The investigation of human attention networks on debugging performance


Akçay A., ALTUN A.

Education and Information Technologies, 2023 (SSCI) identifier

  • Publication Type: Article / Article
  • Publication Date: 2023
  • Doi Number: 10.1007/s10639-023-11955-7
  • Journal Name: Education and Information Technologies
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Communication Abstracts, EBSCO Education Source, Educational research abstracts (ERA), ERIC (Education Resources Information Center), INSPEC
  • Keywords: Attention network, Bug, Debugging, Programming
  • Hacettepe University Affiliated: Yes

Abstract

Debugging is an intellectually rich and a challenging process when learning a programming language. This process is important for increasing the quality of the program and making it functional. Debugging, by its nature, is thought to be a practice with a state of focus and concentration. This study explored whether the debugging performance could be predicted by attention networks of students studying at the IT department of vocational high schools. 108 vocational high school IT department students participated in the research. First, students’ attention levels were determined by utilizing the Attention Network Test. Second, a Debugging Performance Test was administered to determine their debugging performance. The analysis result indicated that the general attention network statistically predicted the debugging performance but the effect size was found to be small. Furthermore, the singular and dual interactions of the alerting attention network, the orienting attention network, and the executive attention network did not predict debugging performances. Yet, the interactions of the three attention networks on predicting the debugging performances were found to be statistically significant. These findings were discussed together with the existing studies in the literature and suggestions to obtain more in-depth information regarding these results were provoked.