ARCS approach to PTA-based programming language practice sessions: Factors influencing Programming Problem-Solving Skills

Benazir Quadir, Kazi Mostafa, Jie Chi Yang, Juming Shen, Rokaya Akter

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This study used the ARCS approach to investigate the effects of university students’ motivation, including attention, relevance, confidence, and satisfaction, to use the Programming Teaching Assistant (PTA) on their Programming Problem-Solving Skills (PPSS). Previous studies have shown that PTA features enhance learners’ programming performance, but relevant studies have not empirically evaluated the factors influencing learners’ problem-solving skills in PTA-based programming language practice. Thus, the current study used the ARCS model to investigate the effects of its core factors, Attention (A), Relevance (R), Confidence (C), and Satisfaction (S), on the learners’ PPSS. A total of 99 students major in computer science and engineering (CSE) participated in this study. Multiple linear regression analysis was conducted and the results indicated that these motivational factors influenced learners’ PPSS in PTA-based programming language practice. The PTA has proven to be a motivating platform for programming language practice. Finally, relevant implications are offered.

Original languageEnglish
JournalEducation and Information Technologies
DOIs
StateAccepted/In press - 2023

Keywords

  • ARCS model
  • Programming performance
  • Programming Problem-Solving Skills
  • PTA

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