An Investigation of Accuracy of Metacognitive Judgments during Learning with an Intelligent Multi-Agent Hypermedia Environment

An Investigation of Accuracy of Metacognitive Judgments during Learning with an Intelligent Multi-Agent Hypermedia Environment Reza Feyzi-Behnagh (reza.feyzibehnagh@mail.mcgill.ca) Zohreh Khezri (zohreh.khezri@mail.mcgill.ca) Roger Azevedo (roger.azevedo@mcgill.ca) Department of Educational and Counselling Psychology Laboratory for Study of Metacognition and Advanced Learning Technologies McGill University 3700 McTavish Street, Montreal, Quebec, H3A 1Y2, Canada Abstract metacognitively monitor their use of strategies or make accurate metacognitive judgments. For example, students’ failure to metacognitively monitor their learning, make accurate metacognitive judgments, and deploy regulatory processes are detrimental and can negatively impact their learning. One approach to address this issue is to develop multi-agent learning environments that embody artificial pedagogical agents that are designed to model, scaffold, and foster students’ metacognitive processes during learning (see Azevedo et al., in press; 2010; Leelwaong & Biswas, 2008; Schwartz et al., 2009; White et al., 2009). The goal of this study was to investigate the effects of a multi-agent hypermedia learning environment, MetaTutor, on the accuracy of learners’ metacognitive judgments during their learning of the human circulatory system. The metacognitive judgments investigated in this study included Judgments of Learning (JOLs) and Feelings of Knowing (FOKs), which were either prompted by one of MetaTutor’s four pedagogical agents or initiated by the students themselves using an SRL (Self Regulated Learning) palette available to them during the learning session. Research by the MetaTutor team has revealed key self-regulatory processes, related to planning, metacognitive monitoring, learning strategies, and methods of handling cognitive task demands, which are deployed by students while learning about complex science topics (see Azevedo & Witherspoon, 2009). One of the main objectives of the MetaTutor project has been to test the effectiveness of pedagogical agents as external regulatory agents in scaffolding students’ learning. Pedagogical agents have the potential to provide students with information that will help them become strategic, motivated, and independent learners. One of the areas where pedagogical agents can help students to better regulate their learning is by improving the accuracy of metacognitive judgments, such as JOLs and FOKs. Nelson (1996) argued that, metacognitive judgments are notoriously inaccurate most of the time. He defined the accuracy of metacognitive judgments in terms of the correlation between the respective metacognitive judgments and a subsequent performance score. When a learner’s metacognitive judgment rating and performance score correlate closely, they are well ‘calibrated’. Lack of confidence as well as overconfidence not justified by one’s performance can threaten short- and Successful learning with advanced learning technologies is based on the premise that students adaptively regulate their cognitive and metacognitive processes. However, research suggests that students are rather dysregulated in their learning. One major source of dysregulation is based on inaccurate metacognitive judgments made during learning. This study investigated learners’ accuracy and confidence in metacognitive judgments made in the context of learning about the human circulatory system with MetaTutor, a multi- agent intelligent hypermedia learning system. 83 college students took part in this study, and their interactions within MetaTutor in the two-hour learning session provided data for this study. In general, the results revealed that learners were overconfident to differing degrees in ratings of their judgments of learning (JOLs) and feelings of knowing (FOKs). It was also found that receiving timely prompts and adaptive feedback from the artificial agent in MetaTutor improved the accuracy of metacognitive judgments. Learners in the Prompt and Feedback condition (PF) were overconfident to a lesser degree than those in other conditions (Prompt Only [PO] and Control). Finally, one-way ANOVA and Tukey post-hoc results indicated that learners who received prompts and feedback attained significantly (p < .05) better learning efficiency scores than learners in Control and Prompt Only conditions. Keywords: Metacognitive Judgments; Hypermedia; JOL; FOK; Accuracy; Multi-Agent Learning Environment. Objectives and Theoretical Framework Successful learning with advanced learning technologies is based on the premise that learners adaptively regulate their cognitive process based on accurate metacognitive judgments during learning (Azevedo, Moos, Johnson, & Chauncey, 2010). However, there is ample empirical evidence to suggest that learners usually do not regulate key cognitive, metacognitive, affective, and motivation processes during learning with advanced learning technologies such as multi-agent environments (Azevedo et al., in press; Biswas et al., 2010; Graesser & McNamara, 2010; McQuiggan & Lester, 2009; White et al., 2009). In other words, learners typically do not deploy effective learning strategies, modify and update internal cognitive standards, correct behavior based on feedback and scaffolding from the learning system or a tutor,