請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98995完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃從仁 | zh_TW |
| dc.contributor.advisor | Tsung-Ren Huang | en |
| dc.contributor.author | 沈家齊 | zh_TW |
| dc.contributor.author | Chia-Chi Shen | en |
| dc.date.accessioned | 2025-08-20T16:35:03Z | - |
| dc.date.available | 2025-08-21 | - |
| dc.date.copyright | 2025-08-20 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-15 | - |
| dc.identifier.citation | Azimi, S., Popa, C.-G., & Cucić, T. (2020). Improving Students Performance in Small-Scale Online Courses—A Machine Learning-Based Intervention. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), 2(2), Article 2. https://doi.org/10.3991/ijai.v2i2.19371
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98995 | - |
| dc.description.abstract | 本研究旨在深入探討影響線上學習成效的關鍵指標,研究目的在於綜合性探討關於過去研究中常見學習活動、行為及著名的間隔效應(Spacing Effect)與學生學業表現的關聯。本研究進一步將學習行為分為「資源瀏覽行為」(Navigation)、「知識獲取行為」(Input)與「知識應用行為」(Output),以求更細緻地探討這些行為的次數、行為分布及學習間隔對學業成效的影響。
首先,研究發現學生的早期表現,特別是在課程初期和中期的作業得分,對最終成績具有高度預測力,其影響力與課程後期的得分相當,強調在遠距學習環境中進行早期干預的必要性。 另外,結果顯示學習行為的間隔對學業成效具有顯著的預測能力。其中,作業完成率及知識獲取行為的點擊頻率為負向因子,而知識獲取行為次數的波動程度以及知識運用行為的間隔天數則呈現正向影響,顯示高成效學生可能展現出更具策略性的學習行為,諸如適當的延長學習行為間隔、選擇性的作業繳交和資源投入。此外,資源瀏覽行為的平均間隔天數過長則顯示出負向預測作用,推測是因瀏覽行為實屬策略規劃行為的範疇,而非實質的學習活動,因此同樣體現了規劃學習策略之行為對最終成績表現的重要性。 總體而言,本研究呼籲針對線上學習的早期預警方法以及學生策略規劃行為的關注,強調未來研究應側重遠距教學場域的學習策略,並建議在教學實踐中加強對學習行為的監測與分析。 | zh_TW |
| dc.description.abstract | This study aims to explore the key indicators influencing online learning outcomes by comprehensively analyzing the relationship between common learning activities, behaviors, and the well-known Spacing Effect, as discussed in previous research. To further investigate these relationships, the study categorizes learning behaviors into "Navigation," "Input," and "Output" behaviors, examining the frequency, distribution, and spacing intervals of these behaviors and their impact on academic performance.
The findings reveal that students' early performance, particularly in the initial and middle stages of the course, strongly predicts their final grades, with its predictive power comparable to that of late-stage performance. This underscores the importance of early intervention in online learning environments. Specifically, early performance serves as an effective early warning indicator, enabling the identification of at-risk students for timely support. Moreover, the study shows that the spacing of learning behaviors significantly predicts academic performance. Lower task completion rates may correlate with better performance, while longer intervals between Output behaviors and greater fluctuations in Input behaviors positively affect learning results. High-performing students may exhibit more strategic learning behaviors, such as maintaining appropriate learning intervals, selectively submitting tasks, and investing focused effort. On the other hand, excessively long intervals between Navigation behaviors negatively predict performance, suggesting that such behaviors, more related to learning strategy planning than actual learning, are important for academic success. Overall, this study emphasizes the need for early warning systems and attention to learning strategies in online learning environments, suggesting that future research should focus on exploring learning strategies in distance education and improving the monitoring and analysis of learning behaviors. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-20T16:35:03Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-20T16:35:03Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 第一章 緒論 1
第一節 研究背景 1 第二節 研究動機 6 第三節 研究目的 9 第二章 研究方法 10 第一節 資料集 10 第二節 資料前處理與樣本篩選 15 第三節 建模分析. 17 第三章 研究結果 22 第一節 資料分布 22 第二節 混合效應模型 24 第三節 類神經網路模型 29 第四章 綜合討論 35 第一節 研究發現與貢獻 35 第二節 研究限制 37 第三節 未來展望 39 第四節 研究結論 40 參考文獻 42 附錄 48 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 線上學習 | zh_TW |
| dc.subject | 學習成效 | zh_TW |
| dc.subject | 學習行為 | zh_TW |
| dc.subject | 早期干預 | zh_TW |
| dc.subject | 學習分析 | zh_TW |
| dc.subject | 混合效應模型 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | 間隔效應 | zh_TW |
| dc.subject | 學習策略 | zh_TW |
| dc.subject | learning behavior | en |
| dc.subject | Online learning | en |
| dc.subject | learning strategies | en |
| dc.subject | early intervention | en |
| dc.subject | Spacing Effect | en |
| dc.subject | learning outcomes | en |
| dc.title | 線上學習成效的關鍵指標 | zh_TW |
| dc.title | Leading Indicators of Online Learning Performance | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 王雅鈴;蔡欣穆 | zh_TW |
| dc.contributor.oralexamcommittee | Ya-Ling Wang;Hsin-Mu Tsai | en |
| dc.subject.keyword | 線上學習,學習成效,學習行為,早期干預,學習分析,混合效應模型,類神經網路,間隔效應,學習策略, | zh_TW |
| dc.subject.keyword | Online learning,learning outcomes,learning behavior,Spacing Effect,early intervention,learning strategies, | en |
| dc.relation.page | 48 | - |
| dc.identifier.doi | 10.6342/NTU202504281 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-18 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 心理學系 | - |
| dc.date.embargo-lift | 2025-08-21 | - |
| 顯示於系所單位: | 心理學系 | |
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