Effects of a peer tutor recommender system (PTRS) with machine learning and automated assessment on vocational high school students' computer application operating skills

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2 Scopus citations

Abstract

Many information technology courses frequently use the learning by doing strategy in vocational high schools. Particularly, learning computer application operating skills is essential for students because excellent computer application operating skills can help them attain good jobs. However, when fostering students’ computer application operating skills by teaching in vocational high school using the learning by doing strategy, a teacher learns that helping all students, evaluating their learning problems, and providing feedback to correct their mistakes are challenging. After investigating the challenge, a machine-learning-based peer tutor recommender system (MPTRS) with automated assessment was proposed to enhance students’ learning performance in computer application operating skills. The advanced automated assessment system (AAS) used computer vision technology to evaluate student assignments and instantly return feedback. The recommendation mechanism of the MPTRS enhanced mutual help among students based on their social relationships, learning performance, and recommendation feedback. Furthermore, machine-learning techniques were used to improve recommendations. In the experiment, the experimental group used the proposed system, and the control group used a conventional commercial automated grading system. From the experimental results, the learning performance of the experimental group significantly improved between the pretest and post-test. Students can correct and complete more assignments using the advanced AAS and students who behind in learning also can use the peer tutor recommender function for asking help. Participants were also satisfied with the proposed advanced AAS and MPTRS. It is worth to promote the proposed system to teachers of adopting the learning-by-doing strategy in computer application classes.

Original languageEnglish
Pages (from-to)435-462
Number of pages28
JournalJournal of Computers in Education
Volume7
Issue number3
DOIs
StatePublished - 1 Sep 2020

Keywords

  • Automated assessment system
  • Computer vision
  • Learning by doing
  • Machine learning
  • Peer tutoring
  • Recommender system

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