Intelligent personalised learning system based on emotions in e-learning.

Autor: Karthika, R., Jesi, V. Elizabeth, Christo, Mary Subaja, Deborah, L. Jegatha, Sivaraman, Audithan, Kumar, Sanjeev
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Zdroj: Personal & Ubiquitous Computing; Dec2023, Vol. 27 Issue 6, p2211-2223, 13p
Abstrakt: One of the greatest challenges in the success of a personalised e-learning system lies in the behaviour identification of the learners during the learning and evaluation phase. The content delivery in an e-learning system must be modified and updated periodically according to the preference and behaviour of the learners. Usually, the behaviour of the learners drastically changes according to their affective states during the learning phase. The accurate identification of the learner's negative emotions and addressing such emotions carefully in a positive sense can greatly provide success to the learners. In this paper, the learner's emotions especially frustration emotion are automatically and accurately estimated using the information received from learning management systems (LMS) using the Takagi sugeno fuzzy inference engine. Based on this estimation, several motivational messages are distributed according to the identified emotions which had greatly helped them to succeed during e-learning. The motivational messages that are used for sending to the learners are based on regulatory fit theory. Several kinds of statistical tests are used in this paper for deep analysis. Two sets of learners, namely, control and experimental sets, are identified from undergraduate students. Experimental results are shown for these two sets to reveal the increase in their emotional strength after receiving the motivational messages. Statistical analysis t-test is also applied to the two sets, and the experimental results have shown that there is a significant difference between the two groups which shows the dominance of the proposed system. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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