In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current recommendation algorithm focuses mainly on the learners’ course ratings. However, the choice of courses is not only based on the learners’ interests and preferences. It is also affected by learners’ knowledge domains and learning capabilities, all of which change dynamically over time. Therefore, this study proposes a personalized hybrid recommendation algorithm combining clustering with collaborative filtering. First, data on learners’ course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits are used based on multidimensional item response theory. Second, considering that learners’ preferences and multidimensional capabilities change dynamically over time, the Ebbinghaus forgetting curve is introduced by integrating memory weights to improve the accuracy and interpretation of the proposed recommendation algorithm for MOOCs. Finally, the performance of the proposed recommendation algorithm is investigated using data from Coursera, an internationally renowned MOOCs platform. The experimental results show that the proposed recommendation algorithm is superior to the baseline algorithms. Accordingly, relevant suggestions are proposed for the development of MOOCs.

/pdf/personalized-hybrid-recommendation-algorithm-for-moocs-based-3aato2da.pdf

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

The process of education informatization has been accelerated in the Internet era. English course teaching in colleges and universities also gradually follows the development of the times and makes efforts to promote the reform of informatization teaching. This paper gives the design and application strategy of the English course development model in China’s colleges and universities and designs a diversified teaching model. In addition, aiming at the unreasonable selection of teaching resources and learning materials, this paper designs a multidimensional course recommendation algorithm with integrated features. The proposed algorithm uses a deep learning model for data feature extraction and extracts the information of course attributes and review texts. Then an improved association rule algorithm is conceived to perform association clustering analysis on the original course categories to increase the coverage of recommendations. Experimental results demonstrate that the proposed content recommendation algorithm can recommend high-quality course resources for English teachers, and its performance is better than other algorithms.

English Online Course Development Model and Course Content Recommendation

Time-aware hybrid recommender for sparse data with nonlinear interactions and dynamic shifts

Different from the search engine system, the recommendation system can help users quickly find their interesting items from the massive data in a personalized way. Traditional collaborative filtering algorithms based on users and items need to define the granularity, dimension, and weight of classification subjectively when calculating similarity based on user behavior. So the accuracy and calculation efficiency of prediction scoring results are not high enough. LFM (latent factor model) based on data itself, adopts automatic clustering according to user behavior and uses a machine-learning method to mine hidden features from the user’s historical scoring data. But when the amount of data is large, there exists data sparsity in the user rating matrix. Also, the user’s interest is always changing with time, and the items themselves have a certain life cycle. Based on the above problems, this paper first proves that the accuracy of LFM model is influenced by the popularity and diversity of negative samples through comparative experiments. Then, a new algorithm called FC-LFM (forgetting curve-latent factor model) is proposed. In this algorithm, the Ebbinghaus forgetting curve function is introduced to improve LFM model and the time decay factor is integrated into the iterative operation of negative sample popularity, matrix filling, user feature matrix, and item feature matrix. In the end, the improved FC-LFM collaborative filtering algorithm is proved to be superior to the traditional UserCF, UserCF-IIF, ItemCF, ItemCF-IUF, and LFM algorithm in accuracy and recall rate by comparing experiments on the MovieLens data set.

Improved FC-LFM Algorithm Integrating Time Decay Factor

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Improved FC-LFM Algorithm Integrating Time Decay Factor