以教育科技和自我評量促進自我調節科學學習的師生研究

Abstract

本論文探討自我評量在自我調節科學學習框架中的作用，強調學習者自我評量的能力對於提高學習成果的重要性，並將自我評量作為實現科學自我調節學習的關鍵機制。本論文採漏斗方法，由相互關聯的研究開展，每項研究對於整合自我評估策略對自我調節學習的影響提供了獨特的視角。第一個研究以主題分析探索教師將自我調節學習策略納入科學教育的看法，並且透過路徑分析建立教師自我調整學習與科技教學內容知識的結構模型，凸顯對於科技輔助自我調節學習價值的共識。第二項研究進一步的探討科技輔助學習環境對學習社會科學議題論證的學生自我調節學習的影響，證明自我評量工具在自我調節成果上的關鍵預測作用。第三項研究評估自我評量設計對學生在社會科學議題論證中的學習和自我調節的影響，發現調適任務在促進認知和後設認知發展上的重要性。第四項研究提供一個全面的文獻綜述，橫跨十年（2013-2023年），以綜合洞察自我評量方法如何增強學生的自我調節科學學習，識別出在促進高層次思考技能方面應用這些策略的研究缺口。綜合這些發現，倡議將自我評量作為一種策略性的學習方法，特別是結合得分預測、以標準進行自我評量和調適任務，以促進科學教育中的自我調節學習。本論文發現其在增強認知和後設認知方面的實際可行性和有效性。此外，本論文也強調在科技輔助學習環境中納入明確的監控和調節策略的必要性，以支持自我調節學習過程。

This dissertation explores the role of self-assessment within the framework of self-regulated science learning, emphasizing the importance of learners' self-assessment abilities in enhancing learning outcomes. It positions self-assessment as a crucial mechanism for achieving self-regulated science learning. The dissertation employs a funnel approach, conducting interrelated studies, with each study offering a unique perspective on integrating self-assessment strategies into self-regulated learning. The first study uses thematic analysis to explore teachers' perceptions of incorporating self-regulated learning strategies into science education. Through path analysis, it establishes a structural model linking teachers' self-regulated learning with technological pedagogical content knowledge, highlighting a consensus on the value of technology-assisted self-regulated learning. The second study further investigates the impact of technology-assisted learning environments on students' self-regulated learning in arguing social science issues, demonstrating the pivotal predictive role of self-assessment tools in self-regulated outcomes. The third study assesses the impact of self-assessment design on students' learning and self-regulation in social science issue argumentation, revealing the importance of adaptive tasks in promoting cognitive and metacognitive development. The fourth study provides a comprehensive literature review spanning a decade (2013-2023), synthesizing insights on how self-assessment methods enhance students' self-regulated science learning and identifying research gaps in applying these strategies to foster higher-order thinking skills. Integrating these findings, the dissertation advocates for self-assessment as a strategic learning approach, particularly by combining score prediction, standard-based self-assessment, and adaptive tasks to promote self-regulated learning in science education. The dissertation demonstrates the practical feasibility and effectiveness of these methods in enhancing cognitive and metacognitive aspects. Additionally, it underscores the necessity of incorporating explicit monitoring and regulation strategies in technology-assisted learning environments to support self-regulated learning processes.