Publications

Book Chapters

Poitras, E. G., Harley, J. M., Compeasu, T., Kee, K., & Lajoie, S.P. (in press). Augmented reality in informal learning settings: Leveraging technology for the love of history. In R. Zheng & G. Michael (Eds.). Handbook of Research on Serious Games for Educational Applications (pp. 272-293). Hershey, PA: IGI Global.
http://www.igi-global.com

Harley, J. M. (2015). Measuring emotions: A survey of cutting-edge methodologies used in computer-based learning environment research. In S. Tettegah & M. Gartmeier (Eds.). Emotions, Technology, Design, and Learning (pp. 89-114). London, UK: Academic Press, Elsevier.
http://store.elsevier.com or http://sciencedirect.com

Azevedo, R., Harley, J., Trevors, G., Feyzi-Behnagh, R., Duffy, M., Bouchet, F., & Landis, R.S. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427-449). Amsterdam, The Netherlands: Springer-Verlag.
http://link.springer.com

Azevedo, R., Behnagh, R., Duffy, M., Harley, J., & Trevors, G. (2012). Metacognition and self-regulated learning with advanced learning technologies. In D. Jonassen & S. Land (Eds.), Theoretical foundations of learning environments (2nd ed.) (pp. 171-197). Mahwah, NJ: Erlbaum.
http://www.tandf.net


Journal Articles

Harley, J.M., Taub, M., Azevedo, R., & Bouchet, F. (in press / online first 2017). “Let’s set up some subgoals”: Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance. IEEE Transactions on Learning Technologies.
Forthcoming

Jarrell, A., Harley, J.M., Lajoie, S.P., & Naismith, L. (2017). Success, failure and emotions: Examining the relationship between performance feedback and emotions in diagnostic reasoning. Educational Technology Research and Development 65(5).
Forthcoming

Harley, J.M., Lajoie, S. P., Frasson, C., & Hall, N.C. (2017). Developing emotion-aware, advanced learning technologies: A taxonomy of approaches and features. International Journal of Artificial Intelligence in Education 27(2).
http://link.springer.com

Jarrell, A., Harley, J.M., & Lajoie, S.P. (2016). The link between achievement emotions, appraisals and task performance: Pedagogical considerations for emotions in CBLEs. Journal of Computers in Education, 3(3), 289-307.
http://link.springer.com

Harley, J. M., Carter, C.K., Papaionnou, N., Bouchet, F., Azevedo, R., Landis, R. L., & Karabachian, L.R. (2016). Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: Towards emotionally-adaptive agent-based learning environments. User Modelling and User-Adapted Interaction, 26, 177-219.
http://link.springer.com

Harley, J.M., Poitras, E. G., Jarrell, A., Duffy, M. C., & Lajoie, S. P. (2016). Comparing virtual and location-based augmented reality mobile learning: Emotions and learning outcomes. Educational Technology Research and Development, 64(3).
http://link.springer.com

Harley, J. M., Bouchet, F., Hussain, S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615-625.
http://www.sciencedirect.com

Harley, J.M., & Azevedo, R. (2014). Toward a feature-driven understanding of students’ emotions during interactions with agent-based learning environments: A selective review. International Journal of Games and Computer Mediated Simulation, 6(3), 17-34.
http://www.igi-global.com

Bouchet, F., Harley, J.M., Trevors, G., & Azevedo, R. (2013). Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5(1), 104-146.
http://www.educationaldatamining.org


Refereed Conference Proceedings

Bouchet, F., Harley, J.M., & Azevedo, R. (2016). Can adaptive pedagogical agents’ prompting strategies improve students’ learning and self-regulation? In A. Micarelli, J. Stamper, & K. Panourgia (Eds.) Lecture Notes in Computer Science: Vol. 9684. Intelligent Tutoring Systems (pp. 368-374). Switzerland: Springer.
http://link.springer.com

Harley, J.M., Rowe, J. P., Lester, J. C., & Frasson, C. (2015). Designing story-centric games for player emotion: A theoretical perspective. Proceedings of the eighth workshop on Intelligent Narrative Technologies (pp. 34-37). Palo Alto, California: AAAI Press.
http://aaai.org

Harley, J. M., Carter, C.K., Papaionnou, N., Bouchet, F., Landis, R. S., Azevedo, R., & Karabachian, L.R. (2015). Examining the predictive relationship between personality and emotion traits and learners’ agent-directed emotions. In C. Conati & N. Heffernan (Eds.), Lectures Notes in Artificial Intelligence: Vol. 9112. Artificial Intelligence in Education (pp. 145-154). Switzerland: Springer.
http://link.springer.com

Harley, J. M., Lajoie, S. P., Frasson, C., & Hall, N.C. (2015). An integrated emotion-aware framework for intelligent tutoring systems. In C. Conati & N. Heffernan (Eds.), Lectures Notes in Artificial Intelligence: Vol. 9112. Artificial Intelligence in Education (pp. 620-624). Switzerland: Springer.
http://link.springer.com

Jarrell, A., Harley, J.M., Lajoie, S.P., & Naismith, L. (2015). Examining the relationship between performance feedback and emotions in diagnostic reasoning: Toward a predictive framework for emotional support. In C. Conati & N. Heffernan (Eds.), Lectures Notes in Artificial Intelligence: Vol. 9112. Artificial Intelligence in Education (pp. 657-660). Switzerland: Springer.
http://link.springer

Benlamine, S., Bouslimi, S., Harley, J., Frasson, C. & Dufresne, A. (2015). Toward Brain-based Gaming: MeasuringEngagement During Gameplay. In S. Carliner, C. Fulford & N. Ostashewski (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2015 (pp. 864-869). Association for the Advancement of Computing in Education (AACE).
http://editlib.org

Harley, J. M., & Azevedo, R. (2014). Understanding students’ emotions during interactions with advanced agent-based learning environments: A selective review. In S. Trausan-Matu., K. Boyer., M. Crosby., K. Panourgia (Eds.), Lecture Notes in Computer Science: Vol. 8474. Intelligent Tutoring Systems (pp. 629-631). Berlin, Heidelberg: Springer-Verlag. http://link.springer.com

Jaques, N., Conati, C., Harley, J. M., & Azevedo, R. (2014). Predicting affect from gaze behavior data during interactions with an intelligent tutoring system. In S. Trausan-Matu., K. Boyer., M. Crosby., K. Panourgia (Eds.), Lecture Notes in Computer Science: Vol. 8474. Intelligent Tutoring Systems (pp. 629-631). Berlin, Heidelberg: Springer-Verlag.
http://link.springer.com

Bouchet, F., Harley, J. M., & Azevedo, R. (2013). The impact of different pedagogical agents’ adaptive self-regulated prompting strategies with MetaTutor. In C. H. Lane, K. Yacef, J. Mostow, P. Pavik (Eds.), Lecture Notes in Computer Science: Vol. 7926. Artificial Intelligence in Education (pp. 815-819). Berlin, Heidelberg: Springer-Verlag.
http://link.springer.com

Bondareva, D., Conati, C., Feyzi-Behnagh, R., Harley, J., Azevedo, R., & Bouchet, F. (2013). Inferring learning from gaze data during interaction with an environment to support self-regulated learning. In C. H. Lane, K. Yacef, J. Mostow, P. Pavik (Eds.), Lecture Notes in Computer Science: Vol. 7926. Artificial Intelligence in Education (pp. 229-238). Berlin, Heidelberg: Springer-Verlag.
http://link.springer.com

Harley, J. M., Bouchet, F., & Azevedo, R. (2013). Aligning and comparing data on learners’ emotions experienced with MetaTutor. In C. H. Lane, K. Yacef, J. Mostow, P. Pavik (Eds.), Lecture Notes in Computer Science: Vol. 7926. Artificial Intelligence in Education (pp. 61-70). Berlin, Heidelberg: Springer-Verlag.
http://link.springer.com

Khosravifar, B., Bouchet, F., Feyzi-Behnagh, R., Azevedo, R., & Harley, J. (2013). Using intelligent multi-agent systems to model and foster self-regulated learning: a theoretically-based approach using Markov decision processes. In L. Barolli, F. Xhafa, M. Takizawa, T. Enokido, H.H. Hsu (Eds.), Proceedings of the 27th IEEE international conference on Advanced Information Networking and Applications (413-420). Los Alamitos, CA: Conference Publishing Services, IEEE.
http://ieeexplore.ieee.org

Azevedo, R., Landis, R.S., Feyzi-Behnagh, R., Duffy, M., Trevors, G., Harley, J., Bouchet, F., Burlison, J., Taub, M., Pacampara, N., Yeasin, M., Rahman, A.K.M.M., Tanveer, M.I., & Hossain, G. (2012). The effectiveness of pedagogical agents’ prompting and feedback in facilitating co-adapted learning with MetaTutor. In S. A. Cerri, W. J. Clancey, G. Papadourakis, and K. Panourgia (Eds.), Lecture Notes in Computer Science: Vol: 7315. Intelligent Tutoring Systems (pp. 212-221). Berlin, Heidelberg: Springer-Verlag.
http://link.springer.com

Harley, J.M., Bouchet, F., & Azevedo, R. (2012). Measuring learners’ co-occurring emotional responses during their interaction with a pedagogical agent in MetaTutor. In S. A. Cerri, W. J. Clancey, G. Papadourakis, and K. Panourgia (Eds.), Lecture Notes in Computer Science: Vol. 7315. Intelligent Tutoring Systems (pp. 40-45). Berlin, Heidelberg: Springer-Verlag.
http://link.springer.com

Harley, J., Bouchet, F., Azevedo, R. (2011). Examining learner’s emotional responses to pedagogical agents’ tutoring strategies. In H. Högni Vilhjálmsson, S. Koop, S. Marsella, and K. R. Thórisson (Eds.), Lecture Notes in Artificial Intelligence: Subseries of Lecture Notes in Computer Science. Vol. 6895. Intelligent Virtual Agents (pp. 449-450). Berlin, Heidelberg: Springer-Verlag
http://link.springer.com

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