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Introduction: The emergence and widespread adoption of generative AI (GenAI) chatbots such as ChatGPT, and programming assistants such as GitHub Copilot, have radically redefined the landscape of programming education. This calls for replication of studies and reexamination of findings from pre-GenAI CS contexts to understand the impact on students. Objectives: Achievement Goals are well studied in computing education and can be predictive of student interest and exam performance. The objective in this study is to compare findings from prior achievement goal studies in CS1 courses with new CS1 courses that emphasize the use of human-GenAI collaborative coding. Methods: In a CS1 course that integrates GenAI, we use linear regression to explore the relationship between achievement goals and prior experience on student interest, exam performance, and perceptions of GenAI. Results: As with prior findings in traditional CS1 classes, Mastery goals are correlated with interest in computing. Contradicting prior CS1 findings, normative goals are correlated with exam scores. Normative and mastery goals correlate with students’ perceptions of learning with GenAI. Mastery goals weakly correlate with reading and testing code output from GenAI.
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How do students’ achievement goals relate to learning from well-designed instructional videos and subsequent exam performance?
Well-designed instructional videos are powerful tools for helping students learn and prompting students to use generative strategies while learning from videos further bolsters their effectiveness. However, little is known about how individual differences in motivational factors, such as achievement goals, relate to how students learn within multimedia environments that include instructional videos and generative strategies. Therefore, in this study, we explored how achievement goals predicted undergraduate students’ behaviors when learning with instructional videos that required students to answer practice questions between videos, as well as how those activities predicted subsequent unit exam performance one week later. Additionally, we tested the best measurement models for modeling achievement goals between traditional confirmatory factor analysis and bifactor confirmatory factor analysis. The bifactor model fit our data best and was used for all subsequent analyses. Results indicated that stronger mastery goal endorsement predicted performance on the practice questions in the multimedia learning environment, which in turn positively predicted unit exam performance. In addition, students’ time spent watching videos positively predicted practice question performance. Taken together, this research emphasizes the availing role of adaptive motivations, like mastery goals, in learning from instructional videos that prompt the use of generative learning strategies.
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- Award ID(s):
- 1821594
- PAR ID:
- 10487840
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Contemporary Educational Psychology
- Volume:
- 73
- Issue:
- C
- ISSN:
- 0361-476X
- Page Range / eLocation ID:
- 102162
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.cedpsych.2023.102162
