Applied Sciences · 2021 · 26 citations · 14 references
ComputingComputer ScienceEducational TechnologyData MiningMachine LearningRecommender SystemsCollaborative FilteringEducational Recommender SystemsOnline RecommendationPersonalized LearningRecommendation SystemsRecommender Systems For Learning ResourcesInformation OverloadInformation Overload ManagementLearning Personalization AlgorithmsPearson Correlation CoefficientPersonalized Content RecommendationPersonalized Learning RecommendationPersonalized RecommendationVideo Recommendation Systems
Finding relevant online learning materials quickly is challenging due to the vast amount of content. The study aims to identify students’ interests from their online learning behaviors and recommend related video resources. The authors use association‑rule mining for students lacking evaluation records and collaborative filtering with Pearson correlation for those with records, applying the appropriate method to recommend videos to all platform users. The approach reduces search time, increases platform stickiness, alleviates information overload, and satisfies learners’ personalized needs.
It is not easy to find learning materials of interest quickly in the vast amount of online learning materials. The purpose of this study is to find students’ interests according to their learning behaviors in the network and to recommend related video learning materials. For the students who do not leave an evaluation record in the learning platform, the association rule algorithm in data mining is used to find out the videos that students are interested in and recommend them. For the students who have evaluation records in the platform, we use the collaborative filtering algorithm based on items in machine learning, and use the Pearson correlation coefficient method to find highly similar video materials, and then recommend the learning materials they are interested in. The two methods are used in different situations, and all students in the learning platform can get recommendation. Through the application, our methods can reduce the data search time, improve the stickiness of the platform, solve the problem of information overload, and meet the personalized needs of the learners.
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Michela Barisone, Annamaria Bagnasco, Giuseppe Aleo et al. · Nurse Education in Practice · 2019 · 93 citations