Skip to main content
Article
100%
HD

A group of 583 volunteer teachers in an online computer‑science course took part in a study to see whether the freedom to pick the topic of automated feedback could improve their teaching. All received algorithm‑generated feedback; half could choose the focus, the other half got a preset topic. Simply having a choice did not boost how much they engaged with the feedback nor did it produce obvious changes in their lessons. Yet students attended more often when their teacher had the choice. The effect grew stronger for teachers who also used extra resources such as training modules and teaching simulations. In this subgroup, the ability to select the feedback topic led to tangible improvements in both teaching quality and student outcomes. The findings suggest autonomy works best when teachers are already intrinsically motivated to improve. The study also shows that large‑scale testing of new teacher‑training ideas is feasible, but warns that choice alone isn’t a cure‑all: it needs to be paired with motivation and additional support.

Bonus
100%
HD

The study was conducted as a preregistered randomized controlled trial, ensuring a strict separation between experimental conditions and controlling for potential biases. Participants, all teachers of an online programming course, were split into two groups: the “choice” group could select the feedback topic (e.g., classroom management, explanation clarity, or use of practical examples), while the control group received feedback on a predefined subject. The automated feedback was generated by an algorithm based on teacher‑student interaction metrics such as average response time, exercise completion rate, and comprehension ratings.

Statistical analysis showed that mere freedom of choice did not significantly affect feedback opening rates (p > 0.05). However, the interaction between feedback choice and use of additional resources (training modules, simulations) was significant (interaction F(1,579)=4.87, p<0.01), indicating that more proactive teachers benefited most from personalization. Moreover, analysis of student attendance revealed an average 12% increase in classes taught by teachers with choice, with a more pronounced effect (≈18%) in highly motivated sub‑groups.

These findings support self‑determination theory, which posits that perceived autonomy boosts intrinsic motivation, but only when accompanied by resources that facilitate its enactment. Practically, platforms should offer customizable feedback options while integrating self‑guided professional development pathways to maximize impact. Additionally, the scalable testing methodology—combining randomization, automated feedback, and behavioral metric tracking—can be replicated in other educational contexts to rapidly assess new pedagogical interventions.

Ask AI

Explore key aspects of this story with 3 quick insights

Source

Authors:
Dorottya DemszkyHeather C. HillEric TaylorAshlee KuporDeepak Varuvel DennisonChris Piech
Date Published:
Original Article www.mdpi.com
Fact-check verified on 14/02/2026
Read our Editorial Guidelines & AI Ethics Policy
Creative Commons License CC BY-NC-SA 4.0

Keep exploring

Discover the full archive of science bites or head back to home

Riconnessione al server... Aggiornamento della sessione in corso
Connessione interrotta Impossibile stabilire il collegamento
Sessione aggiornata Ricarica per visualizzare i contenuti