Li, J. (CM) – Detecting Failure to Adapt: Reading Self-Regulated Learning Breakdowns from Game Telemetry through Plan Recognition

Three learners who fail the same level of an educational game the same number of times can be failing in three different ways, and the difference determines what each should do next. Yet the measures a game’s logs are usually reduced to (completion time, error counts, mastery estimates) render the three identical. This proposal takes one breakdown as its object: failure-to-adapt, the case where the game has repeatedly surfaced evidence that a learner’s current approach is failing and the learner’s approach shows no responsive change. The construct is grounded in Winne and Hadwin’s monitor-and-control model of self-regulated learning and defined at the level of the learner’s plan. To detect it, a plan-recognition engine maintains a continuously updated probability estimate of which strategy the learner is executing across the whole trace; an episode is flagged when that estimate shows no evidence-responsive revision. Because behavior alone cannot settle what broke down, flagged episodes are validated against learners’ own verbal reports, coded blind, and decomposed into monitoring failure, control failure, or control the trace cannot show. Three studies carry the work: detection and diagnosis on real telemetry from an educational game, including a comparison against the analytics the field already runs; a formative study of what a facilitator (an instructor or teaching assistant running a class play session) must see to judge correctly which learners need attention; and a documented authoring case carrying the detection to a second game. The contribution is knowledge for game-based-learning researchers: a theory-grounded construct, a validated way to detect it from play, and the authoring knowledge to embed that detection in new games.

Event Host: Jiahong Li, Ph.D. Student, Computational Media

Advisor: Magy Seif El-Nasr

Zoom: https://ucsc.zoom.us/j/93238603235?pwd=zENRsu82HRj4JYKcMEn9MZibU8kC7F.1

Passcode: 835328