Lack of control on the time spend to use the technology, for example, Facebook, can lead to lesser time use on learners' studies and eventually obtain lower ...
CHAPTER ONE INTRODUCTION
1.0
Introduction This chapter provides the essential indulgent of the research. This chapter will
discuss on the background of the research which including the description for the term information behavior and technology affinity as the subject that will be further discussed throughout the research. Besides that, this chapter also discussed on
statement of
problem, research objective, definition of terms, conceptual framework, research question, research hypotheses, significant of the research as well as the limitation of the study.
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1.1
Background of the study Education system in Malaysia has evolved as the technology evolved. Minister of
Education Malaysia (2013) has proposed a plan which known as “Malaysia Education Blueprint 2013-2025”. The Blueprint has been established with three specific objectives. The first objective is to understanding the current performance and challenges of Malaysia education system. The focus are to improving the accessibility to education, raising the quality of education, closing achievement gaps (equity), promoting unity amongst students, and maximizing system efficiency. The second objective is establishing a clear vision and aspirations for individual students and education system as in one piece over the next 13 years. Lastly, the third objective is to outlining a comprehensive transformation programme for the system, including key changes to the Ministry which permit it to meet new demands and increasing expectation as well as to spark and support on the whole civil service transformation. Based on Malaysia Education Blueprint (2013), the seventh (7th) shift stated that “leverage ICT to scale up quality learning across Malaysia”. Ministry of Education Malaysia (MOE) aim to provide the Internet access and virtual learning environment via programme named 1Bestari for all 10,000 schools by 2013. Besides that, this shift is also target to augment online best practice content starting with a video library of best teacher delivering learning content for critical subject by 2013. Lastly, MOE seek to maximize the use of ICT for distance and self-paced learning to widen the capacity and allow for more customized learning.
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Information and communication technology has been mentioned as a tool which plays as a central, usual role in formal and informal inquest for seeking and sharing information (Mills, Knezek, & Wakefield, 2013). It is a common phenomenon to see all information which is before this embedded within sheets of paper now transferred to the Internet. There are several factors which support informal and formal learning through online, which are the internet coverage which has widen that granted access to the Internet from anywhere and emergence of Web 2.0 (Said & Tahir, 2013). Web 2.0 has become a major technology which supports content publishing and sharing across the Internet. Web 2.0 is a second generation of Web technology which allows users to create, share, publish, exchange, and cooperating knowledge in a new mode of communication and collaboration (Stephen, Irene, Chen & Jeff, 2007). Tredinnick (2006) define Web 2.0 as a process of give control of application to users, enabling users to extract information and data and to use or reuse the information as well as enabling user to alter the structure of the information. Web 2.0 provide a standard platform that make use of Human Computer Interaction (HCI) and the most recognized technology which labelled for Web 2.0 are like e-Learning, professional business and organizational environments, social networking communities (Olaniran, Burley, & Chang, 2010). Web 2.0 also been describe to use Internet as a platform for device that can be connected (Kenney, 2007; O’ Reilly, 2005).
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Technology acts as medium in learning as it is parallel to the use of Web 2.0 in the 21st century learning. Tools that are available such as Padlet, Edmodo, Evernote and other applications promote collaborative and cooperative learning among learners. Prensky (2001) explained that y-generation students spent their time more on, surrounded by, and using computers, video games, digital music players, cell phone and other digital tools. Teens and teenagers have mostly recognized internet sites to be able to connect with their peers, share information, and polish their personas as well as showcase their social lives (Ellison, 2007). Technology has been integrated with learning session which explains the transition from traditional classroom to a smart classroom. The main different between these two settings are the training, skills, and expectation (Mahlenbacher, 2010). In order to prepare a traditional classroom, the instructor needs to equip their own books, reading materials, and lecture plans. Meanwhile, for a smart classroom, the instructor needs to be prepared with knowledge on the “aids” which referred to the technology devices used in this learning environment. Bruner (1964) explained that technology tools are recognized as their ability to assist in cognitive development and to enhance the potential of those who handle the tool. The evidence of Bruner’s explanation which can be seen nowadays is that ICT tools and Internet environment- technology-based provide enhancement on information seeking and information sharing of a person.
Information seeking can be explained as the use
technology to search for information to form an individuals’ perception. While, information sharing can viewed as an act to share information and answer upon request of certain topic (Rafaeli & Raban, 2005).
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1.2
Statement of Problems Technology has improved and evolved each year due to civilization and demand
of human need. It also moved along with 21 st century learning with incorporated technology into learning. Technology is a common toll acts as central, formal and informal information seeking, content sharing, and self-expression of the 21 century (Mills & Knezek, 2012). Since the appearance of technology, human beings now tend to neglect each other and themselves which in some cases it can be resulted in form of poor health, depression, isolation, alienated, alcoholics, drug addicts, overweight, stressed out, overworked, and exhausted (Thiebaud, 2010).
Usage of technology in learning is now a must to any institution. Emerging of the technology which can support learning within and beyond classroom setting calling for an exploration on students’ attitude while using it. Internet, specifically, is the medium which act as the platform for student to explore, search, share as well as develop any knowledge. There is a research which conducted to study the matter which carried out in Texas toward graduate students (Mills, Knezek, & Wakefield, 2013).The research focuses on attitude when learning using ICT which classified into information seeking and information sharing. However, there is not many research found that has been carried out in Malaysia on both attitudes.
Degree of technology affinity measures the level of engagement with ICT while learning. Mills, Knezek, and Wakefield (2013) using the instrument that consist of 22scale items that design for this matter. The result only focuses on the relationship between level of engagement and the attitude toward learning. However, other demographic factor such as gender or social background is not being included as variable.
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Based on the gap presented on previous and current research, a study will be conducted to investigate the behavior that possessed by undergraduate students while using ICT tools which could be classified as information seeking and sharing behavior. The study also seek to find out the degree of technology affinity of a student as they make use of technology in their learning whether they being immersed in daily technology or continuously engaged with it. The research will be conducted at local university located at Kota Samarahan, Sarawak.
1.3
Research Objectives This sub-topic will discuss on the objectives of the research. It has been divided
into two which is general objective and specific objectives. These elements act as guidelines to conduct the research.
1.3.1
General Objective
The general objective for the research is to study undergraduates’ attitude toward learning with ICT and degree of technology usage.
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1.3.2
Specific Objectives
The main objectives of the research are to: a)
To identify the attitude toward learning with ICT based on gender.
b)
To identify the degree of technology affinity against duration of using social media.
c)
To investigate the relationship between gender and degree of technology affinity based on interaction analysis.
d)
To investigate the relationship between attitude toward learning with ICT and degree of technology affinity.
1.4
Research Questions
In this study, the following research questions were investigated: 1.
What is the attitude of undergraduate students toward learning with ICT based on gender?
2.
Does there is any differences between degrees of technology affinity against duration of using social media?
3.
What is the relationship between gender and degree of technology affinity?
4.
What is the relationship between attitude toward learning with ICT and degree of technology affinity?
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1.5
Research Hypotheses
These are the following hypotheses that will be used to support the objectives of the research: H01
There is no significant difference in attitude toward learning with ICT based on gender
H02
There is no significant difference in degree of technology affinity based on duration of using social media.
H03
There is no significant relationship between gender and degree of technology affinity based on interaction analysis
H04
There is no significant relationship between attitude toward learning with ICT and degree of technology affinity.
1.6
Conceptual Framework The conceptual framework of the research represents the general concept of the
research. It explaining the variables involve in the study as use to fulfilling the objective’s requirement. The variables are divided into dependent and independent variable. The dependent variable is consisting of attitude toward learning with ICT tools. Independent variables are represented by demographic information and way of using of technology in learning.
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Dependent Variable
Independent Variables
Gender (Houtz and Gupta, 2001) Duration of using technology (social media)
Attitude toward learning with ICT (Mills & Knezek, 2012) Degree of technology affinity (Mills & Knezek, 2012)
Figure 1.1 Conceptual Framework of the research Figure 1.1 was developed to guide the research process and interpret data from theory. Gender is the demographic information which taken into account for this research. Gender difference is the point of interest for the research as it has been stated that gender could affect individuals’ attitude on using computer (Law, 2008). Besides, duration of using social media also has been taken account as one of the variable that will be tested in this research.
1.6.1
Independent variables Demographic information of respondent has been counted as one of the element
for independent variable. Gender is the main element that will be used in statistical analysis which mentioned by Houtz and Gupta (2001), gender do play part in investigating the engagement in learning using ICT. Others demographic element is also being taken into account as mentioned by Kim, Sin, and Tsai (2014) to be related to personal preference of using ICT. Another variable is duration of using social media.
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1.6.2
Dependent Variables The first dependent variable is the attitude toward learning with ICT by
undergraduate students. The approaches which will be discussed in this research are information seeking and information sharing. Second variable is the degree of technology affinity. By using the Technology Affinity Scale (TAS) as the research instrument, it meant to measure Internet related digital technology use. Two measurement scales has been established from the instrument which is Always On and Immersed (Mills, Knezek, & Wakefield, 2013). It is aligned to Bruner’s theory (1964) which proved over time that ICT tools can enhance individual information seeking and sharing behavior which associated with knowledge construction.
1.7
Significance of the research This research is expected to assist learner and educator in learning that make use
of technology tools. Attitude of learner while using ICT in learning can be categorized as information seeking and information sharing as stated in Information Search Process (ISP) Model by Kuhlthau (1991, 2007). Level of engagement with technology is expected to assist in controlling the use and aftereffect of exposing technology to learner for a certain time. Level of engagement which mention in this research is immersed and always-on (continuously engaged).
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For the first objective, this study is expected to help educator to plan their learning session using technology as a medium based on the attitude of their learners and gender. As if their learner using technology, specifically, Internet to seek for information, task or assignment which encourage them to be engaged in learning can be constructed. In order to complete the task, learners need to seek for needed information which the searching process will undergo several stage and finally, they will present the finding. To measure learners’ attitude toward learning with ICT, this research will make use the instrument by Mills, Knezek, and Wakefield (2011). The outcome is expected to assist to better understanding on learners’ learning preferences and information behavior (information seeking or information sharing) which will be resulted in constructing and implementation of instructional models that can be used in both formal and informal learning. Second objective offer the understanding on level of engagement based on duration of using the technology tools. Learners can learn to be in charged on their own pace while using technology tools. They can easily been distracted and engaged with the technology for a long period. Thus, in order to make sure that the use of technology tools is not being mistreated, as this research completed, learners can monitor their technology usage. Lack of control on the time spend to use the technology, for example, Facebook, can lead to lesser time use on learners’ studies and eventually obtain lower GPAs (Karpinski, 2009).
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Third objective is expected to bring understanding to the relationship between gender and attitude toward learning with ICT tools. There is gender difference among technology tools user. As reported by Arsenth (2007), research on gender differences focuses on numerous areas such as learners’ performance and attitudes toward computers. Thus, by making relationship, instructor as well learner can be informed on this difference and taking action on planning teaching and learning session. Lastly, the fourth objective is expected to highlight the relationship of attitude toward learning with ICT and degree of technology affinity. Statistical test will be executed as all needed data has been collected. The outcome from the test can be used to make relationship between these two variables. The result is expected to help learner to plan their study well as from previous research, it show that learners with high attitude toward learning with ICT were found to have higher technology immersion (Mills, Knezek, & Wakefield, 2013).
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1.8
Limitation of the Research The outcome from the research will identify the most used social media, students’
information behavior and their degree of technology affinity. Nevertheless, there are still limitations to be look upon this research. This research only focuses on tertiary level of education which is the subjects are among undergraduates student from various faculties. The result might not represent the population of the sample as it has been randomly selected throughout the data collection. The number respondent is can be considered as average from the exact population of undergraduate students in the selected university.
1.9
Definition of terms
1.9.1
Information and Communication Technology (ICT)
Conceptual Definition Michiels and Van Crowder (2001) defined ICT as “technologies that facilitate, by electronic means, the acquisition, storage, processing, transmission, and disseminating of information in all forms including voice, text, data, graphics, and video”. In education field, there are four main elements that can be taken into account which are; ICT offers the potential to satisfy the needs of learning to an individual, to promote opportunities, to offer learning material, and also to promote independence of learning among learners (Leach, Ahmed, Makalima, & Power, 2005). Besides that, Longley et al. (1985) mentioned that information technology is described as “microelectronics-based combination of computing and telecommunications is the acquisition processing, storage and dissemination of vocal, pictorial, textual, and numerical information”. 13
Operational Definition In this research, ICT is referring to the technology that has been use by the undergraduate student daily. Some of the technologies are like email, social media, and mobile phones. This research emphasizes the use of ICT in learning to measure the learners’ attitude.
1.9.2
Information Behavior
Conceptual definition Wilson (2000) described information behavior as human behavior which related to sources and channel of information. This includes both active and passive information seeking, and information use. Bates (2010) explained that information behavior can be described as “ways in which human beings interact with information, in particular, the ways in which people seek and utilize information”. Operational Definition Information behavior is referring to undergraduate students’ attitude toward learning using Information and Communication Technology (ICT) tools. This applied to both categories which are information sharing and information seeking.
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1.9.3
Information Sharing
Conceptual Definition Information sharing can be classified as a method of acquiring information in academic environment as information encountering as studied by Erdelez (1997). Rafaeli and Raban (2005) define information sharing as an act of providing feedback or answer upon request of information. Sharing itself is applicable to any environmental influences as the information shared can be in various levels in private and public spaces, at work or normal setting, people from different disciplines depending on the content requested (Rafaeli & Raban, 2005). Operational Definition This research refer information sharing as a way the respondent choose to interact with digital ICT tools by sharing the information across the Internet. Information that been shared is within academic context.
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1.9.4
Information Seeking
Conceptual Definition Wilson (2000) mentioned information seeking behavior as “purposively seeking for information as a consequence of a need to satisfy some goal”. The individual may interact with manual information systems (books, newspaper, etc), or with computerbased systems like the World Wide Web (WWW). Likewise, Dervin (1983) defines information seeking as a process to make sense where a person is forming a personal perception. Spink and Cole (2004) define information seeking as a part of information behavior that includes purposive seeking of information to achieve some goal. Operational Definition Information seeking is explained as attitude shows by the respondent while using ICT tools. They mostly use Internet or ICT tools to seek for information that needed or they wish to explore.
1.10
Chapter Summary This chapter has explained the background of the study, statement of problems,
objectives of the study, research hypotheses and questions, conceptual framework of the research, significant and limitation of the research as well as the definition of terms related to the research. This outcome of the research could help instructor as well as undergraduate student to plan their learning to seek and sharing knowledge as well as understanding the level of engagement showed by the learners while using technology in learning.
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CHAPTER TWO LITERATURE REVIEW
2.0
Introduction This chapter focuses on reading from past study as well as theories which is
related to the topic of research. Literature is the main source of ideas to form a research direction, methodology, procedures, as well as discussion on the outcome of the data analysis (Rusli Ahmad & Hasbee Usop, 2011). In this chapter, the elements that will be discussed are previous study done on social learning theory, social interaction and information behavior.
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2.1
Learning Theories
2.1.1
Social Learning Theory Ison and Watson (2007) define social learning as “achieving concerted action in
complex and uncertain situation”. Bandura (1977) explained social learning as individual learning which takes place in social context and thus influenced by social norms such as imitation of role models. He emphasized that instead of acquiring behavior strictly bounded by rewards or reinforcement, it could be done also by observational learning. Observational learning cannot occur except with the present of cognitive processes. Bandura (1961) has illustrated observational learning onto children learning. Children observe people that surround them which behave in various ways. Individual which they observed are called models. Children easily influenced by models such as their parents, characters on television, peers, and teachers at school. These models provide behaviors which are to be observed and imitate by children. A study has been conducted to illustrate observational learning. The intention is to investigate if social behavior such as aggression can be acquired through observation and imitation. Bandura, Ross, and Ross (1961) had tested 36 boys and 36 girls from the Stanford University Nursery School aged between 3 and 6 years old. The study has been done using 3 stages; modeling, aggression arousal, and test for delayed imitation. This experiment using a toy called “Bobo doll” and a set of scene of aggressive behavior (McLeod, 2014).
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The results of the experiment have been recorded. Children who observed the aggressive model tend to made more aggressive responses compared to those who were in non-aggressive or control group. Another result is the girl in the aggressive model condition showed the tendency to act physically aggressive if the model was male but act verbally aggressive if the model was female. Then, boys were more likely to imitate same-sex model than girl and boys imitated more physically aggressive compared to girls. However, there was little difference in verbal aggression between boys and girls as girls more likely to react verbally aggressive. There are three main concepts to Bandura’s social learning (Kleinman, 2014). Firstly, a person can learn behavior from observation which it could came from a live model (an actual person execute a behavior), verbal model (explanation or description), or symbolic model (model portrayed in books, or film). Secondly, the mental state is an important aspect to learning. This concept refers to internal thoughts which also can play an important role in learning behavior. Example for internal reinforcement or intrinsic are satisfaction, pride, and feelings of accomplishment. Lastly, learning does not mean that a behavior will unavoidably change. This statement explained as a person can learn new information without having to demonstrate this behavior contradicting to behaviorist who they believed that learning a behavior can lead to permanent transform in the individual’s behavior. There are some arguments on the criteria for a process to be considered as social learning as mentioned by Reed et al. (1969). In the article, the criteria has been listed out as; (1) able to demonstrate that a shift of understanding has taken place in the individuals involved; (2) able to demonstrate that this shift goes beyond the individuals and becomes positioned within wider communities of practice; and (3) it occur through social interactions and processes between actors within a social network. 19
2.1.2
Behavioral Theory Behavioral theory or known as behaviorism, a study of stimulus-response
relationships. Behaviorisms concerned with behavior that can be observe which can be objectively and scientifically observed (McLeod, 2013). The history of behaviorism started with Pavlov (1897) that published the result of an experiment that conducted to study dogs’ digestion pattern. Food and bell has been used as the stimulus to trigger the behavior. Before experiment being started, the dog salivates at the present of food. The experiment started by staging two scenes. First scene is by observing the response produce after ringing a bell. However, there is no changing in dogs’ responses. The dog still salivates as before. The second scene conducted by presenting bell and food simultaneously. The dog started to salivated. After conditioning, the dog starts to salivate at the present of bell ringing. As conclusion, classical conditioning by Watson (1913) which has been created after this experiment, involves learning related to unconditioned stimulus that bring a particular response with a new (conditioned) stimulus, so that the new stimulus could bring the same effect which is referring to this experiment, the dog starts to salivate at the present of bell ringing. Operant conditioning has been introduced by Skinner (1938). Operant conditioning referring to changing of behavior by using reinforcement which is given after the preferred response has been obtained. There are three types of responses or operant which are; neutral operants, reinforcers, and punishers (McLeod, 2015).
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Neutral operant referring to responses from the environment with neither increase nor decrease the chances of a behavior to be repeated. Another response is reinforcers. It refers to responses from the environment that increases the chance of a behavior to be repeated and it can be either positive or negative. Positive reinforcement strengthens the behavior by providing reward as the consequence of executing an action. Negative reinforcement strengthens behavior by stop or removing an unpleasant experience. As for punishment, it is the opposite of reinforcement since it is designed to weaken or eliminate a response. There are problems with using punishment such as; it can caused aggression, can creates fear which can generalize undesirable behaviors, and it does not necessarily guide toward desired behavior as punishment only tells a person on what not to do (McLeod, 2015).
2.1.3
Control Theory Control theory has been introduced by Glasser which nowadays known as Choice
Theory (Glasser, 1998). Choice Theory is mainly the behavior that is cause by external stimulus and what a person wants most at any given time and it is internally motivated. Human behavior are intended to meet the needs of these five different internal needs; to survive, to belong and be loved by others, to have power and importance, to have freedom and independence, and to have fun.
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Choicetheory.com (2008) mentioned that there are 10 principles that correspond with choice theory as following: 1. 2. 3. 4. 5.
6. 7. 8. 9.
10.
The only person whose behavior we can control is our own. All we can give another person is information. All long-lasting psychological problems is relationship problems. The problem relationship is always part of our present life. What happened in the past has everything to do with what we are today, but we can only satisfy our basic needs right now and plan to continue satisfying them in the future. We can only satisfy our needs by satisfying the picture in our Quality World. All we do is behave. All behavior is Total Behavior and is made up of four components; acting, thinking, feeling, and physiology. All Total Behavior is chosen, but we only have direct control over the acting and thinking components. We can only control our feeling and physiology indirectly through we choose to act and think. All Total Behavior is designated by verbs and named by the part that is the most recognizable.
This theory helps to explains some of the critics and distractions that students have that eventually will delay their learning. In Glasser’s book, Choice Theory in the Classroom”, he explains: “…students function no differently in school than anywhere else; the attempt to fulfill whatever they need, they detect is most unsatisfied at the time. If they are hungry, they will try to find food, or at least think about food more than what is being taught. If they are lonely, they will spend their time looking for friends, rather than knowledge. If there is no fun, they will attempt to play”, (p. 6)
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2.2
Social Interaction The term of social interaction has broadly been defined by Anderson (2003), can
take eight forms which is learners interacting with instructors, or vice versa; learners interacting with other learners; learners interacting with content; learners interacting with interfaces; instructors interacting with content (form and function); instructors with interfaces; instructors interacting with other instructors; and content interacting with content. Bates (1995) has categorized interaction according to time of interaction and perspective for interaction; asynchronous vs. synchronous, and personal vs. social interaction. He emphasizes that different objective of learning required different types of interaction. Another researcher, Paulsen (1995) mentioned four types of interactions which classified as one-alone, one-to-one, one-to-many, and many-to-many interaction. However, Moore (1993; Moore & Keasley, 1996) provided the dissimilarity types of interaction; learner-content, learner-teacher, and learner-learner interaction. These interaction could occur synchronous or asynchronously. Hillman, Willis, and Gunawardena (1994) added learner-interface to the list which acknowledges learners in a distance education environment which has to interact with the help of interface.
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2.2.1
Learner-learner Learner-learner interaction happens when students interact with each others in
order to complete any assigned tasks, reflecting learning process, and monitoring their learning progress within an e-learning courses (Chou, Peng, & Chang, 2010; Hillman, Willis & Gunawardena, 1994; Miltiadou & Savenye, 2003; Moore, 1989). Empirical evidence shows that students actually wish for learner-learner interaction s, despite the delivery
2.2.2
method
(Grooms,
2000;
King
&
Doerfert,
1996).
Learner-instructor Learner-instructor instruction establishes an environment that promote student to
understand the content better. This type of interaction is considered as fundamental by many educators and highly demanded by many learners (Moore, 1989). Instructor helps learner to maintain their interaction which including motivating learners to learn, assessing their development, and providing proper support and encouragement (Chou, Peng, & Chang, 2010; Hillman, Willis & Gunawardena, 1994; Miltiadou & Savenye, 2003; Moore, 1989). However, the learner-instructor interactions contained by e-learning environment are constrained to enroll students in particular semester of the course (Said & Tahir, 2013).
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2.2.3
Learner-content This interaction is defined as “the process of intellectually interacting with content
that results in changes in the learner’s understanding, the learner’s perspective, or the cognitive structures of the learner’s mind” (Moore, 1989). This interaction happens in leaner’s “head” while endeavor dialogue, constructing meaning, answering question, or finding suitable place to incorporate incoming information with existing schema (Chen, Peng, & Chang, 2010).
2.2.4
Learner-interface Learner-interface interaction occurs between learner and technology to assess
information and content within e-learning environment (Chou, Peng, & Chang, 2010; Hillman, Willis & Gunawardena, 1994; Miltiadou & Savenye, 2003; Moore, 1989). Hillman et al. (1994) mentioned that before learner-interface interaction could takes place, learner needs to able to interact with technology. Then, they could establish interaction successfully with content, instructor and other learners. However, it may face some difficulties due to the fact that people have not experienced having learner-interface interaction in their traditional classroom setting.
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2.3
Social Interaction and Use of Technology According to Gray, Chang and Kennedy (2010) stated that instructors have
reported to use online technologies can encourage online discussion among students outside the classes, beyond the usual class setting. Silius, Miilumaki, Huhtamaki, Tebest, Merilainen, & Pohjolainen, (2010) stated that by creating social media as a medium for college kids, it striving at improving both collaborative study and social interaction. Students particularly higher level of learning can works well collaboratively through exploring the chance given by online social environment to accomplish certain academic matters or any issues with their peers (Kane & Fischman, 2009). This proves that through collaboration or learning in team with help of social media, learners could spark positive contact, make use of the goal of working towards particular final outcome in any situation, offline and online (Lockyer & Patterson, 2008). Guthrie and Carlin (2004) mentioned that usage of social media effect as it increases the quality of perceived interaction within the class. It covers interaction among peers, other students as well as the teacher interaction. Wagner (1994) has defined interaction as “reciprocal events that require at least two objects and two actions”. There are many research which focuses on interaction towards usage of technology in learning which to be specific towards online learning in a Web-based instruction environment.
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There are three types of interaction which are outstanding in technology-based instruction. The first interaction is the academic interaction. It occurs when learners study online material and when learners been given a task-oriented feedback from instructor (Moore, 1993; Moller, 1998). It is a content-centered interaction. Second, collaborative interaction which focuses on learners when they discussing issues related to their learning on bulletin board or solving problem together (Moller, 1998; Adelskold, Alklett, Axelsson & Blomgren, 1999). Third, interpersonal interaction or social interaction occurs when learners get social feedback from instructor or the friends through compliment and motivational assistance (Gunawardena & Zittle, 1997; McDonald & Gibson, 1998).
Technologies are considered as cultural-by-products related to Hatch & Gardner (1993) statement; “Cultural forces influence the kinds of skill people exhibit, the way those skills are developed, and the purposes to which they are directed”.
2.4
Information and Communication Technology (ICT) and Learning Information and Communication Technologies or known as ICTs is a term which
is widely used by any professional field such as education, management and others. The term is referred to various collection technology mechanisms and resources which are made to be use to communicate and it is a new modes in which people can communicate, inquire, make decision, and solve problems (Sarkar, 2012). ICT consist of hardware, software, networks, and media for collection, storage, processing, transmission and presentation of information in form of voice, text, and images.
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The evolution of ICT is shaping traditional teaching and learning (Mills, Knezek, Khaddage, 2014). There is evidence recorded that technology-supported learning are at least as good as in effectiveness to traditional, time-based schedule, face-to-face instruction (Francescato et al., 2006) and appropriate application of IT tools can enhance students learning (Voogt & Knezek, 2008). As mentioned by Sawyer (2005), computer software is the “heart” in the learning sciences as the visual and processing power of current personal computer can support deep learning. Today’s computer enable to represent abstract knowledge in solid form, allow learners to express their developing knowledge in a visual and verbal form, allow learners to use and revise their knowledge through user interface which support real-time articulation, reflection, and learning. Besides that, Sawyer also did mention that computer can support reflection in a mixed mode of visual and verbal and also an Internet-based network of learners can share and merge their ideas of understanding and gain benefit from collaborative learning. There has been listed some advantages and disadvantage of applying Web 2.0 or ICT in the classroom (Harris & Rea, 2009). The advantages that have been listed are such as learners become part of the lesson, the world becomes the classroom, collaboration and competition increases learning and the classroom is always available. The disadvantages are listed as computing resources must be available, web sources can be vandalized as well as plagiarism and the level of openness can cause discomfort to some learners. Besides that, Sarkar (2012) have listed the challenges rose while using ICTs in learning. Those challenges are high cost of obtaining, installing, operating, maintaining, and replacing the technology equipments.
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Roblyer and Edwards (2000) has suggested that there are five important rationales for teacher or instructor to make use of technology in education which are; motivation, distinctive instructional capabilities, higher productivity of instructor or teacher, indispensable skills for the 21st century learning environment, as well as support for new teaching techniques (cited in Samak, 2006). Thus, a research with thorough exploration on the ICT usage toward learning will be conducted to amend learning and teaching environment to fit it with the 21 st century learning.
2.5
Information Behavior Information behavior is described as human behavior which related to sources and
channel of information which includes both active and passive information sharing and information seeking. There is several models constructed based on human information behavior. The first model is Kuhlthau (1993) where she proposed an information search model. The model is divided by seven stages; task initiations, topic selection, prefocus exploration, focus formulation, information collection, search closure and starting writing. This model has integrating some element of feelings such as anxiety, uncertainty, confidence and others. Anxiety and stress can be expected at the early stages of the model. Another model is Wilson (1996) whereby a model is proposed an interdisciplinary which known as general model of human information behavior. Wilson has emphasized on research in health information, advertising, economics, communication, and organizational behavior. The elements that have been included are character or 29
context, activating mechanism, and intervening variables, activating mechanism. These elements combine in a linear manner to yield information seeking behavior which include passive attention, passive search, active search and ongoing search behavior. Evidence reported by Liu, Macmillan, and Timmons (1998) that students’ with positive attitude toward using computer would have positive attitude toward computers for their learning.
2.5.1
Information Sharing Information sharing can be classified as a method of acquiring information in
academic environment as information encountering as studied by Erdelez (1997). Talja (2002) has mentioned by looking from the perspective of document retrieval, information sharing in academic research community can be seen as:
Sharing information about relevant documents,
Sharing relevant documents,
Sharing information about the contents of relevant documents,
Sharing information about novel and efficient ways of finding relevant documents or information sources.
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There are three types of information sharing which are; (1) strategic sharing, (2) paradigmatic sharing, (3) directive sharing, and (4) social sharing. Strategic sharing is explained as conscious strategy of maximizing efficiency in a research group. Paradigmatic sharing focuses on establishing new and different research approach or area within or across a discipline. For directive sharing, in involve sharing between teachers and students. Lastly, social sharing which relate to relationship and community-building activity. There are some factors which serve to promote or hinder sharing which depending on circumstances. The factors had been listed in the table below have received attention in literature while others are approximation and waiting for future research. Table 2.1 Factors that promote or hinder sharing online and variable for further research (Rafaeli & Raban, 2005) Factor
that
promote Factors
that
hinder Variables for research
information sharing online
information sharing online
Ownership
Public goods- free riding
Personal
benefit:
self Diffusion of responsibility
Broadcasting vs. sharing Ownership/ privatization
identity/ respect/ esteem Prior
acquaintance
and Organizational culture and Seeking vs. sharing
similarity
politics
Prosocial transformation
Hierarchical organizational Information vs. advice structure
Social facilitation
Competition
Validation of benchmark
Reciprocity
Selfishness
Cause
or
solution
information overload
31
for
2.5.2
Information Seeking Wilson (2000) has described the term of “information seeking behavior” as the
action which down purposively in order to achieve and satisfy some goal. Dervin (1983) viewed information seeking as “a process of sense-making in which a person is forming a personal point of view”. The literature on information seeking often referring to motivation, critical thinking, and learning theory (Weiler, 2005). Several models have been constructed regarding to information-seeking behavior. The first model is created by Krikelas (1983). The model emphasizes on the step related to information-seeking behavior which are; (1) perceiving a need, (2) the search itself, (3) finding the information, and (4) using the information. The sequences of steps are resulting in either in satisfaction or dissatisfaction. Ellis (1987), a researcher who conducted a study at Shell Research on shift towards a “person-centered” approach and “system-centered approach” that related to information seeking behavior. Ellis has employed a qualitative interview which resulted on the common characteristics of information behavior researchers in social sciences, physical sciences, and most recently engineering. The outcomes has listed the below has some slight growth in the last study (Ellis, 1987; Ellis, Cox, et al., 1993; Ellis & Haugan, 1997).
Starting: user begin to seek information by for example, asking some knowledgeable colleague;
Chaining: following footnotes and citation in known sources or “forward” chaining through citation indexes;
Browsing: “Semi-directed or semi structured searching”;
32
Differentiating: knowing the differences in information as a way to filter the amount of information gathered;
Monitoring: keeping up-to-date;
Extracting: selecting and identify the relevant material in an information source;
Verifying: checking the accuracy of information;
Ending: end up and conclude through a final search. Another model is constructed by Kuhlthau (1994) on a model of information
seeking which initially based on a study of high school students. This model stressing on processes which focus on cognitive skills. When learners’ cognitive skills increase, their information-seeking effectiveness also increases. The findings has listed the stage for the model which are; Initiation, Selection, Exploration, Formulation, Collection, and Presentation. These stages are said to be linked with certain feeling and with specific activities (Wilson, 2000). Those feelings are like confusion, anxiety, doubt, confidence and others. Kuhlthau (1991) has devices a model called Information Search Process (ISP) Model. The model aims to explain the stages of student information activity which consist of initiation, selection, exploration, formulation, collection, and presentation. These stages can assist to explain student information behavior and enable instructor or educator to guide their learner in search activities. This model has emphasizes that human information behavior is seen as a process and understand that cognitive and affective component could shape human information behavior.
33
The first stage, “initiation”, is explain as when an individual acknowledge that he or she is lack of understanding, feeling of uncertainty and anxiety. This is the point that indicates the need for information. Action involve include discussion on several topic and approaches. During “selection” stage, the task now is to recognize and select the common topic to be looked into or the approaches that will be used. After the selection has been made, the uncertainty feelings are replaced with optimism, then the search start. Activity involves are weighing viewpoint against personal interest, the requirement of the task, information available and time allocation. The result of each possible option is predicted and the topic or method evaluated to have the biggest potential to success is selected. There is a point the anxiety level has elevated whenever selection is delayed. The third stage, “exploration”, focuses on inspecting information on the common topic in order to widen personal perceptive. During this stage is the feeling of confusion, uncertainty, and hesitation which regularly elevated. Action involved as executing this stage are locating information of the general matter, reading to know, and making relation between new information and prior information gathered. At this time, the most helpful strategies can be list out the facts of the information to form new construct. However, there is time where the information gathered are seems inconsistent and incompatible with the previous one. The fourth stage, “formation”, is the defining moment when the feelings of uncertainty lessen and confidence level elevated. The task involve at this particular stage is to focus on the information came upon. The action involve is to identify and select the idea in the information to form a focused standpoint of a topic. As the confidence level rose, the topic now gradually constructs and become clearer.
34
The fifth stage, “collection”, is the moment when the interaction between user and information systems occurred most effectively. The task involve is to collect information associated to the focused topic. The sixth stage is “presentation”. At this stage, relief and sense of satisfaction will be presented upon the completion of the search. The task is to complete the search and prepare to present the findings. Table 2.2 Information Search Process (ISP) Stages in ISP
Feeling Common Thought Action to Each Stage Common to Common Each Stage Each Stage
1. Initiation
Uncertainty
2. Selection 3. Exploration
Optimism Confusion / Frustration. Doubt
General Unclear
Seeking relevant information
4. Formulation Clarity 5. Collection
/ Seeking background information
Narrowed Clearer Sense of Direction Increased / Confidence Interest
6. Presentation Relief / Clearer Satisfaction or Focused Disappointment
/
Appropriate to Task According to ISP Recognize
Identify Investigate
Formulate
Seeking Gather relevant or focused information or Complete
Wilson (1999) has proposed a problem-solving model which he suggested that both Ellis’s characteristics and Kulthau’s stages can be related to the model. This model emphasized on perceiving information seeking, searching, and use and linked with different stages of goal-directed process. The stages are problem recognition, problem definition, problem resolution, and solution statement.
35
Context of information need
Activating mechanism
Intervening variable
Activating mechanism
Person-incontext
Stress/ coping theory
Psychological
Risk/ reward theory
Informationseeking behavior
Passive attention
Demographic Role-related or Interpersonal
Social learning theory
Environmental Source characteristic
Selfefficacy
Passive search
Active search
Ongoing search
Information processing and use
Figure 2.1 Model of Information Behavior (Wilson, 1999) Another model by Eisenberg and Berkowitz (1992) which proposed based on the “Big Six Skills” which consists of task definition, information seeking, implementation, use, synthesis, and evaluation. This model is considered as flexible as the used can refine and identify back their information that need and implementing new strategies. Thus, “Big Six Skills” are appropriate to be used to study information seeking and a better match for flexibility of the learning theories and cognitive development theory. It also can be used in different areas and approach.
36
2.6
Gender and Attitude toward Information and Communication Technologies
(ICT) Around the globe, research shows that gender differences exist in all aspects of society. In the field of technology, research on gender difference focuses on several areas such as learners’ performance, attitudes towards computers and skill, as well as the effect on to teacher, parents and peers (Arnseth, 2007; Law, 2008; Lenhart, 2008; Lenhart, 2007; Macgill, 2007; Meelissen, 2008; OECD, 2007, 2008; Pedró,2007;Smihily, 2007; Vekiri, 2008). Houtz and Gupta (2001) discovered that there are significant differences in the way females and males rated themselves by referring to their skills to mastering technology skills, but, males do rated themselves higher than females. Shashaani and Khalili (2001) reported that female undergraduate students had significantly lower confidence compared to males when it came to their ability to use of computers. Females are reported to feel helpless, nervous, and uncomfortable around towards. Current research meant to examine the impacts caused by gender toward learning with ICT as well as the degree of technology affinity.
37
2.7
Chapter Summary This chapter explains on learning theory which included social learning theory,
behavioral theory, control theory and social development theory. This chapter also explaining on social interaction, and its relation with use of technology, Information and Communication Technologies (ICT) and learning, information behavior (information seeking and information sharing) as well as explanation on gender and attitude toward Information and Communication Technologies (ICT). Based on the literature presented, the research will be conducted in the manner to fill out the gap which is to look on the difference between gender and the relationship between attitude toward learning with ICT and degree of technology affinity.
38
CHAPTER THREE METHODOLOGY
3.0
Introduction Research methodology is an element which is crucially needed for a researcher
that could help to plan the studies and design the “manual” to accomplish the objectives of the research. This chapter will discussed on the method that will be used to collect data and other needed information, research design, population and sample, instrument, data collection method and data analysis statistical method. Explanations on instrument of the research are discussed along to make sure the validity and reliability of the research.
39
3.1
Research design Research design is the starting point which help researcher to collect data
correctly based on the objectives of the research and it could ensure the researcher to provide good answer to their research questions (Easterby-Smith, Thorpe, & Lowe, 2002). This research is conducted quantitatively to investigate the social interaction of students as using social media or technology in a higher learning institution. Quantitative research helps the researcher to determine on how many, what, when and where and it focus more on structural issues rather than on complex issues of the process (Van Maanen, 1983). In this case, researcher will be able to discover the preference on social media, way the student use and their perception on social interaction that resulted from using social media. Survey is used in this research to collect information from respondent using a close-ended questionnaire. Information gathered will be used to draw conclusion upon the issue of social interaction related to usage of technology or social media in this case. However, a survey still has some lacking in reliability of the answers given by respondent as researcher cannot force them to provide a truthful answer and it may reflect artificial response of population (Rusli Ahmad & Hasbee Usop, 2011).
40
3.2
Data Analysis Techniques Once the data has been collected, it will be run and analyzed through the IBM
Statistical Package for Social Sciences (SPSS) version 20.0 for Windows. The data will be scrutinized using the techniques used in inferential statistics and descriptive statistics. Descriptive statistic is used to measure the frequency, and mean which will be used to explain the respondents’ demographic information and the information related to daily usage of social media. Inferential statistics which used to test the hypotheses of the research are Independent T-test, One-Way ANOVA, and Pearson Correlation. Table 3.1 Data Analysis Techniques No 1 2
3
4
3.2.1
HYPOTHESES There is no significant difference in attitude toward learning with ICT based on gender There is no significant difference in degree of technology affinity based in duration using social media. There is no significant relationship between gender and degree of technology affinity based on interaction analysis There is no significant relationship between degree of technology affinity and attitude toward learning with ICT.
STATISTICAL TEST Independent T-test One-Way ANOVA
Pearson Correlation
Pearson Correlation
Independent T-test Independent T-test is used to compare the values of means from two samples
(Rusli Ahmad & Hasbee Usop, 2011). The samples used for the research are among undergraduate students which equally 50 respondents for both gender (female and male). Independent T-test is used to test whether there is any difference on the approaches used by the students to interact with technology in learning based on gender.
41
3.2.2
One-Way ANOVA One-way ANOVA test is used to define if there is any difference between the
means of independent variables against dependent variable. The significant (2-tailed) values are observed to conclude whether there is significant difference on attitude toward learning with ICT and degree of technology affinity.
3.2.3
Pearson Product Moment Correlation Test Pearson Correlation test is used to identify the strength of the relationship between
two variables which applied to two hypotheses. The variables that involved are gender, social interaction, and social perception on usage of social media. Rusli Ahmad and Hasbee Usop (2011) mentioned that correlation is a technique which used to examining the relationship between two variables. The r-value which is the correlation coefficient is the value used to measure the strength of the relationship. The ranges are among +1.00 and -1.00. Table 3.2 Strength value of Correlation Coefficient (Chua, 2006) Size of correlation coefficient .91 until 1.00 or -.91 until -1.00 .71 until .90 or -.71 until -.90 .51 until .70 or -.51 until -.70 .31 until .50 or -.31 until -.50 .01 until .30 or -.01 until -.30 .00
Strength of correlation Very Strong Strong Moderate Weak Very Weak No Correlation
42
3.3
Population and Sample Due to limitation of time which within 3 months, close-ended questionnaire is
used to collect the data from respondents. Respondents are among undergraduate students which have been selected randomly across Universiti Malaysia Sarawak (UNIMAS). Possibilities to be chosen as respondents are equal for these faculties; Faculty of Cognitive Sciences and Human Development (FSCHD), Faculty of Engineering (FENG), Faculty of Social Sciences (FSS), Faculty of Applied and Creative Arts (FACA), Faculty of Economics and Business (FEB), Faculty of Resources Science and Technology (FRST) and Faculty of Computer Science and Information Technology (FCSIT).
3.4
Sampling Method For this research, purposive sampling has been used as sampling method.
Respondents are selected purposely according to their gender. The confidence level of the sampling is 95% and the confidence interval 9.77. 106 undergraduate students are selected randomly from various faculties. Participants are given a week to return the questionnaire to the assigned person which selected to ease the distribution of the research instrument to respondents. Briefing on the research objective and the right as respondents has been explained well to the representative. However, only 95 sets of questionnaire have been returned as completed set.
43
3.5
Data Collection Method The data needed to support and answer the research questions is collected by
using close-ended questionnaire. The survey consists of items which adapted from a research done by Mills, Knezek, and Wakefield (2013). The questions are prepared and will be distributed to the respondent which will be selected randomly. The respondents will be informed and briefed on the research objectives as well as their right as respondents which the data collected will be kept confidentially.
Get a letter of acknowledgment from faculty Get respondent’s consent Distribute questionnaire to respondents Collect completed questionnaire from respondents
Figure 3.1 Data Collection Procedure 3.5.1
Close-ended survey For this study, primary data collection method used is close-ended survey. Close-
ended survey used is in form of questionnaire. A questionnaire is a predetermined set of questions which designed to capture data from respondent (Rusli Ahmad & Hasbee Usop, 2011). The advantages of using questionnaire to collect data are it is not affected by the skill and bias of the researcher in implementation, it is possible to cover a large number of samples and the well-designed questionnaire will ease the process to be coded for computer
analysis
(Fink,
44
1995).
3.6
Instrumentation The data needed for the research will be collected using questionnaire which
consists of 3 sections. The statements for each section are adapted from The Learning Preferences (Mills, Knezek, & Wakefield (2013) and translated into Bahasa Malaysia which make the questionnaire in bilingual (English and Bahasa Malaysia). This closed-ended questionnaire is provided to the participants to be filled in and they need to choose only one answer for each statement. For Section B, and C, it comprises of questions with 5 Likert-scale options of answer which varying from 1= strongly disagree until 5= strongly agree. The instruments has been divided into three (3) sections; Section A used to collect demographic information of respondents, Section B is adapted from Information Communication Technology Learning (ICTL) , and Section C which consists of statements from Technology Affinity Survey (TAS).
3.6.1
Consent form Respondents were given a sheet of consent form along with a set of questionnaire.
They have to read, understand and sign it if they agree on the terms and condition stated for data collection process. The respondents are freely to withdraw themselves from the session at any time.
45
3.6.2
Section A: Demographic Information Demographic information is collected from respondents it will be used as a
supportive variable to construct a solution or come up with a conclusion. This section consists of statement related gender, age, ethnicity, type of social media used, and also the duration of usage.
3.6.3
Section B: Information Communications Technology Learning (ICTL) This section consists of questions that related to psychometric measurement and
was expended and validated in a 2011 study of technology tool use which extracted from Mills and Knezek (2012). The instrument meant to seek on how students choose to interact with ICT tools in educational information seeking and sharing. It is a refined version which has been validated in 2011 study (Mills & Knezek, 2012). Table 3.3 Items for Information Communication Technology Learning (ICTL) Groups Information Seeking Information Sharing
3.6.4
Item numbers 1,4,7,8,10,13,14 2, 3, 5,6,9,11,12, 15.
Total items 7 8
Section C: Technology Affinity Scale (TAS) Section C focuses to measure Internet related digital technology and it focusing on
mobile technology use. Initially, the statements in this section is developed in a doctorallevel psychometric class which offered by a university in North Texas during summer of 2011.
46
Table 3.4 Items for Technology Affinity Scale (TAS) Type of Interaction Immersed Always On
3.7
Item numbers Total items 3,4,5,7,8,9,10,11,12,13,14,17,18,19 14 1, 2, 6, 15, 16. 5
Pilot Test The instrument needs to undergo verification for its validity and reliability of the
data. Pilot study in a research investigation is crucial as Payne (1976) mentioned that pilot study hold more than one purpose which firstly it gives an opportunity to practice administering the test and secondly, it may issued any weaknesses in the procedure of administration.
3.7.1
Validity Validity is a crucial stage for any research which to measure the abstract construct
such as depth of interaction. Baker (1999) mentioned that validity deal with “the question whether the instrument developed is measuring what it is supposed to measure”. There are two types of validity, namely, content validity and construct validity (Saunders et al., 2011). As suggested by Churchill (1979), content validity need to be carried out first as the procedure in producing a good measure. Content validity is an assessment of whether the instrument are measuring the core concept (Baker, 1999) which failure to do so will invalidate the instrument as it did not reflecting the full domain of the content. Bryman and Cramer (2011) mentioned that content validity as the minimum prerequisite before establishing construct validity, reliability, and unidemensionality. 47
Content validity will be done through several phase;(1) conducting a meticulous, extensive, and systematic database of literature review, (2) generating the sample items, (3) reviewing items with experts, (4) conducting pilot test, and lastly, (5) purifying items using coefficient alphas and factor analysis (Churchill Jr & Iacobucci, 2009; Bryman, 2012). As the phase has been completed, reliability testing on the instrument will be conducted.
3.7.2
Reliability As all the items passed the content validity test, the next phase is to assessing its
reliability. Peter (1981) defined reliability as “the correlation between a measure and itself”.
3.7.3
Cronbach’s Alpha Cronbach’s alpha is a measure of internal consistency, which is to see the
interrelation a set of items are in a group (Introduction to SAS, 2013). Cronbach’s alpha is also known as “an index of reliability associated with the variation accounted for by the true score of the underlying construct” (Hatcher, 1994). For this research, the instrument that will going to be tested for the reliability need to have the value of Cronbach’s alpha exceeding 0.7. This value has been set as it benchmark to be labelled as reliable instrument.
48
Table 3.5 Cronbach’s Alpha Value Cronbach’s Alpha Value ≥ 0.9 0.8 ≤ α < 0.9 0.7 ≤ α < 0.8 0.6 ≤ α < 0.7 0.5 ≤ α < 0.6 α < 0.5
3.7.4
Internal Consistency Excellent Good Acceptable Questionable Poor Unacceptable
Reliability and Validity Testing
3.7.4.1 Section B: Information and Communication Technology Learning (ICTL) The items are divided into two categories which are information seeking and information sharing. An analysis has been conducted to test the validity and reliability of the items. The Cronbach’s Alpha for Information Seeking with seven items is 0.709 which considered as acceptable. The Cronbach’s Alpha for Information Sharing with eight items is 0.714 is considered as acceptable. However, for all items (15 items), the Cronbach’s Alpha indicated that the items are good as the value is 0.835.
Table 3.6 Cronbach’s Alpha for Information and Communication Technology Learning (ICTL) Section B: ICTL Information Seeking Information Sharing
Item numbers All (15 items) 1,4,7,8,10,13,14 2,3,5,6,9,11,12,15
49
Cronbach’s Alpha Value 0.835 0.709 0.714
3.7.4.2 Section C: Technology Affinity Scale (TAS) The item in this section is divided into two categories of interaction which is immersed and always on. Based on the reliability testing, for Immersed with 14 items, the Cronbach’s Alpha value is 0.823 while Always On with 5 items is 0.380. It shows that items that falls under category “Always On” is unacceptable (less than 0.7) while for category “Immersed “is good (more than 0.7). However, based on overall analysis on all items, it shows that the value of Cronbach’s Alpha is 0.831 which is considered as good. Table 3.7 Cronbach’s Alpha for Technology Affinity Scale (TAS) Cronbach’s
Item numbers
Value
Section C: TAS
All (19 items)
0.831
Immersed
3,4,5,7,8,9,10,11,12,13,14,17,18,19,
0.823
Always On
1,2,6,15,16,
0.380
3.8
Alpha
Summary of the research instruments
Table 3.8 Summary of the research instruments Number
Section Measurement A
of items
Demographic information
5
Sources / Contents Gender, age, ethnicity, choice of social
of
media used and the duration
respondents B
C
Attitude toward learning 15
Information
with ICT
Technology Learning (ICTL)
Degree
of
technology 19
and
Communication
Technology Affinity Scale (TAS)
affinity
50
3.9
Chapter Summary This chapter discussed on the method that used to collect the data as well as the
sources that involve in obtaining information. Research design plats an important roles in constructing the right research framework which be used as a guideline to conduct the research. All the collected data are used to support the facts and theory upon social interaction and technology use in learning.
51
CHAPTER 4 RESULTS AND FINDING
4.0
Introduction This chapter will discuss the results from the research that has been conducted.
The collected data are analyzed and followed by discussion towards the objectives of the research which to investigate the effect on social interaction in social media environment. The analysis of the data has been done using the IBM Statistical Package for Social Science (SPSS) version 20.0 for Windows. Independent T-test, Two-Way ANOVA, and Pearson Correlation Test have been used to test the hypotheses.
52
4.1
Number of Respondents The population of a local university which is located at Kota Samarahan, Sarawak
as of September 2013 consists of 14033 undergraduates. There are 95 questionnaires which have been distributed randomly throughout the university then returned and completed. The respondents have been given freedom to join the research voluntarily and they allowed withdrawing at any time.
4.2
Respondent Demographic Information In the questionnaire, there is a section which is consists of inquiry on respondents’
demographic information. They need to fill in their gender, age, ethnicity, types of social media used regularly and its duration. The data is analyzed using descriptive analysis method which used to measure the frequency.
4.2.1
Gender The number of respondent are equally distributed according to gender which made
up of 47 females and 48 males which make up of 95 respondents. In addition, they are selected randomly throughout the university. Table 4.1 Analysis of gender of respondents Frequency
Percent
Female
47
49.5%
Male
48
50.5%
TOTAL
95
100%
53
4.2.2
Age The respondents are varied in ages. They are classes into 5 groups; 19 years old,
20 years old, 21 years old, 22 years old, and 23 years old and above. The analysis shows that there are no respondent age 19 years old (0.0%), 5 respondents of age 20 years old (5.3%), 24 respondents age 21 years old (25.3%), 53 respondents age 22 years old (55.8%), and 13 respondents age 23 years and above (13.7%). Table 4.2 Analysis for age of respondents
19 Years Old 20 Years Old 21 Years Old 22 Years Old 23 Years Old and above TOTAL
4.2.3
Frequency 0 5 24 53 13 95
% 0.00% 5.3% 25.37% 55.8% 13.7% 100.0%
Ethnicity The table shows the numbers of respondent classified according to their ethnicity.
Malay respondents make up 71.6% (68 respondents), Chinese respondents make up 7.4% (7 respondents), Indian respondents make up 4.2% (4 respondents) and others race make up 16.8% (16 respondents). Other races are consists of Iban Melanau, Bidayuh, Kedayan, Rungus, Sungai, and others. Table 4.3 Analysis for ethnicity of respondents
Malay Chinese Indian Others TOTAL
Frequency 69 7 4 16 95
Percent 71.6% 7.4% 4.2% 16.8% 100%
54
4.2.4
Social Media Frequently Used The table shows the frequency on the social media used by the respondents. Most
of the respondents which are 93 respondents (97.9%) are Facebook user, 93 respondents (97.9%) are Whatsapp user, 36 respondents (37.9%) are Twitter user, 49 respondents (51.6%) are Instagram user, and 67 respondents (70.5%) use email as their social medium and 27 respondents (28.4%) used other social media application to interact with others. Table 4.4 Analysis for social media that frequently used by respondents
Facebook Whatsapp Twitter Instagram Email Others
4.2.5
Frequency Yes 93 (97.9%) 93 (97.9%) 36 (37.9%) 49 (51.6%) 67 (70.5%) 27 (28.4%)
TOTAL No 2 (2.1%) 2 (2.1%) 59 (62.1%) 46 (48.4%) 28 (28.9%) 68 (71.6%)
95 (100%) 95 (100%) 95 (100%) 95 (100%) 95 (100%) 95 (100%)
Time Spend On Social Media The table shows the duration taken by respondents using social media on daily
basis. Most of them used social media more than 2 hours which make up of 51 respondents (53.7%), followed by one hour to two hours which make up of 26 respondents (27.4%), and less than 30 minutes which make up of 18 respondents (18.9%). Table 4.5 Time spend on social media (Duration)
Less than 30 minutes 1-2 hours More than 2 hours TOTAL
Frequency 18 26 51 95
Percent 18.9% 27.4% 53.7% 100.0%
55
4.3
Descriptive Analysis of Research Instrument
4.3.1
Section B: Information and Communications Technology Learning Survey Table 4.6 shows that the mean for Information Seeking items is 3.90 and standard
deviation for this part is 0.518. For item 1, most of the respondents agree (37 respondents) that they would like to be a participating member of an online community. As for item 4 which is “I like to enroll in classes to continue my education”, it found that most of the respondents agree (38 respondents) with the statement. Item 7, “I like to take classes from good professor or teacher” indicated that 46 respondents strongly agree with the statement. Item 8 “I use Internet communication technology tools when I want to learn about something new” received 43 responds as agree with the statement. While, there is 44 respondents agree that Internet technology helps them to be successful in their college classes (Item10). 56 respondents agree that they learn more when they regulate their own learning experience and seek information on things that they want to learn about (Item 13). Lastly, it shows that 51 respondents agree that they use Internet communication technology to keep current on topics related to their field of expertise.
56
Table 4.6 Descriptive Statistics for Information Seeking Strongly Disagree Neutral Disagree Information seeking
Agree
Strongly Mean S.D Agree 3.90 0.518
1. I would like to 7 be a participating (7.4%) member of an online community
9 (9.5%)
30 37 12 (31.6%) (38.9%) (12.6%)
3.40
1.066
4. I like to enroll in 2 classes to continue (2.1%) my education
5 (5.3%)
23 38 27 (24.2%) (40.0%) (28.4%)
3.87
0.959
7. I like to take 1 classes from good (1.1%) professor or teacher
0 (0.0%)
14 34 46 (14.7%) (35.8%) (48.4%)
4.31
0.710
8. I use Internet 1 communication (1.1%) technology tools when I want to learn about something new
2 (2.1%)
20 43 29 (21.1%) (45.3%) (30.5%)
4.02
0.838
10. Internet 1 technology helps (1.1%) me be successful in my college classes
4 (4.2%)
31 44 15 (32.6%) (46.3%) (15.8%)
3.72
0.821
13. I learn more 0 when I regulate my (0.0%) own learning experience and seek information on things that I want to learn about
2 (2.1%)
18 56 29 (18.9%) (58.9%) (20.0%)
3.97
0.691
14. I use Internet 1 2 16 51 25 4.02 communication (1.1%) (2.1%) (16.8%) (53.7%) (26.3%) technology to keep current on topics related to my field of expertise Note. 1= Strongly Disagree, 5= Strongly Agree, S.D = Standard Deviation
0.785
57
Table 4.7 shows the descriptive analysis on each item in the section of “Information Sharing”. The result indicates that the median for all items are 3.74 and the standard deviation is 0.522. For item 2, “I use Internet technology to explore topics of interests”, it found that 49 respondents strongly agree with the statement. There is 35 respondents agree that they like to share their interest and reflection online (Item 3). Meanwhile, for item 6, “I learn many things by interacting with other Internet users”, received 48 feedbacks as agree to the statement. For item 9, there is 45 respondents who felt neutral with the statement, “I learn best in traditional setting”. Item 11 shows 46 respondents agree that more classroom learning should be included interactive communication technology experience. For item 12, 41 respondents agree that the things they should know are taught by instructors in the classroom. Lastly, 42 respondents agree with the statement, “I post information that might be of interest to other people”.
58
Table 4.7 Descriptive Statistics for Information Sharing Strongly Disagree Neutral Disagree Information Sharing
Agree
Strongly Mean S.D Agree 3.74 0.522
2. I use Internet 1 technology to (1.1%) explore topics of interest
1 (1.1%)
4 (4.2%)
40 49 (42.2%) (51.6%)
4.42
0.723
3.I like to share 2 interests and (2.1%) reflection online
7 (7.4%)
32 35 19 (33.7%) (36.8%) (20.0%)
3.65
0.954
5.I use Internet 3 communication and (3.2%) other technology tools for self expression
15 (15.8%)
26 39 12 (27.4%) (41.1%) (12.6%)
3.44
1.008
6. I learn many 0 things by interacting (0.0%) with other Internet users
7 (7.4%)
20 48 20 (21.1%) (50.5%) (21.1%)
3.86
0.829
9.I learn best in 2 traditional (2.1%) classroom setting
12 (12.6%)
45 24 12 (47.4%) (25.3%) (12.6%)
3.34
0.930
11.More classroom 1 learning should be (1.1%) included interactive communication technology experience
3 (3.2%)
17 46 28 (17.9%) (48.4%) (29.5%)
4.02
0.837
12.The things I 3 should know are (3.2%) taught by instructors in the classroom
7 (7.4%)
33 41 11 (34.7%) (43.2%) (11.6%)
3.53
0.909
15.I post 5 4 27 42 17 3.65 information that (5.3%) (4.2%) (28.4%) (44.2%) (17.9%) might be of interest to other people Note. 1= Strongly Disagree, 5= Strongly Agree, S.D = Standard Deviation
0.998
59
4.3.2
Section C: Technology Affinity Scale Table 4.8 shows the descriptive analysis for each item for “Immersed” section.
The median of the items is 3.19 and the standard deviation is 0.610. Item 3, “My attention is often distracted by email or text messages when I am talking to someone”, shows 41 respondents agree with the statement. While for Item 4, 34 respondents choose agree as their respond for the statement “I communicate with my friends mostly text messages”. Item 5 recorded that 41 respondent agree that some people are too absorbed in electronic communication to really face-to-face. Item 7 show that 38 respondents felt neutral that they often messages while walking down the street. Item 8, “I sometimes check email messages while driving” showed that 38 respondents felt neutral with the statement. Item 9 shows that 30 respondents felt neutral with the statement “I sometimes check email messages while driving”. 31 respondents felt agitated when they were away from the Internet for more than one day (Item 10). 36 respondents felt neutral with statement in Item 11 which is they feel disturbed if they go out and forgot their cell phone. 36 respondents felt neutral with Item 12, “I prefer socialize on social media rather than faceto-face”. Item 13 show that 28 respondents choose neutral for the statement, “many relationships are easier to maintain on Facebook-type social media”. For item 14, 37 respondents feel neutral that their computer is just as important to them as their wallet or purse. Item 17 stated that 40 respondents felt neutral for the statement “many people have good friends they met via social network”. 32 respondents felt neutral with the statement “sometimes feel more available to my electronic devices than to my family” (Item 18). Lastly, Item 19 recorded that 38 respondents choose neutral as they sometimes they feel they are a slave to the technologies that surround them.
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Table 4.8 Descriptive Statistics for Immersed Strongly Disagree Neutral Disagree
Agree
Immersed
Strongly Mean S.D Agree 3.19 0.610
3. My attention is 7 often distracted by (7.4%) email or text messages when I am talking to someone
11 (11.6%)
23 41 (24.2%) (43.2%)
13 (13.7%)
3.44
1.096
4. I communicate 5 with my friends (5.3%) mostly by text messages
8 (8.4%)
27 34 (28.4%) (35.8%)
21 (22.1%)
3.61
1.085
5. Some people are 1 too absorbed in (1.1%) electronic communication to really face-to-face
3 (3.2%)
20 41 (21.1%) (43.2%)
30 (31.6%)
4.01
0.869
7. I often type 8 messages while (8.4%) walking down the street
14 (14.7%)
38 24 (40.0%) (25.3%)
11 (11.6%)
3.17
1.088
8. I sometimes 26 check email (27.4%) messages while driving
25 (26.3%)
28 10 6 (29.5%) (10.5%0 (6.3%)
2.42
1.181
9. I sometimes 21 check email (22.1%) messages while meeting
22 (23.2%)
30 17 (31.6%) (17.9%)
5 (5.3%)
2.61
1.170
10. I feel agitated 8 when I am away (8.4%) from the Internet for more than one day
11 (11.6%)
31 22 (32.6%) (23.2%)
23 (24.2%)
3.43
1.217
61
11. I feel disturbed 8 if I go out and (8.4%) forgot my cell phone
9 (9.5%)
28 26 (29.5%) (26.4%)
24 (25.3%)
3.52
1.210
12. I prefer 13 socialize on social (13.7%) media rather than face-to-face
21 (22.1%)
36 19 (37.9%) (20.0%)
6 (6.3%)
2.83
1.098
13. Many 17 relationships are (17.9%) easier to maintain on Facebook-type social media
13 (13.7%)
28 27 (29.5%) (28.4%)
10 (10.5%)
3.00
1.254
14. My computer is 6 just as important to (6.3%) me as my wallet or purse
5 (5.3%)
37 32 (38.9%) (33.7%)
15 (15.8%)
3.47
1.029
17. Many people 1 have good friends (1.1%) they met via social network
12 (12.6%)
40 23 (42.1%) (24.2%)
19 (20.0%)
3.49
0.988
18. Sometimes I 21 feel more available (22.1%) to my electronic devices than to my family
29 (30.5%)
32 9 (33.7%) (9.5%)
4 (4.2%)
2.43
1.068
19. I sometimes I 10 8 38 29 10 3.22 feel I am a slave to (10.5%) (8.4%) (40.0%) (30.5%) (10.5%) the technologies that surround me Note. 1= Strongly Disagree, 5= Strongly Agree, S.D = Standard Deviation
1.093
62
Table 4.9 shows the descriptive analysis on Section C: Always-On with the median 3.27 and standard deviation of 0.576. Item 1 shows that 31 respondents disagree with the statement that it is impossible to work on computer in the audience during a presentation. 37 respondents felt neutral with the statement in Item 2 (There are certain event during which ALL electronic devices should be put away). Item 6 shows that 32 respondents choose neutral for the statement “Is is okay to send text messages while carrying on a face-to-face conversation”. Item 15 shows 30 respondents choose neutral for the statement “for me, a computer is a better companion than a pet”. Finally, 47 respondents strongly agree that many people are too attached to their smartphone (Item 16).
63
Table 4.9 Descriptive Statistics for Always-On Strongly Disagree Neutral Disagree Always-On
Agree
Strongly Mean S.D Agree 3.27 0.576
1.It is impossible to 12 work on computer (12.6%) in the audience during a presentation
31 (32.6%)
28 18 6 (29.5%) (18.9%) (6.3%)
2.74
1.103
2. There are certain 3 events during which (3.2%) ALL electronic devices should be put away
5 (5.3%)
37 27 23 (38.9%) (28.4%) (24.2%)
3.65
1.008
6. It is okay to send 11 text messages while (11.6%) carrying on a faceto-face conversation
19 (20.0%)
32 20 13 (33.7%) (21.1%) (13.7%)
3.05
1.197
15. For me, a 20 computer is a better (21.1%) companion than a pet
24 (25.3%)
30 14 7 (31.6%) (14.7%) (7.4%)
2.62
1.187
16. Many people are 1 too attached to their (1.1%) smartphone
1 (1.1%)
14 32 47 (14.7%) (33.7%) (49.5%)
4.29
0.836
Note. 1= Strongly Disagree, 5= Strongly Agree, S.D = Standard Deviation
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4.4
Inferential Data Analysis towards Hypotheses
4.4.1
Testing the hypothesis by using Independent T-test
H01: There is no significant difference in attitudes toward learning with ICT based on gender. An Independent T-test was performed comparing mean scores for approaches used by female and male students to interact with technology tools in learning. As predicted, result form an independent samples t-test indicated for Information Seeking approach that females (M=3.95, SD = 0.417, n= 47) scores higher on Information Seeking approach than males (M= 3.85, SD = 0.603, n= 48) with t (93) = -0.827, p>0.05. The mean difference was -0.08815 and the 95% confidence interval around the difference between groups means was -0.210 to 0.123. As for Information Sharing approach, the test shows that females (M= 3.68, SD = 0.482, n = 47) score higher on Information Sharing approach than males (M = 3.68, SD = 0.558, n=48) with t (93) = -1.055, p>0.05.The mean difference was -0.1129 and the 95% confidence interval around the difference between groups means was -0.325 to 0.010. For overall analysis on attitude towards learning with ICT based on gender, it reported that females (M= 3.87, SD = 0.399, n= 47) scores higher on attitude towards learning with ICT than males (M= 3.77, SD = 0.557, n= 48) with t(93)= -1.009, p>0.05. The mean difference was -0.1005 and the 95% confidence interval around the difference groups means was -0.2984 to 0.0973. The null hypothesis is failed to be rejected. Thus, there is no significant difference in attitude toward leaning with ICT based on gender.
65
Table 4.10 Group Statistics Gender Information Seeking Male Female Information Sharing Male Female Attitude toward learning Male with ICT Female
N 48 47 48 47 48 47
Mean 3.8571 3.9453 3.6823 3.7952 3.7639 3.8652
Std. Deviation 0.60322 0.41655 0.55780 0.48161 0.55607 0.40156
Table 4.11 Independent Sample T-test for Information Seeking and Information Sharing T-test for Equality of Means t df Sig (2- Mean tailed) Difference
Information_seeking -0.827 Information_sharing -1.055 Attitude toward -1.009 learning with ICT
4.4.2
93 93 93
0.410 0.294 0.316
-0.08815 -0.11292 -0.10053
95% Confidence Interval of the Difference Lower Upper -0.29978 0.12349 -0.32544 0.09959 -.02981 0.09734
Testing the hypothesis by using One-Way ANOVA
H02: There is no significant difference in degree of technology affinity based on duration of using social media One Way Analysis of Variance showed no significant difference in degree of technology affinity (immersive and continuous interaction) from different levels of duration of time. (F (2, 94) =0.013, p>0.05). Tukey HSD post-hoc analyses indicated that technology affinity were highest for usage between one until two hours compared to duration which is less than 30 minutes (p>0.05) and more than 2 hours (p>0.05). Thus, the null hypothesis is failed to be rejected.
66
Table 4.12 Test of Homogeneity of Variances
Immersed Always-On Technology Affinity
Levene Statistic 0.567 0.676 0.980
df1
df2
Sig.
2 2 2
92 92 92
0.569 0.511 0.379
Table 4.13 One Way Analysis of Variance between degree of technology affinity and duration
Immersed
Always-On
Technology affinity
Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total
Sum Squares 0.441
of df 2
Mean Square 0.220
F
Sig.
0.588 0.558
34.484 34.925 1.532
92 0.375 94 2 0.766
2.377 0.099
29.661 31.193 0.027
92 0.322 94 2 0.766
2.377 0.099
28.062 28.089
92 0.322 94
67
Table
4.14
Tukey HSD Dependent (I) Duration Variable
Multiple
(J) Duration
1-2 hours Less than 30 More than 2 minutes hours Less than 30 minutes Always_On 1-2 hours More than 2 hours Less than 30 More than 2 minutes hours 1-2 hours 1-2 hours Less than 30 More than 2 minutes hours Less than 30 minutes Immersed 1-2 hours More than 2 hours Less than 30 More than 2 minutes hours 1-2 hours
Comparison:
Mean Std. Difference Error (I-J)
Tukey
.36838
.17410 .092
95% Confidence Interval Lower Upper Bound Bound -.0464 .7831
.27908
.15567 .178
-.0918
.6499
-.36838
.17410 .092
-.7831
.0464
-.08929
.13683 .791
-.4152
.2367
-.27908
.15567 .178
-.6499
.0918
.08929 -.19719
.13683 .791 .18772 .547
-.2367 -.6444
.4152 .2500
-.15033
.16785 .644
-.5502
.2495
.19719
.18772 .547
-.2500
.6444
.04686
.14753 .946
-.3046
.3983
.15033
.16785 .644
-.2495
.5502
-.04686
.14753 .946
-.3983
.3046
68
Sig.
Post-Hoc
4.4.3
Testing the hypotheses by using Pearson Product Moment Correlation Test
H03: There is no significant relationship between gender and attitude toward learning with ICT. The Pearson Product Moment Correlation shows that there is no significant relationship between gender and attitude toward learning with ICT as the p-value is exceed 0.5 (p= 0.312). Thus, the null hypothesis is failed to be rejected. Table 4.15 Pearson Correlation between gender and attitude toward learning with ICT
Attitude_ICT
Gender
Attitude_ICT 1
Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N
95 0.105 0.312 95
Gender 0.105 0.312 95 1 95
H04: There is no significant relationship between attitudes towards learning with ICT and degree of technology affinity The Pearson Product Moment Correlation result showed that there was a significant (p0.05 H02: There is no significant difference H02 is failed to One-Way ANOVA in degree of technology affinity based be rejected on duration of using social media. p>0.05 H03: There is no significant relationship H03 is failed to Pearson Correlation test between gender and degree of be reject technology affinity based on interaction p-value = 0.312 analysis H04: There is no significant relationship H04 is rejected Pearson Correlation test between degree of technology affinity There is and attitude toward learning with ICT. significant and weak positive relationship
70
4.6
Chapter Summary All the hypotheses have been run using IBM Statistical Package for Social
Sciences (SPSS) for Windows version 20.0.
The inferential as well as descriptive
analyses are run to obtain the means, standard deviation, frequencies and values from the test to investigate the research hypotheses. Inferential test that have been used are Independent T-test, One-Way ANOVA, and Pearson Product Moment Correlation test. The result has been recorded for each hypothesis.
71
CHAPTER 5 DISCUSSION AND CONCLUSION
5.0
Introduction This chapter will summaries the findings of the study and provided with support
from readings. This includes discussion for each objective which is involving students’ attitude toward learning with ICT, degree of technology affinity, the relationship between attitude toward learning with ICT with degree of technology affinity as well as with gender. The implication of the study and recommendation for future researcher are followed by the conclusions of this study.
72
5.1
Summary of the study This study was conducted to identify the gender differences in attitude toward
learning with ICT, difference in degree of technology affinity based on duration of using social media, relationship between gender and degree of technology affinity based on the interaction analysis and the relationship between attitude toward learning with ICT and degree of technology affinity. This study was a quantitative research, using a cross-sectional survey research design. The research instrument used to collect data was a survey and close-ended questionnaire which consisted of three sections. The first section consist of item regarding to respondents demographic information, the second section consist of 15 items on Information and Communication Technology Learning (ICTL). These items have been used to measure the attitude toward learning using ICT that classified by information seeking and information sharing. The third section consist of 19 items which was used to measure the degree of technology affinity that classified as immersive and continuously engage with daily technology tools. The target population for this study is undergraduate students whereby it covered randomly seven faculties across Universiti Malaysia Sarawak (UNIMAS). The respondents consist of ninety-five (95) undergraduate students of various programmes in UNIMAS. The study has been conducted from 30th March 2015 until 30th April 2015. 106 set of questionnaire has been prepared to be distributed and total of 95 set of questionnaire were returned and fully completed. The questionnaire has been constructed as self-administered and collected from the respondents as they had finished completing the set.
73
The data has been collected then being analyzed using Statistical Package for Social Sciences (SPSS) version 20.0 whereby the means, standard deviation, and frequencies has been calculated. Frequencies, means and standard deviation is calculated to explain the demographic data as well as the portion of answer (Liker-scale) for each item in the questionnaire. Moreover, statistical test has been executed to test the research hypothesis. Independent T-test has been used to measure the differences between genders against attitude toward learning using ICT. One-way ANOVA has been used to test the second hypothesis which is to measure the differences in degree of technology affinity based in duration of using social media. Pearson Product Moment Correlation test has been used to test the third and fourth hypothesis which is to measure the relationship between gender and attitude toward learning with ICT as well as the relationship between attitude toward learning with ICT and degree of technology affinity.
74
5.2
Discussion
H01: There is no significant difference in attitude toward learning with ICT based on gender
Independent T-test has been done to test the hypothesis whether there is any significant difference in attitude toward learning with ICT based on gender. The result shows that the null hypothesis is failed to be rejected as it revealed that the p-value is exceeding 0.05 which is 0.316. As a conclusion, it shows that there is no significant difference in attitude toward learning with ICT based on gender. Table 5.1explained on the frequency of respondents based on their attitude toward learning with ICT which divided into 3 subgroups; information seeking, information sharing, and both. For information seeking, it has been dominated by male respondents (n=30) and followed by female respondents (n=29). Information sharing has been scored mostly by female respondents (n=17) and followed by male respondents (n=14). Remaining of respondents has applied both approaches (information sharing and information seeking) which represented by 4 male respondents and one female respondent. Table 5.1 Attitude toward learning with ICT based on gender Attitude
toward
learning Gender
with ICT
Male
Female
Information Seeking
30
29
Information Sharing
14
17
Both
4
1
75
A research done by Dubi and Rutsch (1998) to examined the Internet information search behavior towards students from different level of schooling and the result shows that there are significant gender differences. It was observed that female students are lack of self-confidence as they felt less skilled to deal with search engine as described the systems as “too complicated”. This literature is align with the research finding which indicate female are less likely to linked to information seeking behavior with exception that there is no significant differences between gender on these behavior. A finding which is statistically significant gender differences may not have any values whereas unstudied variables could influence students’ computer-related behavior. This literature shows a contradicting result from the finding obtain from the present research. Students rating on themselves can be particularly challenging due to male students repeatedly observed tendency to overestimate and female students to underestimate their abilities (Bannert & Arbinger, 1996; Copper & Stone, 1996). However, from the descriptive analysis done by comparing social media used by the respondents against gender, it shows that male students engage in using social media more compared to female. It supported by research done by Li and Kirkupb (2007) which indicated that male students are known to use Internet sources regularly compare to female students do. Female students less likely to engage in using computer compared to males could be resulting from their confidence level using computer. Shashaani and Khalili (2001) mentioned that in a research, female undergraduate students reported to have significantly lower in confidence level compared to males based on their ability to use computers.
76
Table 5.2 shows that respondents mostly used Whatsapp and Facebook daily. It follows by Twitter, Instagram, email and others.
Table 5.2 Descriptive analysis between social media and gender Media Whatsapp Facebook Twitter Instagram Email Others
Yes No Yes No Yes No Yes No Yes No Yes No
Gender Male 47 1 46 2 18 30 28 20 31 17 18 30
TOTAL Female 46 1 47 0 18 29 21 26 36 11 9 38
93 2 93 2 36 59 49 46 67 28 27 68
By referring to the instrument, Section B discussed the attitude toward learning with ICT. Respondents are divided into two group based on median from Section B which is 3.8667 for 95 respondents. Respondents with mean below than the median were assigned as low group preference for ICT learning, while for those who score mean more than the median are known as high group on the ICT learning preference. Table 5.2 shows that most of the respondents (n=53) assigned for low ICTL. Table 5.3 Descriptive Statistics for ICT learning preference
ICT Low ICT High TOTAL
Frequency 53 42 95
Percent 55.8% 44.2% 100%
77
Compared to the previous research, there is some similarity and differences. The similarity of the result obtains which it found out that there is no significant difference on gender in relation on using ICT in learning. Tsai, Lin , and Tsai (2001) reported the same result as their finding proved that no significant gender differences in the perceived usefulness of the Internet which in relation to this research is that the usage of Internet of technology in learning. However, there is a different finding which discovered by Houtz and Gupta (2001), they found that there are significant differences in the way females and males classified themselves in their abilities to be advanced on their technology skills. In addition, males rated themselves as higher technological ability compared to females. Thus, as the result of the research does contradicting from the previous research, it is might due to social background and prior knowledge of using ICT in learning. Moreover, as discovered by Kim, Sin, and Tsai (2014), based on their research, they found out that personal preferences or types of tasks and training that the student received could influence information seeking behaviour.
78
There is no significant differences in degree of technology affinity based on H02: duration of using social media
Second hypothesis is tested using One-Way ANOVA. The variable tested is degree of technology affinity and duration of using social media. The null hypothesis has been failed to rejected as the p-value obtained were more than 0.05 (p-value=0.099). Table 5.4 describing the descriptive analysis on degree of technology affinity explains the statistics for immersed group and always-on group. The results shows that 53 respondents tend to continuous (always-on) connection to digital communication technology, 40 respondents tend to immersed in daily technology, and there are 2 respondents react to both immersed and always-on towards usage of technology in daily life. Table 5.4 Degree of Technology Affinity Degree of technology affinity
Count
Immersed
40
Always-On
53
Both
2
Table 5.5 shows the frequency of respondents using social media based on duration taken by them. It shows that respondents aged 22 years old used social media in longer period compared to other age group which represented by 23 respondents. A research done in 2010, prove that social networking is mostly engaged by users with schools and universities. For example about 50% of Facebook and Twitter users are people under 35 years old (Stanciu, Minai, & Aleca, 2012).
79
Table 5.5 Duration of using technology against age Less than 30 minutes
1 -2 hours
More than 2 hours
19 Years Old
0
0
0
20 Years Old
2
0
3
21 Years Old
3
4
17
22 Years Old
10
20
23
23 Years Old and above
3
2
8
As reported by Yellow Pages (2014), Facebook users spend around 17 minutes on the site each time they log into it. The typical user would spend more than 8.5 hours per week on the site. Another platform which score higher visit times are Tumblr and Pinterest.
80
H03: There is no significant relationship between gender and degree of technology affinity based on interaction analysis
Third hypothesis has been analysed using Pearson’s Product Moment Correlation between gender and degree of technology affinity, it found that there is no significant relationship between these two variables, thus it is failed to be rejected. Table 5.5 shows the descriptive analysis on degree of technology affinity according to gender. It shows that 23 male respondents dominated by being immersed in daily technology, but 29 female respondents become the majority group for always-on digital communications. There are equal amount of respondents which is one respondent for both female and male respondent. The finding is supported by Li and Kirkupb (2007) as it reported that male students are known to use Internet source more frequently than female students. The lack of differences between male and female respondents of this study demonstrates that relative similarity has come to degree of technology usage. The finding form the research however is contradicted from Bimber (2000) who cites significant differences in Internet access and use across gender. Table 5.6 Degree of Technology vs. Gender Gender
Degree
Male
Female
23
17
Always-On
24
29
Both
1
1
of Immersed
technology affinity
81
Table 5.6 are presented as it shows male undergraduate student engaged in using ICT compared to female students. 27 male respondents spend their time to use social media more than 2 hours daily compared to female respondents. As mentioned by Lorigo et al. (2006), it has observed that male users had greater fixation duration on selected Web documents than females and female users submitted significantly longer queries to the Google search engine than males. Table 5.6 Duration taken by female and male using ICT
Duration
Male
Female
Less than 30 minutes
10
8
1- 2 hours
11
15
More than 2 hours
27
24
As a conclusion, the statistical test show that there is no significant relationship between gender and degree of technology affinity as the figure on table presented did not have distinct differences between male and female respondents.
82
H04: There is no significant relationship between attitude toward learning with ICT and degree of technology affinity
The fourth hypothesis is tested using Pearson’s Product Moment Correlation test between attitude toward learning with ICT and degree of technology affinity. The result show that there is a significant and weak positive relationship between these two mentioned variables (r= 0.388). It is align with the finding from a research done by Mills, Knezek and Wakefield (2013). The research documented that there are small relationship between attitude toward learning and technology affinity. However, the analysis is done with added variable which is learning with social media that is not included in this research. Table 5.3 showed the distribution of respondents according to the categories for attitude toward learning. The categories are Information Seeking, Information Sharing and there are respondents with both attitudes. The analysis revealed that 59 respondents using ICT in learning for seeking information, followed by 31 respondents used the technology to shared information and there are 5 respondents used both approaches; seeking
and
Table 5.7 Descriptive analysis for Attitude toward Learning with ICT Attitude toward learning with ICT
Count
Information Seeking
59
Information Sharing
31
Both
5
83
sharing.
5.3
Recommendation For further exploration in the future, number of respondent that will be chosen to
participate in the research could be increase to increase the validity and the reliability of the data collected. Furthermore, by adding social media scale as another instrument could help to strengthen the hypothesis which can be related to social media such as to investigate the relationship between social media and degree of technology affinity. The results can be convincing as the element of social media are being compared with other variables. Demographic variable can be varied as for this research it only limited to age, gender and ethnicity whereby not all of these variables are being used in hypotheses testing.
5.4
Conclusion This research reports finding on a study of information behavior (information
seeking and sharing) in current technology-persistent 21st century learning environment. Respondents (n = 95) completed a structured questionnaire which designed to allow exploration on their preferences for using technology in learning. Findings indicated that ICT preference for seeking and sharing information digitally is positively related to preference to daily technology use either immersive or continuous usage.
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Examination find out that gender is not significantly has differences in relation to attitude toward learning with ICT and have no direct relationship with each other. Statistically, it found out that male respondents tend to dominate on information seeking compared to female respondents. Thus, gender does play some role in determining the attitude toward learning even though the finding from the research cannot prove it as mentioned in previous research done by Bimber (2000). This research has indicated that learners’ technology affinity is an important element to be taken into consideration for educator to develop and planning their design models for teaching and learning. The plan will includes the educationally uses of technology.
5.5
Chapter Summary This chapter as enlighten the research hypotheses and summarizing the outcome
obtained after the analysis using appropriate test. Besides that, recommendation for further exploration also has been presented as a guide for future researcher. Moreover, there are elements that have been highlighted as part of the discussion which is research hypothesis and the supported finding which some of them are contradicting with the finding of the research.
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APPENDIX
FACULTY OF COGNITIVE SCIENCES AND HUMAN DEVELOPMENT UNIVERSITI MALAYSIA SARAWAK
MSc. LEARNING SCIENCES SARJANA (SAINS) SAINS PEMBELAJARAN
SOCIAL INTERACTION AND TECHNOLOGY INTERAKSI SOSIAL DAN TEKNOLOGI Dear Mr. /Mrs / Ms, You have been selected as a respondent in this study. This study is conducted by MSc. Learning Sciences student. The purpose of distributing this questionnaire is to gain data about relationship of social interaction and use of technology in everyday lives. All the information given by the respondents will be confidential. Thank you for all the cooperation given.
Tuan / Encik / Cik / Puan, Anda telah dipilih sebagai responden dalam kajian ini. Kajian ini dikendalikan oleh PelajarSarjana Sains Pembelajaran. Tujuan borang soal selidik ini diedarkan adalah untuk mengumpulkan maklumat mengenai hubungan antara interaksi sosial dan penggunaan teknologi dalam kehidupan seharian. Segala maklumat yang diberikan oleh responden adalah sulit. Kerjasama
yang
diberikan
Prepared by / Disediakan oleh: Nurul Muizzah binti Johari (14030227)
98
amat
dihargai.
SECTION A: DEMOGRAPHIC INFORMATION This questionnaire are divided into THREE sections. The Section A is about demographic information of respondent. Section B use to measure how technology is used in learning. Section C comprises the questions on perception related to digital technology use.
Borang soal selidik ini dibahagikan kepada EMPAT bahagian. BAHAGIAN A merangkumi soalan mengenai latar belakang demografik responden. BAHAGIAN B digunakan untuk mengukur bagaimana teknologi digunakan di dalam pembelajaran.BAHAGIAN B mengandungi penyataan berkatian dengan pandangan terhadap media sosial di dalam pembelajaran manakala BAHAGIAN D mengandungi soalan berkaitan dengan persepsi terhadap pengunaan teknologi digital.
SECTION A: DEMOGRAPHIC INFORMATION / MAKLUMAT DEMOGRAFIK Please tick (/) on the suitable answer below. / Sila tandakan (/) pada pilihan jawapan yang sesuai di bawah. 1. Gender / Jantina: Male / Lelaki Female/ Perempuan
2. Age / Umur: 19 Years Old 20 Years Old 21 Year Old 22 Years Old 23 Years Old and Above
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3. Ethnicity / Kaum: Malay / Melayu Chinese / Cina Indian / India Other / Lain-lain:_______________ 4. Did you use any social media to communicate with people? Please tick (/) on the box related to your answer below. You may choose more than one answer. / Adakah anda menggunakan sebarang media sosial untuk berkomunikasi dengan orang lain? Sila tandakan (/) pada kotak yang berkaitan dengan jawapan anda dibawah. Anda boleh menanda lebih dari satu jawapan. Facebook Whatsapp Twitter Instagram E-mail / Emel Others / Lain-lain: _____________________________________
5. How long did you spend your time using social media? / Berapa lamakah masa yang anda ambil untuk menggunakan media sosial? Less than 30 minutes / Kurang daripada 30 minit 1-2 hours / 1-2 jam More than 2 hours / lebih daripada 2 jam ___________________________________________________________________________
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Please read carefully at each statement below. Please tick ( / ) in the space provided according to the given scale. Sila baca setiap pernyataan di bawah dengan teliti. Sila tandakan ( / ) pada ruang yang disediakan berdasarkan skala yang telah diberikan. 1
2
3
4
5
Strongly
Disagree / Tidak
Neutral /
Agree /
Strongly Agree
Disagree
Setuju
Neutral
Setuju
/ Sangat
/ Sangat Tidak
Bersetuju
Bersetuju
SECTION B : INFORMATION AND COMMUNICATION TECHNOLOGY LEARNING (ICTL) NO
STATEMENT(S)
1
1
I would like to be a participating member of an online community / Saya ingin terlibat di dalam perbincangan komuniti atas talian
2
I use Internet technology to explore topics of interest / Saya menggunakan teknologi Internet untuk mencari topik yang diminati
3
I like to share interests and reflection online / Saya ingin berkongsi minat dan pandangan atas talian
4
I like to enroll in classes to continue my education / Saya ingin mendaftar masuk ke kelas untuk menyambung pengajian saya (atas talian)
5
I use Internet communication and other technology tools for self-expression / Saya menggunakan komunikasi atas talian dan alat teknologi lain untuk mengekspresikan diri saya
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2
3
4
5
6
I learn many things by interacting with other Internet users / Saya mempelajari banyak perkara melalui interaksi dengan pengguna Internet yang lain
7
I like to take classes from good professor or teacher / Saya ingin mengikuti kelas dari Professor atau guru yang terbaik
8
I use Internet communication technology tools when I want to learn about something new / Saya menggunakan alat teknologi komunikasi Internet apabila saya ingin mempelajari perkara baharu
9
I learn best in a traditional classroom setting / Saya lebih senang belajar dalam keadaan kelas tradisional
10
Internet technology helps me be successful in my college classes / Teknologi Internet membantu saya untuk cemerlang di dalam kelas
11
More classroom learning should be include interactive communication technology experiences./ Pembelajaran di dalam kelas perlu disertakan dengan pengalaman teknologi komunikasi yang interaktif
12
The things I should know are taught by instructors in the classroom / Perkara yang perlu saya ketahui telah diajar oleh Pengajar di dalam kelas
13
I learn more when I regulate my own learning experience and seek information on things that I want to learn about / Saya belajar lebih banyak apabila saya mempunyai pengalaman mengatur dan mencari maklumat mengenai perkara yang saya ingin belajar
14
I use Internet communication technology to keep current on topics related to my field of expertise / Saya menggunakan teknologi komunikasi Internet bagi memastikan saya mengikuti perkembangan bidang yang berkaitan
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15
I post information that might be of interest to other people / Saya berkongsi maklumat yang boleh menarik minat orang lain
SECTION C: TECHNOLOGY AFFINITY SCALE (TAS) NO
STATEMENT(S)
1
1
It is impolite to work on a computer in the audience during a presentation / Ianya adalah mustahil to menggunakan komputer ketika pembentangan
2
There are certain events during which ALL electronic devices should put away / Ada sesetengah acara yang memerlukan SEMUA alatan eletronik dipadamkan
3
My attention is often distracted by email or text messages when I am talking to someone / Tumpuan saya kadang kala terganggu disebabkan emel atau pesanan ringkas apabila saya bercakap dengan seseorang.
4
I communicate with my friends mostly by text messages / Saya berhubung dengan rakan-rakan kebanyakkannya melalui pesanan ringkas
5
Some people are too absorbed in electronic communication to really listen face-to-face / Sesetengah orang terlalu memberi perhatian kepada komunikasi eletronik daripada perhubungan bersemuka
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2
3
4
5
6
It is okay to send text messages while carrying on a face-to-face converstaion / Ianya adalah perkara biasa untuk menghantar pesanan teks semasa menjalankan perhubungan bersemuka
7
I often type messages while walking down the street / Saya selalu menaip mesej ketika berjalan
8
I sometimes check email messages while driving / Kadangakala saya memeriksa pesanan emel ketika memandu
9
I sometimes check email messages during meetings / Kadangkala saya memeriksa pesanan emel ketika mesyuarat
10
I feel agitated when I am away from the Internet for more than one day / Saya berasa ralat ketika tidak menggunakan Internet lebih daripada sehari
11
I feel disturbed if I go out and forgot my cell phone / Saya berasa terganggu apabila berada di luar dan tidak membawa telefon bersama-sama
12
I prefer socialize on social media rather than face-toface / Saya lebih suka bersosial menggunakan media sosial berbanding dengan perhubungan bersemuka
13
Many relationships are easier to maintain on facebook –type social media / Perhubungan menjadi senang menggunakan media sosial “Facebook”
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14
My computer is just as important to me as my wallet or purse / Komputer saya amat penting seperti beg tangan atau dompet
15
For me, a computer is a better companion than a pet / Bagi saya, komputer lebih baik sebagai teman berbanding binatang peliharaan
16
Many people are too attached to their smartphone / Kebanyakkan orang terlalu bergantung kepada telefon pintar mereka
17
Many people have good friends they met via social network / Kebanyakkan orang mempunyai sahabat baik melalui jariangan sosial
18
Sometimes I feel more available to my electronic devices than to my family / Kadangkala saya berasa lebih berkelapangan untuk alatan eletronik saya berbanding keluarga saya
19
I sometimes I feel I am a slave to the technologies that surround me / Kadangakala saya berasa seperti “hamba” kepada teknologi di sekeliling saya.
-THANK YOU FOR YOUR COOPERATION-
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