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Recommending Personalized Summaries of Teaching Materials
- Source :
- IEEE Access, Vol 7, Pp 22729-22739 (2019)
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Teaching activities have nowadays been supported by a variety of electronic devices. Formative assessment tools allow teachers to evaluate the level of understanding of learners during frontal lessons and to tailor the next teaching activities accordingly. Despite plenty of teaching materials are available in the textual form, manually exploring these very large collections of documents can be extremely time-consuming. The analysis of learner-produced data (e.g., test outcomes) can be exploited to recommend short extracts of teaching documents based on the actual learner's needs. This paper proposes a new methodology to recommend summaries of potentially large teaching documents. Summary recommendations are customized to student's needs according to the results of comprehension tests performed at the end of frontal lectures. Specifically, students undergo multiple-choice tests through a mobile application. In parallel, a set of topic-specific summaries of the teaching documents is generated. They consist of the most significant sentences related to a specific topic. According to the results of the tests, summaries are personally recommended to students. We assessed the applicability of the proposed approach in real context, i.e., a B.S. university-level course. The results achieved in the experimental evaluation confirmed its usability.
- Subjects :
- Monitoring
General Computer Science
Computer science
Usability
Learning analytics
text summarization
Context (language use)
02 engineering and technology
computer.software_genre
Education
Videos
Tools
Formative assessment
Mobile applications
ComputingMilieux_COMPUTERSANDEDUCATION
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
personalized summary recommendation
Set (psychology)
Learning analytics, personalized summary recommendation, text summarization, Education, Dictionaries, Tools, Videos, Mobile applications, Usability, Monitoring
Multimedia
business.industry
General Engineering
020206 networking & telecommunications
Automatic summarization
Test (assessment)
Comprehension
Dictionaries
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
computer
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....f5a1e5cf0cac8da5e63641ee5989caa4