應用指數平滑法實現短期學習成效預測與學習歷程儀表板系統建置

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姓名

黃浩軒(Hao-Hsuan Huang) 查詢紙本館藏

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網路學習科技研究所

論文名稱

應用指數平滑法實現短期學習成效預測與學習歷程儀表板系統建置
(Application of Exponential Smoothing for Short-term Learning Outcome Prediction and Development of Dashboard System for Learning Portfolio)

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至系統瀏覽論文 (2029-8-1以後開放)

摘要(中)

隨著資訊科技進步及網路發展，人們開始利用數位學習平台提升知識。知識追蹤是數位學習系統的核心，根據學習者的學習紀錄追蹤知識掌握程度並預測學習成效。但目前知識追蹤模型仍存在模型複雜及未考慮遺忘因素等問題。因此，本研究嘗試使用指數平滑法進行改善並預測學習成效及設計開發一個學習儀表板，讓學習者及教師迅速的透過視覺化分析瞭解學習狀態。
本研究採取問卷調查法透過科技接受模式以探討不同背景的學習者對於本研究所開發之視覺化學習儀表板ES-Dashboard之知覺有用性及知覺易用性差異，透過Suvery Cake網路問卷發放進行資料蒐集，回收有效樣本共計64份，藉由敘述性統計、單因子變異數分析等統計方法進行分析。研究結果顯示ES-Dashboard具備不錯之知覺可用性及易用性，且職業對學習儀表板的「知覺有用性」與「知覺易用性」有顯著影響；開放問題分析結果為學習者認為不僅可以在ES-Dashboard迅速了解當前的學習狀態，更可透過系統視覺化圖表快速掌握學習重點、分配學習時間並進行進度追蹤。
除此之外，模型驗證結果顯示本研究所提出基於指數平滑法的知識追蹤模型(ES)不論在整體或是分群情況下，模型指標RMSE(12.57<19.01)及MAE(11.2<17.61)皆優於傳統知識追蹤模型(DKT)。

摘要(英)

Along with the advance in information technology and the popularity of the internet, people have started using e-learning platforms to enhance their knowledge. Knowledge tracing is the core of digital learning systems, tracking learners′ knowledge mastery and predicting learning outcomes based on their learning records. However, current knowledge tracing models face issues such as complexity and neglect of forgetting factors. Therefore, this study attempts to use the exponential smoothing method to improve and predict learning outcomes and design and develop a learning dashboard, allowing learners and teachers to quickly understand learning statuses through visual analysis.
This study adopts a questionnaire survey method based on the Technology Acceptance Model to explore the differences in perceived usefulness and perceived ease of use of the visualized learning dashboard, ES-Dashboard, developed in this study among learners with different backgrounds. Data was collected through an online questionnaire distributed via Suvery Cake, with a total of 64 valid samples collected. The data was analyzed using descriptive statistics and one-way ANOVA. The results indicate that the ES-Dashboard has good perceived usability and ease of use, and that occupation significantly influences the perceived usefulness and perceived ease of use of the learning dashboard. Open-ended question analysis revealed that learners believe they can quickly understand their current learning status through the ES-Dashboard, and also quickly grasp key learning points, allocate learning time, and track progress through the system′s visual charts.
Furthermore, the model validation results indicate that the knowledge tracing model proposed in this study based on exponential smoothing (ES) outperforms the traditional knowledge tracing model (DKT) in both overall and subgroup scenarios, with RMSE (12.57 < 19.01) and MAE (11.2 < 17.61).

關鍵字(中)

★ 指數平滑法
★ 學習儀表板
★ 知識追蹤
★ 學習分析

關鍵字(英)

★ Exponential Smoothing
★ Learning Analytics Dashboard
★ Knowledge Tracing
★ Learning Analytics

論文目次

中文摘要 i
Abstract ii
致謝 iii
圖目錄 viii
表目錄 x
一、緒論 1
1-1 研究背景與動機 1
1-2 研究目的 3
1-3 研究問題 3
1-4 研究流程 3
1-5 名詞解釋 4
二、文獻探討 6
2-1 數位學習 6
2-1-1 數位學習發展現況 6
2-1-2 數位學習適性化發展與挑戰 7
2-2 智慧家教系統 8
2-2-1 智慧家教系統與適性化 8
2-2-2 智慧家教與知識追蹤 9
2-3 知識追蹤 9
2-3-1 貝葉斯知識追蹤 10
2-3-2 深度知識追蹤 10
2-3-3 知識追蹤的延伸與應用 11
2-4 時間序列 12
2-4-1 移動平均法 12
2-4-2 指數平滑法 12
2-4-3 指數平滑法在教育領域相關研究與應用 13
2-5 學習分析 14
2-5-1 學習分析儀表板 14
2-5-2 學習分析儀表版在教育之應用 15
2-6 VARK學習風格 16
三、研究方法 18
3-1 知識追蹤模型建模與模型評估 18
3-1-1 實驗數據收集 19
3-1-2 資料前處理 19
3-1-3 測試集資料 20
3-1-4 訓練集資料 20
3-1-5 資料建模 20
3-1-6 模型調校 22
3-1-7 模型預測與評估 22
3-2 不同學習行為模式驗證 22
3-2-1 分群特徵選擇 22
3-2-2 產生分群 23
3-2-3 分群及群組命名 23
3-3 系統可用性及易用性分析 26
3-3-1 研究對象與方式 26
3-3-2 研究工具 26
3-3-3 問卷實施流程 27
3-3-4 統計分析方法 28
3-3-5 受測者編碼及原則說明 28
四、系統設計與實作 29
4-1 系統簡介 29
4-2 系統架構 29
4-3 系統功能介紹 32
4-3-1 登入頁面 32
4-3-2 系統功能介紹 32
4-3-3 題庫試題分布功能介紹 33
4-3-4 學生學習分析功能介紹 37
4-3-5 分群學習分析功能介紹 41
4-3-6 預測模型建置功能介紹 44
五、資料分析與結果 48
5-1 問卷資料分析 48
5-1-1 背景資料之敘述性統計 48
5-1-2 問卷量化分析 50
5-1-3 不同學習者背景對學習儀表板的知覺可用性及易用性之探討 53
5-1-4 問卷開放問題分析 54
5-1-5 問卷開放問題探討 58
5-2 不同答題模式分析 59
5-2-1 不同答題模式群組之前、後測之描述性統計 59
5-2-2 不同答題模式群組之共變數分析 59
5-3 模型預測與評估 61
5-3-1 所有樣本指數平滑最佳α值 61
5-3-2 所有樣本模型預測與評估 61
5-3-3 樣本分群之模型預測與評估 62
5-3-4 不同群組指數平滑最佳α值 63
六、討論 66
6-1 基於短期數據的知識追蹤模型 66
6-1-1 知識追蹤模型在短期學習數據的預測成效 66
6-1-2 知識追蹤模型在短期數據與不同學習模式之預測成效 67
6-1-3 不同學習模式在不同α值之學習成效預測表現 68
6-2 其它知識追蹤相關研究探討 69
6-2-1 指數平滑法預測學習成效相關研究 69
6-2-2 指數平滑法與深度學習模型相關研究 70
6-3 IRT系統相關研究 70
七、結論 73
7-1 研究結論 73
7-1-1 指數平滑法在短期學習數據預測成效顯著且優於傳統知識追蹤 73
7-1-2 指數平滑法知識模型對不同學習行為特徵群體在預測成效上無差異 73
7-1-3 基於指數平滑法的儀表板系統設計 74
7-1-4 學習者透過ES-Dashboard可快速掌握學習狀態並分配學習時間 74
7-2 研究限制 74
7-3 未來展望 75
參考文獻 77
附錄 89
附錄一問卷背景資料 89
附錄二科技接受模式問卷-知覺有用性 90
附錄三科技接受模式問卷-知覺易用性 91
附錄四問卷開放式問題 92
附錄五開放式問題1回覆內容 93
附錄六開放式問題2回覆內容 96
附錄七開放式問題3回覆內容 98

參考文獻

Abdelrahman, G., & Wang, Q. (2019). Knowledge Tracing with Sequential Key-Value Memory Networks. Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval. https://doi.org/10.1145/3331184.3331195
Abdelrahman, G., & Wang, Q. (2023). Learning data teaching strategies via knowledge tracing. Knowledge-Based Systems, 269, 110511. https://doi.org/10.1016/j.knosys.2023.110511
Adiego, J., & Martín-Cruz, N. (2021). Training competences in smart cities: an online program for higher education students. International Journal of Sustainability in Higher Education, 22(7), 1630-1645. https://doi.org/10.1108/IJSHE-08-2020-0307
Alam, A. (2021). Cloud-Based E-Learning: Development of Conceptual Model for Adaptive E-Learning Ecosystem Based on Cloud Computing Infrastructure. International Conference on Artificial Intelligence and Data Science. https://doi.org/10.1007/978-3-031-21385-4_31
Almarzuki, H. F., Samah, K. A. F. A., Rahim, S. K. N. A., Ibrahim, S., & Riza, L. S. (2024). Enhancement of Prediction Model for Students’ Performance in Intelligent Tutoring System. Journal of Artificial Intelligence and Technology. https://doi.org/10.37965/jait.2024.0319
Alshammari, M. (2016). Adaptation based on learning style and knowledge level in e-learning systems University of Birmingham].
Anderson, J. R., Boyle, C. F., & Reiser, B. J. (1985). Intelligent Tutoring Systems. Science, 228(4698), 456-462. https://doi.org/10.1126/science.228.4698.456
Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2018). Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 23, 537-553. https://doi.org/10.1007/s10639-017-9616-z
Bali, M. M. E. I., Aliyah, Z., & Humaidi, D. (2022). Effectiveness of hybrid learning assisted in e-learning media in mathematics learning at elementary school. Journal of Innovation in Educational and Cultural Research, 3(4), 683-690. https://doi.org/10.46843/jiecr.v3i4.340
Barać, D., Bogdanović, Z., Milić, A., Jovanić, B., & Radenković, B. (2011). Developing adaptive e-learning portal in higher education. 14th Toulon-Verona Conf." Organizational Excell. Serv.
Baylari, A., & Montazer, G. A. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 36(4), 8013-8021. https://doi.org/10.1016/j.eswa.2008.10.080
Beldagli, B., & Adiguzel, T. (2010). Illustrating an ideal adaptive e-learning: A conceptual framework. Procedia-Social and Behavioral Sciences, 2(2), 5755-5761. https://doi.org/10.1016/j.sbspro.2010.03.939
Benhamdi, S., Babouri, A., & Chiky, R. (2017). Personalized recommender system for e-Learning environment. Education and Information Technologies, 22, 1455-1477. https://doi.org/10.1007/s10639-016-9504-y
Bezerra, A. K. L., & Santos, É. M. C. (2020). Prediction the daily number of confirmed cases of COVID-19 in Sudan with ARIMA and Holt Winter exponential smoothing. International Journal of Development Research, 10(08), 39408-39413. https://doi.org/10.37118/ijdr.19811.08.2020
Box, G. E., Jenkins, G. M., & Reinsel, G. (1970). Time series analysis: forecasting and control. San Francisco : Holden-Day.
Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of AI Studies in half a century. Sustainability, 13(2), 800. https://doi.org/10.3390/su13020800
Brown, R. G., & Meyer, R. F. (1961). The fundamental theorem of exponential smoothing. Operations Research, 9(5), 673-685. https://doi.org/10.1287/opre.9.5.673
Brusilovsky, P., & Christoph, P. (2003). Adaptive and Intelligent Web-based Educational Systems. Int. J. Artif. Intell. Ed., 13(2–4), 159–172. https://sites.pitt.edu/~peterb/papers/AIWBES.pdf
Brusilovsky, P., & Peylo, C. (2003). Adaptive and Intelligent Web-based Educational Systems. International Journal of Artificial Intelligence in Education, 13, 156–169.
Brusilovsky, P., Schwarz, E., & Weber, G. (1996). ELM-ART: An intelligent tutoring system on World Wide Web. Intelligent Tutoring Systems: Third International Conference, ITS′96 Montréal, Canada, June 12–14, 1996 Proceedings 3. https://doi.org/10.1007/3-540-61327-7_123
Burns, H. L., & Capps, C. G. (2013). Intelligent tutoring systems: an introduction (Vol. 1).
Burton, R. R., & Brown, J. S. (1976). A tutoring and student modelling paradigm for gaming environments. SIGCSE Bull., 8(1), 236–246. https://doi.org/10.1145/952989.803477
Cai, T., Yang, S., Li, J., Sheng, Q. Z., Yang, J., Wang, X., Zhang, W. E., & Gao, L. (2022). Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social Networks. IEEE Transactions on Knowledge and Data Engineering, 35(7), 7030-7044. https://doi.org/10.1109/TKDE.2022.3199494
Carchiolo, V., Longheu, A., Malgeri, M., & Mangioni, G. (2007). A model for a web-based learning system. Information Systems Frontiers, 9(2-3), 267-282. https://doi.org/10.1007/s10796-006-9014-3
Castro-Schez, J. J., Glez-Morcillo, C., Albusac, J., & Vallejo, D. (2021). An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning. Information Sciences, 544, 446-468. https://doi.org/10.1016/j.ins.2020.08.079
Cen, H., Koedinger, K., & Junker, B. (2006). Learning Factors Analysis – A General Method for Cognitive Model Evaluation and Improvement. International Conference on Intelligent Tutoring Systems. https://doi.org/10.1007/11774303_17
Charleer, S., Klerkx, J., Santos Odriozola, J. L., & Duval, E. (2013). Improving awareness and reflection through collaborative, interactive visualizations of badges. ARTEL13: Proceedings of the 3rd Workshop on Awareness and Reflection in Technology-enhanced Learning. https://api.semanticscholar.org/CorpusID:18080786
Chen, T., Chen, J., Or, C. K., & Lo, F. P. (2022). Path analysis of the roles of age, self-efficacy, and TAM constructs in the acceptance of performing upper limb exercises through immersive virtual reality games. International Journal of Industrial Ergonomics, 91, 103360. https://doi.org/10.1016/j.ergon.2022.103360
Chopra, S., & Meindl, P. (2013). Supply Chain Management: Strategy, Planning, and Operation. Pearson. https://books.google.com.tw/books?id=jj-YygAACAAJ
Chowdhury, T. I., Hoque, M. R., Wanke, P., Raihan, M. Z., & Azad, M. A. K. (2022). Antecedents of Perceived Service Quality of Online Education During a Pandemic: Configuration Analysis Based on Fuzzy-Set Qualitative Comparative Analysis. Evaluation Review, 46(3), 235-265. https://doi.org/10.1177/0193841X2210848
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695. https://doi.org/10.1080/13562517.2013.827653
Cole, J., & Foster, H. (2007). Using Moodle: Teaching with the popular open source course management system. " O′Reilly Media, Inc.".
Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4, 253-278. https://doi.org/10.1007/BF01099821
Dargue, B., & Biddle, E. (2014). Just Enough Fidelity in Student and Expert Modeling for ITS. International Conference on Augmented Cognition. https://doi.org/10.1007/978-3-319-07527-3_19
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
Deng, C., Zhang, X., Huang, Y., & Bao, Y. (2021). Equipping seasonal exponential smoothing models with particle swarm optimization algorithm for electricity consumption forecasting. Energies, 14(13), 4036. https://doi.org/10.3390/en14134036
Diao, X.-L., Zheng, C.-H., Zeng, Q.-T., Duan, H., & Zhao, H. (2023). Precise modeling of learning process based on multiple behavioral features for knowledge tracing. Journal of Intelligent & Fuzzy Systems, 44(6), 10747-10764. https://doi.org/10.3233/JIFS-224351
Doleck, T., Lemay, D. J., Basnet, R. B., & Bazelais, P. (2020). Predictive analytics in education: a comparison of deep learning frameworks. Education and Information Technologies, 25, 1951-1963. https://doi.org/10.1007/s10639-019-10068-4
Dong, Y., Fu, Y., Wang, L., Chen, Y., Dong, Y., & Li, J. (2020). A sentiment analysis method of capsule network based on BiLSTM. IEEE access, 8, 37014-37020. https://doi.org/10.1109/ACCESS.2020.2973711
Duncombe, J. (1959). Infrared navigation—Part I: An assessment of feasibility. IEEE Trans. Electron Devices, 11(1), 34-39.
Dunn, R. S., & Dunn, K. J. (1992). Teaching elementary students through their individual learning styles: Practical approaches for grades 3-6. Boston : Allyn and Bacon.
Durall, E., & Toikkanen, T. (2013). Feeler: feel good and learn better. Third International Workshop on Awareness and Reflection in Technology-Enhanced Learning (ARTEL 2013). https://api.semanticscholar.org/CorpusID:2285756
Ennouamani, S., & Mahani, Z. (2017). An overview of adaptive e-learning systems. 2017 eighth international conference on intelligent computing and information systems (ICICIS). https://doi.org/10.1109/INTELCIS.2017.8260060
Erümit, A. K., & Çetin, İ. (2020). Design framework of adaptive intelligent tutoring systems. Education and Information Technologies, 25(5), 4477-4500. https://doi.org/10.1007/s10639-020-10182-8
Erragcha, N., & Babay, H. (2023). Perceived quality and satisfaction with e-Learning during COVID-19: Moderating role of co-production. International Journal of Information and Education Technology, 13(1), 64-72. https://doi.org/10.18178/ijiet.2023.13.1.1781
Eun, C. S., & Shim, S. (1989). International transmission of stock market movements. Journal of financial and quantitative Analysis, 24(2), 241-256. https://doi.org/10.2307/2330774
Few, S. (2006). Information dashboard design: The effective visual communication of data. O′Reilly Media, Inc.
Fleming, N. D. (1995). I′m different; not dumb. Modes of presentation (VARK) in the tertiary classroom. Research and development in higher education, Proceedings of the 1995 Annual Conference of the Higher Education and Research Development Society of Australasia (HERDSA), HERDSA.
Ghosh, A., Heffernan, N., & Lan, A. S. (2020). Context-Aware Attentive Knowledge Tracing Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. https://doi.org/10.1145/3394486.3403282
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216
Gong, Y., Beck, J. E., & Heffernan, N. T. (2011). How to construct more accurate student models: Comparing and optimizing knowledge tracing and performance factor analysis. International Journal of Artificial Intelligence in Education, 21(1-2), 27-46. https://doi.org/10.3233/JAI-2011-016
Graesser, A. C., & D’Mello, S. (2012). Emotions during the learning of difficult material Psychology of learning and motivation. https://doi.org/10.1016/B978-0-12-394293-7.00005-4
Graf, S., Ives, C., Rahman, N., & Ferri, A. (2011). AAT: a tool for accessing and analysing students′ behaviour data in learning systems. Proceedings of the 1st international conference on learning analytics and knowledge. https://doi.org/10.1145/2090116.2090145
Grasha, A. F. (1996). Teaching with style: A practical guide to enhancing learning by understanding teaching and learning styles. Alliance publishers.
Griffith, P. B., Doherty, C., Smeltzer, S. C., & Mariani, B. (2021). Education initiatives in cognitive debiasing to improve diagnostic accuracy in student providers: a scoping review. Journal of the American Association of Nurse Practitioners, 33(11), 862-871. https://doi.org/10.1108/IJSHE-08-2020-0307
Guleryuz, D. (2021). Forecasting outbreak of COVID-19 in Turkey; Comparison of Box–Jenkins, Brown’s exponential smoothing and long short-term memory models. Process Safety and Environmental Protection, 149, 927-935. https://doi.org/10.1016/j.psep.2021.03.032
Guo, L., Wang, D., Gu, F., Li, Y., Wang, Y., & Zhou, R. (2021). Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view. Asia Pacific Education Review, 22(3), 441-461. https://doi.org/10.1007/s12564-021-09697-7
Hakimi, M., Katebzadah, S., & Fazil, A. W. (2024). Comprehensive Insights into E-Learning in Contemporary Education: Analyzing Trends, Challenges, and Best Practices. Journal of Education and Teaching Learning (JETL), 6(1), 86-105. https://doi.org/10.51178/jetl.v6i1.1720
Haldar, N. A. H., Li, J., Ali, M. E., Cai, T., Chen, Y., Sellis, T., & Reynolds, M. (2022). Top-k Socio-Spatial Co-Engaged Location Selection for Social Users. IEEE Transactions on Knowledge and Data Engineering, 35(5), 5325-5340. https://doi.org/10.1109/TKDE.2022.3151095
Huang, S., Liu, C., & Luo, L. (2021). Research on Bayesian knowledge tracking model integrating behavior and forgetting factors. Application Research of Computers, 38(7), 1993-1997. https://doi.org/10.19734/j.issn.1001-3695.2020.10.0356
Huang, X., Liu, Q., Wang, C., Han, H., Ma, J., Chen, E., Su, Y., & Wang, S. (2019). Constructing educational concept maps with multiple relationships from multi-source data. 2019 IEEE International Conference on Data Mining (ICDM). https://doi.org/10.1109/ICDM.2019.00132
Hubalovsky, S., Hubalovska, M., & Musilek, M. (2019). Assessment of the influence of adaptive E-learning on learning effectiveness of primary school pupils. Computers in Human Behavior, 92, 691-705. https://doi.org/10.1016/j.chb.2018.05.033
Hung, H.-C., & Lee, P.-H. (2023). Applying Deep Knowledge Tracing Model for University Students’ Programming Learning. 2023 International Conference on Information Networking (ICOIN). https://doi.org/10.1109/ICOIN56518.2023.10048977
Hussein, A. A., & Al-Chalabi, H. K. M. (2020). Pedagogical Agents in an Adaptive E-learning System. SAR Journal of Science and Research, 3(1), 24-30. https://doi.org/10.18421/SAR31-04
Jacobs, F. R., & Chase, R. (2013). Operations and Supply Chain Management. McGraw Hill.
James, F. (2021). Neural network-based time series forecasting of student enrollment with exponential smoothing baseline and statistical analysis of performance. https://doi.org/10.18260/1-2--41751
Ji, M., Michel, C., Lavoué, E., & George, S. (2014). DDART, a dynamic dashboard for collection, analysis and visualization of activity and reporting traces. Open Learning and Teaching in Educational Communities: 9th European Conference on Technology Enhanced Learning, EC-TEL 2014, Graz, Austria, September 16-19, 2014, Proceedings 9. https://doi.org/10.1007/978-3-319-11200-8_39
Käser, T., Klingler, S., Schwing, A. G., & Gross, M. (2017). Dynamic Bayesian networks for student modeling. IEEE Transactions on Learning Technologies, 10(4), 450-462. https://doi.org/10.1109/TLT.2017.2689017
Latip, M. S. A., Tamrin, M., Noh, I., Rahim, F. A., & Latip, S. (2022). Factors affecting e-learning acceptance among students: The moderating effect of self-efficacy. International Journal of Information and Education Technology, 12(2), 116-122. https://doi.org//10.18178/ijiet.2022.12.2.1594
Li, G., Shuai, J., Hu, Y., Zhang, Y., Wang, Y., Yang, T., & Xiong, N. (2022). DKT-LCIRT: A Deep Knowledge Tracking Model Integrating Learning Capability and Item Response Theory. Electronics, 11(20), 3364. https://doi.org/10.3390/electronics11203364
Liu, C., Liu, H., & Tan, Z. (2023). Choosing optimal means of knowledge visualization based on eye tracking for online education. Education and Information Technologies, 28(12), 15845-15872. https://doi.org/10.1007/s10639-023-11815-4
Liu, S., Zou, R., Sun, J., Zhang, K., Jiang, L., Zhou, D., & Yang, J. (2021). A Hierarchical Memory Network for Knowledge Tracing. Expert Systems with Applications, 177, 114935. https://doi.org/10.1016/j.eswa.2021.114935
Liu, Y., Yang, Y., Chen, X., Shen, J., Zhang, H., & Yu, Y. (2020). Improving Knowledge Tracing via Pre-training Question Embeddings. arXiv preprint arXiv:2012.05031. https://doi.org/10.24963/IJCAI.2020/219
Luckin, R., & Holmes, W. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson.
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of educational psychology, 106(4), 901-918. https://doi.org/10.1037/a0037123
Makram, S., Mourad, A., Adnane, G., & Karim, D. (2020). Adaptive tutoring system based on fuzzy logic. International Journal of Advanced Intelligence Paradigms, 16(2), 132-144. https://doi.org/10.1504/ijaip.2020.107010
May, M., George, S., & Prévôt, P. (2011). TrAVis to enhance students’ self-monitoring in online learning supported by computer-mediated communication tools. Computer Information Systems and Industrial Management Applications, 3, 623-634. https://api.semanticscholar.org/CorpusID:16306455
Mazza, R., & Dimitrova, V. (2004). Visualising student tracking data to support instructors in web-based distance education. Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters. https://doi.org/10.1145/1013367.1013393
McGreal, R. (1998). Integrated Distributed Learning Environments (IDLEs) on the Internet: A Survey. Educational Technology Review, 9, 25-31. https://eric.ed.gov/?id=EJ570626
Michel, C., Lavoué, E., & Pietrac, L. (2012). A dashboard to regulate project-based learning. 21st Century Learning for 21st Century Skills: 7th European Conference of Technology Enhanced Learning, EC-TEL 2012, Saarbrücken, Germany, September 18-21, 2012. Proceedings 7. https://doi.org/10.1007/978-3-642-33263-0_20
Minn, S., Yu, Y., Desmarais, M. C., Zhu, F., & Vie, J.-J. (2018). Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing. 2018 IEEE International conference on data mining (ICDM). https://doi.org/10.1109/ICDM.2018.00156
Mohamed, H., & Lamia, M. (2018). Implementing flipped classroom that used an intelligent tutoring system into learning process. Computers & Education, 124, 62-76. https://doi.org/10.1016/j.compedu.2018.05.011
Muhammad, I., & Utomo, J. (2024). E-LEARNING RESEARCH TRENDS IN EDUCATION IN INDONESIA. Journal of Education Global, 1(2), 122-129. https://penaeducentre.com/index.php/JEdG/article/view/74
Mumtaz, F., Jehangiri, A. I., Ishaq, W., Ahmad, Z., Alramli, O. I., Ala’anzy, M. A., & Ghoniem, R. M. (2024). Quality of interaction-based predictive model for support of online learning in pandemic situations. Knowledge and Information Systems, 66(3), 1777-1805. https://doi.org/10.1007/s10115-023-01995-3
Murray, M. C., & Pérez, J. (2015). Informing and performing: A study comparingadaptive learning to traditional learning. Informing Science: The International Journal of an Emerging Transdicipline, 18, 111. https://doi.org/10.28945/2165
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE access, 10, 81323-81342. https://doi.org/10.1109/ACCESS.2022.3193938
Narciss, S., Proske, A., & Koerndle, H. (2007). Promoting self-regulated learning in web-based learning environments. Computers in Human Behavior, 23(3), 1126-1144. https://doi.org/10.1016/j.chb.2006.10.006
Nedungadi, P., & Remya, M. (2014). Predicting students′ performance on intelligent tutoring system—Personalized clustered BKT (PC-BKT) model. 2014 IEEE Frontiers in Education Conference (FIE) Proceedings. https://doi.org/10.1109/FIE.2014.7044200
Normadhi, N. B. A., Shuib, L., Nasir, H. N. M., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers & Education, 130, 168-190. https://doi.org/10.1016/j.compedu.2018.11.005
Nunnally, J. (1978). Psychometric Theory 2nd edition (New York: McGraw).
Oxman, S., Wong, W., & Innovations, D. (2014). White paper: Adaptive learning systems. Integrated Education Solutions, 6-7. http://kenanaonline.com/files/0100/100321/DVx_Adaptive_Learning_White_Paper.pdf
Pandey, S., & Karypis, G. (2019). A self-attentive model for knowledge tracing. 12th International Conference on Educational Data Mining, EDM 2019. https://doi.org/10.1016/j.patrec.2022.11.016
Pandey, S., & Srivastava, J. (2020). RKT: Relation-Aware Self-Attention for Knowledge Tracing. Proceedings of the 29th ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/3340531.3411994
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological science in the public interest, 9(3), 105-119. https://doi.org/10.1111/j.1539-6053.2009.01038.x
Paturusi, M. A. (2022). Students learning style by using Neil Fleming’s Vark model and inventory. Journal of Language Testing and Assessment, 2(1), 79-86. https://doi.org/10.56983/jlta.v2i1.71
Paul, S. K. (2011). Determination of Exponential Smoothing Constant to Minimize Mean Square Error and Mean Absolute Deviation. Global journal of research in engineering, 11(3), 30-34. https://globaljournals.org/GJRE_Volume11/7-Determination-of-Exponential-Smoothing-Constant-to.pdf
Phielix, C., Prins, F., & Kirschner, P. A. (2011). Group Awareness of Social and Cognitive behavior in a CSCL Environment. https://doi.org/10.22318/icls2010.1.230
Phobun, P., & Vicheanpanya, J. (2010). Adaptive intelligent tutoring systems for e-learning systems. Procedia-Social and Behavioral Sciences, 2(2), 4064-4069. https://doi.org/10.1016/j.sbspro.2010.03.641
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl-Dickstein, J. (2015). Deep Knowledge Tracing. Advances in neural information processing systems, 28. https://papers.nips.cc/paper_files/paper/2015/file/bac9162b47c56fc8a4d2a519803d51b3-Paper.pdf
Prithishkumar, I. J., & Michael, S. A. (2014). Understanding your student: using the VARK model. Journal of postgraduate medicine, 60(2). https://doi.org/10.4103/0022-3859.132337
Quioc, M. A. F., Ambat, S. C., Lagman, A. C., Ramos, R. F., & Maaliw, R. R. (2022). Analysis of Exponential Smoothing Forecasting Model of Medical Cases for Resource Allocation Recommender System. 2022 10th International Conference on Information and Education Technology (ICIET). https://doi.org/10.1109/ICIET55102.2022.9778987
Rafiq, M., Hardiyanto, A., & Sumarno, S. (2023). An Analysis of Student Learning Style in Learning English in Sekolah Indonesia Kuala Lumpur, Malaysia. Voices of English Language Education Society, 7(1), 145-157. https://doi.org/10.29408/veles.v7i1.7869
Rao, C., Zhang, Y., Wen, J., Xiao, X., & Goh, M. (2023). Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model. Energy, 263, 125955. https://doi.org/10.1016/j.energy.2022.125955
Reiners, T., & Dreher, H. (2009). Culturally-based adaptive learning and concept analytics to guide educational website content integration. Journal of Information Technology Education: Research, 8(1), 125-139. https://doi.org/10.28945/163
Sarnato, A. Z., Sari, W. D., Rahmawati, S. T., Hidayat, R., & Patry, H. (2024). The Evolution of E-Learning Platforms: From U-Learning to AI-Driven Adaptive Learning Systems. Journal of Social Science Utilizing Technology, 2(2), 511-522. https://doi.org/10.55849/jssut.v2i2.1131
Schroeder, R., & Goldstein, S. M. (2018). Operations management in the supply chain. Decisions And Cases.
Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet, D., & Dillenbourg, P. (2017). Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research. IEEE Transactions on Learning Technologies, 10(1), 30-41. https://doi.org/10.1109/TLT.2016.2599522
Self, J. (1985). A perspective on intelligent computer-aided instruction. Journal of Computer Assisted Learning, 1, 159-166. https://doi.org/10.1111/j.1365-2729.1985.tb00026.x
Shen, S., Liu, Q., Chen, E., Wu, H., Huang, Z., Zhao, W., Su, Y., Ma, H., & Wang, S. (2020). Convolutional Knowledge Tracing: Modeling Individualization in Student Learning Process. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3397271.3401288
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30. https://doi.org/10.17471/2499-4324/195
Sánchez-Mena, A., Martí-Parreño, J., & Aldás-Manzano, J. (2017). The Effect of Age on Teachers′ Intention to Use Educational Video Games: A TAM Approach. Electronic Journal of E-Learning, 15(4), 355-366. https://files.eric.ed.gov/fulltext/EJ1154704.pdf
Soleimani, S. M., Jaeger, M., Faheiman, A., & Alaqqad, A. R. (2024). Success factors of recently implemented eLearning methods at higher education institutions in Kuwait. Quality in Higher Education, 30(1), 130-142. https://doi.org/10.1080/13538322.2022.2132702
Steenbergen-Hu, S., & Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of educational psychology, 106(2), 331. https://doi.org/10.1037/a0034752
Steiner, C. M., Kickmeier-Rust, M. D., & Albert, D. (2014). Learning Analytics and Educational Data Mining: An Overview of Recent Techniques. Learning analytics for and in serious games. https://graz.elsevierpure.com/en/publications/learning-analytics-and-educational-data-mining-an-overview-of-rec
Su, W., Jiang, F., Shi, C., Wu, D., Liu, L., Li, S., Yuan, Y., & Shi, J. (2023). An XGBoost-Based Knowledge Tracing Model. International Journal of Computational Intelligence Systems, 16(1), 13. https://doi.org/10.1007/s44196-023-00192-y
Subagja, S., & Rubini, B. (2023). Analysis of Student Learning Styles Using Fleming′s VARK Model in Science Subject. JURNAL PEMBELAJARAN DAN BIOLOGI NUKLEUS (JPBN), 9(1), 31-39. https://doi.org/10.36987/jpbn.v9i1.3752
Sun, X., Zhao, X., Li, B., Ma, Y., Sutcliffe, R., & Feng, J. (2021). Dynamic Key-Value Memory Networks With Rich Features for Knowledge Tracing. IEEE transactions on cybernetics, 52(8), 8239-8245. https://doi.org/10.1109/TCYB.2021.3051028
Sun, X., Zhao, X., Ma, Y., Yuan, X., He, F., & Feng, J. (2019). Muti-behavior features based knowledge tracking using decision tree improved DKVMN. Proceedings of the ACM Turing Celebration Conference-China. https://doi.org/10.1145/3321408.3322847
Tang, K.-Y., Chang, C.-Y., & Hwang, G.-J. (2023). Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interactive Learning Environments, 31(4), 2134-2152. https://doi.org/10.1080/10494820.2021.1875001
Taub, M., & Azevedo, R. (2019). How does prior knowledge influence eye fixations and sequences of cognitive and metacognitive SRL processes during learning with an intelligent tutoring system? International Journal of Artificial Intelligence in Education, 29, 1-28. https://doi.org/10.1007/s40593-018-0165-4
Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., & Schmidt-Thieme, L. (2010). Recommender system for predicting student performance. Procedia Computer Science, 1(2), 2811-2819. https://doi.org/10.1016/j.procs.2010.08.006
Thai-Nghe, N., Horv, T., & Schmidt-Thieme, L. (2011). Personalized forecasting student performance. 2011 IEEE 11th International Conference on Advanced Learning Technologies. https://doi.org/10.1109/ICALT.2011.130
Theodossiou, P., & Lee, U. (1993). Mean and volatility spillovers across major national stock markets: Further empirical evidence. Journal of financial Research, 16(4), 337-350. https://doi.org/10.1111/j.1475-6803.1993.tb00152.x
Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55, 1185-1193. https://doi.org/10.1016/j.chb.2015.02.014
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational psychologist, 46(4), 197-221. https://doi.org/10.1080/00461520.2011.611369
Wan, H., Tang, L., Zhong, Z., & Liu, K. (2021). Improving Online Teaching Based on Knowledge Tracing Model. 2021 IEEE International Conference on Engineering, Technology & Education (TALE). https://doi.org/10.1109/TALE52509.2021.9678661
Wang, R., Zhang, W., Deng, W., Zhang, R., & Zhang, X. (2022). Study on Prediction of Energy Conservation and Carbon Reduction in Universities Based on Exponential Smoothing. Sustainability, 14(19), 11903. https://doi.org/10.3390/su141911903
Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British journal of educational technology, 40(1), 92-118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
Wiener, N. (1966). Nonlinear Problems In Random Theory.
Wigner, E. (2005). Theory of traveling-wave optical laser. Phys. Rev, 134, A635-A646.
Wilson, K. H., Xiong, X., Khajah, M., Lindsey, R. V., Zhao, S., Karklin, Y., Van Inwegen, E. G., Han, B., Ekanadham, C., & Beck, J. E. (2016). Estimating student proficiency: Deep learning is not the panacea. In Neural information processing systems, workshop on machine learning for education. http://www.rob-lindsey.com/papers/2016/nips.pdf
Winne, P. H., Hadwin, A. F., & Gress, C. (2010). The learning kit project: Software tools for supporting and researching regulation of collaborative learning. Computers in Human Behavior, 26(5), 787-793. https://doi.org/10.1016/j.chb.2007.09.009
Wu, Z., Jiang, J., Lu, J., Su, Y., Mo, Q., & Li, S. (2023). Knowledge Tracing Modeling Method for Programming Task. 2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS). https://doi.org/10.1109/ISCTIS58954.2023.10213000
Xiaopeng, Y., Hanghang, Z., & Yuntao, W. (2021). Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination. 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE). https://doi.org/10.1109/RCAE53607.2021.9638936
Yang, L.-L., & Hsu, C.-W. (2019). A Better Method for Evaluating Learning Performance Based on Information Entropy and Dynamic Accumulation Level. The 4th International Conference on Economy, Judicature, Administration and Humanitarian Projects (JAHP 2019). https://doi.org/10.2991/jahp-19.2019.22
Yeung, C.-K., & Yeung, D.-Y. (2018). Addressing two problems in deep knowledge tracing via prediction-consistent regularization. Proceedings of the fifth annual ACM conference on learning at scale. https://doi.org/10.1145/3231644.3231647
Yoo, Y., Lee, H., Jo, I.-H., & Park, Y. (2015). Educational Dashboards for Smart Learning: Review of Case Studies. Emerging Issues in Smart Learning. https://doi.org/10.1007/978-3-662-44188-6_21
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
Zhang, J., Shi, X., King, I., & Yeung, D.-Y. (2017). Dynamic key-value memory networks for knowledge tracing. Proceedings of the 26th international conference on World Wide Web. https://doi.org/10.1145/3038912.3052580
Zhang, W., Hu, S., & Qu, K. (2023). Graph Attention Neural Network Model with Behavior Features for Knowledge Tracking. IEEE access, 11, 88329-88338. https://doi.org/10.1109/ACCESS.2023.3300703
Zhao, H., & Sun, Z. (2024). Traditional Cultural Network Online Education Integrating Deep Learning and Knowledge Tracking. Scalable Computing: Practice and Experience, 25(1), 341-354. https://doi.org/10.12694/scpe.v25i1.2299
Zhijun, L., Chen, L., Xianjing, Q., & Yang, G. (2023). Knowledge Tracking Model with Forgetting, Ability and Experience. 2023 42nd Chinese Control Conference (CCC). https://doi.org/10.23919/CCC58697.2023.10241125

指導教授

洪暉鈞(Hui-Chun Hung)

審核日期

2024-7-27

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