LPR: A bio-inspired intelligent learning path recommendation system based on meaningful learning theory
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LPR: A bio-inspired intelligent learning path recommendation system based on meaningful learning theory Mehdi Niknam 1
& Parimala
Thulasiraman 1
Received: 26 July 2019 / Accepted: 6 February 2020/ # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Learning path recommendation . Curriculum sequencing . Ant colony
optimization . Meaningful learning theory . Concept map
* Mehdi Niknam [email protected] Parimala Thulasiraman [email protected]
1
University of Manitoba, Winnipeg, Canada
Education and Information Technologies
1 Introduction The educational community has been interested in having a personalized learning system. Personalized learning refers to adjusting the pedagogy, curriculum, and learning environment for learners to satisfy their learning needs and preferences. A personalized learning system can adapt itself when providing learning support to different learners to overcome the weakness of ‘one size fits all’ approaches in technologyenabled learning systems. The goal is to have a learning system that can dynamically adapt itself based on a learner’s characteristics and needs and provide personalized learning support. One known problem in adaptive learning systems is content planning. Content or curriculum planning (also known as content or curriculum sequencing) refers to the process of selecting appropriate learning objects for a learner or guiding the learner to an appropriate learning path (Wasson 1990). The goal in content planning is to recommend a suitable learning path (a sequence of learning objects) to learners to achieve specific learning objectives. Content planning problem is modeled as a graph problem where the
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