Affect-Adaptive Activities in a Personalised Ubiquitous Learning System

Autor: M. S. Omirin, Emmanuel Onwuka Ibam, S. O. Olatunji, Olumide Sunday Adewale, Oluwatoyin C. Agbonifo, Olutayo Boyinbode, O. Olabode, Bolanle Adefowoke Ojokoh, A. I. Makinde
Rok vydání: 2018
Předmět:
Zdroj: International Journal of Learning, Teaching and Educational Research. 17:43-58
ISSN: 1694-2116
1694-2493
DOI: 10.26803/ijlter.17.7.3
Popis: Many literatures have shown the importance of emotion in learning because of their effect on learner’s performances thereby giving reasons why learners’ affective states are crucial to learning. In this light, this research work aims to identify affect-adaptive activities that would improve learning in a personalised ubiquitous learning system by detecting learning style and affective states through some patterns of  behaviours. In the study, learners’ preference are determined, learners affective states such as confidence, effort, independence and confusion are investigated. The ant colony clustering algorithm is used to determine learners’ activities. The four affective states are determined from the learners’ forum threads, content activities, assessment, timing, and discussion through learners’ engagement. The result of this research on two separate courses, Course A (Affect) and Course B (Non-Affect) shows that the four affective states influence learning. The average mean and standard deviation (SD) value for Course A (Mean=276.23, SD=272.27) over Course B (Mean=200.58, SD=210.51) showed an improvement in learning performance and the t-Test carried out between the courses suggested that students’ performance is dependent on their affective states. https://doi.org/10.26803/ijlter.17.7.3
Databáze: OpenAIRE
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