January 2012 Volume 15 Number 1 - Educational Technology ...

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learning system can be described as a personalized system, which in addition to the content discovery and assembly, is able to provide an adaptive course delivery, an adaptive interaction, and an adaptive collaboration support 0. AWBES (Adaptive Web Based <strong>Educational</strong> System) were developed upon the AHES (Adaptive Hypermedia <strong>Educational</strong> System) theoretical concepts 00. The web based educational system incorporates adaptivity and intelligence by building a model of individual learner’s goals, preferences, and knowledge. The system is then adapted to the learner’s needs by using this model throughout the course delivery. For more details on theoretical concepts and the realization of these systems in practice please see 0 0 0 0 0 0 00. Providing adaptivity in e-education system Adaptive e-learning systems focus on adapting the courses to the individual characteristics of the students. These systems are found to perform better than LMS 00. Adaptive quizzes, intelligent solution analyzers, adaptive class monitoring systems, and adaptive collaborative systems perform their respective functions more efficiently. We should bear in mind, however, that these systems have a certain number of constraints: Creating adaptive e-learning systems and their incorporation into the educational processes is a rather complex and expensive process that requires a high level of engagement from all participants in the process. Such systems are designed for the purpose of ensuring adaptive functionality, however, in practice they frequently turn out not to fit into the learning process. One common problem is that the learning content is impossible to recreate and use anew. The systems are not interoperable. They are developed as isolated applications and they cannot exchange either resources or data on students. The research has shown that these systems are most often isolated applications and experimental solutions with a limited interface 0000. The basic services of the e-learning systems, such as course administration, contents creating, etc. are much more difficult to carry out in adaptive systems. Due to the complexity of the systems, the users are expected to have some pre-existing knowledge of the system itself. The services that often lack most in the communication and social interaction between the participants in the elearning process are chat, forum, and videoconference. For many courses, the time is an important factor, i.e. it is not possible to learn about the students’ needs during a course and then adapt it due to the lack of time. In such scenarios, it is preferred to have information about the students’ preexisting knowledge and experience as well as the intended usage of the course content in advance to maximize the time a student spend in a personalized environment. AWBES does enable full adaptation, but it does not guarantee its timeliness. The key problems in adaptive systems, therefore, are reflected in the architecture and realization of the system, rather than in its essence – adaptivity. On the other hand, the LMS do not provide an adequate level of adapting of contents and services 00. Some authors present new approach to adapting LMS 00 0: embed AH functionality into modern web-based courses using adaptive hypermedia services that can be chosen by the classroom teacher and attached to courses as a single reusable component. Adapting LMS in such a way requires development of new software modules. These software modules can become obsolete and incompatible with each new version of LMS which makes the process inefficient. Personalization based on learning styles Student modeling is the process whereby an adaptive learning system creates and updates a student model by collecting data from several sources implicitly (observing user’s behavior) or explicitly (requesting directly from the user). Traditionally, the majority of the student modeling systems have been limited to maintain assumptions related to student’s knowledge (acquired during evaluation activities) without paying too much attention to student’s preferences 00. 327
Learning is a cognitive activity that differs from a student to a student. Analyzing adaptability in an e-learning system has explicitly pointed out the importance of the modeling learners’ cognitive characteristics, particularly learning styles as the most explored cognitive features. There are several different learning style models presented in literature 0; however, Felder-Silverman Learning Styles Model (FSLSM) is often used for providing adaptability regarding learning styles in e-learning environments 0. Felder-Silverman model describes a single student along four dimensions 0: 1) Active and reflexive learning style; 2) Sensitive and intuitive learning style; 3) Visual and verbal learning style;4) Sequential and global learning style. Research objectives The primary goal of the study was to develop an efficient method for creating adaptive distance education courses in the Moodle LMS. The courses will be organized and adapted to groups of students. Adaptation is based on students’ characteristics, particularly learning styles. The following steps were used: Defining main phases and requirements in developing personalized e-education systems; Clustering students by their learning styles; Personalization and adaptation of e-learning courses. The key idea of this paper is based upon adaptation of courses within the existing Moodle LMS, in order to eliminate the need for developing new modules or designing entirely new solutions. The e-learning system used in the research and described in this paper is used by a large number of users/students daily. The idea was to perform adaptation in such a way to make process economically justified, not time consuming, bearing in mind the environment in which the courses were conducted. Personalization we would achieve in our system is a personalization for a group, not for a person. We claim that this approach is appropriate for three reasons: 1. When the courses are attended by a large number of students, due to the numerous differences in their individual characteristics, the personalization for each student individually is not justified. Taking into consideration the existing differences, some common features can be identified and then they can be assigned into respective groups on the basis of these features. 2. Group personalization can be achieved at the beginning of the course. Therefore, a student will be provided with an adapted online course during the whole learning process. 3. Group personalization is cheap and efficient. Teachers need to adapt learning materials and activities to typical groups of students instead of to each student. This is particularly efficient when a large number of students attend the course. If the adaptation is done at the beginning of the learning process, some of the AHS disadvantages will be overcome. By using students’ learning styles it is possible to define typical learning groups and to assign new students into the groups at the beginning of their courses. The data needed for classifying the new students into these groups can be gathered promptly, by using an appropriate questionnaire or a test, and analyzed with some of the business intelligence tools 0. Each group of students will be provided with specific teaching materials and activities, suited to their learning style. This approach enables adaptation controlled by teacher, and also adaptation that does not depend upon the version of Moodle LMS. It is not necessary to change or add modules. The adaptation is achieved through the customization of Moodle LMS. Personalization is not performed for all the characteristics, but only for those the research findings designate as the most important. It is in such a way that the economic and time efficiency of the adaptation process is achieved. In the proposed model, there are no automated adaptation mechanisms. However, the activities the course organizers have to perform to carry out the course adaptation can be automated to a certain extent. Method for Moodle course adaptation In order to improve the process of personalization in e-learning systems and improve its effectiveness, its main phases and requirements have to be identified first 0. The steps shown in Figure 1. should not be conducted 328
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January 2012 Volume 15 Number 1
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Supporting Organizations Centre for
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Analyzing the Learning Process of a
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Angeli, C., & Valanides, N. (2012).
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ased on an integration and evaluati
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ased on the assumption that no sing
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Diagram type D thinking (shown in F
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collaborative task in terms of inte
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Table 4. Descriptive statistics for
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Hong, N. S., Jonassen, D. H., & McG
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commercial off-the-shelf digital ga
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The post-questionnaire was used aft
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Phase Table 1. Phases and activitie
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Research results Describing student
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Assessment results for each one of
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References Annetta, L.A., Minogue,
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Huang, T.-W. (2012). Aberrance Dete
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Detection power Typically, the rela
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Results Detection rates As seen in
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2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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their easily understandable devices
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Ifenthaler, D. (2012). Determining
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activities is assumed to be reflect
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that the problem representations (i
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deductive reasoning inventory (33 m
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HIMATT structural measures The part
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outcomes, r = .297, p < .01. Accord
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Azevedo, R. (2009). Theoretical, co
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Seel, N. M. (1999b). Educational se
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Initially this paper explores defin
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gamers were boys, and 35% were girl
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Teachers underwent eight hours of p
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A correlation analysis was used to
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playing the game. This would mean P
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Yu, F. Y. (2012). Any Effects of Di
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Considering that identity concealme
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In the nickname group, the student
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Attitudes toward assessors Percepti
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Liu, Z. F., Chiu, C. H., Lin, S. S.
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Appendix A: Attitudes toward Peer-A
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Appendix C: Perception toward the I
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Han, H., & Johnson, S. D. (2012). R
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Research participants The target po
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that its value of cross-loading is
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The canonical variable for the emot
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Literature in the field of online l
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Cornelius, R. R. (1996). The scienc
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Shih, W.-C., Tseng, S.-S., Yang, C.
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To provide personalized (e-)learnin
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Figure 3. An illustrative example o
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elementary schools. Thus they are b
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Table 5. Descriptive statistics for
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References Abowd, G. D., & Atkeson,
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Tseng, K.-H., Chang, C.-C., Lou, S.
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Figure 1. The five stages of KT (Gi
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The use of learning strategies, mon
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Additionally, the above two scales
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Figure 3. Canonical structure betwe
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highly positive perception of CM an
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Langan-Fox, J., Platania-Phung, C.,
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Gömleksiz, M. N. (2012). Elementar
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any statistically significant diffe
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consideration in an educational set
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conditions of a learning environmen
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Kahle, J. B. (1983). The disadvanta
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Appendix A Science and Technology S
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(Guuawardena, Nola, Wilson, Lopez-I
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Questionnaire on student satisfacti
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G4 53 (12.5) 48.9 (13.4) 0.39 G5 51
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across the five levels of knowledge
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Santhanam, R., Sasidharan, S., Webs
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This paper reports only the early e
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Figure 1 illustrates how the abovem
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teacher/instructor continues to dom
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The instructor plays a significant
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Data collection and data sources Fi
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y turning over the responsibility t
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Brophy, J. (1999). Toward a model o
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Lawanto, O., Santoso, H. B., & Liu,
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such as grades and evaluation by ot
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students’ interest, expectancy fo
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constructs that measure students’
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Bandura, A. (1978). Reflections on
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Lin, J. M.-C., & Liu, S.-F. (2012).
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Table 1. The MSWLogo commands learn
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other words, we had prepared a set
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lab. When she returned and found th
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Attitudes toward Programming and Co
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Table 5 successfully, it took four
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Kuter, S., Altinay Gazi, Z., & Alti
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not only the means for trainees’
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Data Collection Techniques and Anal
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organizations was highlighted by on
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provides the means for professional
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Kohonen, V. (2001). Towards experie
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As Rogers (1995) postulates in his
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Instrument and data collection Afte
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“Raising our computer skills in C
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teachers deploy ICT tools in langua
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Ma, W., Anderson, R., & Streith, K.
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APPENDIX A. Questionnaire for Dista
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consistency with other ideas, and a
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conceptions, justifying their belie
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each group. The experimental group
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specifically, instructional approac
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Table 3 shows that the mean frequen
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previous Web-based instructional le
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Millar, R., & Osborne, J.F. (1998).
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Several studies explore the roles t
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understand the content structures o
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teacher was not allowed to provide
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two coding schemes, as illustrated
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also very limited. Teachers and sof
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learning activities. British Journa
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2009; Bernard & Cathryn, 2006) or p
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In this study, the focus of the ins
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The student’s degree of mastery i
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The questionnaire for the acceptanc
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the t-test result, it is found that
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Scale Questionnaire item Mean S.D.
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Hwang, G. J. (2003). A conceptual m
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sharing, problem solving, and achie
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Virtual learning environment In the
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Class Table 1. The survey questions
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“…the distance and the lack of
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collaboration within EVS, thus prev
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Huang, T.-H., Liu, Y.-C., & Chang,
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y personalised context examples in
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Students’ Problem-Solving Guidanc
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Research Method Figure 8. The scree
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Table 4. Abstract of Pairwise Compa
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questions designed for the system h
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Lee, J. (2012). Patterns of Interac
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This study focused on online fora i
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Results and Discussion Due date-cen
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In addition, all groups developed 8
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future. Therefore, they had an oppo
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surprising that most interaction wa
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Pawan, F., Paulus, T. M., Yalcin, S
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systems by using 18 personalization
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Page 287 and 288: DFL. d ( yi ) is the degree of mem
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Page 291 and 292: Finally, Figure 10(A) and Figure 10
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Page 295 and 296: mental models, are cognitive struct
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Page 299 and 300: Data analysis To test the model fit
Page 301 and 302: may be considered an effective inte
Page 303 and 304: Hsu, I.-C. (2012). Intelligent Disc
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Page 307 and 308: RLO denotes a remote learning objec
Page 309 and 310: Figure 2. The flow-oriented LOFinde
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Page 313 and 314: Similarly, the relation metadata of
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Page 317 and 318: References ADL. (2006). Sharable Co
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Page 321 and 322: Mediating effect of learning flow A
Page 323 and 324: The instrument used to measure lear
Page 325 and 326: addition, the cross-loadings of the
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Page 329 and 330: Connell, J. P., Spencer, M. B., & A
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Page 335 and 336: The survey consisted of 30 question
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Page 341 and 342: Conclusion Conducted research showe
Page 343 and 344: Romero, C., Ventura S., García E.,
Page 345 and 346: prompts as scaffolding strategy to
Page 347 and 348: Reflection Types This study attempt
Page 349 and 350: All the three groups completed the
Page 351 and 352: Results Learning Outcomes Pre- and
Page 353 and 354: Map Analysis in Transfer Test Figur
Page 355 and 356: Generic Prompts and Specific Prompt
Page 357 and 358: Biswas, G., Schwartz, D., Bransford
Page 359 and 360: Chen, Y.-H., Looi, C.-K., Lin, C.-P
Page 361 and 362: of correct response, answer until c
Page 363 and 364: Figure 7 shows a screenshot of one
Page 365 and 366: Collaboration Questionnaire results
Page 367 and 368: following diagrams. The double-arro
Page 369 and 370: “Most students were encouraged to
Page 371 and 372: Bangert-Drowns, R.L., Kulick, C. C.
Page 373 and 374: primary research questions. First,
Page 375 and 376: Individual learning Figure 2. Scree
Page 377 and 378: Experimental Tools This study emplo
Page 379 and 380: Table 3 shows that almost all items
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References Bloom, B. S. (Ed.). (195
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APPENDIX 2. Taxonomy for Informatio
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understand how we can effectively u
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5. Is there a relationship between
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correlation between teachers’ IWB
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Q7. IWB provides advantages to me t
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training regarding this topic. This
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Conclusion This study provides a so
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Smith, H. J., Higgins, S., Wall, K.
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