面向未來的數據素養教育：以多元創新教學設計提升人社學生的學習成效

Article Title(Eng.): Data Literacy Education for the Future: Enhancing Humanities and Social Science Students’Learnin Outcomes throuh Innovative Teaching Design

本研究旨在回應當前高等教育在數據驅動時代所面臨的挑戰，探討如何透過多元創新教學策略，培育人文社會領域學生具備跨域整合與產業接軌的數據分析能力。研究以「成果導向學習法」與「專題導向學習法」為核心，建構一套混合式創新教學模式，並融入研究者於產、官、學領域的實務經驗，發展出兼具理論深度與應用廣度的統計與數據分析課程。研究採用前後測量化設計與質性回饋分析，針對36位人社領域學生進行教學介入成效評估。研究結果顯示，導入多元創新教學課程後，學生對統計的恐懼顯著下降，學習興趣、數據解讀能力與統計應用能力均有顯著提升。學生回饋亦指出，課程的專題實作與產業導向設計，有助於將所學知識運用於真實社會問題與職場情境，增進問題解決能力與職場競爭力。研究結果證實，結合「成果導向學習法」與「專題導向學習法」的混合創新教學模式，能有效強化人社學生的數據素養，並促進理論學習與實務應用的連結。綜上所述，本研究不僅對統計與資料分析教學提出具體改進模式，亦對通識教育如何培育具跨域思維與產業適應力之人才提供實證參考，呼應「為未來而學」之教育核心理念。

This study responds to the challenges faced by higher education in the data-driven era and explores how diverse and innovative teaching strategies can cultivate data analysis competencies among humanities and social science students. The study adopts a hybrid innovative teaching model integrating Outcome-Based Education (OBE) and Project-Based Learning (PBL), incorporating the instructor’s practical experiences across industry, government, and academia. Through this integration, the course design aims to combine theoretical depth with practical relevance, thereby fostering students’understanding of statistics and data analysis in both academic and professional contexts. A mixed-methods approach was employed, combining quantitative pre- and post-tests with qualitative feedback analysis, to evaluate the learning outcomes of 36 students in the humanities and social sciences. The results reveal that after the implementation of the innovative teaching design, students’anxiety toward statistics significantly decreased, while their interest in learning, data interpretation skills, and statistical application abilities all improved substantially. Student feedback further indicates that project-based tasks and industry-oriented course components facilitated the application of classroom knowledge to real-world problems and workplace scenarios, enhancing both problem-solving competence and career readiness. Overall, the findings demonstrate that the hybrid OBE–PBL model effectively strengthens students’data literacy and bridges the gap between theoretical learning and practical application. This study not only proposes a pedagogical framework for improving statistics and data analysis instruction but also provides empirical insights into how general education can cultivate graduates with interdisciplinary thinking and industry alignment—embodying the core vision of learning for the future.