Blended Learning in an Introductory Programming Course: A Playful Approach to Coding

摘要

本研究探討在程式設計入門課程中整合混合學習與遊戲式策略之成效。為因應學生先備知識與學習動機差異漸增的挑戰，本研究於台灣某大學「計算機程式及實習」課程中設計並實施了結合線上自學與課堂互動的混合教學架構。課程前期導入 CodeMonkey 與 Scratch 等遊戲式學習工具作為破冰活動，以降低學習焦慮並激發動機；中後期則透過翻轉教室與混合學習模式，結合自動批閱系統與形成性評量，持續追蹤學習進展並提供即時回饋。研究透過三份問卷與課堂觀察收集資料，結果顯示學生在專業技能、問題解決、資訊科技運用及內外在動機上皆有明顯提升。研究亦揭示，策略性結合「遊戲式導入」與「混合翻轉」能有效塑造初學者的學習歷程，並提供具體可複製的課程重構模式。雖受 COVID-19 疫情影響，部分動機呈現波動，本研究仍展現積極成效，並為未來 AI 輔助程式教育的設計奠定基礎，亦與教育部教學實踐研究計畫「生成式 AI 如何改寫教育場景：適性化學習的實驗探索」形成連結。
關鍵詞：混合學習、翻轉教室、遊戲化、學習動機、教學實踐

Abstract

This study investigates the pedagogical impact of integrating a blended learning model with playful, game-based strategies in an introductory programming course. To address the increasing diversity in students’ prior knowledge and motivation, a blended learning framework was implemented in a Taiwanese university’s “Computer Programming and Practice” course. Initial sessions utilized CodeMonkey and Scratch as game-based icebreakers to reduce anxiety and promote engagement. These were followed by a blended and flipped classroom design incorporating digital lecture notes, instructional videos, formative assessments, and an automated grading system. Data were collected through three questionnaires and classroom observations. Results indicated significant improvements in students’ professional competencies, problem-solving skills, IT literacy, and both intrinsic and extrinsic learning motivation. The findings underscore the pedagogical value of strategically sequencing game-based entry activities with sustained blended and flipped learning, offering a replicable model for redesigning introductory programming instruction. While the COVID-19 pandemic disrupted the latter part of the semester, the approach demonstrated robust educational benefits. Furthermore, this study lays the groundwork for AI-assisted programming instruction, directly connecting to the recently completed MOE Teaching Practice Research Project, How Generative AI Rewrites the Educational Landscape: An Experimental Exploration of Adaptive Learning.