| The agency effect: The impact of student agency on learning, emotions, and problem-solving behaviors in a game-based learning environment M Taub, R Sawyer, A Smith, J Rowe, R Azevedo, J Lester Computers & Education 147, 103781, 2020 | 227 | 2020 |
| Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? M Taub, R Azevedo, F Bouchet, B Khosravifar Computers in Human Behavior 39, 356-367, 2014 | 214 | 2014 |
| Understanding and reasoning about real-time cognitive, affective, and metacognitive processes to foster self-regulation with advanced learning technologies R Azevedo, M Taub, NV Mudrick Handbook of self-regulation of learning and performance, 254-270, 2017 | 150 | 2017 |
| How are students’ emotions related to the accuracy of cognitive and metacognitive processes during learning with an intelligent tutoring system? M Taub, R Azevedo, R Rajendran, EB Cloude, G Biswas, MJ Price Learning and Instruction 72, 101200, 2021 | 143 | 2021 |
| Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment M Taub, R Azevedo, AE Bradbury, GC Millar, J Lester Learning and instruction 54, 93-103, 2018 | 129 | 2018 |
| Lessons learned and future directions of metatutor: Leveraging multichannel data to scaffold self-regulated learning with an intelligent tutoring system R Azevedo, F Bouchet, M Duffy, J Harley, M Taub, G Trevors, E Cloude, ... Frontiers in Psychology 13, 813632, 2022 | 122 | 2022 |
| How does prior knowledge influence eye fixations and sequences of cognitive and metacognitive SRL processes during learning with an intelligent tutoring system? M Taub, R Azevedo International Journal of Artificial Intelligence in Education 29, 1-28, 2019 | 93 | 2019 |
| Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island M Taub, NV Mudrick, R Azevedo, GC Millar, J Rowe, J Lester Computers in Human Behavior 76, 641-655, 2017 | 88 | 2017 |
| The effectiveness of pedagogical agents’ prompting and feedback in facilitating co-adapted learning with MetaTutor R Azevedo, RS Landis, R Feyzi-Behnagh, M Duffy, G Trevors, JM Harley, ... Intelligent Tutoring Systems: 11th International Conference, ITS 2012 …, 2012 | 88 | 2012 |
| Self-regulation in computer-assisted learning systems. R Azevedo, NV Mudrick, M Taub, AE Bradbury Cambridge University Press, 2019 | 84 | 2019 |
| Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies: a conceptual framework R Azevedo, GC Millar, M Taub, NV Mudrick, AE Bradbury, MJ Price Proceedings of the seventh international learning analytics & knowledge …, 2017 | 74 | 2017 |
| Are pedagogical agents’ external regulation effective in fostering learning with intelligent tutoring systems? R Azevedo, SA Martin, M Taub, NV Mudrick, GC Millar, JF Grafsgaard Intelligent Tutoring Systems: 13th International Conference, ITS 2016 …, 2016 | 73 | 2016 |
| Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning NV Mudrick, R Azevedo, M Taub Computers in Human Behavior 96, 223-234, 2019 | 72 | 2019 |
| Multiple negative emotions during learning with digital learning environments–Evidence on their detrimental effect on learning from two methodological approaches F Wortha, R Azevedo, M Taub, S Narciss Frontiers in psychology 10, 2678, 2019 | 71 | 2019 |
| The impact of contextualized emotions on self-regulated learning and scientific reasoning during learning with a game-based learning environment M Taub, R Sawyer, J Lester, R Azevedo International Journal of Artificial Intelligence in Education 30, 97-120, 2020 | 67 | 2020 |
| Let's set up some subgoals: Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance JM Harley, M Taub, R Azevedo, F Bouchet IEEE Transactions on Learning Technologies 11 (1), 54-66, 2017 | 67 | 2017 |
| Using multi-channel trace data to infer and foster self-regulated learning between humans and advanced learning technologies R Azevedo, M Taub, NV Mudrick, SA Martin, J Grafsgaard Handbook of self-regulation of learning and performance 2, 254-270, 2018 | 64 | 2018 |
| Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry Based on Levels of Efficiency and Emotions during Game-Based Learning. M Taub, R Azevedo Journal of Educational Data Mining 10 (3), 1-26, 2018 | 48 | 2018 |
| Emotion recognition with facial expressions and physiological signals B Zhong, Z Qin, S Yang, J Chen, N Mudrick, M Taub, R Azevedo, ... 2017 IEEE symposium series on computational intelligence (SSCI), 1-8, 2017 | 46 | 2017 |
| Self-regulation and reflection during game-based learning M Taub, R Azevedo, AE Bradbury, NV Mudrick Handbook of game-based learning 239, 2020 | 39 | 2020 |
