Analysis of 1,425 students' logs shows they coast through 60% of class time on math seatwork, mostly by stopping early, with extra practice effort correlating to higher standardized test performance.
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The Agency Allocation Framework reframes learner agency in AI-mediated education as the explicit allocation of decision authority across human and artificial actors, supported by a literature review and illustrative example.
Response-time propensities estimated from tutoring logs are stable within students and predict learning efficiency conditionally on proficiency and practice stage.
citing papers explorer
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Coasting Through Class: Learning Opportunity Loss from Practice Avoidance During Individual Seatwork
Analysis of 1,425 students' logs shows they coast through 60% of class time on math seatwork, mostly by stopping early, with extra practice effort correlating to higher standardized test performance.
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Who Decides in AI-Mediated Learning? The Agency Allocation Framework
The Agency Allocation Framework reframes learner agency in AI-mediated education as the explicit allocation of decision authority across human and artificial actors, supported by a literature review and illustrative example.
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Understanding Student Effort Using Response-Time Propensities During Problem Solving
Response-time propensities estimated from tutoring logs are stable within students and predict learning efficiency conditionally on proficiency and practice stage.