Analyzing User Interaction to Design an Intelligent e-Learning Environment ARTICLE
Richa Sharma, University of Delhi, India
International Journal on E-Learning Volume 10, Number 4, ISSN 1537-2456 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA
Building intelligent course designing systems adaptable to the learners’ needs is one of the key goals of research in e-learning. This goal is all the more crucial as gaining knowledge in an e-learning environment depends solely on computer mediated interaction within the learner group and among the learners and instructors. The patterns generated out of discussions among e-learners may reveal significant information regarding their learning growth. This paper presents an algorithm to predict the knowledge gain of students by analyzing their online interaction pattern amongst each other. This knowledge gain is used to categorize the students as prospective “gainers” or “non-gainers” through Naïve Bayes Classifier. The preferences of non-gainers in terms of instructor-oriented v/s peer-oriented interaction are subsequently obtained. The paper further suggests some of the remedial plans based on proven instructional strategies, to be adopted that may help learners strengthen their weak areas. The actual knowledge gain of learners is evaluated by performing paired t-test on the previous and post-test score pairs.
Sharma, R. (2011). Analyzing User Interaction to Design an Intelligent e-Learning Environment. International Journal on E-Learning, 10(4), 441-460. Chesapeake, VA: Association for the Advancement of Computing in Education (AACE). Retrieved August 20, 2017 from https://www.learntechlib.org/p/33241/.
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