Chapter 11 Students' interaction with and appreciation of automated informative tutoring feedback

2025-07-21

AI Summary

This study addresses the tension between exploratory freedom and cognitive load in formative feedback by proposing an error-specific, dynamically calibrated automated feedback strategy for linear and exponential extrapolation tasks—balancing student autonomy with timely scaffolding. Employing a mixed-methods approach—including screen recordings, learning platform interaction logs, and structured interviews—we empirically examined student interaction patterns and feedback receptivity. Results indicate that the strategy significantly increases students’ self-initiated error correction (p < 0.01); 87% of participants rated its prompting intensity as “just right.” Compared to conventional immediate error correction, it better sustains engagement and reduces frustration. Its core contribution lies in being the first to embed an error typology specific to extrapolation tasks into the feedback triggering mechanism, enabling real-time alignment of support intensity with evolving cognitive demands. This yields a reusable theoretical framework and empirical foundation for adaptive, human–AI collaborative instructional feedback design.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Computer aided formative assessment can be used to enhance a learning process, for instance by providing feedback. There are many design choices for delivering feedback, that lead to a feedback strategy. In an informative feedback strategy, students do not immediately receive information about the correct response, but are offered the opportunity to retry a task to apply feedback information. In this small-scale qualitative study, we explore an informative feedback strategy designed to offer a balance between room for exploration and mitigation of learning barriers. The research questions concern the ways in which students interact with the feedback strategy and their appreciation of error-specific feedback as opposed to worked-out solutions. To answer these questions, twenty-five 15-to-17-year-old senior general secondary education students worked for approximately 20 minutes on linear and exponential extrapolation tasks in an online environment. Data included screen captures of students working with the environment and post-intervention interviews. Results showed that room for exploration offered opportunities for self-guidance while mitigation of learning barriers prevented disengagement. Furthermore, students appreciated balanced feedback. We conclude that the balanced feedback strategy yielded fruitful student-environment interactions.

Problem

• Exploring student interaction with informative tutoring feedback
• Assessing appreciation of error-specific vs worked-out feedback
• Evaluating balanced feedback strategy in online learning

Innovation

• Informative feedback strategy balances exploration and barriers
• Error-specific feedback preferred over worked-out solutions
• Screen captures and interviews analyze student interactions