Research on Learning Path Recommendation Algorithms in Online Learning Community

Abstract

Aiming at the problem of “learning defiance†and “information overload†brought by educating big data to learners, this paper proposes an online learning community personalized learning path recommendation algorithm based on ant colony algorithm: in terms of computing pheromone, it combines individuality. Based on the characteristics of the learning path, a learning path scoring method based on multi-factor fuzzy evaluation is proposed to quantify the learning path evaluation as a score to solve the problem that it is difficult for the subjective score to accurately represent the pheromone concentration; in terms of pheromone updating rules, The introduction of pheromone restriction intervals avoids the problems associated with excessive or small learning path pheromone concentration in global updating; in the calculation of the selection probability of local search, the positive and negative feedback effects of pheromones can be better used. Search for a local optimal solution. The related experiments show that this algorithm can effectively solve the recommendation of the personalized learning path of the online learning community.