Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Recommender System For Open Educational Videos Based On Skill
  Requirements   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.

16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an