Publications

 

Selected publications below. See Google Scholar.

(+= supervisee; underlined = student author)

 

Harley, J.M., Lou, N.M+., Liu, Y+., Cutumisu, M., Daniels, M., Leighton, J. P., & Nadon, L. (in press). University students’ negative emotions in a computer-based exam: The roles of trait test-emotion, prior test-taking methods, and gender. Assessment and Evaluation in Higher Education. DOI: 10.1080/02602938.2020.1836123

Harley, J. M., Lajoie, S.P., Haldane, C+., McLaughlin, B., & Poitras, E.G. (2020). Beyond Historical Books, Names and Dates: Leveraging Augmented Reality to Promote Knowledge, Reasoning and Emotional Engagement. In Geroimenko, V. (Ed.). Augmented Reality in Education (pp. 199-216). Springer Nature, Switzerland. ISBN: 978-3-030-42155-7

Harley, J.M., Liu, Y+., Ahn, B.T+., Lajoie, S.P., & Grace, A.P. (in press). Examining physiological and self-report indicators of empathy during learners’ interaction with a queer history app, British Journal of Educational Technology. DOI: DOI: 10.1111/bjet.13019

Ahn, B.T+., & Harley, J.M (in press). Facial expressions when learning with a queer history app: Application of the control value theory of achievement emotions, British Journal of Educational Technology. DOI: 10.1111/bjet.12989

Harley, J.M., Lajoie, S.P., Tressel, T., & Jarrell, A. (in press). Fostering positive emotions and history learning with location-based augmented reality and tour-guide prompts. Learning & Instruction. DOI: 10.1016/j.learninstruc.2018.09.001

Harley, J.M., Pekrun, R., Taxer, J.L., & Gross, J.J. (2019). Emotion regulation in achievement situations: An integrated model. Educational Psychologist, 54(2), 106-126. DOI: 10.1080/00461520.2019.1587297

Harley, J.M., Jarrell, A., & Lajoie, S.P. (2019). Emotion regulation tendencies, achievement emotions, and physiological arousal in a medical diagnostic reasoning simulation. Instructional Science, 47(2), 151-180. DOI: 10.1007/s11251-018-09480-z

Poitras, E. G., Harley, J.M., & Liu, Y+. (2019). Achievement emotions with location-based mobile augmented reality: an examination of discourse processes in simulated guided walking tours. British Journal of Educational Technology 50(6), 3345-3360. DOI:10.1111/bjet.12738

Harley, J.M., Liu, Y+., Ahn, T.B+., Lajoie, S.P., Grace, A.P., Haldane, C+., Whittaker, A., & McLaughlin, B. (2019). I’ve got this: Fostering topic and technology-related emotional engagement and queer history knowledge with a mobile app. Contemporary Educational Psychology, 59, 1-18. DOI: 10.1016/j.cedpsych.2019.101790

Harley, J.M., & Beaudin, D. (2019). Fake News and Dinosaurs: The Hunt for Truth Using Media Literacy. Victoria, Canada: Friesen Press.

Harley, J. M., Taub, M., Azevedo, R., & Bouchet, F. (2018). “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, 11(1), 54-66. DOI: 10.1109/TLT.2017.2756629

Bouchet, F., Harley, J.M., & Azevedo, R. (2018). Evaluating adaptive pedagogical agents’ prompting strategies effect on students’ emotions? In J. Vassileva, & R. Azevedo (Eds.) Lecture Notes in Computer Science: Vol. 10858. Intelligent Tutoring Systems (pp. 33-43). Switzerland: Springer.

Harley, J.M., Benlamine, S., Chaouachi., M., Frasson, C., Liu, Y., & Dufresne, A. (2018). Examining how typical gaming behavior influences emotions and achievement during gameplay. In J. Vassileva, & R. Azevedo (Eds.) Lecture Notes in Computer Science: Vol. 10858. Intelligent Tutoring Systems (pp. 438-441). Switzerland: Springer.

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), 1263–1284. DOI: 10.1007/s11423-017-9521-6

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), 268-297. DOI: 10.1007/s40593-016-0126-8

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. DOI: 10.1007/s40692-016-0064-3.

Harley, J. M., Carter, C.K., Papaionnou, N., Bouchet, F., Azevedo, R., Landis, R. L., & Karabachian, L. (2016). Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: Towards emotionally-adaptive agent-based learning environments. User Modeling and User-Adapted Interaction, 26, 177-219. DOI: 10.1007/s11257-016-9169-7.

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), 359-388. DOI: 10.1007/s11423-015-9420-7.

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.

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. DOI: 10.1016/j.chb.2015.02.013.

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.

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. 29-38). Switzerland: Springer.

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.

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.

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 Artificial Intelligence: Vol. 7926. Artificial Intelligence in Education (pp. 815-819). Berlin, Heidelberg: Springer-Verlag.

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 Artificial Intelligence: Vol. 7926. Artificial Intelligence in Education (pp. 229-238). Berlin, Heidelberg: Springer-Verlag.

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 Artificial Intelligence: Vol. 7926. Artificial Intelligence in Education (pp. 61-70). Berlin, Heidelberg: Springer-Verlag.

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.

 

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