A Recommender System For Open Educational Videos Based On Skill Requirements

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Ithaca, NY : Cornell University

In this paper, we suggest a novel method to help learners find relevant open educational videos to master skills demanded on the labour market. We have built a prototype, which 1) applies text classification and text mining methods on job vacancy announcements to match jobs and their required skills; 2) predicts the quality of videos; and 3) creates an open educational video recommender system to suggest personalized learning content to learners. For the first evaluation of this prototype we focused on the area of data science related jobs. Our prototype was evaluated by in-depth, semi-structured interviews. 15 subject matter experts provided feedback to assess how our recommender prototype performs in terms of its objectives, logic, and contribution to learning. More than 250 videos were recommended, and 82.8% of these recommendations were treated as useful by the interviewees. Moreover, interviews revealed that our personalized video recommender system, has the potential to improve the learning experience.

OER, open educational resource, educational recommender system, video recommender system, lifelong learning, machine learning, text mining, text classification
Tavakoli, M., Hakimov, S., Ewerth, R., & Kismihók, G. (2020). A Recommender System For Open Educational Videos Based On Skill Requirements. Ithaca, NY : Cornell University.