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1. Evaluating Gaming Detector Model Robustness Over Time

   https://doi.org/10.5281/zenodo.6852961  

   Levin, Nathan; Baker, Ryan; Nasiar, Nidhi; Fancsali Stephen; Hutt, Stephen (January 2022, Proceedings of the 15th International Conference on Educational Data Mining, International Educational Data Mining Society)

   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 old 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 both new and old data alone. Examining the feature importances provides some explanation for the differences in performance between models, and offers some insight into how we might safeguard against detector rot over time. 

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2. Machine-Learned or Expert-Engineered Features? Exploring Feature Engineering Methods in Detectors of Student Behavior and Affect

   Botelho, A. F.; Baker, R. S.; Heffernan, N. T. (July 2019, The Twelfth International Conference on Educational Data Mining)

   There is a long history of research on the development of models to detect and study student behavior and affect. Developing computer-based models has allowed the study of learning constructs at fine levels of granularity and over long periods of time. For many years, these models were developed using features based on previous educational research from the raw log data. More recently, however, the application of deep learning models has often skipped this feature-engineering step by allowing the algorithm to learn features from the fine-grained raw log data. As many of these deep learning models have led to promising results, researchers have asked which situations may lead to machine-learned features performing better than expert-generated features. This work addresses this question by comparing the use of machine-learned and expert-engineered features for three previously-developed models of student affect, off-task behavior, and gaming the system. In addition, we propose a third feature-engineering method that combines expert features with machine learning to explore the strengths and weaknesses of these approaches to build detectors of student affect and unproductive behaviors. 

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3. Machine-Learned or Expert-Engineered Features? Exploring Feature Engineering Methods in Detectors of Student Behavior and Affect

   Botelho, A. F.; Baker, R. S.; Heffernan, N. T. (January 2019, Proceedings of the Twelfth International Conference on Educational Data Mining)

   There is a long history of research on the development of models to detect and study student behavior and affect. Developing computer-based models has allowed the study of learning constructs at fine levels of granularity and over long periods of time. For many years, these models were developed using features based on previous educational research from the raw log data. More recently, however, the application of deep learning models has often skipped this feature engineering step by allowing the algorithm to learn features from the fine-grained raw log data. As many of these deep learning models have led to promising results, researchers have asked which situations may lead to machine-learned features performing better than expert-generated features. This work addresses this question by comparing the use of machine-learned and expert-engineered features for three previously-developed models of student affect, off-task behavior, and gaming the system. In addition, we propose a third feature-engineering method that combines expert features with machine learning to explore the strengths and weaknesses of these approaches to build detectors of student affect and unproductive behaviors. 

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4. Exploring Differences in Performance between Knowledge Tracing Methods & Gaming the System Behavior

   Almoubayyed, H.; Fancsali, S. (July 2022, Proceedings of The Third Workshop of the Learner Data Institute , The 15th International Conference on Educational Data Mining (EDM 2022))

   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. 

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5. Accurate and Interpretable Sensor-free Affect Detectors via Monotonic Neural Networks

   Lan, A. S.; Botelho, A. F.; Karumbaiah, S.; Baker, R. S.; Heffernan, N. T. (January 2020, Proceedings of the 10th International Conference on Learning Analytics and Knowledge)

   Sensor-free affect detectors can detect student affect using their activities within intelligent tutoring systems or other online learning environments rather than using sensors. This technology has made affect detection more scalable and less invasive. However, existing detectors are either interpretable but less accurate (e.g., classical algorithms such as logistic regression) or more accurate but uninterpretable (e.g., neural networks). We investigate the use of a new type of neural networks that are monotonic after the first layer for affect detection that can strike a balance between accuracy and interpretability. Results on a real- world student affect dataset show that monotonic neural networks achieve comparable detection accuracy to their non-monotonic counterparts while offering some level of interpretability. 

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