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By integrating physics laboratories with engineering design and computer science, students apply physics principles to ill-structured and complex problems, engage in knowledge transfer, and gain interest in STEM. The introductory physics labs at Purdue have been updated to include engineering design and computer science principles that ground physics in authentic problems. Integrated labs have been evaluated using student perception post-surveys, student course performance, interviews, and case-study observations. Preliminary results indicate that integrated physics labs promote transfer, enhanced metacognitive skills, student interest, and motivation.
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How and When: The Impact of Metacognitive Knowledge Instruction and Motivation on Transfer Across Intelligent Tutoring Systems
This paper examines procedural and conditional metacognitive knowledge and student motivation across two ITSs (logic and probability). Students were categorized by metacognitive knowledge and motivation level. Interventions (nudges and worked examples) supported backward-chaining strategy. Results led to an MMI framework combining metacognitive instruction, motivation, and prompting to support effective knowledge transfer.
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- Award ID(s):
- 2013502
- PAR ID:
- 10609428
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- International journal of artificial intelligence in education
- ISSN:
- 1560-4292
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This work-in-progress paper shares findings of the early stage of a 3-year research funded by the National Science Foundation. The major aim of the project is to advance engineering and mathematics (EM) education theory and practice related to students’ self-regulation of cognition and motivation skills during problem-solving activities. The self-regulation includes students’ metacognitive knowledge about task (MKT) and self-regulation of cognition (SRC). The motivational component of self-regulation (SRM) includes self-control of the motivation needed to maintain the level of engagement and deliberate practice necessary for scientific thinking and reasoning. To be effective problem-solvers, students must understand the relationship between the MKT, SRC and SRM throughout the problem-solving activities. Four research questions will guide the research: (1) How do students perceive their self-regulation of cognition (SRC) and motivation (SRM) skills for generic problem-solving activities in EM courses; (2) How does students’ metacognitive knowledge about problem-solving tasks (MKT) inform their Task interpretation?; (3) How do students’ SRC and SRM dynamically evolve?; and (4) How do students’ SRC and SRM reflect their perceptions of self-regulation of cognition and motivation for generic EM problemsolving activities? A sequential mixed-methods research design involving quantitative and qualitative methods are used to develop complementary coarse- and fine-grained understandings of undergraduate students’ SRC and SRM during academic problem-solving activities. Two 2nd year EM courses: Engineering Statics, and Ordinary Differential Equations were purposefully selected for the contexts of the study. One hundred forty two students from both courses were invited and participated in quantitative data collection using two validated surveys during spring 2022 semester. Later in the semester, qualitative data will be generated with twenty students in both courses through one-on-one interviews with students and course instructors, think-aloud protocols with students, and classroom observations. Coarse-grained understandings of students’ SRC and SRM are currently developed through analysis of quantitative data collected using self-report surveys (i.e., BRoMS and PMI). Fine-grained understandings of students’ SRC and SRM will be developed through analysis of qualitative data gathered via one-on-one interviews, think-aloud protocols, classroom observations, and course artifacts gathered as students engage in EM problem-solving activities.more » « less
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In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning (\textit{DRL}) in providing \textit{adaptive} metacognitive interventions to bridge the gap between the three knowledge types and prepare students for future learning across Intelligent Tutoring Systems (ITSs). Students received these interventions that taught \textit{how} and \textit{when} to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that on both ITSs, DRL bridged the metacognitive knowledge gap between students and significantly improved their learning performance over their control peers. Furthermore, the DRL policy adapted to the metacognitive development on the logic tutor across declarative, procedural, and conditional students, causing their strategic decisions to be more autonomous.more » « less
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Introductory computer science for non-majors, often referred to as CS0, is a course that is designed to be more accessible and less intimidating than CS1, with the goal of alleviating barriers and fears associated with learning computer science (CS). However, despite this intention, many students still struggle in CS0 and these courses do not always successfully prepare students for future CS learning experiences. In this paper, we study the experiences of CS0 students with a particular focus on the intersection of their metacognition, affect, and behaviors. To study students’ daily learning experiences, we collected data from 20 participants who completed structured daily diaries and retrospective interviews over the course of a single homework assignment. Through a thematic analysis of the diaries and interviews, we identified three distinct patterns of engagement that highlight the importance of metacognitive knowledge of strategies, or a students’ understanding of when, why, and how to effectively use regulation and disciplinary strategies while working on tasks. The three patterns of engagement include: (1) avoidance behaviors resulting from negative emotions, negative judgements, and a lack of metacognitive knowledge of strategies, (2) persistence or re-engagement behaviors despite negative emotions and judgements aided by metacognitive knowledge of strategies, and (3) persistence behaviors with evidence that metacognitive knowledge of strategies prevented students from forming negative judgements in the first place. We contribute an initial model of the interplay of metacognition, affect, and behaviors in CS learning, showing the role of metacognitive knowledge of strategies in helping students persist in the face of struggle. In our discussion, we advocate for explicit interventions that support students in developing metacognitive knowledge of strategies while also supporting their sometimes challenging emotional experiences.more » « less
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