How to Submit Proof Corrections Using Adobe ...

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IJMBL Editorial Board Editor-in-Chief:

David Parsons, Massey U. - Auckland, New Zealand

Associate Editors:

Hokyoung Ryu, Hanyang U., Korea Elizabeth Stacey, Elizabeth Stacey Educational Consulting, Australia Rosemary Stockdale, Swinburne U. of Technology, Australia John Traxler, U. of Wolverhampton, UK Norman Vaughan, Mount Royal U., Canada Giasemi Vavoula, U. of Leicester, UK International Editorial Review Board:

Sohaib Ahmed, Bahria U., Pakistan Trish Andrews, U. of Queensland, Australia Rajarathinam Arangarasan, The Raj Organization, USA Inmaculada Arnedillo-Sánchez, Trinity College Dublin, Ireland Margaret Baguley, U. of Southern Queensland, Australia Brenda Bannan, George Mason U., USA Adele Botha, Meraka Institute, South Africa Maiga Chang, Athabasca U., Canada Yunhi Chang, Dankook U., Korea, Republic Of Dragan Cisic, U. of Rijeka, Croatia Thomas Cochrane, AUT U., New Zealand John Cook, U. of the West of England, UK Rob Cooper, Southampton Solent U., UK Patrick Danaher, U. of Southern Queensland, Australia Linda De George-Walker, Central Queensland U., Australia Laurel Dyson, U. of Technology, Sydney, Australia Kay Fielden, UNITEC Institute of Technology, New Zealand Elizabeth FitzGerald, The Open U., UK Robert Folden, Texas A&M U.-Commerce, USA Rahul Ganguly, U. of Southern Queensland, Australia Dion Hoe-Lian Goh, Nanyang Technological U., Singapore Tiong-Thye Goh, Victoria U. of Wellington, New Zealand Sam Goundar, Victoria U. of Wellington, New Zealand Joachim Griesbaum, U. of Hildesheim, Germany Margarete Grimus, Graz U. of Technology, Austria Louise Hawkins, Central Queensland U., Australia Aleksej Heinze, U. of Salford, UK Debbie Holley, Anglia Ruskin U., UK Andreas Holzinger, Medical U. Graz (MUG), Austria Joaquim Jorge, U. of Lisboa, Portugal Terry Kidd, U. of Houston-Downtown, USA Michelle Kilburn, Southeast Missouri State U., USA Andrew Kitchenham, U. of Northern British Columbia, Canada Jayne Klenner-Moore, King’s College, USA Agnes Kukulska-Hulme, The Open U., UK

Kwan Lee, U. of Southern Calilfornia, USA Marshall Lewis, Westpac, New Zealand Heide Lukosch, Delft U. of Technology, Netherlands Andrew Luxton-Reilly, U. of Auckland, New Zealand Ross A. Malaga, Montclair State U., USA Masood Masoodian, U. of Waikato, New Zealand David Metcalf, U. of Central Florida, USA Warren Midgley, U. of Southern Queensland, Australia Marcelo Milrad, Linnaeus U., Sweden Mahnaz Moallem, U. of North Carolina-Wilmington, USA Azadeh Nemati, Islamic Azad U., Iran, Islamic Republic Of Julian Newman, Glasgow Caledonian U., UK Hiroaki Ogata, U. of Tokushima, Japan Norbert Pachler, Institute of Education, U. of London, UK Krassie Petrova, Auckland U. of Technology, New Zealand Christoph Pimmer, U. of Applied Sciences, Switzerland Amarolinda Saccol, U. of Vale do Rio dos Sinos, Brazil Jaime Sánchez, U. of Chile, Chile Daniyar Sapargaliyev, Almaty Management U., Kazakhstan Eunice Sari, Online Learning Community for Teacher Professional Development, Singapore Abdolhossein Sarrafzadeh, Massey U., New Zealand Lori Scarlatos, Stony Brook U., USA Eric Seneca, Louisiana State U., USA Robina Shaheen, Coffey international Development, UK Mike Sharples, The Open U., UK Marcus Specht, Open U. of the Netherlands, Netherlands Sue Stoney, Edith Cowan U., Australia Thomas Sweeney, U. of Nottingham, UK Siobhán Thomas, Pervasive Learning, UK Mark Tyler, Griffith U., Australia Ruth Wallace, Charles Darwin U., Australia Marilyn Wells, Central Queensland U., Australia Jocelyn Wishart, U. of Bristol, UK Jane Yau, Malmö U., Sweden Ronda Zelezny-Green, London U., UK

IGI Editorial: Lindsay Johnston, Managing Director Jeff Snyder, Copy Editor Christina Henning, Production Editor Sean Eckman, Development Editor Austin DeMarco, Managing Editor James Knapp, Production Assistant

International Journal of Mobile and Blended Learning July-September 2015, Vol. 7, No. 3

Table of Contents Editorial Preface iv

David Parsons, Massey University, Auckland, New Zealand

Research Articles 1

An Exploration of Pre-Service Teachers’ Intention to Use Mobile Devices for Teaching Jung Won Hur, Auburn University, Auburn, AL, USA Ying W. Shen, University of Northwestern - St. Paul, St. Paul, MN, USA Ugur Kale, West Virginia University, Morgantown, WV, USA Theresa A Cullen, University of Oklahoma, Norman, OK, USA

19

Clustering Students Based on Motivation to Learn: A Blended Learning Approach Maria Alexandra Rentroia-Bonito, Genuinas Consulting Group, Lisbon, Portugal Daniel Gonçalves, INESC-ID, Lisbon, Portugal, & Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal Joaquim A Jorge, INESC-ID, Lisbon, Portugal & Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

41

Evaluating a Mobile and Online System for Apprentices’ Learning Documentation in Vocational Education: Usability, Effectiveness and Satisfaction Alberto A. P. Cattaneo, Swiss Federal Institute for Vocational Education and Training, Lugano-Massagno, Switzerland Elisa Motta, Swiss Federal Institute for Vocational Education and Training, Lugano-Massagno, Switzerland Jean-Luc Gurtner, Department of Education, University of Fribourg, Fribourg, Switzerland

62

Presence and Perceived Learning in Different Higher Education Blended Learning Environments Wan Zah Wan Ali, Department of Foundations of Education, University Putra Malaysia, Selangor, Malaysia & Department of Education, HELP University, Kuala Lumpar, Malaysia Rouhollah Khodabandelou, Department of Foundations of Education, University Putra Malaysia, Selangor, Malaysia Habibah Jalil, Department of Foundations of Education, University Putra Malaysia, Selangor, Malaysia Shaffe Mohd Daud, Department of Foundations of Education, University Putra Malaysia, Selangor, Malaysia

Copyright

The International Journal of Mobile and Blended Learning (IJMBL) (ISSN 1941-8647; eISSN 1941-8655), Copyright © 2015 IGI Global. All rights, including translation into other languages reserved by the publisher. No part of this journal may be reproduced or used in any form or by any means without written permission from the publisher, except for noncommercial, educational use including classroom teaching purposes. Product or company names used in this journal are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. The views expressed in this journal are those of the authors but not necessarily of IGI Global. The International Journal of Mobile and Blended Learning is indexed or listed in the following: ACM Digital Library; Applied Social Sciences Index & Abstracts (ASSIA); Bacon’s Media Directory; Cabell’s Directories; Compendex (Elsevier Engineering Index); DBLP; GetCited; Google Scholar; INSPEC; JournalTOCs; Library & Information Science Abstracts (LISA); MediaFinder; Norwegian Social Science Data Services (NSD); PsycINFO®; SCOPUS; The Index of Information Systems Journals; The Standard Periodical Directory; Ulrich’s Periodicals Directory

62 International Journal of Mobile and Blended Learning, 7(3), 62-73, July-September 2015

Presence and Perceived Learning in Different Higher Education Blended Learning Environments Wan Zah Wan Ali, Department of Foundations of Education, University Putra Malaysia, Selangor, Malaysia & Department of Education, HELP University, Kuala Lumpar, Malaysia Rouhollah Khodabandelou, Department of Foundations of Education, University Putra Malaysia, Selangor, Malaysia Habibah Jalil, Department of Foundations of Education, University Putra Malaysia, Selangor, Malaysia Shaffe Mohd Daud, Department of Foundations of Education, University Putra Malaysia, Selangor, Malaysia

ABSTRACT Blended learning as “third generation” of distance learning has the potential to offer multimethod instruction through the blend, to leverage the strengths of current online and traditional instructions. Therefore, higher education institutions having recognized the fact that blended learning is beneficial, adopted this alternative approach as a new educational delivery method. The study determined the difference in perceived learning among three different blended learning environments in Malaysian higher education institutions. The data were collected from three public universities in Peninsular Malaysia and the respondents were undergraduate students from these universities. The result showed that the students’ presence in classroom meetings contributes to their learning. The results also indicate that high levels of perceived learning were reported by undergraduate student in the blended learning environment face-to-face meeting rather than online sessions Keywords:

Blended Learning Environment, Higher Education, Perceived Learning, Presence

INTRODUCTION The advance of blended learning in higher learning institutions has been anticipated for years. DOI: 10.4018/IJMBL.2015070104

For instance, in the United States, the blended learning programmes grew from 49% in 2003 to 56% in 2005 (Allen & Seaman, 2006; So, 2009). Moreover, in a research in 2003, 70% of participants predicted that more than 40% of higher education courses would be delivered in a blended format by 2013 (Bonk, Kim

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

International Journal of Mobile and Blended Learning, 7(3), 62-73, July-September 2015 63

& Zeng, 2006). Furthermore, the results of a meta-analysis showed that interest in blended learning continues to increase (Means, Toyama, Murphy, Bakia & Jones, 2009). Additionally, Shea and Bidjerano (2009a, b) articulated that the number of student enrolment in blended learning is growing at a rate of ten times more than traditional face-to-face instruction in higher learning institutions. Based on Ambient Insight (2011) prediction, e-learning and blended learning industry income will reach 49.9 billion dollars in 2015. It has been predicted that Asia after America will become the second largest consumer of e-learning and blended learning products and services. Malaysia has been identified as one of the top three largest online markets in Asia (Ambient Insight, 2011). Clearly, with high level of online and blended learning growth, it is important that researchers continue efforts to understand many instructional challenges and complexity of teaching and learning process in blended learning environments. Therefore, based on the above statement, there are various blended learning environment, blended learning approaches, and blended learning models. According to Chew (2009), there is neither a standard nor simple stage-like model to blended learning in higher education for all disciplines and all institutions. Since, in different parts of the world, blended learning has been implemented in various formats higher education institutions. Blended learning is often personalized by different needs and requirements of individual, discipline or organization. Therefore, each blended learning environment has its own advantages, disadvantages, issues and challenges. Graham and Dziuban (2008) emphasized that different blended learning environments may have different effects on student’s learning effectiveness and cost effectiveness. Chew (2009) and Wang (2010) studied and compared different blended learning environments in various higher education institutions in order to obtain an overview of current practices in blended learning. Clearly, different blended learning environments have rendered learning more flexible and accessible and these activities

have greatly enhanced learner autonomy and collaborative learning (Wang, 2010). Moreover, different blended learning environments have created new opportunities for students to have new learning experience (Vaughan, 2010). Currently, only a small (but growing) body of research is specifically comparing blended environments. In this respect, there is a need of more studies to focus on this area. Thus, the main focus of the current study is comparing different blended learning environments in Malaysian higher education context. Since students’ learning is a very broad concept; its application should be narrowed down. Students’ learning has many definitions including successful completion of a course, course withdrawals, grades, added knowledge, and skill building (Picciano, 2002). Using grades to operationalize students’ learning may not always provide the best results. As argued by Rovai, Wighting, Baker and Grooms (2009), grades particularly for higher education courses, tend to have very restricted ranges, such as uniformly superior achievement. Thus, there is a limitation of using grades in higher education studies. Additionally, as Corrallo (1994) indicated, the grades have little relationship with students’ output. Thus, there is a need to have other approaches to measure students’ learning. In contrast to grades, another approach to measuring student’s learning is perceived learning that measures student’s perceptions about their own learning. Although grades have been the primary criterion for establishing the validity of student evaluations, the grades reflect only what students have learned (Centra & Gaubatz, 2002). Research evidence suggested that perceived learning can be a valid measure of students’ learning (Rovai et al., 2009). Based on Centra and Gaubatz (2002) in contrast to grades, perceived learning can be studied across a wide variety of courses. In blended learning environments there is a separation between the instructor and the student and among the students themselves. This separation obviously leads to feelings of isolation on the part of instructors and students as well as low participation of students



in academic activities. The separation and low participation of students in academic activities have been identified as main reasons for student dissatisfaction and decrease of student’s success in the blended learning environments (Lehman & Conceição, 2010). Currently, only a small (but growing) body of research is specifically comparing blended environments particularly in Malaysia. In this respect, there is a need of more studies to focus on this area. Thus, the main focus of the current study is comparing different blended learning environments in Malaysian higher education context. Part of the aim of this study is to identify the role of presence in students learning in blended learning environments. Other purpose of this research is to determine if there is a difference in perceived learning among three different blended learning environments in Malaysian selected higher education institutions. Based on the above objectives two research questions were raised. 1. Is there a statistically significant difference in students’ perceived learning in face to face and online sessions of blended learning environments? 2. Is there a statistically significant difference in perceived learning among distance education students in different blended learning environments?

METHOD 332 undergraduate students from 3 Malaysian public universities which offer blended learning environments participant in this study. The gender composition of respondents was less balanced with 215 or 65% female respondents and 117 or 35% male respondents. Based on the questionnaire information, there were 153 (46%) students from Uni1, 115 (35%) of them were from Uni3, while only 64 (19%) of the respondents were from Uni2. Furthermore, based on the questionnaire information, 71.4% of respondents were the Malays, 21% were Chinese, and only 4% were Indians. This research

employed quantitative technique for gathering data. As mentioned earlier, blended learning environment consists of online and face-to face-sessions. To test the degree of students’ perceived learning in blended learning class, 12 items were designed by the researchers in two parts. The first part includes 6 items which measured the degree of students’ perceived learning in online session. The second part includes same items to test the degree of students’ perceived learning in face-to-face session of the blended learning environment. It is necessary to point out that the two parts of the test measured the same concept. The items of Perceived learning were modified from the CAP Perceived Learning Scale created by Rovai et al. (2009), and the perceived learning achievement scale (Kim, 2011). Both questionnaires contain a seven-point Likert ranging from 0 = not et all to 6 = very much so. The Cronbach’s Alpha coefficient was used to test internal consistency. Perceived learning construct was considered to be a reliable factor with an alpha level of 0.89. However the degree of students’ perceived learning scale was found to be a strong and highly reliable factor with an alpha level of 0.87.

FINDINGS Research Objective and Research Question No. 1 The first research objective in this study was to determine the role of presence in student’s perceived learning in different blended learning environments. In order to examine this objective, the following research question was raised. Is there a statistically significant difference in students’ perceived learning in face to face and online sessions of blended learning environments? The researcher used paired-samples t-test to test significant role for students’ perceived learning in different blended learning environments as measured by perceived learning questionnaire. Table 4-4 shows the result of the test. In Table 4-4 there is a need to study



Table 4. Multiple comparisons of the means of the three blended learning environments in the perceived learning scale (I) University

(J) University

Mean Difference (I-J)

Std. Error

Sig.

Uni1

Uni3

-4.35*

0.50

.000

Uni2

-1.68*

0.60

.015

Uni3

Uni2

2.66*

0.63

.000

* The mean difference is significant at the 0.05 level.

the final column, labelled Sig. (2-tailed), in which is the probability (p) value. If this value is less than .05 (e.g. .04, .01, .001), it can be concluded that there is a significant difference between the two scores. Take note of the t value (Online, -85.76; F2F, -72.83) and the degrees of freedom (df = 331). It should also be noted that the mean difference in the two scores is 2.85, with a 95 per cent confidence interval stretching from a Lower bound of -58.74 to an Upper bound of -56.11 for online construct and Lower bound of -56.05 to an Upper bound of -53.11 for face-to-face construct. Hence, there is a significant difference somewhere among the mean scores on dependent variables for the contribution of face-toface and online on learning. To understand the amount of magnitude of the intervention’s effect, the researcher needs to calculate the effect size for paired-samples t-test from the following formula: Eta squared = Eta squared =

t2

t + (N − 1) 2

12.602

12.602 + (348 − 1)

158.76 = 0.31 505.76 Based on Cohen’s (1988) guideline on the interpreting the strength of effect size considers ð2 = 0.1 is a small effect, 0.25 is a medium effect and 0.4 is a large effect. Given eta squared value of 0.31, it can be concluded that there has been a medium effect, with a substantial difference in the sores on perceived learning. In summary, paired-samples t-test has been conducted to identify student’s perceived learning in face-to-face and online session in blended learning environment. There was a statistically significant difference in perceived learning scores in face-to-face (M= 26.96, SD= 4.93) and online session of blended learning environment (M= 22.75, SD= 5.46), t (347) = 12.60, p< 0005 (two-tailed). The mean increase in perceived learning scores was 4.20 with a 95% confidence interval ranging from 3.55 to 4.86. The eta squared statistic (0.31) indicated a medium effect size.

Table 1. Paired sample t-test to examine the perceived learning differences between online and face-to-face sessions Paired Differences

F2F-Online

Mean

SD

SD. ErrorMean

95% Difference Lower

Upper

4.20

6.22

0.33

3.55

4.86

t

df

Sig

12.60

347

0.000



Research Objective and Research Question No. 2 The second research objective in this study is to identify the perceived learning in different blended learning environments. In order to examine this objective, the following research question was raised. Is there a statistically significant difference in perceived learning among distance education students in different blended learning environments? Till now the researcher found out that there are differences between face-to-face and online scores and presence plays a significant role in blended learning environments. To examine the second research question and understand the amount of differences between groups in students’ perceived learning three statistical measures were used. First, descriptive statistics were performed to illustrate the means, the standard deviations of the data. Second, to investigate the between-group and within-group differences, a one-way ANOVA, was used for the null hypothesis. Third, after the results of the one-way ANOVA were obtained, a post hoc multiple comparison test was performed to determine which means were significantly different from each other as well as to show that whether if there is any differences between three blended learning environments (Uni1, Uni2 and Uni3) undergraduate students’ perception about their learning.

Differences among the Three Different Blended Learning Environments in Students’ Perceived Learning The results of descriptive statistical analysis are shown in Table 4-5. Table 4-5 shows the mean scores and standard deviation of student’s perceived learning in three different blended learning environments. The result of descriptive analysis also indicates that ANOVA can be used. The table also shows the number of participation of each blended learning environment in face-toface session. For example, Uni1 undergraduate students attending two times of face-to-face

sessions during each semester. While the Uni2 undergraduate students attending 3 times, the Uni3 undergraduate students attending 4 times of face-to-face sessions during each semester. As Table 2 shows the mean score of the first blended learning environment (Uni1) with the number of face-to-face session which is 2 times in each semester in the perceived learning is 23.81. The mean score of the second blended learning environment (Uni2) with the number of face-to-face session which is 3 in the perceived learning is 25.50. Finally, the mean score of perceived learning for Uni3 students is 28.16 while they participate 4 times in the face-to-face session each semester. From the above table we can understand that the number of face-to-face session is related to the mean score of students’ perceived learning.

One-Way Analysis of Variance (ANOVA) Before considering ANOVA, the homogeneity of variance of three groups was calculated by Levene’s test to observe whether the variance is the same for each of the three groups. The observed value is 0.058 which is greater than 0.050; therefore, the homogeneity of variance assumption has not been violated. It indicates the variance of the three groups is the same. It means that we can use one-way ANOVA to test the second hypothesis. Having only one dependent variable, namely the students’ perceived learning; the most appropriate statistical measure is a oneway ANOVA. To check between-groups and within-groups sums of squares, degrees of freedom, and significant value, the researcher presents them in the ANOVA table (Table 3). The main interesting thing in this table is the Sig column. If the Sig. value is less than or equal to 0.05 (e.g. 0.03, 0.001), there is a significant difference somewhere among the mean scores on the dependent variable (perceived learning) for the three undergraduate students groups. The results of the one-way ANOVA showed there was variability in the perceived learning within each group and between the



Table 2. Summary table of students’ perceived learning differences in the blended learning environments University

N

No of Face-to- Face Session

Mean

Std. Deviation

Uni1

160

2

23.81

4.36

Uni2

67

3

25.50

3.86

Uni3

121

4

28.16

3.71

Total

348

25.64

4.48

three undergraduate students groups. Table 4, clearly shows that there was a significant difference in the perceived learning both between and within the three undergraduate students groups in Uni1, Uni2, and Uni3 F(2, 329) = 37,912 at the p

How to Submit Proof Corrections Using Adobe ...

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