Abstract
This paper addresses the issue of content sequencing in computer-based learning (CBL). In doing so, it proposes a Skill-Challenge Balancing (SCB) approach as a way to enhance the CBL experience. The approach is based on the Flow Theory, allowing self-adjustment of the given levels of challenges in a given learning tasks so that the learner will consistently be adaptively able to engage in the CBL activity. An empirical study with 70 students suggested that the SCB-based learners were significantly better in their learning experience specifically in their focus of attention and intrinsic interests compared to the learners in the system without SCB. The results also revealed that SCB was fully utilised by the learners to regulate the levels of difficulty of the CBL tasks.
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References
Chen, C.M.: Intelligent Web-Based Learning System with Personalized Learning Path Guidance. Computers & Education 51(2), 787–814 (2008)
de Marcos, L., Martinez, J.J., Gutierrez, J.A., Barchino, R., Gutierrez, J.M.: A New Sequencing Method in Web-Based Education. In: IEEE Congress on Evolutionary Computation (CEC 2009), pp. 3219–3225 (2009)
Chi, Y.L.: Ontology-Based Curriculum Content Sequencing System with Semantic Rules. Expert Systems with Applications 36(4), 7838–7847 (2009)
Idris, N., Yusof, N., Saad, P.: Adaptive Course Sequencing for Personalization of Learning Path Using Neural Network. International Journal of Advanced Soft Computing Applications 1(1), 49–61 (2009)
Katuk, N., Ryu, H.: Finding an Optimal Learning Path in Dynamic Curriculum Sequencing: An Account of the Flow Learning Experience. In: 2010 International Conference on Computer Applications & Industrial Electronics (ICCAIE 2010), pp. 227–232. IEEE Computer Society, New York (2010)
Katuk, N., Ryu, H.: Does a Longer Usage Mean Flow Experience? An Evaluation of Learning Experience with Curriculum Sequencing Systems (CSS). In: Gupta, G.S., Bailey, D., Demidenko, S., Osseiran, A., Renovell, M. (eds.) Sixth IEEE International Symposium on Electronic Design, Test and Application, pp. 13–18. IEEE Computer Society, New York (2011)
Sun, P.C., Tsai, R.J., Finger, G., Chen, Y.Y., Yeh, D.: What Drives a Successful E-Learning? An Empirical Investigation of the Critical Factors Influencing Learner Satisfaction. Computers & Education 50(4), 1183–1202 (2008)
Deepwell, F., Malik, S.: On Campus, but out of Class: An Investigation into Students’ Experiences of Learning Technologies in Their Self-Directed Study. Research in Learning Technology 16(1), 5–14 (2008)
Engelbrecht, E.: Adapting to Changing Expectations: Post-Graduate Students’ Experience of an E-Learning Tax Program. Computers & Education 45(2), 217–229 (2005)
Csikszentmihalyi, M.: Beyond Boredom and Anxiety. Jossey-Bass Publishers, San Francisco (1975)
Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper & Row Publishers, New York (1990)
Leontidis, M., Halatsis, C., Grigoriadou, M.: Mentoring Affectively the Student to Enhance His Learning. In: Aedo, I., Chen, N.S., Kinshuk, Sampson, D., Zaitseva, L. (eds.) 9th IEEE International Conference on Advanced Learning Technologies (ICALT 2009), pp. 455–459. IEEE Computer Society, New York (2009)
Sabine, A.M.: A Learner, Is a Learner, Is a User, Is a Customer: QOS-Based Experience-Aware Adaptation. In: 16th ACM International Conference on Multimedia, pp. 1035–1038. ACM, Vancouver (2008)
Ryoo, W., Jung, H., Yoo, M., Hwang, S.: Development of Motivational E-Learning Environment Based on Flow Theory. In: The World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2008, pp. 1208–1211 (2008)
Woolf, B., Arroyo, I., Cooper, D., Burleson, W., Muldner, K.: Affective Tutors: Automatic Detection of and Response to Student Emotion. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems. SCI, vol. 308, pp. 207–227. Springer, Heidelberg (2010)
Muldner, K., Burleson, W., VanLehn, K.: “Yes!”: Using Tutor and Sensor Data to Predict Moments of Delight During Instructional Activities. In: De Bra, P., Kobsa, A., Chin, D., Muldner, K., Burleson, W., VanLehn, K. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 159–170. Springer, Heidelberg (2010)
Kaklauskas, A., Krutinis, M., Seniut, M.: Biometric Mouse Intelligent System for Student’s Emotional and Examination Process Analysis. In: Aedo, I., Chen, N.S., Kinshuk, Sampson, D., Zaitseva, L. (eds.) 9th IEEE International Conference on Advanced Learning Technologies (ICALT 2009), pp. 189–193. IEEE Computer Society, New York (2009)
Park, J., Parsons, D., Ryu, H.: To Flow and Not to Freeze: Applying Flow Experience to Mobile Learning. IEEE Transactions on Learning Technologies 3(1), 56–67 (2010)
Chiu, C.M., Hsu, M.H., Sun, S.Y., Lin, T.C., Sun, P.C.: Usability, Quality, Value and E-Learning Continuance Decisions. Computers & Education 45(4), 399–416 (2005)
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Katuk, N., Wang, R., Ryu, H. (2011). Enhancement of Learning Experience Using Skill-Challenge Balancing Approach. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_72
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DOI: https://doi.org/10.1007/978-3-642-25832-9_72
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