隨著數位轉型發展，程式設計已成為各領域重要的數位素養核心能力，涵蓋邏輯思維、問題解決與跨領域應用等關鍵技能。對非資訊背景參與者而言，學習程式設計時常因語法理解、邏輯推理與錯誤修正等挑戰，導致挫折感、信心不足及學習動機下降，影響學習成效與持續性。為降低學習門檻，視覺化程式設計工具如 MakeCode 被廣泛應用，可協助初學者掌握程式邏輯概念與操作流程，惟在錯誤排除與概念整合歷程中仍存在困難。回饋機制被視為提升學習成效的重要教學策略，透過即時與具體的指引，協助參與者釐清問題來源、調整學習策略並強化自我效能。然而，針對不同回饋型態應用於非資訊背景初學者之實證研究仍相對有限，特別缺乏整合學習歷程數據進行系統性探討。
本研究採準實驗設計，探討形成性與總結性回饋對程式學習成效之影響，透過程式教育任務分析系統（Programming Education Task Analysis System, PETAS），用以記錄與分析學習歷程行為數據。26 位非資訊相關背景且具備基本電腦操作能力初學者隨機分派至兩組，課程以 MakeCode 平台與 Maqueen 自走車進行五項任務。學習成效評量包含程式邏輯能力、學習動機與電腦自我效能之前後測，並輔以操作行為數據進行綜合分析。
研究結果顯示，PETAS 可穩定紀錄行為數據並支持教學診斷。總結性回饋組在程式邏輯能力後測表現顯著優於形成性回饋組，顯示整體性回饋有助於推理與統整能力發展；學習動機與電腦自我效能則於短期介入下無顯著差異。本研究驗證回饋機制應用成效，並確認 PETAS 於學習分析之可行性，結果可供課程設計與教學優化之參考。

With the advancement of digital transformation, programming has become a core digital competence across fields, involving logical reasoning, problem-solving, and interdisciplinary applications. Beginners with non-computer-related backgrounds and basic computer operation skills often face challenges in syntax, logic, and error correction, affecting confidence and motivation. Visual tools like MakeCode lower learning barriers and support logic acquisition, yet difficulties in debugging and conceptual integration persist. Feedback mechanisms provide timely guidance for problem-solving and self-efficacy. However, empirical studies on feedback types for novice learners with non-computer-related backgrounds remain limited, especially those integrating process data.
This quasi-experimental study examined formative and summative feedback effects using the Programming Education Task Analysis System (PETAS) for behavior data analysis. 26 beginners with non-computer-related backgrounds and basic computer operation skills were randomly assigned to two groups. The intervention included five tasks via MakeCode and Maqueen robot. Outcomes were assessed through pre- and post-tests of Programming Logic Ability Test, Learning Motivation Scale, and Computer Self-Efficacy Scale, combined with behavioral data.
Results indicated PETAS effectively recorded behavior data for analysis. The summative feedback group outperformed the formative group in programming logic ability post-tests, suggesting comprehensive feedback improved reasoning and integration. No significant group differences were found in learning motivation and computer self-efficacy. This study confirms feedback mechanism effects and demonstrates PETAS’s value for learning analytics, offering insights for instructional design.

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