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Analyzing Learner Characteristics and Courses Based on Cognitive Abilities, Learning Styles, and Context

  • Moushir M. El-Bishouty
  • Ting-Wen Chang
  • Renan Lima
  • Mohamed B. Thaha
  • Kinshuk
  • Sabine Graf
Chapter
Part of the Lecture Notes in Educational Technology book series (LNET)

Abstract

Student modeling and context modeling play an important role in adaptive and smart learning systems, enabling such systems to provide courses and recommendations that fit students’ characteristics and consider their current context. In this chapter, three approaches are presented to automatically analyze learners’ characteristics and courses in learning systems based on learners’ cognitive abilities, learning styles, and context. First, a framework and a system are presented to automatically identify students’ working memory capacity (WMC) based on their behavior in a learning management system. Second, a mechanism and an interactive tool are described for analyzing course contents in learning management systems (LMSs) with respect to students’ learning styles. Third, a framework and an application are presented that build a comprehensive context profile through detecting available features of a device and tracking the usage of these features. All three approaches contribute toward building a foundation for providing learners with intelligent, adaptive, and personalized support based on their cognitive abilities, learning styles, and context.

Keywords

Cognitive abilities Learning styles Context profile Student modeling Personalization 

Notes

Acknowledgments

The authors acknowledge the support of nserc, icore, xerox, mitacs, and the research-related gift funding by mr. a. markin.

References

  1. Atman, N., Inceoğlu, M. M. & Aslan, B. G. (2009). Learning styles diagnosis based on learner behaviors in web based learning. Proceedings of ICCSA 2009, 5593/2009, 900–909. Springer, Heidelberg.Google Scholar
  2. Broadway, J. M., & Engle, R. W. (2011). Lapsed attention to elapsed time? Individual differences in working-memory capacity and temporal reproduction. Acta Psychologica, 137(1), 115–126.CrossRefGoogle Scholar
  3. Chen, G. D., Chang, C. K., & Wang, C. Y. (2008). Ubiquitous learning website: Scaffold learners by mobile devices with information-aware techniques. Computers and Education, 50(1), 77–90.CrossRefGoogle Scholar
  4. Clay, J. & Orwig, C. J. (1999). Your learning style and language learning. Lingual Links Library, Summer Institute of Linguistic, Inc (SIL) International version 3.5.Google Scholar
  5. El-Bishouty, M. M., Ogata, H., & Yano, Y. (2007). PERKAM: Personalized knowledge awareness map for computer supported ubiquitous learning. Educational Technology and Society, 10(3), 122–134.Google Scholar
  6. El-Bishouty, M. M., Chang, T.-W., Kinshuk, & Graf, S. (2012). A framework for analyzing course contents in learning management systems with respect to learning styles. In G. Biswas et al. (eds.) The 20th International Conference on Computers in Education (ICCE 2012), pp. 91–95. Asia-Pacific Society for Computers in Education, Singapore.Google Scholar
  7. El-Bishouty, M. M., Saito, K., Chang, T.-W., Kinshuk, & Graf, S. (2013). An interactive course analyzer for improving learning styles support level. In Proceedings of the 3rd International Workshop On Human-Computer Interaction And Knowledge Discovery In Complex, Unstructured, Big Data (HCI-KDD 2013), Lecture Notes in Computer Science, Springer, Vol 7947, pp. 136–147.Google Scholar
  8. Engle, R. W. (2010). Role of working-memory capacity in cognitive control. Current Anthropology, 51(1), Working Memory: Beyond Language and Symbolism, 17–26.Google Scholar
  9. Felder, R., & Silverman, L. (1988). Learning and teaching styles. Journal of Engineering Education, 94(1), 674–681.Google Scholar
  10. Felder, R. M., & Soloman, B. A. (1997). Index of learning styles questionnaire. North Carolina State University, http://www.engr.ncsu.edu/learningstyles/ilsweb.html. (Accessed on October, 2013).
  11. Gathercole, S. E., & Alloway, T. P. (2008). Working memory and learning: A practical guide for teachers. London: Sage Press.Google Scholar
  12. Graf, S., & Kinshuk, K. (2007). Providing adaptive courses in learning management systems with respect to learning styles. In: G. Richards (Ed.), Proceedings of the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (e-Learn), 2576–2583, AACE Press, Chesapeake, VA.Google Scholar
  13. Graf, S., Kinshuk, & Liu, T.-C. (2009a). Supporting teachers in identifying students’ learning styles in learning management systems: An automatic student modelling approach. Educational Technology and Society, 12 (4), 3–14.Google Scholar
  14. Graf, S., & Tortorella, R. (2012). Personalized mobile learning via an adaptive engine. Exploring the future of Technology Enhanced Learning ICALT: IEEE Computer Society.Google Scholar
  15. Graf, S., Lin, T., Kinshuk, Chen, N. S., & Yang, S. J. H. (2009b). Learning styles and cognitive traits—their relationship and its benefits in web-based educational systems. Computers in Human Behavior, 25(6), 1280–1289.Google Scholar
  16. Huai, H. (2000). Cognitive style and memory capacity: Effects of concept mapping as a learning method. Doctoral Dissertation, Oct 2000, Twente University, The Netherlands.Google Scholar
  17. Joiku Phone Usage (2013) https://play.google.com/store/apps/details?id=com.jupiterapps. Phoneusage&hl=en. (Accessed on Dec 2013).
  18. Kuljis, J., & Liu, F. (2005). A comparison of learning style theories on the suitability for eLearning. In M. H. Hamza (Ed.), Proceedings of IASTED, pp. 191–197. ACTA Press.Google Scholar
  19. Lima, R. H. P., El-Bishouty, M. M, & Graf, S. (2013). A framework for automatic identification and visualization of mobile device functionalities and usage. Proceedings of SouthCHI 2013, Lecture Notes in Computer Science, Springer, 7947, 148–159.Google Scholar
  20. Lin, T., Kinshuk, & Patel A. (2003). Cognitive trait model—a supplement to performance based student models. In Proceedings of international conference on computers in education (pp. 629–632). Hong Kong.Google Scholar
  21. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an early warning system for educators: A proof of concept. Computers and Education, 54, 588–599.CrossRefGoogle Scholar
  22. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.CrossRefGoogle Scholar
  23. Ogata, H., Li, M., Hou, B., Uosaki, N., El-Bishouty, M. M., & Yano, Y. (2010). SCROLL: Supporting to share and reuse ubiquitous learning log in the context of language learning. Proceedings of mLearn, 2010, 40–47.Google Scholar
  24. Onyejegbu, L. N. & Asor, V. E. (2011). An efficient model for detecting LEARNING STYLE preferences in a personalized E-Learning management system. Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Software Engineering (JSSE), May Edition.Google Scholar
  25. Paredes, P., & Rodríguez, P. (2004). A mixed approach to modelling learning styles in adaptive educational hypermedia. Advanced Technology for Learning, 1(4), 210–215.CrossRefGoogle Scholar
  26. Park, O., & Lee, H. (2003). Adaptive instructional systems. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (pp. 651–684). Bloomington, Indiana: AECT.Google Scholar
  27. Roman, M. & Campbell, R. H. (2002). A user-centric, resource-aware, context-sensitive, multi-device application framework for ubiquitous computing environments. CS Technical Report.Google Scholar
  28. Tseng, J. C. R., Chu, H. C., Hwang, G. J., & Tsai, C. C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers and Education., 51, 776–786.CrossRefGoogle Scholar
  29. Unsworth, N., Redick, T. S., Gregory, J., Spillers, J., & Brewer, G. A. (2012). Variation in working memory capacity and cognitive control: Goal maintenance and microadjustments of control. The Quarterly Journal of Experimental Psychology, 65(2), 326–355.CrossRefGoogle Scholar
  30. Woehrle, J. L., & Magliano, J. P. (2012). Time flies faster if a person has a high working-memory capacity. Acta Psychologica, 139(2), 314–319.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Moushir M. El-Bishouty
    • 1
    • 3
  • Ting-Wen Chang
    • 1
    • 2
  • Renan Lima
    • 1
    • 4
  • Mohamed B. Thaha
    • 1
  • Kinshuk
    • 1
  • Sabine Graf
    • 1
  1. 1.Athabasca UniversityAthabascaCanada
  2. 2.Beijing Normal UniversityBeijingPeople’s Republic of China
  3. 3.City for Scientific Research and Technological ApplicationsAlexandriaEgypt
  4. 4.Federal University of São CarlosSão CarlosBrazil

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