後設認知表現對心智建模推理歷程的影響之研究

Abstract

本研究將探討後設認知表現對學習者利用心智建模能力進行推理與思考歷程的影 響。本計畫將以曾選修普通化學的大學生為對象，探討其心智建模推理過程中的後設認 知表現。本計畫將從三方面描繪學生的後設認知表現：(1)利用Inventory of Metacognitive Self-Regulation (IMSR)問卷分析學生自我覺察的後設認知行為；(2)發展概念監測表現評 量工具來分析學生對既有概念理解多寡的自我監測能力；(3)設計與進行事例晤談 (interview-about-instance)，要求學生放聲思考，以評量學生實際在思考過程中的後設認 知表現。 研究者將利用普通化學課程中的化學概念，設計對心智建模能力有高度需求以及 需求程度較低的事例晤談情境，藉此來探討學生在此兩類事例晤談過程中的後設認知表 現，以及後設認知表現對心智建模推理歷程與成效的影響。本研究亦將進一步分析後設 認知表現與心智建模能力間的關聯，探討是否某些後設認知能力對心智建模推理歷程有 重要的影響。研究者相信，了解後設認知表現在心智建模過程中所扮演的角色以及影 響，將能增進我們對於學生科學建模學習(model-based learning)歷程的了解；此外，本 研究所發展的概念監測表現評量以及研究設計模式將可提供其他有關心智建模學習歷 程的研究利用。

This study explored university chemistry students’ metacognitive skills while solving two types of interview-about-instances: instances with high demand versus low demand of mental-modeling ability. Three instruments will be used to portray how chemistry students perform metacognitive skills to facilitate construction and utilization of mental models while thinking through the two types of instances. These three instruments include: (1) Inventory of Metacognitive Self-Regulation (IMSR), which students will be asked to self-report metacognitive skills; (2) a modified version of Knowledge Monitoring Assessment (KMA) test; and (3) video-taped interviews using a combination of think-aloud protocol and interview-about-events to elicit students’ thinking processes and use of metacognitive skills. The KMA test was originally developed by Tobias and Everson (1996) to assess students’ ability to monitor their use of existing knowledge while solving problems. In this study, the KMA test will be modified so it can be used to not only diagnose students’ knowledge monitoring ability but also their prior knowledge and misconceptions. By comparing students’ metacognitive skills while thinking about instances with high demand versus low demand of mental-modeling ability, the project investigator hopes to explore the relationship between metacognitive skills and mental-modeling ability, especially, to investigate whether metacognition during mental-modeling process is domain-specific. In addition, the project investigator will categorize participants into novices and experts based on their amount of existing knowledge revealed from their successfulness of answering questions in the KMA test. The comparisons between novices and experts on their metacognitive skills during the two types of interview-about-instances may provide empirical evidence for how students’ metacognitive skills, knowledge monitoring ability, and mental-modeling ability influence their thinking with mental models. The findings have implications for future research on model-based learning.