Experience beyond knowledge: Pragmatic e-learning systems design
with learning experience

Computers in Human Behavior (2012)

Norliza Katuk, Jieun Kim & Hokyoung Ryu

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Abstract:With the growing demand in e-learning system, traditional e-learning systems have dramatically evolved to provide more adaptive ways of learning, in terms of learning objectives, courses, individual learning processes, and so on. This paper reports on differences in learning experience from the learner’s perspectives when using an adaptive e-learning system, where the learner’s knowledge or skill level is used to configure the learning path. Central to this study is the evaluation of a dynamic content sequencing system (DCSS), with empirical outcomes being interpreted using Csikszentmihalyi’s flow theory (i.e., Flow, Boredom, and Anxiety). A total of 80 participants carried out a one-way between-subject study controlled by the type of e-learning system (i.e., the DCSS vs. the non-DCSS). The results indicated that the lower or medium achievers gained certain benefits from the DCSS, whilst the high achievers in learning performance might suffer from boredom when using the DCSS. These contrasting findings can be suggested as a pragmatic design guideline for developing more engaging computer-based learning systems for unsupervised learning situations.

The fact that some medium- and expert learners did not have the optimal learning experience with the dynamic content sequencing system was highly dependent on the matching algorithm that determines the forthcoming content. Our algorithm was one of the best predictive content-based learning methods, which are suitable for situations where users tend to exhibit an idiosyncratic behaviour (Zukerman & Albrecht, 2001). Several different statistical models have been proposed, but Breese, Heckerman, and Kadie’s (1998) comparative study on the predictive performance of several predictive models indicated that Bayesian Network outperform the other models for a wide range of conditions. This technique is particularly useful when building an initial model on the basis of limited data and homogeneous subjects groups, since only a few learning contents are required to identify possible topics of interest for each subject group.
Link to read more, http://dx.doi.org/10.1016/j.chb.2012.12.014