Learning Analytics of Students’ Interaction with ChatGPT in Programming Education: A Process-Oriented Analysis
Artykuł w czasopiśmie
MNiSW
70
Lista 2024
| Status: | |
| Autorzy: | Omarbekova Assel, Miłosz Marek, Bekmanova Gulmira, Rakymkan Yershin , Nazyrova Aizhan, Lamasheva Zhanar |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 9 |
| Wolumen/Tom: | 16 |
| Numer artykułu: | 1382 |
| Strony: | 1 - 44 |
| Impact Factor: | 3,5 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 27 sierpnia 2026 |
| Abstrakty: | angielski |
| Generative AI tools are now widely used in undergraduate programming, yet most ev- idence about how students use them comes from self-report rather than from observed behaviour. This study examined the sequential structure of students’ ChatGPT (GPT- 4o, OpenAI)-supported programming work and the cognitive complexity of their queries. Screen recordings of 363 second-year students completing an individual Python 3.12 data- visualisation assignment were coded into 3985 activity episodes and analysed using de- scriptive statistics, lag-1 sequential analysis, and cognitive network analysis; because recordings capture actions rather than cognition, the coded categories are treated as be- havioural indicators interpreted within, rather than as measurements of, the Self-Regulated Learning framework. Programming activities accounted for 63% of coded actions and ChatGPT interactions for 20%. Behaviour was organised around a troubleshooting cycle, the strongest association being between submitting error messages and reviewing ChatGPT responses (PCM → RF, Yule’s Q = 0.84, a descriptive association measure, rather than a transition probability, whose stability across students was not tested). ChatGPT use was concentrated in activities indicative of monitoring and control and was largely absent from planning and reflection. High-achieving students produced a higher proportion of deep- level submissions (32.2% vs. 19.2%) and a lower proportion of surface-level submissions (23.2% vs. 38.4%) than low-achieving students; because submissions are nested within students, this difference is reported as a property of the observed distribution rather than as an inferential finding. Comparisons computed at the level of the student were tested inferentially and reached significance with small effect sizes; comparisons computed at the level of coded actions or query submissions are reported throughout as observed properties of the aggregate distributions rather than as inferentially established differences. These episode-level findings support instructional scaffolding that structures query formulation and reflection. |
