Investigating patterns of study persistence on self-assessment platform of programming problem-solving

Cheng Yu Chung, I. Han Hsiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A student's short-term study behavior may not necessary infer his/her long-term behavior. It is very common to see a student changes study strategy throughout a semester and adapts to learning condition. For example, a student may work very hard before the first exam but gradually reducing the effort due to several possible reasons, e.g., being overwhelmed by various course work or discouraged by increasing complexity in the subject. Consistency or differences of one student's behavior is more likely to be discovered by multiple granularity of learning analytics. In this study, we investigate students' study persistence on a self-assessment platform and explore how such a behavioral pattern is related to the performance in exams. A probabilistic mixture model trained by response streams of log data is applied to cluster students' behavior into persistence patterns, which are further categorized into micro (short-term) and macro (long-term) patterns according to the span of time being modeled. We found four types of micro persistence patterns and several macro patterns in the analysis and analyzed their relations with exam performances. The result suggests that the consistency of persistence patterns can be an important factor driving student's overall performance in the semester, and students achieving higher exam scores show relatively persistent behavior compared to students receiving lower scores.

Original languageEnglish (US)
Title of host publicationSIGCSE 2020 - Proceedings of the 51st ACM Technical Symposium on Computer Science Education
PublisherAssociation for Computing Machinery
Pages162-168
Number of pages7
ISBN (Electronic)9781450367936
DOIs
StatePublished - Feb 26 2020
Event51st ACM SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2020 - Portland, United States
Duration: Mar 11 2020Mar 14 2020

Publication series

NameAnnual Conference on Innovation and Technology in Computer Science Education, ITiCSE
ISSN (Print)1942-647X

Conference

Conference51st ACM SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2020
CountryUnited States
CityPortland
Period3/11/203/14/20

Keywords

  • Learning analytics
  • Poisson mixture model
  • Self- assessment
  • Self-regulated learning
  • Study persistence

ASJC Scopus subject areas

  • Management of Technology and Innovation
  • Education

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  • Cite this

    Chung, C. Y., & Hsiao, I. H. (2020). Investigating patterns of study persistence on self-assessment platform of programming problem-solving. In SIGCSE 2020 - Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 162-168). (Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE). Association for Computing Machinery. https://doi.org/10.1145/3328778.3366827