UNDERSTANDING AND REASONING ABOUT REAL-TIME COGNITIVE, AFFECTIVE, AND METACOGNITIVE PROCESSES TO FOSTER SELF-REGULATION WITH ADVANCED LEARNING TECHNOLOGIES

Handbook of Self-Regulation of Learning and Performance, Second Edition

Volumn , Issue , 2017, Pages 254-270

  Azevedo, Roger ^a   Taub, Michelle ^a   Mudrick, Nicholas V ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85141491790     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9781315697048-17     Document Type: Chapter

References (61)

1. Aleven, V. (2013). Help seeking and intelligent tutoring systems: Theoretical perspectives and a step towards theoretical integration. In R. Azevedo and V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 311-336). Amsterdam, The Netherlands: Springer.
2. Aleven, V., and Koedinger, K. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26, 147-179.
3. Anderson, J. R., Corbett, A. T., Koedinger, K., and Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of Learning Sciences, 167-207.
4. Anderson, J. R., and Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence Erlbaum.
5. Anderson, J. R., and Schunn, C. D. (2000). Implications of the ACT-R learning theory: No magic bullets. In R. Glaser (Ed.), Advances in instructional psychology (Vol. 5). Mahwah, NJ: Erlbaum.
6. Arroyo, I., Woolf, B. P., Burelson, W., Muldner, K., Rai, D., and Tai, M. (2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. International Journal of Artificial Intelligence in Education, 24, 387-426.
7. Attention Tool 6.0 [Computer software] (2016). Boston, MA: iMotions Inc.
8. Azevedo, A., Millar, G. C., Taub, M., Mudrick, N. V., Bradbury, A. E., and Price, M. J. (2017, March). Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies: A conceptual framework. Paper to be presented at the 7th International Conference on Learning Analytics and Knowledge Conference, Vancouver, BC, Canada.
9. Azevedo, R. (2014). Multimedia learning of metacognitive strategies. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 647-673). New York: Cambridge University Press.
10. Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50, 84-94.
11. Azevedo, R., and Aleven, V. (Eds.). (2013a). International handbook of metacognition and learning technologies. Amsterdam, The Netherlands: Springer.
12. Azevedo, R., and Aleven, V. (2013b). Metacognition and learning technologies: An overview of the current interdisciplinary research. In R. Azevedo and V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 1-16). Amsterdam, The Netherlands: Springer.
13. Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., and 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 and V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427-449). Amsterdam, The Netherlands: Springer.
14. Azevedo, R., Moos, D., Johnson, A., and Chauncey, A. (2010). Measuring cognitive and metacognitive regulatory processes used during hypermedia learning: Issues and challenges. Educational Psychologist, 45, 210-223.
15. Azevedo, R., Taub, M., and Mudrick, N. (2015). Technologies supporting self-regulated learning. In M. Spector, C. Kim, T. Johnson, W. Savenye, D. Ifenthaler, and G. Del Rio (Eds.), The SAGE Encyclopedia of educational technology (pp. 731-734). Thousand Oaks, CA: SAGE.
16. Azevedo, R., Taub, M., Mudrick, N., Farnsworth, J., and Martin, S. (2016). Using research methods to investigate emotions in computer-based learning environments. In P. Schutz and M. Zembylas (Eds.), Methodological advances in research on emotion and education (pp. 231-244). Amsterdam, The Netherlands: Springer.
17. Beaudoin, L., and Winne, P. H. (2009, June). nStudy: An internet tool to support learning, collaboration and researching learning strategies. Presented at the Canadian e-Learning Conference, Vancouver, Canada.
18. Bernacki, M. L. (2018/this volume). Examining the cyclical, loosely sequenced, and contingent features of selfregulated learning: Trace data and their analysis. In D. H. Schunk and J. A. Greene (Eds.), Handbook of selfregulation of learning and performance (2nd ed.). New York: Routledge.
19. Bernacki, M. L., Nokes-Malach, T. J., and Aleven, V. (2013). Fine-grained assessment of motivation over long periods of learning with an intelligent tutoring system: Methodology, advantages, and preliminary results. In R. Azevedo and V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 629-644). Amsterdam, The Netherlands: Springer.
20. Biswas, G., Baker, R. S., and Paquette, L. (2018/this volume). Data mining methods for assessing self-regulated learning. In D. H. Schunk and J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed.). New York: Routledge.
21. Biswas, G., Segedy, J. R., and Kinnebrew, J. S. (2013). Smart open-ended learning environments that support learners’ cognitive and metacognitive processes. In A. Holzinger and G. Pasi (Eds.), Human-computer interaction and knowledge discovery in complex, unstructured, big data: Lecture notes in computer science (Vol. 7947, pp. 303-310). Berlin, Germany: Springer.
22. Bondareva, D., Conati, C., Feyzi-Behnagh, R., Harley, J. M., and Azevedo, R. (2013). Inferring learning from gaze data during interaction with an environment to support self-regulated learning. In H. C. Lane, K. Yacef, J. Mostow, and P. Pavlik (Eds.), Proceedings of the international conference on artificial intelligence in education: Lecture notes in computer science (Vol. 7926, pp. 229-238). Berlin, Germany: Springer.
23. D’Mello, S. K., and Graesser, A. C. (2012a). AutoTutor and affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems, 2, 23-39.
24. D’Mello, S. K., and Graesser, A. C. (2012b). Dynamics of affective states during complex learning. Learning and Instruction, 22, 145-157.
25. D’Mello, S. K., and Graesser, A. C. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In R. Calvo, S. K. D’Mello, J. Gratch, and A. Kappas, A. (Eds.), The Oxford handbook of affective computing (pp. 419-434). New York: Oxford University Press.
26. D’Mello, S. K., Olney, A., Williams, C., and Hays, P. (2012). Gaze Tutor: A gaze-reactive intelligent tutoring system. International Journal of Human-Computer Studies, 70, 377-398.
27. Du, S., Tao, T., and Martinez, A. M. (2014). Compound facial expressions of emotion. In D. J. Heeger (Ed.), Proceedings of the National Academy of Sciences of the United States of America PNAS (pp. E1454-E1462). Redwood City, CA: HighWire Press.
28. Ekman, P. (1973). Darwin and facial expression: A century of research in review. New York: Academic Press.
29. Empatica E4 [Apparatus and software] (2015). Boston, MA: Empatica, Inc.
30. Greene, J. A., and Azevedo, R. (2009). A macro-level analysis of SRL processes and their relations to the acquisition of sophisticated mental models. Contemporary Educational Psychology, 34, 18-29.
31. Greene, J. A., and Azevedo, R. (2010). The measurement of learners’ self-regulated cognitive and metacognitive processes while using computer-based learning environments. Educational Psychologist, 45, 203-209.
32. Greene, J. A., Deekens, V. M., Copeland, D. Z., and Yu, S. (2018/this volume). Capturing and modeling self-regulated learning using think-aloud protocols. In D. H. Schunk and J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed.). New York: Routledge.
33. Harley, J. M., Bouchet, F., Hussain, S., Azevedo, R., and 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.
34. Harley, J. M., Carter, C. K., Papaionnou, N., Bouchet, F., Azevedo, R., Landis, R. L., and 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.
35. Heidig, S., and Clarebout, G. (2011). Do pedagogical agents make a difference to student motivation and learning? Educational Research Review, 6, 27-54.
36. Jackson, G. T., Boonthum-Denecke, C., and McNamara, D. (2015). Natural language processing and game-based practice in iSTART. Journal of Interactive Learning Research, 26, 189-208.
37. Jaques, N., Conati, C., Harley, J., and Azevedo, R. (2014). Predicting affect from gaze data during interaction with an intelligent tutoring system. In S. Trausan-Matu, K. E. Boyer, M. Crosby, and K. Panourgia (Eds.), Proceedings of the 12th international conference on Intelligent Tutoring Systems (ITS 2014) (pp. 29-38). Amsterdam, The Netherlands: Springer.
38. Johnson, A. M., Azevedo, R., and D’Mello, S. K. (2011). The temporal and dynamic nature of self-regulatory processes during independent and externally assisted hypermedia learning. Cognition and Instruction, 29, 471-504.
39. Lester, J., Mott, B., Robison, J., Rowe, J., and Shores, L. (2013). Supporting self-regulated science learning in narrative-centered learning environments. In R. Azevedo and V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 471-483). Amsterdam, The Netherlands: Springer.
40. Mendicino, M., Razzaq, L., and Heffernan, N. T. (2009). Improving learning from homework using intelligent tutoring systems. Journal of Research on Technology in Education (JRTE), 41, 331-346.
41. Molenaar, I., and Järvelä, S. (2014). Sequential and temporal characteristics of self and socially regulated learning. Metacognition and Learning, 9, 75-85.
42. Moos, D. C. (2018/this volume). Emerging classroom technology: Using self-regulation principles as a guide for effective implementation. In D. H. Schunk and J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed.). New York: Routledge.
43. Olney, A., D’Mello, S., Person, N., Cade, W., Hays, P., Williams, C.,… Graesser, A. (2012). Guru: A computer tutor that models expert human tutors. In S. A. Cerri, W. J. Clancey, G. Papadourakis, and K. K. Panourgia (Eds.), Proceedings of the 11th international conference on Intelligent Tutoring Systems (pp. 256-261). Amsterdam, The Netherlands: Springer.
44. Poitras, E. G., and Lajoie, S. P. (2018/this volume). Using technology-rich environments to foster self-regulated learning in the social sciences. In D. H. Schunk and J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed.). New York: Routledge.
45. Reimann, P., and Bannert, M. (2018/this volume). Self-regulation of learning and performance in computersupported collaborative learning environments. In D. H. Schunk and J. A. Greene (Eds.), Handbook of selfregulation of learning and performance (2nd ed.). New York: Routledge.
46. Rowe, J., Shores, L., Mott, B., and Lester, J. (2011). Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education, 21, 115-133.
47. Scherer, K. (2009). Emotions are emergent processes: They require a dynamic computational architecture. Philosophical Transactions of the Royal Society, 364, 3459-3474.
48. Singh, R., Saleem, M., Pradhan, P., Heffernan, C., Heffernan, N., Razzaq, L.,… Mulchay, C. (2011). Feedback during web-based homework: The role of hints. In G. Biswas et al. (Eds.), Proceedings of the artificial intelligence in education conference 2011 (pp. 328-336). Berlin, Germany: Springer.
49. SMI Experiment Center 3.4.165 [Apparatus and software] (2014). Boston, MA: SensoMotoric Instruments.
50. Taub, M., and Azevedo, R. (2016). Using eye-tracking to determine the impact of prior knowledge on self-regulated learning with an adaptive hypermedia-learning environment? In A. Micarelli, J. Stamper, and K. Panourgia (Eds.), Proceedings of the 13th international conference on intelligent tutoring systems-lecture notes in computer science 9684 (pp. 34-47). Dordrecht, The Netherlands: Springer.
51. Taub, M., Azevedo, R., Bouchet, F., and 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.
52. Taub, M., Mudrick, N. V., Azevedo, R., Markhelyuk, M., and Powell, G. S. (2016, April). Assessing middle school students’ use of a metacognitive monitoring tool during learning with SimSelf. Paper presented at the annual meeting of the American Educational Research Association, Washington, DC.
53. Taub, M., Mudrick, N. V., Azevedo, R., Millar, G., Rowe, J., and Lester, J. (2016). Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with Crystal Island. In A. Micarelli, J. Stamper, and K. Panourgia (Eds.), Proceedings of the 13th international conference on intelligent tutoring systems-lecture notes in computer science 9684 (pp. 240-246). Dordrecht, The Netherlands: Springer.
54. Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., and Lester, J. (in press). Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island. Computers in Human Behavior.
55. VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., and Rose, C. P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31, 3-62.
56. Winne, P. H. (2018/this volume). Cognition and metacognition within self-regulated learning. In D. H. Schunk and J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed.). New York: Routledge.
57. Winne, P. H., and Azevedo, R. (2014). Metacognition. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 63-87). Cambridge, England: Cambridge University Press.
58. Winne, P. H., and Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, and A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277-304). Hillsdale, NJ: Lawrence Erlbaum Associates.
59. Winne, P. H., and Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. In D. H. Schunk and B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297-314). Mahwah, NJ: Erlbaum.
60. Winne, P. H., and Hadwin, A. F. (2013). nStudy: Tracing and supporting self-regulated learning in the Internet. In R. Azevedo and V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 293-310). Amsterdam, The Netherlands: Springer.
61. Woolf, B. P., Arroyo, I., Muldner, K., Burleson, W., Cooper, D. G., Dolan, R., and Christopherson, R. M. (2010). The effect of motivational learning companions on low achieving students and students with disabilities. In V. Aleven, J. Kay, and J. Mostow (Eds.), Proceedings of the 10th international conference on intelligent tutoring systems (pp. 327-337). Amsterdam, The Netherlands: Springer.