團隊合作與個人轉化之歷程探究;Explore the process of teamwork and personal transformation

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題名:	團隊合作與個人轉化之歷程探究;Explore the process of teamwork and personal transformation
作者:	張嫈慈;Chang, Ying-Tzu
貢獻者:	學習與教學研究所
關鍵詞:	合作型問題解決能力;思考風格;密室脫逃;collaborative problem solving (CPS);thinking style;escape room
日期:	2022-09-27
上傳時間:	2022-10-04 12:10:30 (UTC+8)
出版者:	國立中央大學
摘要:	世界日新月異，人們如今所面臨的問題已經越來越複雜，僅憑一人之力來解決相當的困難。因此，合作型問題解決能力(Collaborative Problem Solving，簡稱 CPS)成為各國積極培育的關鍵能力之一。在這樣的背景下，學者們積極發展相關的教學理論與評估架構。其中，國際學生能力評量計畫( the Programme for International Student Assessment, 簡稱 PISA)與 21 世紀技能評估與教學計畫(the Assessment and Teaching of Twenty-first Century Skills, 簡稱 ATC21S)所提出的架構最廣為人用。然而，大部分都以研究中學生個人能力的展現與任務成果之間的關係為主，而且時常運用控制程度較高的評估環境，不僅研究的面向單一，而且探究的合作解題行為也比較偏離真實的狀況，要將研究成果轉化並運用於現實生活中也較為困難。為了解決研究環境對參與者控制度高、研究面向較單一等問題，本研究將採用新興的研究環境—密室脫逃來設置研究情境，並增加個人思考風格的面向，來研究個人與團隊在解決問題歷程中合作互動的行為展現。研究問題分為以下兩項: 1. 個人與團隊在密室脫逃中的互動行為與問題解決樣貌為何? 2. 個人的思考風格與他們在密室脫逃中的行為表現之間有什麼關係? 本研究挑選了三組高等教育階段的學生共 12 位為研究對象，使用 Sternberg 的思考風格量表(Thinking Styles Inventory, TSI)進行背景資料的收集。接著，運用 CPS 概念架構觀察密室脫逃中團隊解決問題的歷程以及成員間的合作互動行為。最後，將所有資料進行綜合分析。研究結果呈現了三種不同團隊的合作解題型態，而思考風格會形塑團隊的樣貌，並影響團隊在合作互動與問題解決的展現。依據研究結果延伸出於現實生活中的應用參考，包含: 1. 創造溝通互動的合作氛圍 2. 從任務特性與問題解決歷程找團隊困境與因應方法 3. 團隊思考風格組合的理解及應用根據研究結論，建議未來可以嘗試在團隊的組成上進行控制，並結合機器學習等科技工具來進行資料的處理，以期有更多不同的發現。;The world is changing with each passing day, and the problems people are facing today have become more and more complex, and it is quite difficult to solve it by one person alone. Therefore, Collaborative Problem Solving (CPS) has become one of the key capabilities that countries actively cultivate. In this context, scholars are actively developing relevant teaching theories and assessment frameworks. Among them, the Programme for International Student Assessment (PISA) and the Assessment and Teaching of Twenty-first Century Skills (ATC21S) have the most extensive frameworks for human use. However, most of them focus on the study of the relationship between the display of personal abilities of middle school students and task outcomes, and often use a highly controlled assessment environment. Not only is the research oriented to a single aspect, but also the cooperative problem-solving behavior of inquiry is relatively deviated from the real one. It is also difficult to transform and apply research results in real life. In order to solve the problems that the research environment has a high degree of control over participants and the research orientation is relatively single, this research will use the emerging research environment - escape room to set up the research situation, and increase the aspect of personal thinking style to study the problem-solving between individuals and teams The behavioral display of cooperation and interaction in the process. The research questions fall into the following two categories: 1. Whatistheinteractionbehaviorandproblemsolvingofindividualsand teams in Escape Room? 2. Whatistherelationshipbetweenanindividual′sthinkingstyleandtheir behavior in Escape Room? This study selected a total of 12 students from three groups of higher education as research objects, and used Sternberg′s Thinking Styles Inventory (TSI) to collect background data. Then, the CPS conceptual ii framework is used to observe the team′s problem-solving process and the cooperative and interactive behavior of members in the escape room. Finally, all data are analyzed comprehensively. The results of the study showed three different types of cooperative problem-solving in teams, and the thinking style shaped the appearance of the team and affected the team′s performance in cooperative interaction and problem-solving. Based on the research results, the reference is extended to real-life applications, including: 1. Create a cooperative atmosphere for communication and interaction 2. Find team dilemmas and coping methods from the task characteristics and problem solving process 3. Understanding and application of team thinking style combination According to the conclusion of the research, it is suggested that in the future, we can try to control the composition of the team, and combine technological tools such as machine learning to process the data, so as to have more different discoveries.
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