Using Eye-Tracking to Determine the Impact of Prior Knowledge on Self-Regulated Learning with an Adaptive Hypermedia-Learning Environment

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• 关键词：Metacognition ; Self ; regulated learning ; Eye tracking ; Prior knowledge ; Adaptive hypermedia ; learning environments ; Process data
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9684
• 期：1
• 页码：34-47
• 全文大小：660 KB
• 参考文献：1.Azevedo, R., et al.: Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 427–449. Springer, Amsterdam (2013)CrossRef
  2.Graesser, A.C.: Evolution of advanced learning technologies in the 21st century. Theor. Into Pract. 52, 93–101 (2013)CrossRef
  3.Lester, J.C., et al.: Supporting self-regulated science learning in narrative-centered learning environments. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 471–483. Springer, Amsterdam (2013)CrossRef
  4.Winne, P.H., Hadwin, A.F.: The weave of motivation and self-regulated learning. In: Schunk, D.H., Zimmerman, B.J. (eds.) Motivation and Self-Regulated Learning: Theory, Research and Applications, pp. 298–314. Erlbaum, New York (2008)
  5.Conati, C., et al.: Understanding attention to adaptive hints in educational games: an eye-tracking study. Int. J. Artif. Intell. Educ. 23, 136–161 (2013)CrossRef
  6.D’Mello, S.K., et al.: Gaze tutor: a gaze-reactive intelligent tutoring system. Int. J. Hum. Comput. Stud. 70, 377–398 (2012)CrossRef
  7.Bondareva, D., Conati, C., Feyzi-Behnagh, R., Harley, J.M., Azevedo, R., Bouchet, F.: Inferring learning from gaze data during interaction with an environment to support self-regulated learning. In: Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 229–238. Springer, Heidelberg (2013)CrossRef
  8.Jaques, N., Conati, C., Harley, J.M., Azevedo, R.: Predicting affect from gaze data during interaction with an intelligent tutoring system. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 29–38. Springer, Heidelberg (2014)CrossRef
  9.Taub, M., et al.: Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? Comput. Hum. Behav. 39, 356–367 (2014)CrossRef
  10.Trevors, G., et al.: Note-taking within MetaTutor: interactions between an intelligent tutoring system and prior knowledge on note-taking and learning. Educ. Technol. Res. Dev. 62, 507–528 (2014)CrossRef
  11.Pekrun, R., et al.: Measuring emotions in students’ learning and performance: the achievement emotions questionnaire (AEQ). Contemp. Educ. Psychol. 36, 36–48 (2011)CrossRef
  12.SMI Experiment Center 3.4.165 [Apparatus and Software]. SensoMotoric Instruments, Boston, Massachusetts, USA (2014)
  13.Salvucci, D.D., Goldberg, J.H.: Identifying fixations and saccades in eye-tracking protocols. In: Duchowski, A.T. (ed.) Eye-Tracking Research and Application, pp. 71–78. ACM Press, Palm Beach Gardens (2000)
  14.Mayer, R.E. (ed.): The Cambridge Handbook of Multimedia Learning, 2nd edn. Cambridge University Press, New York (2014)
  15.Calvo, R.A., et al. (eds.): The Oxford Handbook of Affective Computing. Oxford University Press, New York (2015)
  16.Azevedo, R.: Defining and measuring engagement and learning in science: conceptual, theoretical, methodological, and analytical issues. Educ. Psychol. 50, 84–94 (2015)CrossRef
  17.Baker, R.S.: Educational data mining: an advance for intelligent systems in education. IEEE Intell. Syst. 29, 78–82 (2014)CrossRef
  
• 作者单位：Michelle Taub (16)
  Roger Azevedo (16)
  
  16. Department of Psychology, Laboratory for the Study of Metacognition and Advanced Learning Technologies, North Carolina State University, Raleigh, NC, USA
  
• 丛书名：Intelligent Tutoring Systems
• ISBN：978-3-319-39583-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9684

文摘

Recent research on self-regulated learning (SRL) includes multi-channel data, such as eye-tracking, to measure the deployment of key cognitive and metacognitive SRL processes during learning with adaptive hypermedia systems. In this study we investigated how 147 college students’ proportional learning gains (PLGs), proportion of time spent on areas of interest (AOIs), and frequency of fixations on AOI-pairs, differed based on their prior knowledge of the overall science content, and of specific content related to sub-goals, as they learned with MetaTutor. Results indicated that students with low prior sub-goal knowledge had significantly higher PLGs, and spent a significantly larger proportion of time fixating on diagrams compared to students with high prior sub-goal knowledge. In addition, students with low prior knowledge had significantly higher frequencies of fixations on some AOI-pairs, compared to students with high prior knowledge. The results have implications for using eye-tracking (and other process data) to understand the behavioral patterns associated with underlying cognitive and metacognitive SRL processes and provide real-time adaptive instruction, to ensure effective learning.