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Individual Differences in Online Learning ( Han, Li, and Advani)

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Individual Difference in Online Learning

 
1. Introduction


1.1 Introduction to Online Learning


Online learning could be defined as any class that delivers its curriculum online and uses the internet as a source of communication, as well as information transmission between teachers and students (Berge, 1995). Over the years, online learning has been promoted as being more cost­efficient and more effective than traditional teaching methods (Richardson et. al, 2003).

  

Raiij and Schepers (2006) mention that globalisation goes ‘hand in hand’ with the increase in the number of e­learning programs, which is further utilised by universities to promote online degrees and courses. The courses offered could either be purely online or incorporated with other aspects such as face to face classes, which is also known as blended learning. However, according to Harasim et. al (1995) the term online learning in today’s world is used to address any classes which offers their curriculum, and deliver it via the internet, which allows students to participate in learning, regardless of their geographical distance, time and place. Online education has progressed that it no longer requires educators and students to interact face to face. It offers convenience and flexibility, that allows students to access courses and learning materials 24 hours a day, regardless of their location and time (Simonson et. al, 2000). Unlike traditional classrooms, students are able to work at their own pace, which largely favours non­native speakers. With their identities concealed, there is often no bias when it comes to appearance, gender, race or sexual orientation (Simonson et. al, 2000). Another advantage of online learning is also the fact that it promotes greater autonomy among students, with instructors often seen as facilitators as opposed to lecturers (Richardson et. al, 2003). Online courses also allow students who might not have the time or budget to sit in traditional classroom to experience the same quality of education.

     

According to Seacstudent (2013) the origins of online learning could be tracked to be around the same time as computers made its way to personal use, despite limited online courses and degree programs. Subsequently, many institutions start offering their degrees online. The term ‘online learning’ is also used interchangeably with the term CSCL, or Computer­ Supported Collaborative Learning. This is mainly due to its propensity to encourage students to collaborate in virtual spaces as they learn new lessons and tasks (Seacstudent, 2013). E­learning has also led to the development of Virtual Learning Space (VLE). VLEs aim to assist students with their online courses and promote collaborative learning, which is incorporated in the form of course time­table, discussion groups, chat forums and sometimes links to social media group (Raiij & Schepers, 2006).

 

Despite being praised for its efficiency and convenience, online learning has raised certain questions. Many have questioned its ability to function as effectively as traditional teaching methods. Certain critics have claimed that it fails to generate the same outcome as traditional methods due to lack of face to face interactions (Ward & Newlans, 1998). Bullen (1998) conducted a case study involving the use of computer mediated conferencing methods among college students. He examined the participation and critical thinking of students and found that they felt ‘disconnected’ from others while using the technology; with most citing lack of facial expression as the main factor.


1.2 Introduction to Individual Differences in online learning

 

The 19th century brought about a rapid growth on the research of individual differences in the educational field (Samah et. al, 2011). For a long time, experienced educators maintained that individual differences play an important role in terms of learning and instruction (Jonassen & Grabowski, 1993). Researchers believe that by taking into account their learning rate, style, orientation, cognitive style, multiple intelligence and talents. Students tend to learn in different rates, perceive information in different rates through different learning contexts (Felder, 1993).

    

Experiments over the years have shown that students’ different styles of learning and thinking styles have affected their academic achievements (Kim & Michael, 1995). Aviram et al. (2008) and Weber (2005) mention that challenges and opportunities offered by the research will offer students higher chance of self­development and learning. When greater emphasis is put on individual difference in learning, it is found that students have higher motivation, which could develop into learning satisfaction and end up giving them better grade (Lim et. al, 2008). Deci & Ryan (2005) mentioned how learning is an active process that requires internal motivation in order for students to engage and assimilate information. A greater autonomy is also required by learners to effectively absorb the information and learning materials (Ryan, Connel & Deci, 1985). It is also found that certain lecturers have different learning styles, and those whose learning styles match with the given methods tend to absorb and retain information for a longer time than those whose learning styles don’t match with theirs (Riding & Grimly, 1999). Cleveland­Innes and Emes (2005) added that social and academic interaction amongst learners in their respective environment, be it online or face to face has largely demonstrated impact on the approach as well as learning outcomes.

   

1.3 Aims

    

This literature review aims to investigate the factors that influence individual differences in online learning. We aim to conduct an in depth review of the internal and external factors which influence online learning. The internal factors we indentified are the cognitive control and cognitive style, and different cultures.

 

When it comes to the external factors, we examined differences in the use of Personalised Learning Environment as well as demographical issues. We will then explore further how the internal and external factors are related, especially when it comes to interaction and collaboration in online learning. After in depth exploration, we then draw conclusions of the study.

    

2.Internal Factors


2.1 Cognitive Control and Cognitive Style

    

Jonassen and Grabowski (1993) summarised a systematic classification of measurable variables to explain the individual differences. The main aspects are cognitive controls, cognitive styles, learning styles, and personality types in their study. Cognitive controls placed first because the elements in this catalogue can affect and take control of one’s perception and reaction of the surrounding stimuli. Jonassen and Grabowski (1993) displayed 6 distinctive layers of cognitive controls in their work. These are: Field Dependence/Independence; cognitive flexibility; impulsivity and reflectivity; focal attention; category width, and automization. This classification is widely cited in the post studies (David J. Ayersman & Avril von Minden, 1995). As the definition of Jonassen & Grabowski (1993), the field­dependent group prefer to external references while the field­independent ones tend to the dependence of internal resource. Besides, they find the FD person easily accept the objects as whole, by contrast an FI person tend to lay emphasis on separate detailed parts of the object.Cognitive flexibility contributes to identifying an individual’s ability of concentration. Precisely, it determines the degree a person can focus on an on­going task with external distractions. A person in constricted cognition is vulnerable to be distracted from the surrounding whereas the opposite group is the people high in flexibility. In other words, flexible persons are better at managing information because the skill of ignoring the useless parts, by contrast, the constricted ones require more energy to process the acquired massive information (Jonassen & Grabowski, 1993).

 

Cognitive style defined by Messick (1984), is an inertial thinking of how to process and analyse information and the ways of solving problems. Sternberg and Grigorenko (1997) argued that cognitive style is a bridge of two study fields ­ cognition and personality in psychology. It was stated by Riding and Cheema (1991) that there are two basic dimensions of cognitive style: wholiest­analytics (the way of information processing) and verbaliser­imager, which are existing independently. Jonassen and Grabowski’s (1993) gave a more specific and easily­understood explanation of cognitive style that there are five fundamental types classified into two catalogues. To distinguish cognitive style from cognitive control, the concept is used to depict the traits of individual learners, while cognitive control determines

 

the whole process of perceiving and understanding. In other words, one’s cognitive control impact his/her behaviours and habits including cognitive style (Jonassen & Grabowski, 1993). In terms of collecting information, people can be divided into visual/haptic, visualizer/ verbalizer, and levelling/sharpening styles. Serialist/holist and analytical/relational styles are used to describe the way people consolidating gathered messages.
       
According to Jonassen & Grabowski (1993), the element of visual/haptic is to describe an individual’s predilection of visual or physical sensory when receiving messages. Visualizer/verbalizer, is a more precise catalogue to group the learners whether they are tend to accept words or pictures. However, nowadays more learners are found undifferentiated in accepting words and pictures. (Jonassen & Grabowski, 1993) Serialist/Holist cognitive style, on the other hand, describes the form of representing messages. Holists are tend to interpret what they receive in an all­around way while serialists are easy to pay their attention on the specifics. However, Pask (1976) stated that versatile learners are able to transfer their cognitive styles properly according to the context.
      
In addtion, Multiple Intelligence Theory (Gardner, 1999) should be considered as it also focus on individual differences from learners. He indicated intelligence is based on cultural and physiological factors. Denig (2004) summarised the combined theory of Multiple Intelligence Theory and Learning Style Theory that will include not only difference in potential of further learning, but also differences in current learning process.
      
Therefore, many researches investigated several specific intelligences, such as musical intelligences (Dara­Abrams, 2002) and mechanical intelligences (Carla Lane, 2000). The capability of the web­based instructions with a variety forms of graph,audio and video, will support Multiple Intelligences theory (Krichen, 2007).
     
Nevertheless, long before the study of Jonassen & Grabowski, Tamaoka (1985) had already pointed out that no strong evidence is found to prove that an individual’s cognitive style is connecting to his/her intelligence. Most of researcher acknowledge it is difficult to identify learner’s intelligence, especially in the online context. Therefore, in order to match learner's cognitive style and multiple intelligence, and learners should be encouraged to self­choose more relevant cognitive style in open activities.
        

2.2 Cultural Difference and Learning Styles

    

Culture is an irregardless variable in distinctive disciplines when doing research such as anthropology, psychology and business management. However, a consensus of definition is reached (Simy Joy and David A. Kolb, 2008) that culture is a vehicle carrying “shared motives, values, beliefs, identities, and interpretations or meanings of significant events”, which has been gained via long­term common experiences and passed on across generations (House et al., 2004, p. 15). Triandis (1994) suggested that a culture can be formed when people interact because the ways of thinking, feeling and behaving would influence each other and then develop in an automatic similarity of reaction”. In additional, culture is an invisible civilisation ligament (Barmeyer, 2004; Hayes & Allinson, 1988) which would rooted in one’s preference of information processing and cognition (Earley & Ang, 2003). There are plenty of cultural typologies based on different significant features introduced in different disciplines. For instance, high context and low context cultures (Hall, 1976); low trust and high trust cultures (Fukuyama, 1995); independent and interdependent self cultures (Markus & Kitayama, 1991); and shame and guilt cultures (Benedict, 1946). Hofstede (2001) introduced the concept of continuous cultural dimensions which classified the significant elements into various categories and then enabled comparisons among cultures. After the improvement by House et al. (2004), the nine dimensions of the concept were: in­group collectivism, institutional collectivism, power distance, uncertainty avoidance, future orientation, performance orientation, humane orientation, assertiveness and gender egalitarianism.
      
Online courses usually involved people from different countries with different cultural background. That require a high level of embodiment for moderation and adaptability. Cultural differences have significantly affect the learning process (Cole and Engestrom, 1993; Lim, 2004). On one hand, cultural differences have a large impact on individual learners that may interfere their expressions with tutors or other learners (Bates and Poole, 2003). Online tutors should be mindful of relevant cross­cultural issues during learning (Juwah, 2006). On the other hand, tutor­directed instructional activities not fit for learners with lower power distance culture (Stavredes, 2011).
       
Research of cultural differences have indicated significant influence on the learner‘s behaviours. Grant and Dweck (2000) and Li (2000) stated the behaviours of Asian students were oriented by authoritarian and examination. Li (2000) pointed out that learning is treated as a lifelong process of self­perfection in Asian while western culture views learning as a cognitive process psychologically. Online courses usually involved people from different countries with different cultural background. That require a high level of embodiment for moderation and adaptability. Cultural differences have significantly affect the learning process (Cole and Engestrom, 1993; Lim, 2004). On one hand, cultural differences have a large impact on individual learners that may interfere their expressions with tutors or other learners (Bates and Poole, 2003). Online tutors should be mindful of relevant cross­cultural issues during learning (Juwah, 2006). On the other hand, tutor­directed instructional activities not fit for learners with lower power distance culture (Stavredes, 2011).
       
Learning Style Theory has shown individual difference positively affect learning outcome and satisfaction (Dunn and Griggs, 2000). Sternberg & Grigorenko (2001) identified learning style is the characteristics of both teachers and learners. This definition emphasise learning style is a set of habitual patterns or preferred approaches during learning activities. Moreover, the learning outcomes will be improved by adjusting teaching styles of instruction by tutors to match up with the individual’s learning preference. Years later, Klasnja et al (2010) defined learning style is the cognitive behaviours of learner, which aims at how they interact with the learning environment with teacher‘s guide. With the development of web­based instruction, new investigations of learning style are explored by recent researchers. Several findings concentrate on the impact of instructional activities based on multi­approaches (Krichen,2007).
        
Research of cultural differences have indicated significant influence on the learner‘s behaviours. Grant and Dweck (2000) and Li (2000) stated the behaviours of Asian students were oriented by authoritarian and examination. Li (2000) pointed out that learning is treated as a lifelong process of self­perfection in Asian while western culture views learning as a cognitive process psychologically. Learning Style Theory and Multiple Intelligence mentioned before is the best examples. Lim (2004) compared Korean students and US student in an online leaning test and found the concept of "meaning system" may explain the cultural difference. As Grant and Dweck (2000) mentioned before, culture is a meaning system that affects learning style. Except that, other individual variables, such as religion can also influent learning (Stavredes, 2011). For example, the female learners from masculine cultures are usually less active in online discussions. Learning Style Theory and Multiple Intelligence mentioned before is the best examples.
       
Further more, Yamazaki (2005) did a meta­analysis providing a summary of the studies relating to the learning style preferences and different cultural background. The conclusion can be displayed in a chart depicting the mean scores on AC­CE and AE­RO of the samples from the pre­studies (Figure 1). Besides, Yamazaki illustrated the conceptual relationships between six cultural typologies and learning abilities (Table 1. & Figure 2.) in his meta­research.

 

 

Fig. 1. Mean AC­CE and AE­RO scores summarized in Yamazaki’s (2005) meta­analysis.
      


Table 1. Conceptual relationships between six cultural typologies and learning abilities

 

 

Figure 2. Conceptual relationships between six cultural typologies and learning abilities.
     

Simy Joy and David A. Kolb (2008) did a study to exam the relationship between culture differences and learning styles of adult learners with a sample of 533 individuals from the USA, Italy, Germany, Poland, Brazil, India and Singapore. In each country, the maximum and minimum of the sample size were 116 and 28 (mean: 76, and merely in 2 countries were less than 65). The results of the study indicated that culture does impact the learning style as well as some other demographic elements. They concluded that the most significant effect is an individual’s preference for abstract conceptualization or concrete experience.

         

3. External Factors

      
3.1 Personalised Learning Environment

 

Martinez (2001) explored Intentional Learning Theory by Bereiter & Scardamalia (1989) and suggested design of learning environment should consider of individual difference as an important implication. Although it discussed in fundamental social aspects, such as “socially situated learning environment”, it focus more on how individuals control their own learning. The extension of learning depends on multiple factors such as learning style, learning motivation and learning Environment. Individual student with different learning needs and characteristics require a personalised learning environment. Online learning environment has become one of the most important task for online learning researchers (Lim et al, 2006). Many researchers take learner’s differences into first consideration in order to designing an appropriate learning environment (Meyer,2002; Twigg,2000; Russell et al, 2006; Krichen,2007 and Kim, 2009).
   
Some researchers reported people learn better and more satisfied in online learning environment than traditional face­to­face learning (Russell et al, 2006). However, others refuted this view and suggested any kind of learning environment can foster learning when it took advantages of technology and easy to access (Meyer,2002; Twigg,2000, Krichen.2007). Bach et al (2007) mention that management and encouragement of learning will be more challenging than creating a learning environment for online tutors. It will help students to build a social presence which is essential for learners to start investigation(Gui.G et al,2012). Online tutors should aware of the individual distinctiveness and modify the learning environment to maximum learner’s social presence. Consequentially, they can gain knowledge more effectively at their own pace, reinforce self­development and increase the motivation of learning (Bach et al, 2007; Krichen, 2007, Gui.G,2012). Weber et al (2005) also suggest more opportunities to participate in further learning activities if individual difference takes into account learning­environmental factors.
     
Chapman (2006) mentioned meaningful learning happened when individual learners actively engaged in the learning environment and understand how the intention of learning meets their individual requirements. As the characteristics of online learning environment, the knowledge construction will become a more independently process than the traditional one that all learning activities are under the supervision. Bach et al (2007) mention that the key perspective of manage online course is that online tutors should provide ongoing support during learning. However, the responsibility of online tutors could be a little different. They play a role of ice­breaker and aims at encouraging reflections and respond only when required (Salmon. G, 2012).
    
Although more researchers focus on the designing principals of instruction, there several meaningful learning style theories, such as Felder­Silverman Learning Style Theory and Honey and Mumford Learning Style Theory (Jarvis. 2005). These insights suggest that in learners perspective, control and manage their learning with clear understand of how to take use of learning resource online, how to adjust the suitable learning time and how to achieve their personal goals. Lim (2004) mentioned

 

individual control of the learning process will motivate their behaviours and explored classification of individual control: learner control of content, leaner control of sequence, learner pace and learner control of instructional display. These theories connect learning motivation to the learning environment, and Jonassen and Land (2000) stated the distinction of learning motivation of each learner required a comprehensive learning environment. This is supported by research from Krichen (2007) that learning motivation includes influent elements of individual’s emotion and intention in online learning context.
    
Lim (2004) classify learning motivation into three types: reinforcement oriented from incentive theory, relevance based on cognition theory, and interest from basic human emotion.The reinforced motivator means an increase of the response from learners during learning, which is necessary for their knowledge construction. Relevant motivator refers that a learner can find value in learning to meet their own needs. Intrinsic motivator is a collection of emotional motivation such as higher scores, encourage from tutors, and achieving challengeable task.
      

3.2 Demographic factors

   
Demographic factors are indispensable parts to measure the how individual difference impact the outcomes of online learning. The factors include age, gender, past experience, family background, etc. which may largely influence the persistence of online learning. (O.J. Jegede & J. Kirkwood, 1992). Adult learners are deemed to have better performances in an online learning environment because web­based technologies enable them to cope with their different restrictions, to meet their demands, or to tailor their learning preference. ( S. Carr, 2000; A.H. Digilio, 1998) Proost (1997) argued that female le arners have a lower drop rate in distance learning than male peers. Besides, Astin (1991) stated that an employed learner no matter full­time or part­time was relatively poor in learning performance and course persistence. Whereas Ben­Jacob & Levin (2000) reached a completely opposite conclusion that a full­time employed online learner can do as well as full­time students and stay in the online courses. June Lua et al. (2003) conducted a study with 96 students from two grades of Web­based courses during one academic year from 2000 to 2001. They were offered the same learning materials with the same course tutor and the same test was used to assess the outcomes. However the result showed that there is no significant relationship between learning performance and gender, age and employment. And their finding of the relationship between learners’ achievement and ethnicity required further supports.
      
In addition, learners’ academic backgrounds exert a subtle influence on learning outcomes because the past experience of learning subjects and computer technologies can alter one’s cognition on learning especially distance learning (J. Cano, 1999) As a result, some studies summarized that the first­time online learners showed a lower level of achievements and persistence due to their inexperience of managing time and independent learning (Eisenberg & Dowsett, 1990)

 

Family factors include online learners’ current family support and past parenting especially parents’ scholarly culture. Regardless of other factors, a higher degree of parents’ scholarly culture can boost children’s academic achievements (Evans & Kelley, 2002), even the influence is always subtle but not negligible (Aschaffenburg & Maas, 1997). Park (2008) supplemented that children who grew up in cultured families gained relatively high marks in school works. A recent research was done by M.D.R. Evans et al. (2010) covering 73,249 cases from 27 nations which are in different stages of economic development to test the relationship between parents’ scholarly culture and next generation’s academic achievements. A positive correlation was shown significantly after a series of quantities analysis that parents’ scholastic aptitude and a home with the strong academic atmosphere can increase their offspring’s learning achievements. The outcome was attested true in both advanced peaceful nations like western European countries and developing countries like China and South Africa. The variable maintain its significance after altering other elements like parents’ occupation, gender, regional economic changes during children’s growing up. Moreover, the effect can hardly be eliminated under any political environment in all 27 countries with complementary back forward data.
      
Family support differs largely of different online learners and determines individuals’ persistence and efficiency when studying with new technologies. To involve learners in web­based learning environment, family members are essential in a majority of adult learners’ acceptance of online education (Thather et al. 2007). Regina Ju­Chun Chu (2010) refined the division of family support into tangible dimension and emotional dimension. Tangible support is the positive information and financial facilities given by family members while emotional support is the positive attitudes sharing in families. 290 adult learners were participated in Regina Ju­Chun Chu’s (2010) research to verify the relationship between family support and Internet self­efficacy and its further influence of e­learning. Although the ages of the participants were 50 or above and they were all from Taiwan, the results of the this mono­background study can still provide some reference points. Besides, the majority of the participants were at least senior high school graduates. According to R. J. Chu (2010), Internet self­efficacy (ISE) is one person’s self assessment of commanding the Internet usage (Torkzadeh & Van Dyke, 2002), which is a basic quality in e­learning (Tsai & Tsai, 2003). Figure 3. Is the research model of R. J. Chu as follows:

 

 

Fig. 3. The relationship between ISE and family support further affect e­learning according to Regina Ju­Chun Chu’s (2010)
    
The results of the study showed that the emotional support from family is more supportive and demanding to the adult learners than the tangible support of family members. Precisely, although emotional support will impact Internet self­ self­efficacy indirectly but it showed a strong bond when measuring one’s learning outcomes, which agreed with Kim and Park (2006) that emotional support is more helpful from the learners’ perspectives than other forms of support for adult learning. However, another important findings of Regina Ju­Chun Chu’s (2010) is: the variables of age and gender played moderated roles of the family support and ISE. For example, the female older learners required more from their family, especially emotional support. Then the significant relationship was shown between it and general ISE after data analysis and demonstrated that higher e­learning outcomes followed a high ISE and positive family emotional support.
        

4. Interwork between Internal and External factors

     

  4.1 Interactivity in online context

    
A study conducted by UCLA (2000) found that students’ enthusiasm towards online learning has increased throughout the years, with an increased completion rate of 85­90% in 1999, as opposed to 50­60% in 1996. The department has worked on various methods to increase interaction between students and instructors in online learning, and improved its asynchronous sessions in order to maintain engagement between learners, since it was discovered that those who interact more with instructors and with their fellow online learners performed better throughout the years (Garrison and Anderson, 2003). Interactivity, as defined by Evans and Sarby (2003) is the ability to be familiar and work with computer mediated content and receive feedback. Bannan and Ritman (2002) however define it as a learner’s ability to get involved in instructional activities and technologies which also includes interaction and networking. Muirhead and Juwah (2004) on the other hand referred to interactivity as the functions and impact made by interactions in terms of online learning. With three definitions of interactivity, Chou et al. (2010) mention that there are three different forms of interactivity that need to be accommodated and integrated in a personalised learning environment. The first one is the relations between the learner himself with fellow learners, instructor, content and interface. The second is the interactivity dimension, which includes the ability to add or edit information, access, adaptability, responsiveness as well as facilitation of interpersonal communication. The third one is described as the interactivity function, which is a technical operation that corresponds to the interactive dimension and allows technical operations such as taking notes and jokes. Garrison and Cleveland (2005) mentioned that interactions between instructors and learners as well as learners with their colleagues does not imply that students are engaged and are at the same cognitive level.
         
Piccino (2002) cited the difference between interaction and learning rates in the virtual space. Like Garrison and Cleveland (2005) it was found that interaction among learners does not presume that learners are engaged in the process of learning. The quantity of interaction was also found to not always be related to the quality of discourse (Meyer, 2003). Individuals happen to perceive information differently, and learning rates are qualitative measures, which often do not give a clear picture of a student’s progress (Jiang and Ting 2000). It is important to note that interaction between learners do not often lead to collaboration.
          
In terms of external conditions of online learning environment, interaction is a necessary part that the designer should take into account. Researchers have shown 20 to 43 percentage of wastage rate in online courses (Henke and Russum, 2000; Juwah, 2006). Such the high level of attrition is mainly caused by a lack of interactions (Juwah, 2006). So a global interest in interactivity has shown by many instructional designers.
         
Juwah (2006) stated interactivity had promoted learning within many educational computing discourses. The definition of interactivity is summarised as “an active involvement in instructional activities” (Bannan­Ritland, 2002). Juwah (2006) explored it and defined interactivity is “a key determinant of quality of any learning environment, online or otherwise”. He explained interactivity mixed cognitive, social, and behavioural components. Since then, many researches provide both the theoretical and practical views of interactivity in online courses (Wright et al 2006, Chou et al,2010, Samah.2011). For example, Krichen (2007) stated the interaction with the learning environment based on three stable indicators. They are cognition, efficiency, and physiology. Meanwhile, Wright et al (2006) specified a hybrid model for interactive online learning and Barrueco (2011) stated recent evaluations of web­based courses indicated the need to interactivity from both instructors and learners.
         
Wright et al (2006) and Chou et al (2010) highlighted the interaction is not only between student and the instructor. Juwah (2006) and Chou et al (2010) summarised 5 types of interaction: Learner­self interaction, learner­interface, learner­content interaction, learner instructor interaction and learner­learner interaction. Most researchers have increasingly focus on the interaction between student and online learning environment (Hedberg and Sims, 2001; Bach et al, 2007). Juwah (2006) supported this view and also explored the level of interactivity. He suggested there are three levels of interactivity: activity, action and operation. Recent works are more in socio­cognitive and socio­cultural aspects on interactivity in an online context.
       

4.2 Collaboration in Online Learning

      
Over the years, researchers have been largely interested in online discussion boards due to its potential to promote learning (Swan et. al, 2000). One of the main issues that has been thoroughly investigated is its ability to promote collaborative learning. Collaborative learning could be defined as an activity in which learners interact and exchange ideas in order to solve one or more problems (Dillenbourg and Schneider, 1995). Interaction among learners does not necessary imply collaboration, as collaboration measures the quality of discourse as opposed to conversation (Johnson and Johnson, 1996). Collaboration involves contributions from other online learners, which could be via e­mail or discussion groups. They could share resources, exchange opinions and provide each other with appropriate feedback (Swan et. al, 2000). Examples among diversity in collaborative learning environments include the construction of knowledge bases, collaborative investigation of scientific phenomenon, engagement of groups in game­like learning activities as well as simulations, peer review and evaluation of learning products, peer mentoring online, analysis and case studies as well as discussion groups (Ben­Haym et. al, 1999; Naidu et. al, 2000).
    
Dillenbourg and Schneider (1995) drew a difference between cooperative and collaborative learning. Co­operative learning is a medium which allows tasks to be split into subtasks which are solved independently by the learners. Collaborative learning, however, is a situation where learners interact to produce a joint solution to a problem. Curtis and Lawson (2001) mention that in cooperative tasks, participants could agree over the division of tasks and distribute them to other group members to work on it independently. The separate components will then be gathered to produce the final outcome. Some authors such as Johnson and Johnson (1994) argued that co­operative learning is more beneficial and describes higher level of processes as opposed to collaborative learning. However, Presley and
McCormick (1995) mention how discussions and tasks engagement that take place during collaborative learning has higher cognitive benefits. This theory is supported by Swan et. al (2000) who mentioned that collaborative learning has repeatedly proven to enhance effectiveness of online learning, especially in small groups where interaction and collaboration between individuals is generally higher. Some researchers such as Barros and Verdejo (2000) base collaborative learning on a conversation or dialogue paradigm. Dillenbourg & Schneider (1995) believe that in collaborative tasks, there is a greater potential for students to be more articulate, share workload and their ideas in learning tasks. Collaboration provides opportunity for scaffolding their thoughts which often lead towards better understanding of the subject. Curtis and Lawson (2001) in their study found that students are more keen to collaborate in asynchronous forms of communication, such as e­mails and discussion boards. This is mainly due to the fact that asynchronous sessions allow them to have more time to think and reflect on their work, as opposed to synchronous ones which is more spontaneous.
     
However, certain researchers have expressed concerns over problems when collaborating online. One of the problems is ensuring proper division of tasks. Swan et. al (2000) however express concern over the fact that it is generally hard to assess the amount of collaboration which takes place online. Some learners see collaboration as means to an end, while some see it as a process. Assessment is yet another problem. A good way to assess the amount of collaboration in online learning is to ask each member of the group to rate the amount of contribution given by their peers for each task (Ben­Haym et. al, 1999). Johnson and Johnson (1994) further argued that even within these various grouping within different groups, not one single assessment method would be suitable, as learning goals vary from implementation to implementation.
    

5. Conclusion

     
Firstly, we expatiate the study of current conditions including the presence and development of online learning as well as the reason why we should take individual differences into account. The individual differences comprise several factors: cultural backgrounds, level of education, gender, charater traits, social awareness, to name a few. We further analysed from both internal and external aspects to explore more detailed results of studies. And then, we combined them to each other, in terms of the interaction and collaboration between tutors, learners and learning environment. Importantly, the interactivity is regarded as the centric important factor in order to create an ideal learning environment. However, it does not fully measure the level of intellectual discourse among learners. Interestingly, evidence­based studies have shown that the collaboration among learners provides a better measure, as it allows them to solve problems, exhange ideas and construct knowledge. In a word, there is no doubt that teachers should take individual differences into account when designing an online course as it will affect the learning outcomes considerably.
      

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