Abstract
In linear e-Learning management systems, also referred to as Learning Management Systems (LMS), content is presented to the learners in the same way irrespective of their different learning styles, educational, social and historical background, their interests and learning abilities. In education recommender-based adaptive systems, learning is personalized and differentiated, taking into consideration the students’ different attributes. Adaptivity is automatic adjustment of the content provided to learners to suit their individual attributes. Personalisation is the ability to provide content and services that are tailored to individuals based on knowledge about their preferences and behavior. This research applies pedagogical foundations of teaching and learning in identifying learner attributes to go into an educational recommender-based adaptive system. Through a literature review, 40 attributes of personalized/differentiated learning were identified. A user-centric approach was adopted to prioritise the attributes in order to identify the 10 top attributes. This was done by using the Multi-Attribute Utility Theory (MAUT). The 40 attributes of personalised learning initially fed into questionnaires for students. From a population of 1203 students from a higher education college called EDU-REC, for the purpose of this research and to preserve anonymity of the college, a sample of 200 students was purposively selected for the research on the basis of their familiarity with the college’s eLearning system, and 103 students responded to the questionnaire representing a response rate of 52%. From the responses of the students, the following top ten (10) attributes were identified for inclusion in an educational recommender platform: culture, emotional/mental state, socialisation, motivation, learning preferences, prior knowledge, educational background, learning/cognitive style, and navigation and learning goals.
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Maravanyika, M., Dlodlo, N. (2018). Selecting Attributes for Inclusion in an Educational Recommender System Using the Multi-attribute Utility Theory. In: Florez, H., Diaz, C., Chavarriaga, J. (eds) Applied Informatics. ICAI 2018. Communications in Computer and Information Science, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-01535-0_18
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