An improved adaptive learning path recommendation model driven by real-time learning analytics

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Abstract
The advancements in the education sector made e-learning more popular in recent years. The velocity of learning content creation and its availability is also growing exponentially day after day. It is challenging for a learner to find a learning path for a course with a vast content repository. So, recommending a learning path helps the learners streamline the learning materials systematically and achieve their goals. This article proposes a learning path recommendation approach focused on knowledge building and learning performance analysis. The model considers both static and dynamic learner parameters for learning path generation. The difficulty level of the learning resources is tuned based on the real-time performance analysis of the students. The learning resources are recommended based on learning preferences and the ability of a learner to learn the specific learning resource. The model also predicts the learning time and the expected score for each learner. Root Mean Square Deviation and Coefficient of Determination (R-Squared error) measures are used to find the correctness of the prediction. The model is also checked for its adaptivity to the learners’ changing behavior and diversity of the LOs recommended for different learners. Ninety-six undergraduate learners participated in the study. The experimentations are conducted with 530 LOs from selected courses. The comparison results with three existing models show a better performance from the proposed approach with an average accuracy rise of 30% in learning path prediction based on the expected duration of learning 27.8% in expected score prediction with the second-best performing model. It is observed that the real-time learning analytics using the implicit learner log data benefits the recommendation process. LO rating strongly indicated the enhancement of learner satisfaction and experience with a rise of 25.5% when comparing the rating share with the second-best model.

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