漸進釋放責任模式結合開放教育資源於運動大數據分析課程之實踐

緒論：運動大數據分析是一門新興課程，目標是培養學生面對物聯網與人工智慧蓬勃發展的時代，能具備足夠數據素養，以因應各類感知器所收集的動作、位置、生理等大量訊號，以及競賽表現、運動迷社群、運動行銷等巨量網路數據。但在體育運動領域教育中，程式設計、統計學等先備知識課程相對較少，墊高了大數據分析的學習門檻，使得教學上普遍存在學習困難、學習信心低落、學習成效不佳等問題。本研究以漸進釋放責任模式結合開放教育資源重新設計運動大數據分析課程，主要目的在探討這個課程採用的教學模式對於學生學習動機、學習成效、學習自信心之影響。方法：課程設計以開放教育資源為教材，逐步依“我做”、“我們做”、“你們一起做”、“你單獨做”漸進釋放責任模式的四個階段，設計了三個課程模組來學習專業知識並實作資料分析操縱技能。研究對象為某運動相關科系大學部連續兩年度共31位修課學生，透過行動研究法、訪談、以及自我效能前後測問卷，收集研究資料。結果：問卷量化數據依魏克生符號檢定（Wilcoxon signed-rank test）之結果顯示，學生修課前後對於專業知識、處理技能之自我效能皆有顯著提升。質性訪談結果指出，引入開放教育資源能提升學習動機，而漸進釋放責任模式能與合作學習、專題導向學習、做中學、主動學習的概念相結合，有助於提升學習成效。結論：透過漸進釋放責任模式結合開放教育資源之課程設計，能巧妙地在各個學習階段中漸進式地讓學生理解和動手作建立學習信心，達到顯著優異的教學效能，可縮小運動大數據分析學習上先備知識的落差，使學生願意學、有信心學會，具備未來所需之數據素養。這個教學策略，未來可嘗試應用到其他學習門檻較高、學習信心不足的課程上。

關鍵字

數據素養；運動分析；課程設計；資料科學；自我效能

並列摘要

Introduction: Big data analytics in sports is an emerging course that aims to cultivate students' data literacy to cope with the massive amounts of movement, location, and physiology signals collected by various sensors, as well as voluminous internet data on areas such as game performance, sports fan communities, and sports marketing in the era of explosive development of the Internet of Things and artificial intelligence. However, in sports education, there are relatively few courses on programming, statistics, and other prerequisite knowledge. This deficiency raises the learning threshold of big data analytics, resulting in low confidence in learning and poor learning outcomes. The main purpose of this study was to investigate the impacts on students' learning motivation, learning effectiveness, and self-confidence by combining the gradual release of responsibility (GRR) model with open educational resources (OER) to reformulate the big data analysis in sports course. Methods: This study applied the GRR model with OER to big data education in sports and designed three curriculum modules to improve professional knowledge and implement data manipulation skills in four stages: "I do it," "We do it together," "You do it together," and "You do it alone." Thirty-one students from a university department of a sports-related discipline were enrolled in the study. Data were collected through action research, interviews, and self-efficacy pre- and posttest questionnaires. Results: The results of the Wilcoxon signed-rank test of quantitative data from the questionnaire showed that students' self-efficacy in professional knowledge and processing skills improved significantly after the course. The results of the interviews indicated that OER can enhance learning motivation and GRR can be combined with the concepts of cooperative learning, topic-oriented learning, learning by doing, and active learning to help improve learning effectiveness. Conclusions: The GRR model, combined with an OER curriculum, design can effectively and progressively establish students' understanding of and confidence in each learning stage, resulting in highly effective teaching. It can narrow the gap between the prerequisite knowledge of sports and big data analysis and improve students' willingness to learn and confidence in learning and equip them with the data literacy required in the future. This teaching strategy may be applied to other courses with a higher learning threshold and lower learning confidence.

並列關鍵字

data literacy ； sports analytics ； curriculum design ； data science ； self-efficiency

參考文獻

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