The Relative Importance of Cognitive and Behavioral Engagement to Task Performance in Self-regulated Learning with an Intelligent Tutoring System

被引:1
|
作者
Huang, Xiaoshan [1 ]
Li, Shan [2 ]
Lajoie, Susanne P. [1 ]
机构
[1] McGill Univ, Montreal, PQ H3A 0G4, Canada
[2] Lehigh Univ, Bethlehem, PA 18015 USA
关键词
Self-Regulated Learning; Cognitive Engagement; Behavioral Engagement; Relative Importance; Intelligent Tutoring System; MOTIVATION;
D O I
10.1007/978-3-031-32883-1_39
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Self-regulated learning (SRL) is essential in promoting students' learning performance, especially in technology-rich environments where learning can be disorienting. Student engagement is closely associated with SRL, although the regulation of engagement in SRL is still underexplored. In this study, we aimed to compare the relative importance of cognitive and behavioral engagement in the three SRL phases (i.e., forethought, performance, self-reflection) to learning performance in the context of clinical reasoning. Specifically, students were tasked to solve two virtual patients in BioWorld, an intelligent tutoring system. We measured student behavioral engagement as their time spent on diagnostic behaviors. Students' cognitive engagement was extracted from their think-aloud protocols as they verbalized their thinking and reasoning process during the tasks. We analyzed the relative importance of cognitive and behavioral engagement in the three SRL phases to diagnostic efficacy. Results suggested that the effects of engagement on student performance depend on task complexity. In the complex task, the six predictors (i.e., two types of engagement in the three SRL phases) explained 36.81% of the overall variances in learner performance. Cognitive engagement in SRL played a more significant role than behavioral engagement in predicting students' performance in clinical reasoning.
引用
收藏
页码:430 / 441
页数:12
相关论文
共 50 条
  • [21] Effects of self-regulated learning on cognitive engagement and learning achievement in online discussions
    Liu, Zhi
    Gao, Ya
    Zhang, Ning
    Long, Taotao
    Liu, Sannyuya
    Peng, Xian
    CURRENT PSYCHOLOGY, 2024, 43 (35) : 28147 - 28162
  • [22] Pedagogical Agent Support and Its Relationship to Learners' Self-regulated Learning Strategy Use with an Intelligent Tutoring System
    Dever, Daryn A.
    Sonnenfeld, Nathan A.
    Wiedbusch, Megan D.
    Azevedo, Roger
    ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I, 2022, 13355 : 332 - 343
  • [23] A complex systems approach to analyzing pedagogical agents' scaffolding of self-regulated learning within an intelligent tutoring system
    Dever, Daryn A.
    Sonnenfeld, Nathan A.
    Wiedbusch, Megan D.
    Schmorrow, S. Grace
    Amon, Mary Jean
    Azevedo, Roger
    METACOGNITION AND LEARNING, 2023, 18 (03) : 659 - 691
  • [24] A complex systems approach to analyzing pedagogical agents’ scaffolding of self-regulated learning within an intelligent tutoring system
    Daryn A. Dever
    Nathan A. Sonnenfeld
    Megan D. Wiedbusch
    S. Grace Schmorrow
    Mary Jean Amon
    Roger Azevedo
    Metacognition and Learning, 2023, 18 : 659 - 691
  • [25] The Effectiveness of Peer Tutoring Method on Self-Regulated Learning
    Arjanggi, Ruseno
    Suprihatin, Titin
    MAKARA HUMAN BEHAVIOUR STUDIES IN ASIA, 2010, 14 (02): : 91 - 97
  • [26] The Effects of Self-Regulated Learning Support on Learners' Task Performance and Cognitive Load in Computer Programing
    Shin, Yoonhee
    Song, Donggil
    JOURNAL OF EDUCATIONAL COMPUTING RESEARCH, 2022, 60 (06) : 1490 - 1513
  • [27] Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System
    Azevedo, Roger
    Bouchet, Francois
    Duffy, Melissa
    Harley, Jason
    Taub, Michelle
    Trevors, Gregory
    Cloude, Elizabeth
    Dever, Daryn
    Wiedbusch, Megan
    Wortha, Franz
    Cerezo, Rebeca
    FRONTIERS IN PSYCHOLOGY, 2022, 13
  • [28] Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system
    Duffy, Melissa C.
    Azevedo, Roger
    COMPUTERS IN HUMAN BEHAVIOR, 2015, 52 : 338 - 348
  • [29] Forecasting Students' Performance Through Self-Regulated Learning Behavioral Analysis
    Rodrigues, Rodrigo Lins
    Cavalcanti Ramos, Jorge Luis
    Sedraz Silva, Joao Carlos
    Dourado, Raphael A.
    Gomes, Alex Sandro
    INTERNATIONAL JOURNAL OF DISTANCE EDUCATION TECHNOLOGIES, 2019, 17 (03) : 52 - 74
  • [30] School Engagement, Academic Achievement, and Self-Regulated Learning
    Estevez, Iris
    Rodriguez-Llorente, Carolina
    Pineiro, Isabel
    Gonzalez-Suarez, Rocio
    Valle, Antonio
    SUSTAINABILITY, 2021, 13 (06)