@article{mcquiggan_mott_lester_2008, title={Modeling self-efficacy in intelligent tutoring systems: An inductive approach}, volume={18}, ISSN={["1573-1391"]}, DOI={10.1007/s11257-007-9040-y}, number={1-2}, journal={USER MODELING AND USER-ADAPTED INTERACTION}, author={McQuiggan, Scott W. and Mott, Bradford W. and Lester, James C.}, year={2008}, month={Feb}, pages={81–123} }
 @article{mcquiggan_lester_2007, title={Modeling and evaluating empathy in embodied companion agents}, volume={65}, ISSN={["1095-9300"]}, DOI={10.1016/j.ijhcs.2006.11.015}, abstractNote={Affective reasoning plays an increasingly important role in cognitive accounts of social interaction. Humans continuously assess one another's situational context, modify their own affective state accordingly, and then respond to these outcomes by expressing empathetic behaviors. Synthetic agents serving as companions should respond similarly. However, empathetic reasoning is riddled with the complexities stemming from the myriad factors bearing upon situational assessment. A key challenge posed by affective reasoning in synthetic agents is devising empirically informed models of empathy that accurately respond in social situations. This paper presents Care, a data-driven affective architecture and methodology for learning models of empathy by observing human–human social interactions. First, in Care training sessions, one trainer directs synthetic agents to perform a sequence of tasks while another trainer manipulates companion agents’ affective states to produce empathetic behaviors (spoken language, gesture, and posture). Care tracks situational data including locational, intentional, and temporal information to induce a model of empathy. At runtime, Care uses the model of empathy to drive situation-appropriate empathetic behaviors. Care has been used in a virtual environment testbed. Two complementary studies investigating the predictive accuracy and perceived accuracy of Care-induced models of empathy suggest that the Care paradigm can provide the basis for effective empathetic behavior control in embodied companion agents.}, number={4}, journal={INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES}, author={McQuiggan, Scott W. and Lester, James C.}, year={2007}, month={Apr}, pages={348–360} }
 @article{mcquiggan_lester_2006, title={Diagnosing self-efficacy in intelligent tutoring systems: An empirical study}, DOI={10.1007/11774303_56}, abstractNote={Self-efficacy is an individual’s belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a students’ level of self-efficacy. This paper investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. In an empirical study, two families of self-efficacy models were induced: a static model, learned solely from pre-test (non-intrusively collected) data, and a dynamic model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. The resulting static model is able to predict students’ real-time levels of self-efficacy with reasonable accuracy, while the physiologically informed dynamic model is even more accurate.}, number={4053}, journal={Lecture Notes in Computer Science}, author={McQuiggan, S. W. and Lester, J. C.}, year={2006}, pages={565–574} }
 @inproceedings{robison_mcquiggan_lester, title={Modeling task-based vs. affect-based feedback behavior in pedagogical agents: An inductive approach}, volume={200}, booktitle={Artificial intelligence in education - building learnning systems that care: from knowledge representation to affective modelling }, author={Robison, J. L. and McQuiggan, S. W. and Lester, J. C.}, pages={25–32} }