當AI遇上教育大數據：讓智慧教育儀表板成為教育工作好幫手

When AI Meets Educational Big Data: Intelligent Dashboards as Partners in Education

因應教育數位轉型的推進與學習行為資料的大量累積，如何有效解讀教育數據並轉化為具體可行的教學與政策行動，已成為當代教育治理的重要課題。結合學習分析（Learning Analytics）與生成式人工智慧（Generative Artificial Intelligent, GenAI）技術，設計一套具備自動解讀數據並生成行動建議的智慧教育儀表板，有助於決策者、教師與學生等不同層級之教育利害關係人在不同的情境脈絡下取得數據支持的具體建議。本研究以2024年臺灣教育大數據開放資料為基礎，此資料集涵蓋三個主要數位學習平台中的練習作答、影片觀看、測驗表現、互動紀錄與遊戲學習等行為資料，近1,500萬筆。我們透過宏觀趨勢分析、群體比較與異常偵測等分析模組，展示整合生成式AI的行動建議模組的學習儀表板可以如何根據不同使用者角色（決策者、教師、學生）動態調整資料視角與建議內容，實現個人化、任務導向的資料應用體驗。我們期望這樣的智慧儀表板系統能減少教育現場在洞察學習數據與應用的斷層，提升教育利害關係人對學習數據的理解與運用，為未來教育、數據驅動教學實踐提供實務參考。

With the advancement of digital transformation in education and the massive accumulation of learning behavior data, how to effectively interpret educational data and translate it into actionable teaching and policy initiatives has become a critical issue in contemporary educational governance. By integrating Learning Analytics with Generative Artificial Intelligence (GenAI), this study proposes the design of an intelligent educational dashboard capable of automatically interpreting data and generating actionable recommendations. Such a system can provide data-driven insights tailored to the needs of multiple educational stakeholders, including policymakers, teachers, and students across diverse contexts. This research is based on Taiwan's 2024 open educational big data, comprising nearly 15 million records collected from three major digital learning platforms, including practice responses, video engagement, test performance, interaction logs, and game-based learning activities. Through analytical modules such as macro-level trend analysis, group comparison, and anomaly detection, we demonstrate how the integration of a GenAI-powered recommendation module allows the learning dashboard to dynamically adjust data perspectives and suggested actions according to user roles. This enables a personalized and task-oriented data application experience. We anticipate that such an intelligent dashboard system can reduce the gap between data insights and practical application in educational settings, enhance stakeholders' understanding and utilization of learning data, and provide practical implications for data-driven teaching practices in the future.