基於間隔效應與知識追蹤之適性化學習演算法系統設計與應用：以多益英語學習為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

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、勘誤回報

、線上人數：49

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姓名

徐世凡(Shih-Fan Hsu) 查詢紙本館藏

畢業系所

網路學習科技研究所

論文名稱

基於間隔效應與知識追蹤之適性化學習演算法系統設計與應用：以多益英語學習為例
(Design and Application of an Adaptive Learning Algorithm System based on Spacing Effects and Knowledge Tracing: Case Study on TOEIC English Learning)

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摘要(中)

英語作為全球通用的語言之一，在現今全球化的時代學習英語技能變得極為重要，而精通第二語言需要時間並且持續地學習和積極練習。適應性間隔複習方法對於第二語言學習具有正向影響，可提高學習者的長期記憶和學習品質。而透過知識追蹤演算法可以針對學習者的學習軌跡進行知識建模，了解學習者當下的知識掌握程度，協助學習者針對弱點進行練習與複習。
本研究拓展了以往的知識追蹤演算法，開發了Tagorithm知識追蹤演算法。並於兩所臺灣北部某國立大學招募學習者進行實驗，研究對象共計61人，對照組29人、實驗組32人。對照組練習時，系統從題庫中隨機抽取題目練習與複習；實驗組練習時，系統依照Tagorithm知識追蹤預測的知識點掌握程度推送學習者需要練習和複習的題目。本研究實驗進行12周，於第一周與第十二周分別進行前測與後測，包含英語能力測驗、線上自我調節學習問卷以及自我導向學習問卷。在實驗後，使用英語能力前測與練習紀錄作為模型的訓練集，英語能力後測作為測試集以評估模型的預測能力。
研究結果發現，Tagoritm知識追蹤演算法能夠準確預測學習者們各知識點的掌握程度，使用預測結果推薦題目的實驗組在學習成效有顯著的提升以及反應時間有顯著的下降。整體而言，相較於對照組的隨機出題方式，Tagoritm知識追蹤演算法能夠幫助學習者們有效率的提升學習成效。並提供視覺化儀表板呈現各知識點的掌握程度，讓學習者與管理者了解學習狀況。而經過實驗後對於線上自我調節學習能力以及自我導向學習能力兩組皆無顯著差異。最後，依據訪談結果，實驗參與者對於專業性與實用性面向表示肯定，並認為適性化間隔複習機制能夠幫助他們有效地複習並提升英語能力。

摘要(英)

English, as one of the globally recognized languages, has become increasingly important in the era of globalization. Achieving fluency in a second language requires time, continuous learning, and active practice. Adaptive spaced repetition methods have positively affected second language learning, enhancing learners′ long-term memory and learning quality. Through knowledge tracing algorithms, it is possible to model learners′ knowledge trajectories, understand their current grasp of knowledge, and assist them in practicing and reviewing areas of weakness.
This study expands on previous knowledge tracing algorithms and develops the Tagorithm knowledge tracing algorithm. It recruited 61 participants from two universities in northern Taiwan, with 29 in the control group and 32 in the experimental group. During practice sessions, the control group randomly selected questions from a question bank for practice and review, while the experimental group received practice and review recommendations based on Tagorithm′s predicted knowledge mastery. The experiment lasted 12 weeks, with pre-tests and post-tests conducted in the first and twelfth weeks, including English proficiency tests, online self-regulated learning questionnaires, and self-directed learning questionnaires. After the experiment, the English proficiency pre-tests and practice records were used as the training set, and the English proficiency post-tests were used as the test set to evaluate our proposed model′s predictive ability.
The research findings indicate that the Tagorithm knowledge tracing algorithm accurately predicts learners′ mastery of various knowledge points. The experimental group, which received practice recommendations based on the predictive results, showed significant improvement in learning outcomes and significantly reduced response times. Overall, compared to the control group′s random question approach, the Tagorithm knowledge tracing algorithm efficiently improved learning outcomes for learners and provided a visualized dashboard displaying the mastery levels of various knowledge points, enabling learners and administrators to understand learning progress. Following the experiment, there were no significant differences in online self-regulated learning and self-directed learning abilities between the two groups. Finally, based on the interview results, participants appreciated the algorithm′s professionalism and acknowledged the benefits of adaptive spaced repetition mechanism in helping them efficiently review and improve their English proficiency.

關鍵字(中)

★ 學習分析
★ 教育資料探勘
★ 知識追蹤
★ 第二外語學習

關鍵字(英)

★ Learning Analytics
★ Educational Data Mining
★ Knowledge Tracking
★ Second Language Acquisition

論文目次

中文摘要 i
Abstract ii
誌謝 iv
目錄 v
圖目表 ix
表目錄 x
一、緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究問題 2
1-4 名詞解釋 2
二、文獻探討 4
2-1 自我導向與自我調節 4
2-1-1 自我導向 4
2-1-2 自我調節於線上學習 5
2-2 第二外語學習與學習策略 7
2-2-1 第二外語學習 7
2-2-2 間隔效應 8
2-2-3 間隔效應應用於第二外語學習 9
2-3 教育資料探勘 10
2-3-1 學習管理系統 10
2-3-2 教育資料探勘 11
2-4 學習預測與知識追蹤 12
2-4-1 學習成效的預測 12
2-4-2 知識追蹤 14
2-4-3 知識追蹤相關應用 16
三、研究方法 17
3-1 研究對象 17
3-2 研究流程 18
3-3 研究工具 20
3-3-1 學習者背景問卷 20
3-3-2 多益英語考古題 20
3-3-3 英語能力測驗 22
3-3-4 線上自我調節學習問卷 22
3-3-5 自我導向問卷 23
3-3-6 訪談大綱 25
3-3-7 學習者學習log相關紀錄 26
3-4 分析方法 26
3-4-1 信度分析 26
3-4-2 斯皮爾曼相關性分析 27
3-4-3 Shapiro-Wilk檢定 27
3-4-4 機器學習中分類器的評估方法 28
四、系統設計與實作 29
4-1 TAG EASY學習管理平台系統架構 29
4-2 Tagorithm知識追蹤演算法 30
4-3 TAG EASY學習管理平台介紹 32
4-3-1 登入頁面 32
4-3-2 主頁面（學習口袋） 33
4-3-3 作答頁面與log收集機制 34
4-3-4 適性化推題機制 35
4-3-5 視覺化儀表板（知識庫） 35
五、研究結果 37
5-1 英語能力測驗 37
5-1-1 英語能力測驗之獨立樣本T檢定 38
5-1-2 英語能力測驗之成對樣本T檢定 38
5-2 線上自我調節學習問卷 39
5-3 自我導向學習問卷 45
5-4 TAG EASY學習管理平台系統log 49
5-4-1 對照組與實驗組對於學習特徵的影響 50
5-4-2 對照組與實驗組對於反應速度的影響 51
5-5 不同學習特徵對於學習表現的差異 52
5-5-1 不同參與度組對於學習成效的影響 52
5-5-2 不同反應時間組對於學習成效的影響 54
5-5-3 不同反應時間組對於反應時間的影響 56
5-6 學習者背景問卷 57
5-6-1 不同學習動機對於學習成效的影響 58
5-6-2 不同學習目標對於學習成效的影響 60
5-7 開放式問題與訪談結果 61
5-7-1 開放式問題 61
5-7-2 訪談結果 62
5-8 Tagorithm知識追蹤預測結果 64
六、討論 66
6-1 活躍學習者的學習特徵與學習表現 66
6-1-1 活躍學習者學習成效的差異 66
6-1-2 活躍學習者反應時間的差異 67
6-2 不同學習動機與目標對於學習成效的影響 67
6-3 Tagorithm知識追蹤演算法對於知識點的預測能力 68
6-4 與相關研究的比較 69
七、結論 71
7-1 研究結論 71
7-1-1 適性化間隔學習系統能夠有效率的提升學習成效 71
7-1-2 適性化間隔學習系統對學習者的認知能力並無影響 72
7-1-3 Tagorithm知識追蹤演算法模型能夠有效預測學生能力 72
7-2 研究限制 73
7-3 未來展望 73
參考文獻 74
附件一、行為與社會科學研究倫理委員審查 85
附件二、研究參與者知情同意書 86
附件三、英語能力測驗 90
附件四、學習者背景問卷 110
附件五、線上自我調節學習問卷 111
附件六、自我導向學習問卷 113
附件七、訪談大綱 116

參考文獻

Achchuthan, S., Thuseethan, S, Kuhanesan, & S. (2015). Usability Evaluation of Learning Management Systems in Sri Lankan Universities. Global Journal of Computer Science and Technology, 15(C1), 15-24. https://computerresearch.org/index.php/computer/article/view/1138
Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A. S. (2022). Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Education and Information Technologies, 27(6), 7805-7826. https://doi.org/10.1007/s10639-022-10934-8
Ambridge, B., Theakston, A. L., Lieven, E. V. M., & Tomasello, M. (2006). The distributed learning effect for children′s acquisition of an abstract syntactic construction. Cognitive Development, 21(2), 174-193. https://doi.org/10.1016/j.cogdev.2005.09.003
Artino, A., & Ioannou, A. (2008). Promoting Academic Motivation and Self-Regulation: Practical Guidelines for Online Instructors Society for Information Technology & Teacher Education International Conference 2008, Las Vegas, Nevada, USA. https://www.learntechlib.org/p/27160
Asarta, C. J., & Schmidt, J. R. (2013). Access Patterns of Online Materials in a Blended Course. Decision Sciences Journal of Innovative Education, 11(1), 107-123. https://doi.org/10.1111/j.1540-4609.2012.00366.x
Baker, R. S. J. d., Corbett, A. T., & Aleven, V. (2008). More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing. Intelligent Tutoring Systems, Berlin, Heidelberg.
Baker, R. S. J. d., Corbett, A. T., Gowda, S. M., Wagner, A. Z., MacLaren, B. A., Kauffman, L. R., Mitchell, A. P., & Giguere, S. (2010). Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor. User Modeling, Adaptation, and Personalization, Berlin, Heidelberg.
Baker, R. S. J. d., & Yacef, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1), 3-17. https://doi.org/10.5281/zenodo.3554657
Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S.-L. (2009). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1-6. https://doi.org/10.1016/j.iheduc.2008.10.005
Baturay, M., Yildirim, S., & Daloglu, A. (2009). Effects of Web-Based Spaced Repetition on Vocabulary Retention of Foreign Language Learners. Eurasian Journal of Educational Research, 8(34), 17-36.
Beck, J. E., & Woolf, B. P. (2000). High-level student modeling with machine learning. In G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Intelligent Tutoring Systems, Proceedings (Vol. 1839, pp. 584-593).
Calvet Liñán, L., & Juan Pérez, Á. A. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. RUSC. Universities and Knowledge Society Journal, 12(3). https://doi.org/10.7238/rusc.v12i3.2515
Cen, H., Koedinger, K., & Junker, B. (2006). Learning Factors Analysis – A General Method for Cognitive Model Evaluation and Improvement. Intelligent Tutoring Systems, Berlin, Heidelberg.
Chao, T. Y., & Sung, Y. T. (2019). An investigation of the reasons for test anxiety, time spent studying, and achievement among adolescents in Taiwan. Asia Pacific Journal of Education, 39(4), 469-484. https://doi.org/10.1080/02188791.2019.1671804
Chen, C.-M., & Chung, C.-J. (2008). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624-645. https://doi.org/10.1016/j.compedu.2007.06.011
Chen, C. M., Chen, L. C., & Yang, S. M. (2019). An English vocabulary learning app with self-regulated learning mechanism to improve learning performance and motivation. Computer Assisted Language Learning, 32(3), 237-260. https://doi.org/10.1080/09588221.2018.1485708
Chen, T. G., Peng, L. J., Yang, J. J., & Cong, G. D. (2021). Analysis of User Needs on Downloading Behavior of English Vocabulary APPs Based on Data Mining for Online Comments. Mathematics, 9(12), Article 1341. https://doi.org/10.3390/math9121341
Chen, Y., Liu, Q., Huang, Z., Wu, L., Chen, E., Wu, R., Su, Y., & Hu, G. (2017). Tracking Knowledge Proficiency of Students with Educational Priors Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, Singapore. https://doi.org/10.1145/3132847.3132929
Cheung, L. P., & Yang, H. (2017). Heterogeneous Features Integration in Deep Knowledge Tracing. Neural Information Processing, Cham.
Choffin, B., Popineau, F., Bourda, Y., & Vie, J.-J. (2019). DAS3H: modeling student learning and forgetting for optimally scheduling distributed practice of skills. Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019), 29-38. https://educationaldatamining.org/edm2019/awards/
Corbett, A. (2001). Cognitive Computer Tutors: Solving the Two-Sigma Problem. In M. Bauer, P. J. Gmytrasiewicz, & J. Vassileva, User Modeling 2001 Berlin, Heidelberg.
Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253-278. https://doi.org/10.1007/BF01099821
Cull, W. L. (2000). Untangling the benefits of multiple study opportunities and repeated testing for cued recall. Applied Cognitive Psychology, 14(3), 215-235. https://doi.org/10.1002/(SICI)1099-0720(200005/06)14:3<215::AID-ACP640>3.0.CO;2-1
Dabbagh, N., & Kitsantas, A. (2004). Supporting self-regulation in student-centered web-based learning environments. International Journal on E-Learning, 3(1), 40-47. https://www.learntechlib.org/primary/p/4104/.
Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). Current state and future trends: a citation network analysis of the learning analytics field Proceedings of the Fourth International Conference on Learning Analytics And Knowledge, Indianapolis, Indiana, USA. https://doi.org/10.1145/2567574.2567585
Dempster, F. N. (1996). Distributing and Managing the Conditions of Encoding and Practice. In E. L. Bjork & R. A. Bjork (Eds.), Memory (pp. 317-344). Academic Press. https://doi.org/10.1016/b978-012102570-0/50011-2
Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17-26. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880710773&partnerID=40&md5=8ccdc1568df4267036c3e3eb27303c41
Driscoll, M. P. (1994). Psychology of learning for instruction. Allyn & Bacon.
Dweck, C. (2015). Carol Dweck revisits the growth mindset. Education week, 35(5), 20-24.
Ezin, C. C., & Yilmaz, R. (2022). The effect of learning analytics-based interventions in mobile learning on students′ academic achievements, self-regulated learning skills, and motivations. Universal Access in the Information Society. https://doi.org/10.1007/s10209-022-00905-8
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317. https://doi.org/10.1504/IJTEL.2012.051816
Ferguson, R., & Clow, D. (2017). Where is the evidence? a call to action for learning analytics Proceedings of the Seventh International Learning Analytics & Knowledge Conference, Vancouver, British Columbia, Canada. https://doi.org/10.1145/3027385.3027396
Gan, W., Sun, Y., Ye, S., Fan, Y., & Sun, Y. (2019). Field-Aware Knowledge Tracing Machine by Modelling Students′ Dynamic Learning Procedure and Item Difficulty. 2019 International Conference on Data Mining Workshops (ICDMW),
García Botero, G., Questier, F., & Zhu, C. (2019). Self-directed language learning in a mobile-assisted, out-of-class context: do students walk the talk? Computer Assisted Language Learning, 32(1-2), 71-97. https://doi.org/10.1080/09588221.2018.1485707
Garrison, D. R. (2003). Cognitive presence for effective asynchronous online learning: The role of reflective inquiry, self-direction and metacognition. Elements of Quality Online Education: Practice and Direction, 4, 47-58. https://www.scopus.com/inward/record.uri?eid=2-s2.0-27644532404&partnerID=40&md5=eca9e36446a19c3c6f3c79027129207c
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71. https://doi.org/10.1007/s11528-014-0822-x
Graham, S. (2007). Learner strategies and self-efficacy: Making the connection. The Language Learning Journal, 35(1), 81-93. https://doi.org/10.1080/09571730701315832
Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2(2), 107-124. https://doi.org/10.1007/s11409-007-9016-7
Hiemstra, R. (1994). Self-directed learning.
Hirschel, R., & Fritz, E. (2013). Learning vocabulary: CALL program versus vocabulary notebook. System, 41(3), 639-653. https://doi.org/10.1016/j.system.2013.07.016
Hsieh, T. C., Wang, T. I., Su, C. Y., & Lee, M. C. (2012). A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning. Educational Technology & Society, 15(1), 273-288.
Hwang, G.-J., Spikol, D., & Li, K.-C. (2018). Guest Editorial
Trends and Research Issues of Learning Analytics and Educational Big Data. Journal of Educational Technology & Society, 21(2), 134-136. http://www.jstor.org/stable/26388386
Jeong, H. Y., Choi, C. R., & Song, Y. J. (2012). Personalized Learning Course Planner with E-learning DSS using user profile. Expert Systems with Applications, 39(3), 2567-2577. https://doi.org/10.1016/j.eswa.2011.08.109
Johnson, G. M. (2005). Student alienation, academic achievement, and WebCT use. Educational Technology and Society, 8(2), 179-189. https://www.scopus.com/inward/record.uri?eid=2-s2.0-23844551233&partnerID=40&md5=ec3143e8faa6d5856395c2de3286397d
Joo, Y. J., Joung, S., & Kim, J. (2014). Structural relationships among self-regulated learning, learning flow, satisfaction, and learning persistence in cyber universities. Interactive Learning Environments, 22(6), 752-770. https://doi.org/10.1080/10494820.2012.745421
Joseph, L., Abraham, S., Mani, B. P., & Rajesh, N. (2022). Exploring the Effectiveness of Learning Path Recommendation based on Felder-Silverman Learning Style Model: A Learning Analytics Intervention Approach. Journal of Educational Computing Research, 60(6), 1464-1489, Article 07356331211057816. https://doi.org/10.1177/07356331211057816
Juhanak, L., Zounek, J., & Rohlikova, L. (2019). Using process mining to analyze students′ quiz-taking behavior patterns in a learning management system. Computers in Human Behavior, 92, 496-506. https://doi.org/10.1016/j.chb.2017.12.015
Kennedy, C., & Levy, M. (2009). Sustainability and computer-assisted language learning: factors for success in a context of change. Computer Assisted Language Learning, 22(5), 445-463. https://doi.org/10.1080/09588220903345218
Kew, S. N., & Tasir, Z. (2022). Developing a Learning Analytics Intervention in E-learning to Enhance Students′ Learning Performance: A Case Study. Education and Information Technologies, 27(5), 7099-7134. https://doi.org/10.1007/s10639-022-10904-0
Kim, R., Olfman, L., Ryan, T., & Eryilmaz, E. (2014). Leveraging a personalized system to improve self-directed learning in online educational environments. Computers & Education, 70, 150-160. https://doi.org/10.1016/j.compedu.2013.08.006
Kim, S. K., & Webb, S. (2022). The Effects of Spaced Practice on Second Language Learning: A Meta-Analysis. Language Learning, 72(1), 269-319. https://doi.org/10.1111/lang.12479
Klingsieck, K. B., Fries, S., Horz, C., & Hofer, M. (2012). Procrastination in a distance university setting. Distance Education, 33(3), 295-310. https://doi.org/10.1080/01587919.2012.723165
Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers.
Koedinger, K., Cunningham, K., Skogsholm, A., & Leber, B. (2008). An open repository and analysis tools for fine-grained, longitudinal learner data. Educational data mining 2008,
Kornell, N. (2009). Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23(9), 1297-1317. https://doi.org/10.1002/acp.1537
Kornell, N., & Bjork, R. A. (2008). Learning concepts and categories: is spacing the "enemy of induction"? Psychol Sci, 19(6), 585-592. https://doi.org/10.1111/j.1467-9280.2008.02127.x
Kornell, N., Bjork, R. A., & Garcia, M. A. (2011). Why tests appear to prevent forgetting: A distribution-based bifurcation model. Journal of Memory and Language, 65(2), 85-97. https://doi.org/10.1016/j.jml.2011.04.002
Kosareva, L., Demidov, L., Ikonnikova, I., & Shalamova, O. (2021). Ispring platform for learning Russian as a foreign language. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1913423
Kurata, Y., Bano, R. M. L., & Marcelo, M. C. (2018). Effectiveness of Learning Management System Application in the Learnability of Tertiary Students in an Undergraduate Engineering Program in the Philippines. https://doi.org/10.1007/978-3-319-60018-5_15
Lasfeto, D. B., & Ulfa, S. (2023). Modeling of Online Learning Strategies Based on Fuzzy Expert Systems and Self-Directed Learning Readiness: The Effect on Learning Outcomes. Journal of Educational Computing Research, 60(8), 2081-2104, Article 07356331221094249. https://doi.org/10.1177/07356331221094249
Lee, C. (2012). Tertiary students′ perceptions and use of information computer technology for language consultation. Teaching in Higher Education, 17(3), 323-339. https://doi.org/10.1080/13562517.2011.641007
Lee, C., Yeung, A. S., & Ip, T. (2017). University english language learners′ readiness to use computer technology for self-directed learning. System, 67, 99-110. https://doi.org/10.1016/j.system.2017.05.001
Lin, B., & Hsieh, C.-t. (2001). Web-based teaching and learner control: a research review. Computers & Education, 37(3), 377-386. https://doi.org/10.1016/S0360-1315(01)00060-4
Lindsey, R. V., Shroyer, J. D., Pashler, H., & Mozer, M. C. (2014a). Improving Students′ Long-Term Knowledge Retention Through Personalized Review. Psychological science, 25(3), 639-647. https://doi.org/10.1177/0956797613504302
Lindsey, R. V., Shroyer, J. D., Pashler, H., & Mozer, M. C. (2014b). Improving students′ long-term knowledge retention through personalized review. Psychol Sci, 25(3), 639-647. https://doi.org/10.1177/0956797613504302
Liu, H., Zhang, T., Li, F., Gu, Y., & Yu, G. (2021). Tracking Knowledge Structures and Proficiencies of Students With Learning Transfer. IEEE Access, 9, 55413-55421. https://doi.org/10.1109/ACCESS.2020.3032141
Long, H. B. (1989). Self-Directed Learning: Emerging Theory & Practice. ERIC.
Long, H. B. (1994). Resources related to overcoming resistance to self-direction in learning. new Directions for Adult and Continuing Education, 1994(64), 13-21. https://doi.org/10.1002/ace.36719946404
Louhab, F. E., Bahnasse, A., Bensalah, F., Khiat, A., Khiat, Y., & Talea, M. (2020). Novel approach for adaptive flipped classroom based on learning management system. Education and Information Technologies, 25(2), 755-773. https://doi.org/10.1007/s10639-019-09994-0
Luan, J. (2002). Data Mining and Its Applications in Higher Education. new Directions for Institutional Research, 2002(113), 17-36. https://doi.org/10.1002/ir.35
Ma, Y., Han, P., Qiao, H., Cui, C., Yin, Y., & Yu, D. (2022). SPAKT: A Self-Supervised Pre-TrAining Method for Knowledge Tracing. IEEE Access, 10, 72145-72154. https://doi.org/10.1109/ACCESS.2022.3187987
Mega, C., Ronconi, L., & De Beni, R. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology, 106, 121-131. https://doi.org/10.1037/a0033546
Mercer, S., & Ryan, S. (2009). A mindset for EFL: learners’ beliefs about the role of natural talent. ELT Journal, 64(4), 436-444. https://doi.org/10.1093/elt/ccp083
Miles, S. W. (2014). Spaced vs. massed distribution instruction for L2 grammar learning. System, 42, 412-428. https://doi.org/10.1016/j.system.2014.01.014
Morris, L. V., Finnegan, C., & Wu, S.-S. (2005). Tracking student behavior, persistence, and achievement in online courses. The Internet and Higher Education, 8(3), 221-231. https://doi.org/10.1016/j.iheduc.2005.06.009
Nagatani, K., Zhang, Q., Sato, M., Chen, Y.-Y., Chen, F., & Ohkuma, T. (2019). Augmenting Knowledge Tracing by Considering Forgetting Behavior The World Wide Web Conference, San Francisco, CA, USA. https://doi.org/10.1145/3308558.3313565
Nedungadi, P., & Remya, M. S. (2015). Incorporating forgetting in the Personalized, Clustered, Bayesian Knowledge Tracing (PC-BKT) model. 2015 International Conference on Cognitive Computing and Information Processing(CCIP),
Onem, E., & Ergenc, I. (2013). Testing a model of teaching for anxiety and success for English language teaching. Cambridge Journal of Education, 43(3), 357-376. https://doi.org/10.1080/0305764x.2013.792786
Ortega, L. (2014). Understanding second language acquisition. Routledge.
Pardos, Z. A., & Heffernan, N. T. (2010). Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing. User Modeling, Adaptation, and Personalization, Berlin, Heidelberg.
Pardos, Z. A., & Heffernan, N. T. (2011). KT-IDEM: Introducing Item Difficulty to the Knowledge Tracing Model. User Modeling, Adaption and Personalization, Berlin, Heidelberg.
Pavlik Jr, P., Cen, H., & Koedinger, K. (2009). Performance Factors Analysis - A new Alternative to Knowledge Tracing (Vol. 200). https://doi.org/10.3233/978-1-60750-028-5-531
Pavlik Jr., P. I., & Anderson, J. R. (2005). Practice and Forgetting Effects on Vocabulary Memory: An Activation-Based Model of the Spacing Effect. Cognitive Science, 29(4), 559-586. https://doi.org/10.1207/s15516709cog0000_14
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
Pemberton, R., & Cooker, L. (2012). Self-directed Learning: Concepts, Practice, and a Novel Research Methodology. In S. Mercer, S. Ryan, & M. Williams (Eds.), Psychology for Language Learning: Insights from Research, Theory and Practice (pp. 203-219). Palgrave Macmillan UK. https://doi.org/10.1057/9781137032829_14
Pham, X. L., Chen, G. D., Nguyen, T. H., & Hwang, W. Y. (2016). Card-based design combined with spaced repetition: A new interface for displaying learning elements and improving active recall. Computers & Education, 98, 142-156. https://doi.org/10.1016/j.compedu.2016.03.014
Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92, 544-555. https://doi.org/10.1037/0022-0663.92.3.544
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and Self-Regulated Learning Components of Classroom Academic Performance. Journal of Educational Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33
Pintrich, P. R., & Zusho, A. (2002). Chapter 10 - The Development of Academic Self-Regulation: The Role of Cognitive and Motivational Factors. In A. Wigfield & J. S. Eccles (Eds.), Development of Achievement Motivation (pp. 249-284). Academic Press. https://doi.org/10.1016/B978-012750053-9/50012-7
Psaromiligkos, Y., Orfanidou, M., Kytagias, C., & Zafiri, E. (2011). Mining log data for the analysis of learners′ behaviour in web-based learning management systems. Operational Research, 11(2), 187-200. https://doi.org/10.1007/s12351-008-0032-4
Puzziferro, M. (2008). Online Technologies Self-Efficacy and Self-Regulated Learning as Predictors of Final Grade and Satisfaction in College-Level Online Courses. American Journal of Distance Education, 22(2), 72-89. https://doi.org/10.1080/08923640802039024
Qiu, Y., Qi, Y., Lu, H., Pardos, Z. A., & Heffernan, N. T. (2011). Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing. Educational Data Mining,
Rafique, A., Khan, M. S., Jamal, M. H., Tasadduq, M., Rustam, F., Lee, E. E., Washington, P. B., & Ashraf, I. (2021). Integrating Learning Analytics and Collaborative Learning for Improving Student′s Academic Performance. IEEE Access, 9, 167812-167826. https://doi.org/10.1109/access.2021.3135309
Reyes, J. A. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends, 59(2), 75-80. https://doi.org/10.1007/s11528-015-0842-1
Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618. https://doi.org/10.1109/TSMCC.2010.2053532
Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining and Knowledge Discovery, 3(1), 12-27. https://doi.org/10.1002/widm.1075
Şahin, M., & Yurdugül, H. (2021). Educational Data Mining and Learning Analytics: Past, Present and Future. Bartın University Journal of Faculty of Education, 9(1), 121-131. https://dergipark.org.tr/en/pub/buefad/issue/51796/606077
Seibert Hanson, A. E., & Brown, C. M. (2020). Enhancing L2 learning through a mobile assisted spaced-repetition tool: an effective but bitter pill? Computer Assisted Language Learning, 33(1-2), 133-155. https://doi.org/10.1080/09588221.2018.1552975
Seliger, H. W. (1977). DOES PRACTICE MAKE PERFECT?: A STUDY OF INTERACTION PATTERNS AND L2 COMPETENCE1. Language Learning, 27(2), 263-278. https://doi.org/10.1111/j.1467-1770.1977.tb00122.x
Seng, L. C., & Hok, T. T. (2003). Humanizing e-learning. Proceedings. 2003 International Conference on Cyberworlds, 418-422.
Serrano, R., & Huang, H. Y. (2018). Learning Vocabulary Through Assisted Repeated Reading: How Much Time Should There Be Between Repetitions of the Same Text? Tesol Quarterly, 52(4), 971-994. https://doi.org/10.1002/tesq.445
Shadach, E., Levy-Frank, I., Levy, S., Amitai, T., & Shadach, E. (2017). Preparatory Test Anxiety: Cognitive, Emotionality, and Behavior Components. Studia Psychologica, 59(4), 271-279. https://doi.org/10.21909/sp.2017.04.747
Shioya, S. (1995). RELATIONSHIP OF TEST ANXIETY AND LEARNING-BEHAVIOR TO COGNITIVE APPRAISALS IN HIGH-SCHOOL-STUDENTS. Japanese Journal of Educational Psychology, 43(2), 125-133. https://doi.org/10.5926/jjep1953.43.2_125
Song, D., & Bonk, C. J. (2016). Motivational factors in self-directed informal learning from online learning resources. Cogent Education, 3(1), 1205838. https://doi.org/10.1080/2331186X.2016.1205838
Song, L., Singleton, E. S., Hill, J. R., & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics. The Internet and Higher Education, 7(1), 59-70. https://doi.org/10.1016/j.iheduc.2003.11.003
Spaulding, S., Gordon, G., & Breazeal, C. (2016). Affect-aware student models for robot tutors.
Stone, N. J. (2000). Exploring the Relationship between Calibration and Self-Regulated Learning. Educational Psychology Review, 12(4), 437-475. https://doi.org/10.1023/A:1009084430926
Sung, Y. T., Chao, T. Y., & Tseng, F. L. (2016). Reexamining the relationship between test anxiety and learning achievement: An individual-differences perspective. Contemporary Educational Psychology, 46, 241-252. https://doi.org/10.1016/j.cedpsych.2016.07.001
Tempelaar, D., Rienties, B., & Nguyen, Q. (2021). The Contribution of Dispositional Learning Analytics to Precision Education. Educational Technology & Society, 24(1), 109-122. https://www.jstor.org/stable/26977861
Teo, T., & Lee, C. (2010). Fostering self-directed learning with ICT. ICT for self-directed and collaborative learning, 39-51.
Thai-Nghe, N., Horvath, T., & Schmidt-Thieme, L. (2011). Factorization Models for Forecasting Student Performance.
Troussas, C., Chrysafiadi, K., & Virvou, M. (2019). An intelligent adaptive fuzzy-based inference system for computer-assisted language learning. Expert Systems with Applications, 127, 85-96. https://doi.org/10.1016/j.eswa.2019.03.003
Tuckman, B. W. (2005). Relations of Academic Procrastination, Rationalizations, and Performance in a Web Course with Deadlines. Psychological Reports, 96(3_suppl), 1015-1021. https://doi.org/10.2466/pr0.96.3c.1015-1021
Underwood, B. J. (1970). A breakdown of the total-time law in free-recall learning. Journal of Verbal Learning and Verbal Behavior, 9(5), 573-580. https://doi.org/10.1016/S0022-5371(70)80104-9
Victori, M., & Lockhart, W. (1995). Enhancing metacognition in self-directed language learning. System, 23(2), 223-234. https://doi.org/10.1016/0346-251X(95)00010-H
Vie, J.-J., & Kashima, H. (2019). Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 750-757. https://doi.org/10.1609/aaai.v33i01.3301750
Vlach, H. A., & Sandhofer, C. M. (2012). Distributing learning over time: the spacing effect in children′s acquisition and generalization of science concepts. Child development, 83(4), 1137-1144. https://doi.org/10.1111/j.1467-8624.2012.01781.x
Wang, T. H., Sun, Y., & Huang, N. W. (2021). Implementation of web-based dynamic assessment in improving low English achievers′ learning effectiveness. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2021.1998129
Wang, Y. H., & Liao, H. C. (2011). Adaptive learning for ESL based on computation. British journal of educational technology, 42(1), 66-87. https://doi.org/10.1111/j.1467-8535.2009.00981.x
Witten, I. H., & Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record, 31(1), 76-77.
Wolters, C. A. (2003). Understanding procrastination from a self-regulated learning perspective. Journal of Educational Psychology, 95(1), 179-187. https://doi.org/10.1037/0022-0663.95.1.179
Xodabande, I., Iravi, Y., Mansouri, B., & Matinparsa, H. (2022). Teaching Academic Words With Digital Flashcards: Investigating the Effectiveness of Mobile-Assisted Vocabulary Learning for University Students. Frontiers in Psychology, 13, Article 893821. https://doi.org/10.3389/fpsyg.2022.893821
Xu, Y. Q. (2022). An Adaptive Learning System for English Vocabulary Using Machine Learning. Mobile Information Systems, 2022, Article 3501494. https://doi.org/10.1155/2022/3501494
Yang, C. C. Y., Chen, I. Y. L., & Ogata, H. (2021). Toward Precision Education: Educational Data Mining and Learning Analytics for Identifying Students′ Learning Patterns with Ebook Systems. Educational Technology & Society, 24(1), 152-163.
Yang, C. C. Y., & Ogata, H. (2023). Personalized learning analytics intervention approach for enhancing student learning achievement and behavioral engagement in blended learning. Education and Information Technologies, 28(3), 2509-2528. https://doi.org/10.1007/s10639-022-11291-2
Yang, Y.-F., Hsieh, W.-M., Wong, W.-K., Hong, Y.-C., & Lai, S.-C. (2022). Reducing students’ foreign language anxiety to improve English vocabulary learning in an online simulation game. Computer Assisted Language Learning, 1-23. https://doi.org/10.1080/09588221.2022.2039203
Yang, Y. X. (2017). Test Anxiety Analysis of Chinese College Students in Computer-based Spoken English Test. Educational Technology & Society, 20(2), 63-73. <Go to ISI>://WOS:000400882300006
You, J. W. (2015). Examining the Effect of Academic Procrastination on Achievement Using LMS Data in e-Learning. Journal of Educational Technology & Society, 18(3), 64-74. http://www.jstor.org/stable/jeductechsoci.18.3.64
You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23-30. https://doi.org/10.1016/j.iheduc.2015.11.003
You, J. W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Computers & Education, 77, 125-133. https://doi.org/10.1016/j.compedu.2014.04.018
Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Educational Technology and Society, 10(2), 71-83. http://www.jstor.org/stable/jeductechsoci.10.2.71
Yuling, M., Han, P., Qiao, H., Cui, C., Yin, Y., & Yu, D. (2022). SPAKT: A Self-supervised Pre-trAining method for Knowledge Tracing. IEEE Access, 10, 1-1. https://doi.org/10.1109/ACCESS.2022.3187987
Zaharias, P., & Pappas, C. (2016). Quality Management of Learning Management Systems: A User Experience Perspective.
Zaidi, A., Caines, A., Moore, R., Buttery, P., & Rice, A. (2020). Adaptive Forgetting Curves for Spaced Repetition Language Learning. Artificial Intelligence in Education, Cham.
Zhang, C. Y., Patras, P., & Haddadi, H. (2019). Deep Learning in Mobile and Wireless Networking: A Survey. Ieee Communications Surveys and Tutorials, 21(3), 2224-2287. https://doi.org/10.1109/comst.2019.2904897
Zhang, R. F., Zou, D., & Xie, H. R. (2022). Spaced repetition for authentic mobile-assisted word learning: nature, learner perceptions, and factors leading to positive perceptions. Computer Assisted Language Learning, 35(9), 2593-2626. https://doi.org/10.1080/09588221.2021.1888752
Zhu, M. N., Bonk, C. J., & Doo, M. Y. (2020). Self-directed learning in MOOCs: exploring the relationships among motivation, self-monitoring, and self-management. Etr&D-Educational Technology Research and Development, 68(5), 2073-2093. https://doi.org/10.1007/s11423-020-09747-8
Zimmerman, B. J. (2013). From Cognitive Modeling to Self-Regulation: A Social Cognitive Career Path. Educational psychologist, 48(3), 135-147. https://doi.org/10.1080/00461520.2013.794676
Zimmerman, B. J., & Martinez-Pons, M. (1988). Construct Validation of a Strategy Model of Student Self-Regulated Learning. Journal of Educational Psychology, 80(3), 284-290. https://doi.org/10.1037/0022-0663.80.3.284

指導教授

洪暉鈞(Hui-Chun Hung)

審核日期

2023-7-26

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