探索基於預測產生教材的個人化干預對學生學習成效和SRL學習策略的影響

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張介韋(Jei-Wei Chang) 查詢紙本館藏

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資訊工程學系

論文名稱

探索基於預測產生教材的個人化干預對學生學習成效和SRL學習策略的影響
(Exploring the effect of prediction-based content intervention on students’ learning performance and SRL learning strategy.)

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至系統瀏覽論文 (2027-8-1以後開放)

摘要(中)

在過去的研究中發現透過行為分析相較於考試成績可以更好的預測學生的學習成效。但是透過學習分析找出高風險的學生後該如何為他們提供及時的干預以提高學生的學習成效是重要的。
本研究根據大學生在線上學習環境的課程中的行為日誌，透過機器學習的方法識別學生的概念是否精熟。另外，進一步研究了從學習日誌中提取的哪些特徵會影響學生各概念的精熟程度，分類器識別學生為不精熟之後，透過學生的學習日誌來找出學生缺乏哪些行為、進而根據學生的情況提供不同個人化的教材當作干預。在個人化教材的生成上，透過自然語言處理的方式幫助教師生成個人化教材以減輕教師的負擔。
研究結果顯示分類器識別了大多數有風險的學生，也就是說根據學生的線上學習行為可以找出有風險的學生，進一步幫助教師可以了解學生的學習情況。在不同的概念上影響學生概念精熟與否的特徵也不相同，且實施個人化干預對於提高學生的學習成效有顯著的幫助，在干預措施之成效上，實驗組的學生在程式概念上相較於干預前更為精熟，且相較於控制組在程式測驗成績上的表現顯著較佳。

摘要(英)

In past studies, behavioral analysis has been found to be a better predictor of student learning outcomes than test scores. However, after identifying at-risk students through learning analysis, it is important to provide them with timely interventions to improve students′ learning outcomes.
This study uses machine learning to identify students′ conceptual mastery level based on the learning logs of college students in courses that support an online learning environment. In addition, we further studied which features extracted from the learning logs would affect the mastery level of each concept of. After the classifier identified the students as not mastery, the students′ learning logs were used to find out which behaviors the students lacked, and then based on it provide personalized materials as interventions. In the personalized teaching materials generation, we help teachers generate personalized teaching materials through natural language processing to reduce the burden on teachers.
The results of the study show that the classifier identified most of at-risk students, which means that at-risk students can be identified based on students′ online learning log, which further helps teachers understand students′ learning situations. Different concepts have different characteristics that affect students′ concept mastery level, and the implementation of individualized interventions can significantly help improve students′ learning effectiveness. In terms of the effectiveness of intervention measures, the students in the experimental group are similar in terms of program concepts. More proficient than before the intervention, and significantly better than the control group in programming test.

關鍵字(中)

★ 精準教育
★ 學習分析
★ 個人化干預
★ 自然語言處理
★ 機器學習

關鍵字(英)

★ Precision Education
★ Learning Analytics
★ Personalized Intervention
★ Natural Language Processing
★ Machine Learning

論文目次

目錄
摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 v
表目錄 vi
1. 緒論 1
2. 文獻探討 3
2.1. 自我調節學習(Self-Regulated Learning, SRL) 3
2.2. 學習分析 4
2.3. 個人化學習 5
3. 自我調節的個人化干預方法 7
4. 研究方法與實驗設計 12
4.1. 參與者 12
4.2. 實驗設計與課程活動 12
4.3. 預測模型訓練 13
4.3.1. 特徵整理 13
4.3.2. 特徵選擇 14
4.3.3. 模型訓練 18
4.3.4. 模型效能評估 20
4.4. 個人化教材生成 20
4.4.1. 摘要生成 22
4.4.2. 練習題生成 23
5. 結果 26
5.1. 模型效能評估 26
5.2. 各概念重要特徵 27
6. 分析結果討論 28
6.1. 各概念重要特徵分析 28
6.2. 學習成效分析 29
6.3. 學習策略與學習行為的分析 31
6.4. 學生反饋 43
7. 結論 44
參考文獻 45
附錄 51
自我調節學習問卷(MSLQ) 51

參考文獻

Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). Text summarization techniques: a brief survey. arXiv preprint arXiv:1707.02268.
Amra, I. A. A., & Maghari, A. Y. (2017). Students performance prediction using KNN and Naïve Bayesian. Paper presented at the 2017 8th International Conference on Information Technology (ICIT).
Arroway, P., Morgan, G., O’Keefe, M., & Yanosky, R. (2015). Learning analytics in higher education. Retrieved from
Baghaei, N., Mitrovic, A., & Irwin, W. (2007). Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams. International Journal of Computer-Supported Collaborative Learning, 2(2), 159-190.
Benedict, N. (2010). Virtual patients and problem-based learning in advanced therapeutics. American journal of pharmaceutical education, 74(8).
Borthwick, A. C., Anderson, C. L., Finsness, E. S., & Foulger, T. S. (2015). Special article personal wearable technologies in education: Value or villain? Journal of Digital Learning in Teacher Education, 31(3), 85-92.
Braunstein, A. W., Lesser, M. N., & Pescatrice, D. R. (2008). The Impact of a Program for the Disadvantaged on Student Retention. College Student Journal, 42(1).
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
Chang, K.-m., Beck, J., Mostow, J., & Corbett, A. (2006). A Bayes net toolkit for student modeling in intelligent tutoring systems. Paper presented at the International Conference on Intelligent Tutoring Systems.
Chen, C. S. (2002). Self-regulated learning strategies and achievement in an introduction to information systems course. Information technology, learning, and performance journal, 20(1), 11.
Cook, C. R., Kilgus, S. P., & Burns, M. K. (2018). Advancing the science and practice of precision education to enhance student outcomes. Journal of School Psychology, 66, 4-10.
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Eckles, J. E., & Stradley, E. G. (2012). A social network analysis of student retention using archival data. Social Psychology of Education, 15(2), 165-180.
Education, U. D. o. (2017). Reimagining the role of technology in education: 2017 national education technology plan update. US Department of Education.
Falcão, T. P., e Peres, F. M. d. A., de Morais, D. C. S., & da Silva Oliveira, G. (2018). Participatory methodologies to promote student engagement in the development of educational digital games. Computers & Education, 116, 161-175.
Ferjaoui, D., & Cheniti Belcadhi, L. (2020). A Conceptual Model for Personalized Learning based on Educational Robots. Paper presented at the Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality.
Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009). An experimental comparison of performance measures for classification. Pattern recognition letters, 30(1), 27-38.
Foshee, C. M., Elliott, S. N., & Atkinson, R. K. (2016). Technology‐enhanced learning in college mathematics remediation. British Journal of Educational Technology, 47(5), 893-905.
Foss, K. A., Foss, S. K., Paynton, S., & Hahn, L. (2014). Increasing College Retention with a Personalized System of Instruction: A Case Study. Journal of Case Studies in Education, 5.
Frey, J. R. (2019). Assessment for special education: Diagnosis and placement. The ANNALS of the American Academy of Political and Social Science, 683(1), 149-161.
Gómez, S., Zervas, P., Sampson, D. G., & Fabregat, R. (2014). Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. Journal of King Saud University-Computer and Information Sciences, 26(1), 47-61.
Hendel, D. D. (2007). Efficacy of participating in a first-year seminar on student satisfaction and retention. Journal of College Student Retention: Research, Theory & Practice, 8(4), 413-423.
Horváth, I. (2020). Personalized learning opportunity in 3D VR. Paper presented at the 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom).
Hsieh, T.-C., Lee, M.-C., & Su, C.-Y. (2013). Designing and implementing a personalized remedial learning system for enhancing the programming learning. Journal of Educational Technology & Society, 16(4), 32-46.
Huang, L.-M., Yang, W.-J., Huang, Z.-Y., Tang, C.-W., & Li, J. (2020). Artificial intelligence technique in detection of early esophageal cancer. World journal of gastroenterology, 26(39), 5959.
Huang, Y., & Shen, J. (2018). An Implicit Knowledge Oriented Algorithm for Learning Path Recommendation. Paper presented at the 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA).
Imran, H., Belghis-Zadeh, M., Chang, T.-W., & Graf, S. (2016). Plors: a personalized learning object recommender system. Vietnam Journal of Computer Science, 3(1), 3-13.
Jin, Q., Imbrie, P., Lin, J. J., & Chen, X. (2011). A multi-outcome hybrid model for predicting student success in engineering. Paper presented at the 2011 ASEE Annual Conference & Exposition.
Johnson, D., & Samora, D. (2016). The potential transformation of higher education through computer-based adaptive learning systems. Global Education Journal, 2016(1).
Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016 higher education edition: The New Media Consortium.
Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2012). The 2011 Horizon Report. Austin, TX: The New Media Consortium; 2011. In.
Jones, E. R., & Martinez, M. (2001). Learning Orientations in University Web-Based Courses.
Jong, B.-S., Chan, T.-Y., Wu, Y.-L., & Lin, T.-W. (2006). Applying the adaptive learning material producing strategy to group learning. Paper presented at the International Conference on Technologies for E-Learning and Digital Entertainment.
Kerr, P. (2016). Adaptive learning. Elt Journal, 70(1), 88-93.
Kingsford, C., & Salzberg, S. L. (2008). What are decision trees? Nature biotechnology, 26(9), 1011-1013.
Kira, K., & Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings 1992 (pp. 249-256): Elsevier.
Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2021). Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems. International Journal of Artificial Intelligence in Education, 1-27.
Kurdi, G., Leo, J., Parsia, B., Sattler, U., & Al-Emari, S. (2020). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30(1), 121-204.
Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models. In: McGraw-Hill New York.
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., Vaclavek, J., & Wolff, A. (2015). OU Analyse: analysing at-risk students at The Open University. Learning Analytics Review, 1-16.
Labuhn, A. S., Zimmerman, B. J., & Hasselhorn, M. (2010). Enhancing students’ self-regulation and mathematics performance: The influence of feedback and self-evaluative standards. Metacognition and learning, 5(2), 173-194.
Lackey, L. W., Lackey, W. J., Grady, H. M., & Davis, M. T. (2003). Efficacy of using a single, non‐technical variable to predict the academic success of freshmen engineering students. Journal of Engineering Education, 92(1), 41-48.
Lai, C.-H., Lee, T.-P., Jong, B.-S., & Hsia, Y.-T. (2014). Using SPRT+ to Reduce Measure Time on Student Learning Efficiency by Pre-defined Student′s Confidence Indicator. International Journal of Emerging Technologies in Learning (iJET), 9(3), 55-58.
Lee, H. W., Lim, K. Y., & Grabowski, B. (2009). Generative learning strategies and metacognitive feedback to facilitate comprehension of complex science topics and self-regulation. Journal of Educational Multimedia and Hypermedia, 18(1), 5-25.
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017). Feature selection: A data perspective. ACM computing surveys (CSUR), 50(6), 1-45.
Lin, C. C., Guo, K. H., & Lin, Y. C. (2016). A simple and effective remedial learning system with a fuzzy expert system. Journal of Computer Assisted Learning, 32(6), 647-662.
Lu, J. (2004). A personalized e-learning material recommender system. Paper presented at the International conference on information technology and applications.
Lu, O. H., Huang, A. Y., Huang, J. C., Lin, A. J., Ogata, H., & Yang, S. J. (2018). Applying learning analytics for the early prediction of Students′ academic performance in blended learning. Journal of Educational Technology & Society, 21(2), 220-232.
Malmivuori, M.-L. (2006). Affect and self-regulation. Educational studies in mathematics, 63(2), 149-164.
Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, 103, 1-15.
Meyer, J., & Land, R. (2005). Overcoming barriers to student understanding: Taylor & Francis Limited.
Moos, D., & Azevedo, R. (2006). The role of goal structure in undergraduates’ use of self-regulatory variables in two hypermedia learning tasks. Journal of Educational Multimedia and Hypermedia, 15(1), 49-86.
NMC. (2015). NMC Horizon Report: 2015 Higher Education Edition: NMC [New Media Consortium].
Noble, W. S. (2006). What is a support vector machine? Nature biotechnology, 24(12), 1565-1567.
Ogata, H., Yin, C., Oi, M., Okubo, F., Shimada, A., Kojima, K., & Yamada, M. (2015). E-Book-based learning analytics in university education. Paper presented at the International conference on computer in education (ICCE 2015).
Pearson, K. (1900). X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302), 157-175.
Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence, 27(8), 1226-1238.
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International journal of educational research, 31(6), 459-470.
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In Handbook of self-regulation (pp. 451-502): Elsevier.
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational psychology review, 16(4), 385-407.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.
Rokach, L., & Maimon, O. (2005). Top-down induction of decision trees classifiers-a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35(4), 476-487.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J., & Hlosta, M. (2014). Developing predictive models for early detection of at-risk students on distance learning modules.
Yang, S. J., Huang, J. C., & Huang, A. Y. (2017). MOOCs in Taiwan: The movement and experiences. In Open education: From oers to moocs (pp. 101-116): Springer.
Yoon, C.-H. (2009). Self-regulated learning and instructional factors in the scientific inquiry of scientifically gifted Korean middle school students. Gifted Child Quarterly, 53(3), 203-216.
Yusri, G., & Rahimi, N. M. (2010). Self-Regulated Learning Strategies Among Students Of Arabic Language Course And Intensive Arabic Course In Mara University Of Technology Malaysia (Uitm). International Journal of Applied Educational Studies, 8(1).
Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary educational psychology, 11(4), 307-313.
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of educational psychology, 81(3), 329.
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational psychologist, 25(1), 3-17.

指導教授

楊鎮華(Jhen-Hua Yang)

審核日期

2022-7-11

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