博碩士論文 111524008 完整後設資料紀錄

DC 欄位 語言
DC.contributor網路學習科技研究所zh_TW
DC.creator柯炘德zh_TW
DC.creatorXin-De Keen_US
dc.date.accessioned2024-11-18T07:39:07Z
dc.date.available2024-11-18T07:39:07Z
dc.date.issued2024
dc.identifier.urihttp://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=111524008
dc.contributor.department網路學習科技研究所zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract學習程式語言涉及許多複雜概念,需要學生持續練習並調節其學習。為了應對這些挑戰,本研究基於自我調節學習理論及共同調節學習理論設計了兩個學習分析儀表板。自我調節學習儀表板主要展示學生的個人學習狀態,並根據學生當前知識點掌握情況預測學生的學習透過GenAI提供適性化學習建議。共同調節學習儀表板展示小組成員的學習狀態,透過排行榜展示學生與同儕之間的差異,並透過學習歷程記錄追蹤學習過程。 本研究的研究對象為台灣北部一門為期17週的Python程式語言課程中的38名研究生。透過實驗前後的Python能力測驗和線上自我調節學習問卷及自我導向學習問卷,評估學生的能力,學生可依照自由意願使用本學習系統進行題目練習、觀看儀表板,並且隨時調整學習目標。系統日誌記錄蒐集了學生滑鼠點擊和瀏覽行為。實驗結束後透過科技接受模型、學習分析評價框架和開放式問題收集了學生對於系統及學習分析儀表板的回饋。 研究發現,使用學習系統一學期後,學生的學習成效顯著提升,自我導向學習能力中的學習動機和自我監控構面均顯著提高,整體自我導向學習能力也顯著提升。學生對系統的感知易用性給予高分,並提出了相關建議和想法。然而,自我調節學習能力在所有構面及整體上未達顯著水準。結果顯示,學習系統及學習分析儀表板能促進學生學習,使他們對學習程式設計的態度更加積極,並能更有效管理學習過程,學生在學習成效、自我導向學習能力、學習動機和自我監控能力上均有顯著進步。zh_TW
dc.description.abstractLearning programming languages involves many complex concepts, requiring students to practice continuously and regulate their learning. To address these challenges, this study designed two learning analytics dashboards based on self-regulated learning theory and co-regulated learning theory. The self-regulated learning dashboard primarily displays the student′s personal learning status and provides adaptive learning recommendations through GenAI based on the student′s current knowledge mastery. The co-regulated learning dashboard shows the learning status of group members, highlighting differences between students and their peers through leaderboards and tracking the learning process via learning history records. The study′s participants were 38 graduate students enrolled in a 17-week Python programming course in northern Taiwan. The study evaluated the students′ abilities through pre- and post-experimental Python proficiency tests and online self-regulated learning and self-directed learning questionnaires. Students could voluntarily use the learning system for practice, view the dashboards, and adjust learning goals at any time. The system logs collected data on students′ mouse clicks and browsing behaviors. After the experiment, feedback on the system and learning analytics dashboards was gathered using the Technology Acceptance Model, the Learning Analytics Evaluation Framework, and open-ended questions. The study found that after using the learning system for a semester, students′ learning outcomes significantly improved. There were significant increases in the learning motivation and self-monitoring aspects of self-directed learning ability, and overall self-directed learning ability also significantly improved. Students rated the system′s perceived ease of use highly and provided relevant suggestions and ideas. However, the self-regulated learning ability did not reach significant levels in all dimensions and overall. The results indicate that the learning system and learning analytics dashboards can enhance students′ learning, making their attitudes toward learning programming more positive and enabling them to manage the learning process more effectively. Significant improvements were observed in learning outcomes, self-directed learning ability, learning motivation, and self-monitoring ability.en_US
DC.subject學習系統zh_TW
DC.subject程式教育zh_TW
DC.subject自我導向學習zh_TW
DC.subject調節學習zh_TW
DC.subject學習分析儀表板zh_TW
DC.subjectlearning systemen_US
DC.subjectprogramming educationen_US
DC.subjectself-directed learningen_US
DC.subjectregulated learningen_US
DC.subjectlearning analysis dashboardsen_US
DC.title結合自我調節及共同調節學習分析儀表板於Python程式教育之研究zh_TW
dc.language.isozh-TWzh-TW
DC.titleCombining Self-Regulated and Co-Regulated Learning Analysis Dashboards for Python Programming Educationen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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