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1. Says Who? How different ground truth measures of emotion impact student affective modeling

   https://doi.org/10.5281/zenodo.12729799  

   Zambrano, Andres Felipe; Nasiar, Nidhi; Ocumpaugh, Jaclyn; Goslen, Alex; Zhang, Jiayi; Rowe, Jonathan; Esiason, Jordan; V, Jessica; enberg; Hutt, Stephen (January 2024, International Educational Data Mining Society)

   Benjamin, Paaßen; Carrie, Demmans Epp (Ed.)

   Research into student affect detection has historically relied on ground truth measures of emotion that utilize one of three sources of data: (1) self-report data, (2) classroom observations, or (3) sensor data that is retrospectively labeled. Although a few studies have compared sensor- and observation-based ap-proaches to student affective modeling, less work has explored the relationship between self-report and classroom observa-tions. In this study, we use both recurring self-reports (SR) and classroom observation (BROMP) to measure student emotion during a study involving middle school students interacting with a game-based learning environment for microbiology educa-tion. We use supervised machine learning to develop two sets of affect detectors corresponding to SR and BROMP-based measures of student emotion, respectively. We compare the two sets of detectors in terms of their most relevant features, as well as correlations of their output with measures of student learning and interest. Results show that highly predictive features in the SR detectors are different from those selected for BROMP-based detectors. The associations with interest and motivation measures show that while SR detectors captured underlying motivations, the BROMP detectors seemed to capture more in-the-moment information about the student申fs experience. Evi-dence suggests that there is benefit of using both sources of data to model different components of student affect. 

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2. The Half-Life of Epistemic Emotions: How Motivation Influences Affective Chronometry

   https://doi.org/10.5281/zenodo.15870172  

   Zambrano, Andres Felipe; Ocumpaugh, Jaclyn; Baker, Ryan S; Vanacore, Kirk; Esiason, Jordan; Vandenberg, Jessica (January 2025, International Educational Data Mining Society)

   Mills, Caitlin; Alexandron, Giora; Taibi, Davide; Lo_Bosco, Giosuè; Paquette, Luc (Ed.)

   Research on epistemic emotions has often focused on how students transition between affective states (e.g., affect dynamics). More recently, studies have examined the properties of cases where a student remains in the same affective state over time, finding that the duration of a student's affective state is important for multiple learning outcomes. However, the likelihood of remaining in a given affective state has not been widely studied across different methods or systems. Additionally, the role of motivational factors in the persistence or decay of affective states remains underexplored. This study builds on two prior investigations into the exponential decay of epistemic emotions, expanding the analysis of affective chronometry by incorporating two detection methods based on student self-reports and trained observer labels in a game-based learning environment. We also examine the relationship between motivational measures and affective decay. Our findings indicate that boredom exhibits the slowest decay across both detection methods, while confusion is the least persistent. Furthermore, we found that higher situational interest and self-efficacy are associated with greater persistence in engaged concentration, as identified by both detection methods. This work provides novel insights into how motivational factors shape affective chronometry, contributing to a deeper understanding of the temporal dynamics of epistemic emotions. 

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3. Sensor-Free Predictive Models of Affect in an Online Learning Environment

   Harrison, A (January 2018, Proceedings of the Eleventh International Conference on Educational Data Mining)

   A significant amount of research has illustrated the impact of student emotional and affective state on learning outcomes. Just as human teachers and tutors often adapt instruction to accommodate changes in student affect, the ability for computer-based systems to similarly become affect-aware, detecting and personalizing instruction in response to student affective state, could significantly improve student learning. Personalized and affective interventions in tutoring systems can be realized through affect-aware learning technologies to deter students from practicing poor learning behaviors in response to negative affective states and to optimize the amount of learning that occurs over time. In this paper, we build off previous work in affect detection within intelligent tutoring systems (ITS) by applying two methodologies to develop sensor-free models of student affect with only data recorded from middle-school students interacting with an ITS. We develop models of four affective states to evaluate and determine significant predictors of affect. Namely, we develop a model which discerns students’ reported interest significantly better than majority class. 

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4. Says Who? How different ground truth measures of emotion impact student affective modeling.

   Zambrano, A; Ocumpaugh, J; Nasiar, N; Goslen, A; Zhang, J; Rowe, J; Esiason, J; Vandenberg, J; Hutt, S (January 2024, Proceedings of the 17th International Conference on Educational Data Mining.)

   Research into student affect detection has historically relied on ground truth measures of emotion that utilize one of three sources of data: (1) self-report data, (2) classroom observations, or (3) sen- sor data that is retrospectively labeled. Although a few studies have compared sensor- and observation-based approaches to student af- fective modeling, less work has explored the relationship between self-report and classroom observations. In this study, we use both recurring self-reports (SR) and classroom observation (BROMP) to measure student emotion during a study involving middle school students interacting with a game-based learning environment for microbiology education. We use supervised machine learning to develop two sets of affect detectors corresponding to SR and BROMP-based measures of student emotion, respectively. We compare the two sets of detectors in terms of their most relevant features, as well as correlations of their output with measures of student learning and interest. Results show that highly predictive features in the SR detectors are different from those selected for BROMP-based detectors. The associations with interest and moti- vation measures show that while SR detectors captured underlying motivations, the BROMP detectors seemed to capture more in-the- moment information about the student’s experience. Evidence sug- gests that there is benefit of using both sources of data to model different components of student affect. 

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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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