Meta-Heuristic Algorithms for Learning Path Recommender at MOOC

Ngo Tung Son

doi:10.1109/ACCESS.2021.3072222

Meta-Heuristic Algorithms for Learning Path Recommender at MOOC

2021-01-01

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AI

DOI：10.1109/ACCESS.2021.3072222

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Abstract

En 中文

Online learning platforms, such as Coursera, Edx, Udemy, etc., offer thousands of courses with different content. These courses are often of discrete content. It leads the learner not to find a learning path in a vast volume of courses and contents, especially when they have no experience in advance. Streamlining the order of courses to create a well-defined learning path can help e-learners achieve their learning goals effectively and systematically. The learners usually ask the necessary skills that they expect to earn (query). The need is to develop a recommender system that can search for suitable learning paths. This study proposes a multi-objective optimization model as a knowledge-based recommender. Our model can generate an appropriate learning path for learners based on their background and job goals. The recommended studying path satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners, and cost. We have developed Metaheuristic algorithms includes the Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), to solve the proposed model. Finally, we tested proposed methods with a dataset consisting of Coursera's courses and Vietnam work's jobs. The test results show the effectiveness of the proposed method.

Keywords:

Optimization

Electronic learning

Data mining

Genetic algorithms

Computer aided instruction

Java

Ant colony optimization

Learning path

Knowledge-based recommendation

Knowledge graph

multi-objective optimization

compromise programming

genetic algorithm

ant colony optimization algorithm

Meta-Heuristic Algorithms for Learning Path Recommender at MOOC

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