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We report work-in-progress that aims to better understand prediction performance differences between Deep Knowledge Tracing (DKT) and Bayesian Knowledge Tracing (BKT) as well as “gaming the system” behavior by considering variation in features and design across individual pieces of instructional content. Our“non-monolithic”analysis considers hundreds of “workspaces” in Carnegie Learning’s MATHia intelligent tutoring system and the extent to which two relatively simple features extracted from MATHia logs, potentially related to gaming the system behavior, are correlated with differences in DKT and BKT prediction performance. We then take a closer look at a set of six MATHia workspaces, three of which represent content in which DKT out-performs BKT and three of which represent content in which BKT out-performs DKT or there is little difference in performance between the approaches. We present some preliminary findings related to the extent to which students game the system in these workspaces, across two school years, as well as other facets of variability across these pieces of instructional content. We conclude with a road map for scaling these analyses over much larger sets of MATHia workspaces and learner data.Free, publicly-accessible full text available July 1, 2023
Understanding how students with varying capabilities think about problem solving can greatly help in improving personalized education which can have significantly better learning outcomes. Here, we present the details of a system we call NeTra that we developed for discovering strategies that students follow in the context of Math learning. Specifically, we developed this system from large-scale data from MATHia that contains millions of student-tutor interactions. The goal of this system is to provide a visual interface for educators to understand the likely strategy the student will follow for problems that students are yet to attempt. This predictive interface can help educators/tutors to develop interventions that are personalized for students. Underlying the system is a powerful AI model based on Neuro-Symbolic learning that has shown promising results in predicting both strategies and the mastery over concepts used in the strategy.Free, publicly-accessible full text available July 1, 2023
Free, publicly-accessible full text available July 1, 2023
Exploring Predictors of Achievement-Goal Profile Stability during Mathematics Learning in an Intelligent Tutoring SystemUsing archived student data for middle and high school students’ mathematics-focused intelligent tutoring system (ITS) learning collected across a school year, this study explores situational, achievement-goal latent profile membership and the stability of these profiles with respect to student demographics and dispositional achievement goal scores. Over 65% of students changed situational profile membership at some time during the school year. Start-of-year dispositional motivation scores were not related to whether students remained in the same profile across all unit-level measurements. Grade level was predictive of profile stability. Findings from the present study should shed light on how in-the-moment student motivation fluctuates while students are engaged in ITS math learning. Present findings have potential to inform motivation interventions designed for ITS math learning.Free, publicly-accessible full text available July 1, 2023
Research into "gaming the system" behavior in intelligent tutoring systems (ITS) has been around for almost two decades, and detection has been developed for many ITSs. Machine learning models can detect this behavior in both real-time and in historical data. However, intelligent tutoring system designs often change over time, in terms of the design of the student interface, assessment models, and data collection log schemas. Can gaming detectors still be trusted, a decade or more after they are developed? In this research, we evaluate the robustness/degradation of gaming detectors when trained on older data logs and evaluated on current data logs. We demonstrate that some machine learning models developed using past data are still able to predict gaming behavior from student data collected 16 years later, but that there is considerable variance in how well different algorithms perform over time. We demonstrate that a classic decision tree algorithm maintained its performance while more contemporary algorithms struggled to transfer to new data, even though they exhibited better performance on unseen students in both New and Old data sets by themselves. Examining the feature importance values provides some explanation for the differences in performance between models, and offers some insight into howmore »