MetaTutorrevisión sistemática de una herramienta para la evaluación e intervención en autorregulación del aprendizaje

Referencias bibliográficas

• *Publicaciones que forman parte de la revisión sistemática.
• Azevedo, R. (2005). Computers as metacognitive tools for enhancing learning. Educational Psychologist, 40(4), 193-197. doi:10.1207/ s15326985ep4004_1
• Azevedo, R., Behnagh, R., Duffy, M., Harley, J., y Trevors, G. (2012). Metacognition and self-regulated learning in studentcentered leaning environments. En D. Jonassen, y S. Land (Eds.) Theoretical foundations of student-centered learning environments (pp. 171-197). Florence, USA: Routledge, Taylor & Francis Group.
• Azevedo, R., y Chauncey, A. (2011). Integrating cognitive, metacognitive, and affective regulatory processes with MetaTutor. En R.A. Calvo y S.K. D´Mello (Eds.) New perspectives on affect and learning technologies (pp. 141-154). New York, USA: Springer. doi:10.1007/978- 1-4419-9625-1
• Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., y Landis, R. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional selfregulatory processes during learning with multi-agent systems. En R. Azevedo y V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427-449). New York, USA: Springer.
• *Azevedo, R., Johnson, A., Chauncey, A., y Burkett, C. (2010A). Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. En M. S. Khine e I. M. Saleh (Eds.), New science of learning (pp. 225-247). New York: Springer. doi:10.1007/978- 1-4419-5716-0
• *Azevedo, R., Martin, S. A., Taub, M., Mudrick, N. V., Millar, G. C., y Grafsgaard, J. F. (2016). Are pedagogical agents’ external regulation effective in fostering learning with intelligent tutoring systems? En A. Micarelli, J. Stamper y K. Panourgia (Eds.) International Conference on Intelligent Tutoring Systems (pp. 197- 207). Zagreb, Croacia: Springer, Cham. doi:10.1007/978-3-319-39583-8_19
• Azevedo, R., Moos, D. C., Johnson, A. M., y Chauncey, A. D. (2010B). Measuring cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges. Educational psychologist, 45(4), 210-223.
• Azevedo, R., y Strain, A. C. (2011). Integrating cognitive, metacognitive, and affective regulatory processes with
• MetaTutor. En R.A. Calvo y S.K. D´Mello (Eds.) New perspectives on affect and learning technologies (pp. 141-154). New York, USA: Springer.
• Azevedo, R., Witherspoon, A. M., Chauncey, A., Burkett, C., y Fike, A. (2009). MetaTutor: A MetaCognitive Tool for Enhancing Self-Regulated Learning. En R. Pirrone, R. Azevedo y G. Biswas (Eds.) AAAI Fall Symposium: Cognitive and Metacognitive Educational Systems. Recuperado de www.aaai. org/ocs/index.php/FSS/FSS09/paper/ viewFile/995/1253
• Azevedo, R., Witherspoon, A. M., Graesser, A. C., McNamara, D. S., Chauncey, A., Siler, E., Cai, Z.Q., Rus, V., y Lintean, M. C. (2009). MetaTutor: Analyzing Self-Regulated Learning in a Tutoring System for Biology. En D. Dicheva y D. Dochey (Eds.), Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 635- 637). Brighton, UK: Sirma Group Corp. doi:10.3233/978-1-60750-028-5-635
• Beheshitha, S. S., GaÅ¡ević, D., y Hatala, M. (2015). A process mining approach to linking the study of aptitude and event facets of self-regulated learning. En J. Baron, G. Lynch y O. Maziarz (Eds.), Proceedings of the fifth international conference on learning analytics and knowledge (pp. 265-269). New York, USA: Association for Computing Machinery.
• Bogarin, A., Cerezo, R., y Romero, C. (2018). Discovering learning processes using inductive miner: a case study with Learning Management Systems (LMSs). Psicothema, 30(3), 322-329. doi:10.7334/psicothema2018.116
• *Bouchet, F., Harley, J. M., y Azevedo, R. (2016). Can Adaptive Pedagogical Agents’ Prompting Strategies Improve Students’ Learning and Self-Regulation? En A. Micarelli, J. Stamper y K. Panourgia (Eds.). 13th International Conference on Intelligent Tutoring Systems (pp. 368- 374). Zagreb, Croacia: Springer, Cham. doi:10.1007/978-3-319-39583-8_43
• Bulu, S. T., y Pedersen, S. (2012). Supporting problem-solving performance in a hypermedia learning environment: The role of students’ prior knowledge and metacognitive skills. Computers in Human Behavior, 28(4), 1162-1169. doi:10.1016/j.chb.2012.01.026
• Cerezo, R., Esteban, M., Rodríguez, L., Bernardo, A., Sánchez, M., Amiero, N., y Pereles, A (2017a). Learning Difficulties in Computer-Based Learning Environments. En J. A. González-Pienda, A. Bernardo, J. C. Núñez, y C. Rodríguez (Eds.), Factors Affecting academic performance (pp. 157-171). New York, USA: Nova Science.
• Cerezo, R., Esteban, M., Sánchez-Santillán, M., y Núñez, J. C. (2017b). Procrastinating behavior in computer-based learning environments to predict performance: A case study in Moodle. Frontiers in psychology, 8, 1403. doi:10.3389/ fpsyg.2017.01403
• *Cloude, E. B., Taub, M., y Azevedo, R. (2018). Investigating the Role of Goal Orientation: Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring Systems. En R. Nkambou, R. Azevedo, y J. Vassileva (Eds.) International Conference on Intelligent Tutoring Systems (pp. 44-53). Nueva York, USA: Springer, Cham. doi:10.1007/978-3- 319-91464-0_5
• Dahlstrom, E., Brooks, D. C., y Bichsel, J. (2014). The current ecosystem of learning management systems in higher education: Student, faculty, and IT perspectives (Research report). Recuperado de www.digitallernen.ch/ wp-content/uploads/2016/02/ers1414. pdf
• Dinsmore, D. L., Alexander, P. A., y Loughlin, S. M. (2008). Focusing the conceptual lens on metacognition, self-regulation, and self-regulated learning. Educational Psychology Review, 20(4), 391-409. doi:10.1007/s10648-008-9083-6
• Dodge, A. M. (2013). From Research to Practice: Understanding Self-Regulation. Newsletter Talk to Teachers. Education Matters, 1(1), 4-6. Recuperado de
• www.punyamishra.com/wp-content/ uploads/2014/02/Dodge-selfregulation.pdf
• *Duffy, M. C., y Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338-348. doi:10.1016/j.chb.2015.05.041
• Elliot, A. J., y McGregor, H. A. (2001). A 2x2 achievement goal framework. Journal of Personality and Social Psychology, 80, 501-519. doi:10.1037/0022- 3514.80.3.501
• *Graesser, A., y McNamara, D. (2010). Self-regulated learning in learning environments with pedagogical agents that interact in natural language. Educational Psychologist, 45(4), 234- 244. doi:10.1080/00461520.2010.51 5933
• Gross, J. J., y John, O. P. (2003). Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348. doi:10.1037/0022- 3514.85.2.348
• *Harley, J. M., Bouchet, F., Hussain, M. S., Azevedo, R., y Calvo, R. (2015a). A multicomponential 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., Carter, C. C., Papaionnou, N., Bouchet, F., Landis, R. S., Azevedo, R., y Karabachian, L. (2015b). Examining the predictive relationship between personality and emotion traits and learners’ agent-direct emotions. En C. Conati, N. Heffernan, A. Mitrovic y M. F. Verdejo (Eds.) International Conference on Artificial Intelligence in Education (pp. 145-154). Nueva York, USA: Springer, Cham. doi:10.1007/978-3-319-19773- 9_15
• *Harley, J. M., Carter, C. K., Papaionnou, N., Bouchet, F., Landis, R. S., Azevedo,
• R., y Karabachian, L. (2016). Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agentbased learning environments. User Modeling and User-Adapted Interaction, 26(2-3), 177-219. doi:10.1007/ s11257-016-9169-7
• *Harley, J. M., Taub, M., Azevedo, R., y Bouchet, F. (2017). “Let's Set Up Some Subgoals”: Understanding HumanPedagogical 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
• *Jaques, N., Conati, C., Harley, J. M., y Azevedo, R. (2014). Predicting affect from gaze data during interaction with an intelligent tutoring system. En S. Trausan-Matu, K. E. Boyer, M. Crosby y K. Panourgia (Eds.), International Conference on Intelligent Tutoring Systems (pp. 29-38). Nueva York, USA: Springer, Cham. doi:10.1007/978-3- 319-07221-0_4
• Järvelä, S., y Hadwin, A. F. (2013). New frontiers: Regulating learning in CSCL. Educational Psychologist, 48(1), 25-39. doi:10.1080/00461520.2012.748006
• Lajoie, S. P. (2008). Metacognition, selfregulation, and self-regulated learning: A rose by any other name? Educational Psychology Review, 20(4), 469-475. doi:10.1007/s10648-008-9088-1
• *Lallé, S., Conati, C., y Azevedo, R. (2018). Prediction of student achievement goals and emotion valence during interaction with pedagogical agents. En E. Andre y S. Koenig (Eds.), Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (pp. 1222-1231). International Foundation for Autonomous Agents and Multiagent Systems.
• *Lallé, S., Mudrick, N. V., Taub, M., Grafsgaard, J. F., Conati, C., y Azevedo, R. (2016). The impact of individual
• differences on affective reactions to pedagogical agents scaffolding. En J. Kolkmeier, J. Vroon y D. Heylen (Eds.), International Conference on Intelligent Virtual Agents (pp. 269-282). Verlag: Springer International Publishing. doi:10.1007/978-3-319-47665-0_24
• *Lallé S., Taub, M., Mudrick, N. V., Conati, C., y Azevedo, R. (2017). The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor. En E. André, R. Baker, X. Hu, M. Rodrigo y B. Du Boulay (Eds.), Artificial Intelligence in Education. Lecture Notes in Computer Science (pp 149-161). Nueva York, USA: Springer, Cham. doi:10.1007/978-3-319-61425- 0_13
• Link, T. M., y Marz, R. (2006). Computer literacy and attitudes towards e-learning among first year medical students. BMC Medical Education, 6(1), 34. doi:10.1186/1472-6920-6-34
• *Lintean, M., Rus, V., y Azevedo, R. (2012). Automatic detection of student mental models based on natural language student input during metacognitive skill training. International Journal of Artificial Intelligence in Education, 21(3), 169- 190. doi:10.3233/JAI-2012-022
• Martín-Albo, J., Núñez, J. L., Navarro, J. G., y Grijalvo, F. (2007). The Rosenberg SelfEsteem Scale: translation and validation in university students. The Spanish Journal of Psychology, 10(2), 458-467.
• *Martin, S. A., Azevedo, R., Taub, M., Mudrick, N. V., Millar, G. C., y Grafsgaard, J. F. (2016). Are There Benefits of Using Multiple Pedagogical Agents to Support and Foster Self-Regulated Learning in an Intelligent Tutoring System? En A. Micarelli, J. Stamper y K. Panourgia (Eds.), International Conference on Intelligent Tutoring Systems (pp. 273- 279). Nueva York, USA: Springer, Cham. doi:10.1007/978-3-319-39583-8_29
• Panadero, E., Klug, J., y Järvelä, S. (2016). Third wave of measurement in the self-regulated learning field: when measurement and intervention come
• hand in hand. Scandinavian Journal of Educational Research, 60(6), 723-735. doi:10.1080/00313831.2015.106643 6
• Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., y Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36-48. doi:10.1016/j.cedpsych.2010.10.002
• *Price, M. J., Mudrick, N. V., Taub, M., y Azevedo, R. (2018). The Role of Negative Emotions and Emotion Regulation on Self-Regulated Learning with MetaTutor. En R. Nkambou, R. Azevedo, y J. Vassileva (Eds.), International Conference on Intelligent Tutoring Systems (pp. 170- 179). Springer, Cham.
• Rosenberg, M. (1979). Conceiving the Self. New York, USA: Basic Books. doi:10.1007/978-3-319-91464-0_17
• Rosenberg, M. (1979). Conceiving the self. New York: Basic Books.
• *Sinclair, J., Jang, E. E., Azevedo, R., Lau, C., Taub, M., y Mudrick, N. V. (2018). Changes in Emotion and Their Relationship with Learning Gains in the Context of MetaTutor. En R. Nkambou, R. Azevedo, y J. Vassileva (Eds.), International Conference on Intelligent Tutoring Systems (pp. 202-211). Springer, Cham. doi:10.1007/978-3-319-91464-0_20
• Stahl, E., y Bromme, R. (2007). The CAEB: An instrument for measuring connotative aspects of epistemological beliefs. Learning and Instruction, 17(6), 773-785. doi:10.1016/j. learninstruc.2007.09.016
• *Taub, M., y Azevedo, R. (2016). Using eyetracking to determine the impact of prior knowledge on self-regulated learning with an adaptive hypermedia-learning environment. En A. Micarelli, J. Stamper y K. Panourgia (Eds.), 13th International Conference on Intelligent Tutoring Systems (pp. 37-47). Zagreb, Croacia: Springer, Cham. doi:10.1007/978-3- 319-39583-8_4
• *Taub, M., y Azevedo, R. (2019). How
• Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System? International Journal of Artificial Intelligence in Education, 29(1), 1-28. doi:10.1007/s40593-018-0165-4
• *Taub, M., Azevedo, R., Bouchet, F., y Khosravifar, B. (2014). Can the use of cognitive and metacognitive selfregulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? Computers in Human Behavior, 39, 356- 367. doi:10.1016/j.chb.2014.07.018
• *Taub, M., Mudrick, N. V., Rajendran, R., Dong, Y., Biswas, G., y Azevedo, R. (2018). How Are Students’ Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSs? En R. Nkambou, R. Azevedo, y J. Vassileva (Eds.), International Conference on Intelligent Tutoring Systems (pp. 233-242). Springer, Cham. doi:10.1007/978-3-319-91464-0_23
• *Trevors, G., Duffy, M., y Azevedo, R. (2014). Note-taking within MetaTutor: interactions between an intelligent tutoring system and prior knowledge on note-taking and learning. Educational Technology Research and Development, 62(5), 507-528. doi:10.1007/s11423- 014-9343-8
• Trevors, G., Feyzi-Behnagh, R., Azevedo, R., y Bouchet, F. (2016). Selfregulated learning processes vary as a function of epistemic beliefs and contexts: Mixed method evidence from eye tracking and concurrent and retrospective reports. Learning and Instruction, 42, 31-46. doi:10.1016/j. learninstruc.2015.11.003
• Winne, P. H. (2017). Cognition and metacognition within self-regulated learning. En D.H. Schunk y J.A. Green (Eds.) Handbook of self-regulation of learning and performance (pp. 52-64). Routledge.
• Winne, P. H., y Hadwin, A. F. (2013). nStudy: Tracing and supporting self-regulated
• learning in the internet. En R. Azevedo y V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies (pp. 293-307). Nueva York: Springuer. doi:10.1007/978-1-4419-5546-3_20
• Witherspoon, A., Azevedo, R., y Cai, Z. (2009). Learners' exploratory behavior within MetaTutor. En V. Dimitrova, R. Mizoguchi, B. du Boulay y A. Graesser (Eds.) Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling (pp. 644-646). Amsterdam, Holanda: IOS Press. doi:10.3233/978- 1-60750-028-5-644
• Witherspoon, A. M., Azevedo, R., y D’Mello, S. (2008). The dynamics of self-regulatory processes within self-and externally regulated learning episodes during complex science learning with hypermedia. En B. P. Woolf, E. Aïmeur, R. Nkambou y S. Lajoie (Eds.), 9th International Conference on Intelligent Tutoring Systems (pp. 260-269). Berlin, Alemania: Springer. doi:10.1007/978- 3-540-69132-7_30
• *Wortha, F., Azevedo, R., Taub, M., y Narciss, S. (2019). Multiple negative emotions during learning with digital learning environments–Evidence on their detrimental effect on learning from two methodological approaches. Frontiers in Psychology, 10, 2678. doi:10.3389/ fpsyg.2019.02678
• Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American educational research journal, 45(1), 166-183. doi:10.3102/0002831207312909