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LPR: A bio-inspired intelligent learning path recommendation system based on meaningful learning theory

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Abstract

The educational community has been interested in personalized learning systems that can adapt itself while providing learning support to different learners to overcome the weakness of ‘one size fits all’ approaches in technology-enabled learning systems. In this paper, one known problem in adaptive learning systems called curriculum sequencing is addressed. A learning path recommendation (LPR) system is designed and implemented that clusters the learners into groups and selects an appropriate learning path for learners based on their prior knowledge. The clustering component uses Fuzzy C-Mean (FCM) algorithm that can recommend more than one learning path to learners located on the cluster boundaries. Using bioinspired ant colony optimization (ACO) algorithm and meaningful learning theory, the ACO path finder component searches for a suitable learning path for the learners while incorporating their continuous improvements. The effectiveness of the LPR system is evaluated by developing and offering a database course to actual learners. The results of the experiment showed that the group using the LPR system had a significantly higher performance and knowledge improvement in the course than the control group. This indicated that the LPR system has a moderate to large impact on the learners’ performance and knowledge improvement.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, Canada

    Mehdi Niknam & Parimala Thulasiraman

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  1. Mehdi Niknam
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  2. Parimala Thulasiraman
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Correspondence to Mehdi Niknam.

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Niknam, M., Thulasiraman, P. LPR: A bio-inspired intelligent learning path recommendation system based on meaningful learning theory. Educ Inf Technol 25, 3797–3819 (2020). https://doi.org/10.1007/s10639-020-10133-3

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