quality of Graduate Students by Data Mining. Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Thailand. About the Author Pimpa Cheewaprakobkit, instructor in the Computer Information System Program, Faculty of Business Administration, Asia-Pacific International University.

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Catalyst ISSN: 2408-137X, Volume 12, No. 2, 2015 Institute Press

Risk Factors for Hypertension among a Church-based, Black Population in London

Maxine A Newell, Naomi N Modeste, Helen Hopp Marshak, Colwick Wilson, Sherma J. Charlemagne-Badal

Abstract Compared to other ethnic groups, there is an increased prevalence of hypertension, and subsequent morbidity and mortality, among people of African descent residing in the United Kingdom. We studied a group of people of African descent living in London to examine the impact of their lifestyle on hypertension. A cross-sectional study, using a convenience sample of individuals aged 25-79 from 17 predominantly Black Seventh-day Adventist churches across London. Linear regressions were run between the main variables of RR score for hypertension and blood pressure levels. Hypertension was more prevalent among males (34%) than females (21.6%). Relative Risk Estimates for hypertension were predictive of diastolic blood pressure (p 25) and obesity (a BMI > 30) are major risk factors for many chronic conditions such as diabetes, cardiovascular disease and coronary heart disease (Goldberg, 2003). They are related to HTN, independent of age or gender, (Appleby, Davey, & Key, 2002; Lopes, Bortolotto, Szlejf, Kamitsuji, & Krieger, 2001) and account for 78% and 65% of HTN in males and female, respectively (Pausova, 2006; El-Atat, Aneja, McFarlane, & Sowers, 2003). The prevalence rates of overweight (including obesity) for Caribbean (64.5%) and African (69.8) women in the UK are somewhat higher than those of the general female population (51.7%). The overweight prevalence rates for African men (61.8%) are lower than the general male population (66.5%) while those for Caribbean men (67.4%) are higher (National Health Services, 2005). Specific to the prevention and treatment of HTN is physical activity (PA): 20-30 minutes of moderate to vigorous cardiovascular exercise on 4-5 days of the week reduces BP and is effective for long-term HTN control (Pescatello et al., 2004). Additionally, many practitioners recommend accumulated PA, which is less intense, as a modality for treating HTN which can significantly reduce BP in both pre-hypertensive and hypertensive individuals (Brookes, 2005; Padilla, Wallace, & Park, 2005). Sodium intake has also been consistently linked to HTN. He and Macgregor (2002) conducted a meta-analysis of randomized control trials on salt intake and HTN. The results demonstrated that a reduction of sodium had a significant effect on SBP and DBP: On average the BP of hypertensives decreased by 5/3 mm Hg and normotensives by 2/1 mm Hg. They suggest that, in the long-term, population salt reduction would have a positive impact on public health by decreasing BP and therefore cardiovascular mortality. Hooper et al.’s (2004) meta-analysis also led them to conclude that sodium reduction resulted in an average decrease of 1.1 mm Hg for SBP and 0.6 mm Hg for DBP, and that the low salt diet enabled those who were hypertensive to discontinue their medications without a subsequent loss of BP control. Diet also plays a key role in the prevention and treatment of HTN. In 2002, John et al. (2002) carried out a randomized control trial to examine the specific effects of fruit and vegetable consumption on BP. They reported that after six months of a diet with a minimum of five daily portions of fruit and vegetables, the SBP and DBP of the intervention group fell 4 mm Hg and 1.5 mm Hg respectively, more than in the control group. Comparisons of the effects of vegetarianism, and non-vegetarianism show that the SBP and DBP of vegetarians are 3 to 14 mm Hg and 5 to 6 mm Hg lower, respectively, than non-vegetarians. For vegetarians, the prevalence of HTN ranges from 2% to 40%, while the range is from 8% to 60% in non-vegetarians (Berkow & Bernard, 2005). As part of its policy on health, the British government set one of the 2010 targets as the reduction of deaths from heart disease and stokes in those less than 75 years of age by two fifths. One of the means by which they planned to achieve this goal was by the improvement of individual lifestyles (Department of Health, 1999). Most of these lifestyle recommendations are part of the emphasis on diet and healthy living advocated by the Seventh-day Adventist (SDA) church (Fraser, 1999; Fønnebø, 1994). In the United States (US), the health profile of Black SDAs is better than that of non-SDA Blacks (Montgomery et al, 2007). Therefore, this study aimed to examine the lifestyles of SDAs living in London and to compare their risk factors for HTN to US Black SDAs and to the non-SDA Black population in the UK. Methods This was a cross-sectional study of Black SDA Christians living in London. Using a convenience sampling technique, 352 participants from 17 predominantly Black SDA churches, across London, self-selected to be part of the study. The participants completed a questionnaire and had their blood pressure and anthropometric measurements taken by qualified nurses. 45

Participants were included if they: (a) were current members of the SDA church, (b) resided in any of the London boroughs, (c) were between the ages of 25 and 79 years, and (d) self-identified as Black. Participants were excluded if they: (a) reported current use of alcohol or tobacco, (b) had a diagnosis of HTN and currently taking medication to control BP. Measures Blood Pressure. Blood pressure was recorded using digital monitors and classified according to the British Hypertension Society (BHS)(Mead, 2004). It was measured seated, once in each arm and then repeated in the arm with the highest reading. An average of the repeated measure in one arm was calculated. BMI. Anthropometric measures were taken with participants in light clothing and without shoes. Weight was measured using a Conair Weight Watchers Glass Memory Precision Electronic Scale (WW43). Height was measured using a portable Seca Leicester height measure. Body mass index was calculated as the body weight in kilograms divided by height in meters squared, and classified as < 18.5 being underweight, 18.5 – 24.9 being normal, 25.0 – 29.9 being overweight and 30 – 39.9 being obese, and > 40 being extremely obese. Waist circumference (WC) was measured and recorded in centimeters using a flexible measuring tape. (Increased cardiovascular disease risk when WC is > 102 cm in men and 88 cm > in women.) Survey Instrument. A structured questionnaire was developed for use in this study. The items in the first section focused on demographic questions such as age, gender, education, and yearly income. Questions were formulated to assess the participants’ knowledge and lay beliefs about HTN. The health belief model (HBM) was the theoretical framework used to develop the section examining perceptions about HTN. Most of the items for the HBM constructs were drawn from the instrument developed by Desmond, Price, Roberts, Pituch and Smith et al (1992) or adapted from Champion’s (1984) HBM scale. Cohen’s 10-item Perceived Stress Scale was used for measuring stress (Cohen, Kamark, & Mermelstein, 1983). Also included were items on diet (salt, fruit, and vegetable consumption, whether participants were vegan or vegetarian) and levels of daily PA to evaluate participants’ current practices related to HTN prevention. The questionnaire was checked for clarity before it was used for data collections. Building on the 10-year relative risk estimates (RRE) for cancers developed by researchers at Harvard Medical School and Harvard School of Public Health (Colditz et al, 2000), researchers at the Siteman Cancer Center expanded the RRE to include heart disease and stroke (Siteman Cancer Center, nd.). Using the heart disease and stroke estimates as guidelines a 10-year RRE was developed to score the risk of HTN for the participants in this study (Table 1). Table 1. Ten-Year Relative Risk Estimates for Hypertension Risk Factor Age: > 50 Female Family History Parental Sibling BMI Women 25-28.9 > 30 Men 25-29.9 > 30 Waist circumference Women >35in Men >40in

46

RR Score +2 +1 +2 +3

+2 +3 +2 +3 +1 +2

Risk Factor Stress Score >14.7 Salt Added during cooking Generally added at table without tasting food Generally taste then food then add salt at the table Taste food and occasionally add salt at the table Rarely/never add salt to food at the table Vegan Vegetarian Fish > 3 servings per week Red meat consumption > 3 servings per week White meat consumption > 3 servings per week Fruit/Vegetable > 5 servings per day Physical Activity at least 30 minutes per day for five days or three hours per week

RR Score +2 +1 +3 +2 +1 -1 -4 -3 -2 +2 +1 -1 -2

Data Entry/Analysis. The Statistical Package for the Social Sciences (SPSS) for Windows software program version 14 was used for data entry and analysis Linear regressions were run to examine the associations between the RRE score for HTN, SBP and DBP, as classified by the BHS. Results Demographics A total of 352 questionnaire were distributed. 27 (7.67%) were not returned, and 13 of those returned could not be used. Ultimately the sample size used for analyses was 312. Table 2 provides details on the respondents surveyed. Of the 312 respondents, the majority were born in the Caribbean, followed by those born in the UK. Two thirds were female. The mean age for all of the respondents was 44.37 years. When age was examined by place of birth, African Blacks were on the average younger than Caribbean Blacks. The majority (43.2%) reported being married for the first time followed by 36.4% who were single/never married. A little over one third (34.7%) completed graduate degrees. Table 2. Demographic Characteristics of Participants Total* N=312

Caribbean 171 (55%)

African U.K. born 32 (10.3%) 108 (34.7%)

Gender‡ Male Female

94 (31.1) 208 (68.9)

53 (31.7) 114 (68.3)

11 (37.9) 18 (62.1)

30 (28.6) 75 (71.4)

Mean Age (SD)

44.37 (12.7)

49.24 (14.1)

37.38 (10.8)

38.9 (6.4)

Marital Status Single/Never married First time married Remarried Divorced

p-value