A Recommender System For Open Educational Videos Based On Skill Requirements

TL;DR

This paper introduces a personalized recommender system for open educational videos tailored to skill requirements derived from job market data, aiming to enhance learning relevance and quality.

Contribution

It presents a novel method combining text mining, skill prediction, and personalized recommendations for educational videos based on labor market demands.

Findings

82.8% of recommended videos were deemed useful by experts.

The system has potential to improve personalized learning experiences.

Prototype successfully matches videos to skill requirements in data science jobs.

Abstract

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.