Introduction
Instant Message (IM) based instruction is increasingly used in many e-learning programs, and the reported benefits include higher learner interest, increased participation in coursework and improved outcomes (Du & Li, 2010; Lu, Chiou, Day, Ong, & Hsu, 2006; Lan & Jiang, 2009; Sotillo, 2006). IM based instruction is generally most effective when used as a supplement to, rather than a replacement for, traditional education (Sotillo, 2006), and there is certainly increased interest in using IM based instruction in this fashion. As an integrated curriculum component, IM based instruction can be used in the instructional process, as a Virtual Learning Companion (VLC), including individual tutorial practice, and testing. For example, Lu et al. (2006) used chatbot technique to design a VLC based on IM based instruction for student on-line coaching in English learning. Lan and Jiang (2009) also designed a VLC to improve undergraduate programming courses. Du and Li (2010) used and designed an IM based instruction VLC as collaborative supporting tools in e-learning program.
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In view of the above, the goal of the present work was to develop a VLC that incorporated emerging best practices for IM based instruction development and that could also be used as a prototype for other e-learning programs. However, those studies focus on the VLCs' conversation ability (Brennan, 2006), instead of paying much attention to users' cognitive differences in e-learning programs. Johnson and Aragon (2003) contend that powerful instructional framework for e-learning need to contain a combination of seven principles (see Figure 1). According to their study, recognition of individual differences has, for the most part, been taken into account in e-learning. Individual differences specific to learning and instruction can be found within cognitive styles, learning styles, cognitive controls, intelligence, etc. (Jonassen & Grabowski, 1993).
Individual cognitive differences among learners mean that no one instructional method is appropriate for the array of cognitive styles. Contemporary researchers suggest that instructors need to learn a different set of teaching skills for teaching online (Brower, 2003; Easton, 2003). The limitation imposed by the presentation of learning materials that are not based on learners' needs can result in an opposite effect. Drummond (2000) believed that one of the main reasons that situations in which opposite learning effects came into play was the disregard of learners' cognitive styles. Dunn and Dunn (1994) found that when the teaching methods and formats of materials fit learners' cognitive styles, it improved not only the student's learning performance but also their attitude toward learning. Any well designed IM based instruction system must be adaptive to learners' cognitive styles so as to increase both the efficacy and the satisfaction of the learning experience.
This paper designed a VLC using chatbot system technique and considered individual cognitive difference in IM based instruction environment. Cognitive theory has presented a very broad and useful classification for understanding individual cognitive difference. This is Dillon and Gabbard's (1988) construct of Field Dependence Independence (FD-I). FD-I places learners on spectrum that designates one end as field-dependent (FD), and the other end as field-independent (FI). This model has been successfully utilized in studies regarding more traditional educational environments, but the results of research that have used this model to study the performance of learners interacting with new technologies to accomplish a learning task are still inconclusive (Davis, 1991; Dillon & Gabbard, 1998), and, at times, contradictory. Understanding the different cognitive styles of learners, and which instruction method is most beneficial to that style remains open to much greater research.
According to the statistics of InsightXplorer Ltd. in March, 2008, MSN Messenger with over eight million users was the most popular IM system in Taiwan. Because of MSN Messenger popularity (Hsu, 2007; Kinzie, Whitaker, & Hofer, 2005) and its ease of use and recognition factor, this study designed a VLC system, Confucius, using chat-bot technique based on the MSN Messenger platform. Created specifically for this study, Confucius can enhance traditional classroom instruction by offering a learning format that is ideally matched to the user's individual cognitive style.
Literature Review
Virtual learning companion (VLC)
Chan and Baskin (1988) first proposed the concept of VLCs to be a partner that can accompany learners in the e-learning environment. Beyond the traditional binary relationship of the instructor and the learner, a VLC is a third participant in the learning project. Because the VLC is Internet-based, it can facilitate the acquisition of knowledge at any time and from almost any location.
Webb (1982) discovered that the guidance and information supplied among learning companions can increase learning performance. Through the interaction with VLCs, learners often become more immersed in their learning situation which increases their concentration, engagement, and attention (Hsu et al., 2007). When there is the absence of interaction with learning companions in an e-learning environment, learners feel isolated (Hong, 2002) and their sense of learning satisfaction decreases (Hiltz & Wellman, 1997; Rovai, 2002; Rovai & Wighting, 2005). Arbaugh (2002) also elucidated the positive relationship between learning satisfaction and the interaction among the learners, the instructor and VLCs. Numerous studies (El-Bishouty, Ogata & Yano, 2007; Kim & Baylor, 2006; Hooper, 1992; Slavin, 1995) and common sense suggest that the encouragement, explanation, interpretation, instruction and demonstration available from the interactive relationships among learning companions make it easier to reach study goals. The inclusion of a VLC component in any learning project is not vital to a learner's success, but it can significantly increase a learner's acquisition of knowledge and skills by offering related material and alternative approaches to concepts that cannot be covered within the classroom because of time constraints.
A key consideration in designing a VLC environment is the recognition that there are vast differences of cognitive styles among learners. The format of a successful VLC system that is applicable to all learners cannot rely on only one method of assistance. Renzulli (1994) showed that when the instruction format and the learner's cognitive style are consistent with each other, knowledge is more easily acquired, the process is more enjoyable, and the learner's attitude toward the project is positively affected (Dunn & Dunn, 1994). Drummond (2000) is emphatic in stating that offering a variety of formats, suitable to a variety of cognitive styles, will not decrease learning performance. She and Fisher (2003) found that if the instruction methods correspond to learners' cognitive styles, they will affect the learner's concept of the subject more than any other factor. It is apparent that efficacy of any VLC system is highly dependent on the inclusion of a variety of formats so that users can choose the format best suited to their cognitive style.
Cognitive style
Messick (1984) defined cognitive style as "characteristic self-consistencies in information processing that develop in congenial ways around the underlying personality trends". Witkin, Moore, Goodenough and Cox (1977) referred cognitive style to the individual differences in perception, thinking, problem solving and learning. Cognitive style is a hypothetical construct. It is the special individual style or method used when engaging in cognitive activities (Witkin & Goodenough, 1981; Riding & Cheema, 1991; Morgan, 1997). Within the field of cognition studies, a continuum defined as FI and FD is very prominent. This division of cognitive style was first purposed by Witkin et al. in 1954 and was also named psychological differentiation (Witkin et al., 1962) or field articulation. This cognitive style is also an important learner characteristic for educational technologies (Chinien & Boutin, 1992/1993). This division of cognitive styles uses the Embedded Figures Test (EFT) as the measurement instrument to measure the field independency degree of the subjects (Messick, 1962). Field independency of FD-I describes learners along a continuum, such that learners who fall in the two extremes of the continuum are characterized as FD and FI. Field independency is the cognitive characteristic in which the subject overcomes the influence of the irrelevant field elements while recognizing the relevant aspects in a specific situation. The less a person is influenced by the irrelevant elements, the more analytical or FI the person is. Subjects that fall under the other end of the spectrum are more influenced by irrelevant elements and considered more global or FD (Wu, 1987).
Chapelle and Roberts (l986) found that FI learners are not as influenced by social orientation or extrinsic motives as FD learners, and prefer analytical learning and independent study. FI learners believe they can learn more, faster and easier through independent study, whereas FD learners are more influenced by the external environment, social orientation and extrinsic motives. The latter prefer global and collaborative learning, and enjoy the peer guidance which can reduce learning anxiety and foster greater learning interest. Garger and Guild (1984) also found that FD learners prefer a learning environment in which they can interact and discuss with others, and that FI learners prefer a teaching method that is purely a dissemination of the facts. In short, cognitive style is the individual's form of perception during information processing and is equally apparent in the manner that an individual approaches and solves a given problem. It is a non-intelligence personal characteristic but it can significantly influence the process and thereby the results of learning (Johnson & Aragon, 2003). Any comparative discussion of learning performance in e-learning environment should consider the influence of cognitive styles.
Chatbot system
A chatbot system is the software that can "chat" with a human user in natural language (Mauldin, 1994). Different terms have been used to denote chatbot systems: machine conversation system, virtual agent and chatterbot. Brennan (2006) defined a chatbot system as "an artificial construct that is designed to converse with human beings using natural language as input and output". The aim of a chatbot system is to simulate a human conversation; the chatbot architecture integrates a language model and computational algorithms to emulate informal communication between a computer and a user. Initially, developers built and used chatbots for fun, and used simple keyword matching techniques to find a match to a user input, such as ELIZA (Weizenbaum, 1966) and PARRY (Colby, Weber, & Hilf, 1971). A large body of text and natural-language interface research was conducted in the seventies and eighties before the advent of graphical user interfaces such as Cliff and Atwell (1987), and Wilensky et al. (1988). Since that time, a range of new chatbot architectures have been developed, such as CONVERSE (Batacharia et al., 1999), ELIZABETH (Abu Shawar & Atwell, 2002), Jabberwacky (Fryer & Carpenter, 2006) and ALICE (2010). As the design of chatbots became increasingly sophisticated, their use was adopted for learning support. For example, Kerfoot et al. (2006) used chatbots for training medical students. Fryer and Carpenter (2006) used a chatbot for language acquisition and Robin (2007) used one to assist listening comprehension.
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Research Method
Experiment system--Confucius
Confucius, a VLC chatbot system interacts with others either by recognizing certain commands tied to its statistical information gathering or by conversational pattern-matching techniques. Confucius can assist instructors in the provision of extra-class assistance for their students. As it is an Internet-based program it is neither limited by location nor by time. Confucius helps learners practice class content through real-time two-way interaction. The program is designed around a Question and Answer (Q&A) format. If a user chooses an incorrect answer to a given question, Confucius provides two modes, a lecture mode and a discussion mode, that will help users to find and comprehend the correct answer.
The lecture mode supplies information and content related to the question that was erroneously answered. As shown in Figure 2, when Confucius poses a question to the user (block A) and the learner gives a wrong answer, Confucius will provide the correct answer (block B) and a webpage of supplementary related materials that have been prepared by the instructor (block C). The discussion mode provides peer discussion opportunities when learners give erroneous answers. As shown in the Figure 3, when Confucius poses a question to the user (block D) and the user subsequently gives a wrong answer, Confucius will search the database to find the online learners who have the correct answer and randomly choose some of them to be listed for potential peer-to-peer (P2P) discussion (block E). The learner, at this time, can then choose one of the listed peers and initiate a discussion of the topic in question. Confucius will then inquire if the selected peer learner agrees to participate in an interactive discussion (block F) and if so, connect them to the discussion window (block G).
Experiment design
This study uses the Microsoft technical specialist certification examination 70620 (Exam-70620) (Microsoft, 2010), as the basis for the instruction goals and the students' examination results as a measurement tool, to explore how different guidance modes offered by a VLC system influence learning performance. The experiment consisted of four stages. In the first stage, students received a prior knowledge test of Exam-70620 (Prior-test) and the computerized EFT to determine their cognitive styles. In the second stage, students were divided according to their cognitive styles and were then randomly assigned to one of two guidance methods of VLC. They were then introduced to the Confucius VLC system in Microsoft certification training project. This project was tough by one instructor and students were asked to use the VLC system after class. In the third stage, students took the official examination of Exam-70620 (Official-exam) held at PROMETRIC test center (http://www.prometric.com) and answered a questionnaire of subject-reported satisfaction levels (SRSL). In the final stage, another self developed examination based on the contents of the official Exam-70620 was used as a follow-up examination (Follow-up exam) to evaluate students' ability to recall the course content.
The independent variable of the study was the different guidance methods of VLC and the dependent variables were the learning performance (i.e. grades of Official-exam, grades of Follow-up exam and SRSL). Although a user's SRSL with a given system is likely tied to their level of success within a given project, satisfaction is not merely a derivation of results. A user's sense of satisfaction or dissatisfaction of a certain system can be better captured through a direct questionnaire. Using SRSL to determine the success of an information system remains a vibrant discussion (DeLone & McLean, 1992; Zviran & Erlich, 2003). Contemporary research on the impact of e-learning environments in higher education has adopted SRSL as an integral measure of success (Bekele & Menchaca, 2008; Bekele, 2010). The operational definition of satisfaction for this paper is the measurement of learners' holistic perspective towards their experience of using the VLC system. A questionnaire modified according to the related literature of satisfaction, as shown in Table 1, was used in this study to determine users' perception towards the Confucius system (please see appendix A).
Experiment Content
Exam-70620 is used as the instruction content in the experiment and it became available in 2007. Exam-70620, which includes testing descriptive and procedure knowledge, measures the ability to resolve issues concerning network connectivity, desktop operating systems, security, log on problems, password resets, and most desktop application issues. Examinees that pass official-exam earn "Microsoft Certified Technology Specialist: Windows Vista, Configuration" credentials. The detail description and skill requirement of Exam-70620 can be retrieved at the web site of Microsoft (http://www.microsoft.com/learning/en/us/exam.aspx? id=70-620).
The participants
The experiment subjects were selected from the students who had participated in the Microsoft certification training project and had not taken the Microsoft certification examination before. In order to maximize the heterogeneity of the sample populations, this training project was accessible to different grades and classes. The subjects of the study consisted of 192 randomly selected volunteers (simple random sampling) as the experiment subjects, which included four different grades at a university in Taiwan. Sixty-one point five percent of them were male students and 38.5% were female students; 30.7% of the students were 19 years or younger, 21.4% of the students were 20 years old, 27.6% of the students were 21 years old, and 20.3% of the students were 22 years or older.
At the beginning of the experiment, participants took the EFT to classify their cognitive styles. The main measurement in the EFT included two parts and each part had 16 items in which the subjects attempted to find simple figures embedded in complicated figures within ten minutes. Because the EFT identifies cognitive styles along a continuum which is scored between the ranges of 0 (FD) to 32 (FI) depending on the number of figures traced correctly, this study has followed the statistical procedure of using the upper and lower 27% of the EFT scores to identify extreme FD and FI subjects (Spanier & Tate, 1988). Fifty-two out of the 192 subjects who took the EFT are considered as FD (their EFT scores are located at the lower 27% and their average score is 5.38). Fifty-two subjects are considered as FI (their EFT scores are located at the upper 27% and their average score is 22.14). Each cognitive style learners were randomly assigned to the two guidance methods of VLC. The grouping of students is presented in Table 2.
Research Results and Discussions
The effect of Prior-test
In order to examine if their prior knowledge of Exam-70620 was significantly different, a self developed examination based on the contents of the official Exam-70620 was adopted as a prior-test. The prior-test grades were analyzed by one-way ANOVA to determine if the grouping of students had significantly different prior knowledge related to the knowledge of Exam-70620, as shown in Table 3. The result shows that their prior knowledge was not significantly different, 17(3,100)=2.114, p=.103. That is, the students had equivalent prior knowledge of Exam-70620 before participating in the learning activity.
Learning performance on Official-exam, Follow-up exam and SRSL
Descriptive statistics of students' learning performance were shown in Table 4. Since this study discusses how, if any, the variables of different guidance methods offered in a VLC and the variables of students' cognitive styles affect learning performance. Hence, we used cognitive styles and guidance methods of VLC as independent variables; and learning performance as dependent variable to conduct the two-way ANOVA, as shown in Table 5-7.
The results shown in Table 5-7 illustrate that both the main effects of cognitive styles on Official-exam, Follow-up exam and SRSL [F(1,100)=0.108, p=.743; F(1,100)=0.724, p=.397; F(1,100)=1.448, p=.232] and guidance methods of VLC on Official-exam, Follow-up exam and SRSL [F(1,100)=0.808, p=.371; F(1,100)=1.404, p=.239; F(1,100)=0.006, p=.938] were not statistically significant. The effect of the interaction between cognitive styles and guidance methods of VLC on Official-exam, Follow-up exam and SRSL were significant [F(1,100)=10.079, p=.002; F(1,100)=11.979, p=.001; F(1,100)=4.903, p=.029]. The interaction effects could also be found at the estimated marginal means plot in Figure 4. These results showed that the cognitive styles or guidance methods of VLC had an interactive effect on learning performance.
A statistical interaction occurs when the effect of one independent variable (cognitive styles) on the dependent variable (learning performance) changes depending on the level of another independent variable (guidance methods of VLC). In our current design, this is equivalent to asking whether the effect of guidance methods of VLC changes depending on the cognitive styles of learners. To determine if this is the case, we need to look at the simple main effects (Weinberg & Abramowitz, 2002).
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In order to further understand the interactive effect between cognitive styles and guidance methods of VLC, this study used simple main effect as the post-hoc analysis, as shown in Table 8-9. Table 8 showed that when the guidance method of VLC was discussion mode, the learning performance (Official-exam, Follow-up exam and SRSL) of FD and FI was significantly different [F(1,50)=4.755, p=.034; F(1,50)=4.812, p=.033; F(1,50)=5.035, p=.029]. It showed that when the guidance method of VLC was discussion mode, learning performance of FD learners was higher than that of FI learners.
When the guidance method of VLC was lecture mode, the learning performance (Official-exam and Follow-up exam) of FD and FI was significantly different [F(1,50)=5.344,p=.025; F(1,50)=7.195,p=.010]. It showed that when the guidance method of VLC was lecture mode, learning performance of FI learners was higher than that of FD learners.
Table 9 also showed that the learning performance (Official-exam and Follow-up exam) of FI learners in discussion mode and lecture mode was significantly different [F(1,50)=9.111, p=.004; F(1,50)=10.178, p=.002]. It showed that the learning performance (Official-exam and Follow-up exam) of FI learners in lecture mode was higher than in discussion mode.
Learning performance of Adaptive/Non-adaptive groups
These results are similar to the findings of Garger and Guild (1984). Based on Garger and Guild's (1984) suggestions regarding the matching of cognitive styles and guidance methods of VLC, the combination of cognitive styles and guidance methods of VLC was classified into Adaptive/Non-adaptive groups. Learners in the Adaptive group were FD learners guided with discussion mode, and FI learners guided with lecture mode. Learners assigned to the Non- adaptive group were FD learners who were guided by using lecture mode, and FI learners who were guided by using discussion mode.
a. Adaptive Group: Within this group students received the matched guidance method. That is, the group of students with FD style received the "discussion mode" guidance method. And, those who are characteristic with FI style received the "lecture mode" guidance method.
b. Non-adaptive Group: Within this group students received the mismatched guidance method. That is, the group of students with FD style received the "lecture mode" guidance method. And, those who are characteristic with FI style received the "discussion mode" guidance method.
A one-way ANOVA was used to test the learners' learning performance of the Adaptive and Non-adaptive groups, as shown in Table 10. The result suggested that learners involving in the Adaptive group had significantly better learning performance on Official-exam than those in the Non-adaptive groups, F(1,102)=10.187, p=.002. That is, matching the cognitive styles of learners with the associated guidance methods will significantly improve the learners' scores of Official-exam within a VLC learning context. The Follow-up exam [F(1,102)=11.965, p=.001] and SRSL [F(1,102)=4.929, p=.029] also showed that the learning performance of the adaptive group was significantly higher than that of the non-adaptive group.
Conclusion
The role of technology is becoming increasingly prominent in the provision of extra-classroom learning assistance. Starting with the preposition that all learners utilize distinct cognitive approaches to information gathering and comprehension, and that one of the most common classifications of cognitive styles uses a scale of FD versus FI, this study sought to undertake a quantitative analysis of the role of multi-mode functions in a VLC system. Specifically, it analyzed whether offering the user choices of modes, in this case lecture versus P2P discussion, produces measurable effects on the user's ability to ingest and understand information. The goal of this study was to better understand how the design features of a VLC affect the efficacy of learning. The VLC system used in this study, Confucius, was created specifically for this purpose and designed to help students that were studying for the Exam70620. Confucius offers two distinct modes that can assist the user. These two modes, lecture and discussion, are designed to meet the cognitive styles of FI and FD learners respectively. The findings are summarized as below:
1. When the instruction mode of the VLC is restricted to the discussion, or P2P, mode for both FD and FI learners, the former achieve substantially higher test results.
2. When the instruction mode of the VLC is restricted to the lecture mode for both FD and FI learners, the latter achieve substantially higher test results.
3. The learning performance of the adaptive group is significantly higher than that of the non-adaptive group.
The findings suggest that when the guidance methods of the VLC, lecture or discussion mode, correspond to the learner's specific cognitive style, the program can increase their learning performance. Previous studies based on traditional instruction formats (not Internet-based), like that of Dunn and Dunn (1994), also found that when the instruction and teaching resources correspond to learners' unique cognitive styles, their learning performance will be elevated and their attitude toward learning become more positive.
The results generated by this study are unequivocal and parallel the findings of Meyer (2003) that successful learning within an Internet environment is highly related to learners' cognitive styles. Any VLC system that seeks to assist learners in the most efficient and productive manner must recognize and incorporate design features that are appropriate for different cognitive styles. Learners that are offered a learning platform that appeals to their individual cognitive style are able to derive greater benefits from a VLC system and experience greater levels of satisfaction while participating in the system. Increased comprehension of a given knowledge set and increased satisfaction with the use and generated results of a system are to an extent mutually reinforcing. The more comfortable users feel when using a VLC system the more they are likely to use and thereby benefit from the system. Vice versa, the more benefit users derive from a system the more likely they are to use that system.
As educators increasingly utilize e-learning technology as an adjunct to their classroom instruction they must be cognizant that the format of that technology will have a profound effect on the learning performance of their students. While establishing an after class on-line P2P support group will inevitably be of great assistance to some students, the benefits of participation for another group will be extremely limited. Conversely, simply offering links to similar web-based information will benefit one group of students, but it will neither be of interest nor great assistance to another group of students.
This exploratory study shows that educators could create, design and offer redundant formats of the same information for each type of user to engage in the format that best suits their cognitive styles. By doing so facilitators will make learning more enjoyable, rewarding, and ultimately more productive for each of their students. The future confirmatory study of this research should include increasing the sample size to increase the power of the found effects. In addition, many studies are explicitly in demonstrating numerous factors which affect learning, such as IQ, gender, and personal characteristics. The exploration of learning in MSN VLC environments should also take these factors into consideration in a holistic way to make learning as well as enjoyable and more productive experience.
Appendix A: The Questionnaire 1 2 Very Somewhat dissatisfied dissatisfied1.1 think the satisfaction ofusing the virtual learningcompanion guidance is higherthan I expect.2.1 think the quality ofvirtual learning companions isbetter than I imagined.3.1 think the function ofvirtual learning companionsis more helpful than I expected.4.1 think the use of virtuallearning companions makes melearn faster and I am satisfiedwith the results.5.1 think the use of virtuallearning comp anions makes melearning easier and I amsatisfied with the results.Gender [] Male [] Female [] l9 or belowAge [] 20 [] 21 [] 22 or above [] Under 4 years [] 4~5 yearsInternet Experience [] 5~6 years [] 6~7 years [] Over 7 years 3 4 5 Neither satisfied nor Somewhat Very- dissatisfied satisfied satisfied1.1 think the satisfaction ofusing the virtual learningcompanion guidance is higherthan I expect.2.1 think the quality ofvirtual learning companions isbetter than I imagined.3.1 think the function ofvirtual learning companionsis more helpful than I expected.4.1 think the use of virtuallearning companions makes melearn faster and I am satisfiedwith the results.5.1 think the use of virtuallearning comp anions makes melearning easier and I amsatisfied with the results.Gender [] Male [] Female [] l9 or belowAge [] 20 [] 21 [] 22 or above [] Under 4 years [] 4-5 yearsInternet Experience [] 5-6 years [] 6-7 years [] Over 7 years
Acknowledgments
This study is partially supported by the National Science Council under contract number NSC99-2410-H-269-005
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Sheng-Wen Hsieh
Department of Management Information Systems, Far East University, Taiwan // onyx@cc.feu.edu.tw
Table 1. Related Literature of Satisfaction Authors Dependent Variable Information SystemNegash, Ryan, & Customer satisfaction Customer support systemIgbaria (2003)Landrum & Prybutok User satisfaction Library information(2004) serviceRodgers, Negash, & User Satisfaction Online experienceSuk (2005)Bharati & Chaudhury User Satisfaction Web-based systems(2006)Lin, Huang, Joe, & User Satisfaction IM SystemMa (2008)Sahin & Shelley User Satisfaction Distance Learning(2008)Table 2. The participants' number of each groupCognitive styles Guidance methods of VLC NFD Discussion mode 26 52 Lecture mode 26FI Discussion mode 26 52 Lecture mode 26Table 3. Descriptive statistics of students' grades on Prior-testCognitive Guidance methods Prior-test styles of VLC (mean/S.D.) F-valueFD Discussion mode 349.231/88.450 2.114 Lecture mode 350.769/86.576FI Discussion mode 301.154/91.186 Lecture mode 318.462/73.031Table 4. Descriptive statistics of students' learning performanceCognitive Guidance Official-exam Follow-up exam styles methods of VLC mean / S.D. mean / S.D.FD Discussion mode 818.462/82.996 746.731/75.955 Lecture mode 780.192/95.544 706.385/97.881FI Discussion mode 770.615/75.020 700.462/76.142 Lecture mode 839.115/88.105 782.808/107.351Cognitive Guidance SRSL styles methods of VLC mean / S.D.FD Discussion mode 3.577/0.481 Lecture mode 3.413/0.385FI Discussion mode 3.314/0.353 Lecture mode 3.490/0.331Table 5. Two-way ANOVA of students' learning performanceon Official-exam Source SS df MS F-valueCognitive styles 797.538 1 797.538 0.108Guidance methods of VLC 5940.346 1 5940.346 0.808Cognitive styles x Guidance methods of VLC 74097.846 1 74097.846 10.079 **Error 735183.308 100 7351.833** p < .01.Table 6. Two-way ANOVA of students' learning performanceon Follow-up exam Source SS df MS F-valueCognitive styles 5910.154 1 5910.154 0.724Guidance methods of VLC 11466.000 1 11466.000 1.404Cognitive styles x Guidance methods of VLC 97847.115 1 97847.115 11.979 **Error 816789.769 100 8167.898** p < .01.Table 7. Two-way ANOVA of students' learning performance on SRSL Source SS df MS F-valueCognitive styles 0.222 1 0.222 1.448Guidance methods of VLC 0.001 1 0.001 0.006Cognitive styles x Guidance methods of VLC 0.753 1 0.753 4.903 *Error 15.361 100 0.154* p < .05.Table 8. Simple main effects of Cognitive Styles (CS) at each levelof guidance methods of VLC Learning FD Marginal performance Source Mean/S.E.Official-exam CS at discussion mode 818.462/15.514 CS at lecture mode 780.192/18.023Follow-up exam CS at discussion mode 746.731/14.914 CS at lecture mode 706.385/20.146SRSL CS at discussion mode 3.577/0.083 CS at lecture mode 3.413/0.070 Learning FI Marginal performance Source Mean/S.E. F-valueOfficial-exam CS at discussion mode 779.615/15.514 4.755 * CS at lecture mode 839.115/18.023 5.344 *Follow-up exam CS at discussion mode 700.462/14.914 4.812 * CS at lecture mode 782.808/20.146 7.195 *SRSL CS at discussion mode 3.314/0.083 5.035 * CS at lecture mode 3.490/0.070 0.608* p < .05.Table 9. Simple main effects of Guidance Methods (GM) of VLCat each level of cognitive styles Discussion Lecture Learning mode Marginal mode Marginal performance Source Mean/S.E. Mean/S.E.Official-exam GM at FD 818.462/17.550 780.192/17.550 GM at FI 770.615/16.047 839.115/16.047Follow-up exam GM at FD 746.731/17.181 706.385/17.181 GM at FI 700.642/18.251 782.808/18.251SRSL GM at FD 3.577/0.085 3.413/0.085 GM at FI 3.314/0.067 3.490/0.067 Learning performance Source F-valueOfficial-exam GM at FD 2.377 GM at FI 9.111 **Follow-up exam GM at FD 2.757 GM at FI 10.178 **SRSL GM at FD 1.845 GM at FI 3.442** p < .01.Table 10. Descriptive data and ANOVA of the learning performance Groups\ Adaptive Non-adaptive Learning Group (a) Group (b) performance N mean / S.D. mean / S.D. F-valueOfficial-exam 52 828.788/85.384 52 775.404/85.188 10.187 **Follow-up exam 52 764.769/93.856 52 703.423/86.875 11.965 **SRSL 52 3.534/0.411 52 3.363/0.369 4.929 *
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