改善大數據分析課程的學生參與策略研究

Research on Strategies to Improve Student Engagement in Big Data Analysis Course

在技職體系中，很多學生是數學相關學科的「傷痕族」，在國高中的階段，數學的學習過程經常受挫，總認為再怎麼努力也於事無補。學生因而缺乏學習的動機，視「學習」為畏途，而產生習得無助感。為改善此一狀況，建議從減輕學生的心理負荷開始。本研究選擇數學相關的大數據分析課程為例，導入遊戲化、分組協作、實作性的教學策略，讓各組之間互相遊戲競合中消除習得無助感、提升學習動機、參與課程，並提升學生的學習成效。本研究以一個商業領域的主題：以RFM模型探討消費者類型，讓學生分組協作完成報告並發表成果。本研究依照學生的入學成績以及在學成績進行分組，再透過前後測的比較分析、成果競賽（包含學生自評、互評以及參與者的評比），作為多元學習成效評量的參考。另外亦針對學生對本教學實踐研究的滿意度調查，作為以後教學改進的參考。研究結果發現D類學生（入學成績低/在學成績低）進步幅度高於A類學生（入學成績高/在學成績高）20分以上，也發現100%的學生對大數據分析的學習動機或意願均有明顯提升（53.8%）或部分提升（46.2%）。

Within the technical and vocational education system, a significant number of students exhibit a detrimental impact on their mathematical abilities, often described as being "scarred" by their experiences in mathematics-related subjects. During their junior high and high school years, these students frequently encounter frustrations in the learning process, leading them to believe that their efforts are futile. Consequently, they tend to lack motivation, perceive learning as an overwhelming challenge, and develop a sense of learned helplessness. To address this issue, it is advisable to begin by alleviating the psychological burden on students. This study focuses on a course centered on statistical data analysis in mathematics, implementing strategies such as gamification, collaborative group work, and practical teaching methods. These approaches aim to help students overcome learned helplessness, boost their motivation to learn, and engage actively in the course through competitive and cooperative gaming activities, ultimately enhancing their learning outcomes. The research employs a business-oriented theme, specifically the exploration of consumer types utilizing the RFM model, which facilitates group collaboration for report completion and result dissemination. Students were organized into groups based on their entrance scores and academic performance. The study utilized comparative analyses of pre- and post-test results, as well as performance competitions that included self-evaluations, peer evaluations, and participant assessments, to evaluate various aspects of learning effectiveness. Additionally, a survey was conducted to gauge student satisfaction with the teaching practices implemented, serving as a basis for future pedagogical enhancements. The findings revealed that students categorized as D (those with low admission scores and low academic performance) demonstrated an improvement exceeding 20 points compared to students in category A (those with high admission scores and high academic performance). Furthermore, it was noted that 100% of the student’s expressed motivation or willingness to engage in big data analysis, with significant improvements observed in 53.8% of the participants and partial improvements in 46.2%.