使用機器學習識別課堂中的風險學生：12門課程的實證研究

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呂欣澤(Hsin-Tse Lu) 查詢紙本館藏

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資訊工程學系

論文名稱

使用機器學習識別課堂中的風險學生：12門課程的實證研究
(Applying Machine Learning for Identifying Risk Student in Classroom: An Empirical Study of Twelve Courses)

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摘要(中)

學習分析的理念上是透過學習者在課堂中產生的數位足跡，促使其在課堂中獲得成功，實作上許多學者們倡導早期識別風險學生，適時給予學習介入為主要手段，因此，在研究的領域中興起了使用機器學習訓練風險學生識別模型的風潮。然而，在系統性探討文獻後，我們發現許多研究中忽略了模型訓練的細節，包含了：學習風險早期預測可行性、降低資料維度以增加模型準確度與找出關鍵因子、風險類別不平衡的影響等。因此，本研究先著手探討學習風險預測的現況定義，再收集了12個來自於線上學習平台的課程資料，並且以監督式/非監督式學習、回歸、分類以及分群等方法，透過資料的切片、特徵工程導入、資料重新取樣等進行探索。最後，透過交叉驗證機制與現況比對，證實了透過學生在平台上的關鍵學習行為，能夠在學期前三分之一針對風險程度進行提前識別，並且歸納出教師常用的鑑別型、嚴格型與寬鬆型等三種給分策略，以及每一種策略對於機器學習的成效影響以及改善方式。在機器學習方法套用於實際的課程之前，我們提出了兩點限制，第一、風險識別模型若需在同類型的課程通用，需限制課程長度、學習教材、作業、小考、學習活動以及給分策略的一致性；第二、對於低數位足跡學生族群無法透過機器進行風險識，教師仍需投入投入合適的干預手段在該族群，若是採取小考成績輔助，需要特別注意考題的鑑別度以提高識別的準確度。

摘要(英)

The concept of learning analytics is to motivate student achieving success in the classroom by the support of digital footprint that generated from the learning environment. In practice, many researchers advocate early identification of risk students and timely access to learning intervention as the main approach. Therefore, a trend of adopting machine learning to train risk student identification models has emerged in the field of learning analytics. However, after systematically exploring the recent literature, we identified several details of model training were overlooked by many studies, which including an innovated early warning system for classroom, reducing data dimensions for improving model accuracy and identifying key factors that affected students′ learning performance, and impact of the number of failure students that caused by teachers′ grading policy. Therefore, this thesis collected 12-courses data and adopted supervised/unsupervised learning under the method of classification, regression, and clustering to fill the gap from previous studies. Through the process of feature engineering and resampling, it is confirmed that students′ risk level can be identified by one-third of the semester and three grading policies have been summarized, which is discrimination, stringency, and leniency. Moreover, a resampling process is necessary to avoid issues caused by teachers′ grading policy. Furthermore, we propose two limitations when adopting machine learning into the classroom: the first one is the risk identification model could be applied to different courses only if the course duration, learning materials, homework, exam, learning activities, and grading policy were consistent. Second, machine cannot identify the risk population with a low digital footprint, exam discrimination is necessary if the instructor would consider the exam results as well.

關鍵字(中)

★ 學習分析
★ 學習資料探勘
★ 機器學習
★ 特徵工程
★ 給分策略

關鍵字(英)

★ Learning analytics
★ Educational data mining
★ Machine learning
★ Feature engineering
★ Grading policy

論文目次

Contents
?要 ix Abstract xi Contents xiii
1 Introduction 1
1.1 Learning Analytics ............................................................ 1
1.2 Identify at-risk Students ...................................................... 2
1.2.1 Dropout................................................................ 2
1.2.2 Outcome ............................................................... 3
1.2.3 Grades ................................................................. 3
1.2.4 Score ................................................................... 4
1.3 Machine Learning ............................................................. 4
2 Literature Review 7
2.1 State of the art in Risk Students Identification .............................. 7
2.2 Features in Educational Data................................................. 10
2.2.1 Feature Selection and Feature Extraction............................. 10
2.2.2 Measureable Features in Classroom................................... 11
2.3 Labels in Educational Data................................................... 12
2.3.1 2.3.2
3 Method
Grading Policy......................................................... 12 Class Imbalance........................................................ 14
17
3.1 Data Set....................................................................... 17 3.1.1 Data from 12 Online Courses ......................................... 17
xv
page
CONTENTS
3.1.2 Data from DS01 blended Calculus Course ............................ 18
3.2 Data Slicing ................................................................... 21
3.3 Regression..................................................................... 24
3.3.1 Multiple Linear Regression ............................................ 24
3.3.2 Principal Component Analysis ........................................ 24
3.3.3 Principal Component Regression...................................... 24
3.4 Clustering ..................................................................... 25
3.5 Classification .................................................................. 25
3.6 Resample ...................................................................... 26
3.7 Evaluation..................................................................... 27
3.7.1 Cross Validation ....................................................... 27
3.7.2 Metrics for Regression ................................................. 27
3.7.3 Metrics for Classification .............................................. 28
4 Results and Discussions 31
4.1 Reply RQ1: Concept Proof on Innovation Application ...................... 31
4.2 Reply RQ2: Dimension Reduction for Improve Performance ................ 37
4.3 Reply RQ3: Class Imbalanced avoidance on Grading Policy ................ 40
5 Limitations 47
5.1 Model Generalization ......................................................... 47
5.2 Population that machine cannot identify the risk level ...................... 49
6 Conclusion 53
References 55

參考文獻

Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-Garcı́a, Á. (2014). Can we predict success from log data in vles? classification of interactions for learning analytics and their relation with performance in vle-supported f2f and online learning. Computers in human behavior, 31, 542–550.
Akçapınar, G., Hasnine, M. N., Majumdar, R., Flanagan, B., & Ogata, H. (2019). Developing an early-warning system for spotting at-risk students by using ebook interaction logs. Smart Learning Environments, 6(1), 4.
Albán, M., & Mauricio, D. (2018). Decision trees for the early identification of university students at risk of desertion. International Journal of Engineering & Technology, 7(4.44), 51–54.
Alston, G. L., Lane, D., & Wright, N. J. (2014). The methodology for the early identification of students at risk for failure in a professional degree program. Currents in Pharmacy Teaching and Learning, 6(6), 798–806.
Arroway, P., Morgan, G., O’Keefe, M., & Yanosky, R. (2016). Learning analytics in higher ed- ucation. EDUCAUSE, available at: https://library. educause. edu/~/media/files/library/ 2016/2/ers1504la. pdf (accessed 28 February 2017).[Google Scholar].
Asif, R., Merceron, A., & Pathan, M. K. (2014). Predicting student academic performance at degree level: A case study. International Journal of Intelligent Systems and Applications, 7(1), 49.
Beatty, I. D. (2013). Standards-based grading in introductory university physics. Journal of the Scholarship of Teaching and Learning, 1–22.
Bellman, R. E. (2015). Adaptive control processes: A guided tour. Princeton university press. Bhuyan, M. H., Khan, S. S. A., & Rahman, M. Z. (2014). Teaching analog electronics course for electrical engineering students in cognitive domain. Journal of Electrical Engineering,
the Institute of Engineers Bangladesh (IEB-EE), 40(1-2), 52–58.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin
classifiers. In Proceedings of the fifth annual workshop on computational learning theory
(pp. 144–152). ACM.
Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance
problem in convolutional neural networks. Neural Networks, 106, 249–259.
Caragiannis, I., Krimpas, G. A., & Voudouris, A. A. (2016). How effective can simple ordinal peer grading be? In Proceedings of the 2016 acm conference on economics and computation
(pp. 323–340). ACM.
55
REFERENCES
Çevik, Y. D. (2015). Predicting college students’online information searching strategies based on epistemological, motivational, decision-related, and demographic variables. Computers & Education, 90, 54–63.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321–357. Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004). Special issue on learning from imbalanced
data sets. ACM Sigkdd Explorations Newsletter, 6(1), 1–6.
Chen, W., Brinton, C. G., Cao, D., Mason-singh, A., Lu, C., & Chiang, M. (2018). Early detec-
tion prediction of learning outcomes in online short-courses via learning behaviors. IEEE
Transactions on Learning Technologies.
Choi, S. P., Lam, S. S., Li, K. C., & Wong, B. T. (2018). Learning analytics at low cost: At-risk
student prediction with clicker data and systematic proactive interventions. Journal of
Educational Technology & Society, 21(2), 273–290.
Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M. (2018). Predicting at-risk university
students in a virtual learning environment via a machine learning algorithm. Computers
in Human Behavior.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273–297. Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and roc curves. In
Proceedings of the 23rd international conference on machine learning (pp. 233–240). ACM. Devijver, P. A., & Kittler, J. (1982). Pattern recognition: A statistical approach. Prentice hall. Elikai, F., & Schuhmann, P. W. (2010). An examination of the impact of grading policies on
students’achievement. Issues in Accounting Education, 25(4), 677–693.
Estabrooks, A., Jo, T., & Japkowicz, N. (2004). A multiple resampling method for learning from
imbalanced data sets. Computational intelligence, 20(1), 18–36.
Fan, J., & Li, R. (2006). Statistical challenges with high dimensionality: Feature selection in
knowledge discovery. arXiv preprint math/0602133.
Fawcett, T. (2004). Roc graphs: Notes and practical considerations for researchers. Machine
learning, 31(1), 1–38.
Golub, G. H., Heath, M., & Wahba, G. (1979). Generalized cross-validation as a method for
choosing a good ridge parameter. Technometrics, 21(2), 215–223.
Green, S. B. (1991). How many subjects does it take to do a regression analysis. Multivariate
behavioral research, 26(3), 499–510.
Gui, C. (2017). Analysis of imbalanced data set problem: The case of churn prediction for
telecommunication. Artif. Intell. Research, 6(2), 93.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of
machine learning research, 3(Mar), 1157–1182.
Guyon, I., Gunn, S., Nikravesh, M., & Zadeh, L. A. (2008). Feature extraction: Foundations and
applications. Springer.
Hachey, A. C., Wladis, C. W., & Conway, K. M. (2014). Do prior online course outcomes
provide more information than gpa alone in predicting subsequent online course grades and retention? an observational study at an urban community college. Computers & Education, 72, 59–67.
56

He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In 2008 ieee international joint conference on neural networks (ieee world congress on computational intelligence) (pp. 1322–1328). IEEE.
Hira, Z. M., & Gillies, D. F. (2015). A review of feature selection and feature extraction methods applied on microarray data. Advances in bioinformatics, 2015.
Hu, Y.-H., Lo, C.-L., & Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36, 469–478.
Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dy- namics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, 133–145.
Hwang, G.-J., Chu, H.-C., & Yin, C. (2017). Objectives, methodologies and research issues of learning analytics. Taylor & Francis.
Jain, A., & Zongker, D. (1997). Feature selection: Evaluation, application, and small sample performance. IEEE transactions on pattern analysis and machine intelligence, 19(2), 153– 158.
Janecek, A., Gansterer, W., Demel, M., & Ecker, G. (2008). On the relationship between feature selection and classification accuracy. In New challenges for feature selection in data mining and knowledge discovery (pp. 90–105).
Jenke, R., Peer, A., & Buss, M. (2014). Feature extraction and selection for emotion recognition from eeg. IEEE Transactions on Affective Computing, 5(3), 327–339.
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. F., & Witchey, H. (2011). The 2010 horizon report: Museum edition. Curator: The Museum Journal, 54(1), 37–40.
Jolliffe, I. T. (1982). A note on the use of principal components in regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 31(3), 300–303.
Kamal, P., & Ahuja, S. (2019). An ensemble-based model for prediction of academic perfor- mance of students in undergrad professional course. Journal of Engineering, Design and Technology.
Khalid, S., Khalil, T., & Nasreen, S. (2014). A survey of feature selection and feature extraction techniques in machine learning. In 2014 science and information conference (pp. 372–378). IEEE.
Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of cluster in k-means clustering. International Journal, 1(6), 90–95.
Kulick, G., & Wright, R. (2008). The impact of grading on the curve: A simulation analysis. International Journal for the Scholarship of Teaching and Learning, 2(2), n2.
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., & Wolff, A. (2015). Ou analyse: Analysing at-risk students at the open university. Learning Analytics Review, 1–16.
Lara, J. A., Lizcano, D., Martı́nez, M. A., Pazos, J., & Riera, T. (2014). A system for knowl- edge discovery in e-learning environments within the european higher education area– application to student data from open university of madrid, udima. Computers & Educa- tion, 72, 23–36.
57

REFERENCES
Liu, S., Wang, Y., Zhang, J., Chen, C., & Xiang, Y. (2017). Addressing the class imbalance problem in twitter spam detection using ensemble learning. Computers & Security, 69, 35–49.
Macfadyen, L. P., & Dawson, S. (2010). Mining lms data to develop an“early warning system” for educators: A proof of concept. Computers & education, 54(2), 588–599.
MacQueen, J. et al. (1967). Some methods for classification and analysis of multivariate ob- servations. In Proceedings of the fifth berkeley symposium on mathematical statistics and probability (Vol. 1, 14, pp. 281–297). Oakland, CA, USA.
Mani, I., & Zhang, I. (2003). Knn approach to unbalanced data distributions: A case study involving information extraction. In Proceedings of workshop on learning from imbalanced datasets (Vol. 126).
Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: A case study with high school students. Expert Systems, 33(1), 107–124.
Meier, Y., Xu, J., Atan, O., & Van der Schaar, M. (2016). Predicting grades. IEEE Transactions on Signal Processing, 64(4), 959–972.
Melli, G., Zaı̈ane, O. R., & Kitts, B. (2006). Introduction to the special issue on successful real-world data mining applications. SIGKDD Explorations, 8(1), 1–2.
Millard, J. P. (2016). How can instructional staff be effectively introduced to a standards-based grading policy.
Motoda, H., & Liu, H. (2002). Feature selection, extraction and construction. Communication of IICM (Institute of Information and Computing Machinery, Taiwan) Vol, 5(67-72), 2.
Nam, S., Frishkoff, G., & Collins-Thompson, K. (2018). Predicting students’disengaged behaviors in an online meaning-generation task. IEEE Transactions on Learning Technologies, 11(3), 362–375.
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), 1345–1359.
Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4), 49–64.
Pearson, K. (1901). Liii. on lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559–572.
Peng, C.-C. (2017). Grading on a curve in prerequisite courses and student performance in online introductory corporate finance classes. Journal of Higher Education Theory & Practice, 17 (9).
Raman, K., & Joachims, T. (2015). Bayesian ordinal peer grading. In Proceedings of the second (2015) acm conference on learning@ scale (pp. 149–156). ACM.
Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students’ final per- formance from participation in on-line discussion forums. Computers & Education, 68, 458–472.
Sadler, P. M., & Good, E. (2006). The impact of self-and peer-grading on student learning. Educational assessment, 11(1), 1–31.
58

Thammasiri, D., Delen, D., Meesad, P., & Kasap, N. (2014). A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition. Expert Sys- tems with Applications, 41(2), 321–330.
Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013). Auto-weka: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th acm sigkdd international conference on knowledge discovery and data mining (pp. 847–855). ACM.
Van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2013). Teacher interventions in a synchronous, co-located cscl setting: Analyzing focus, means, and temporality. Computers in Human Behavior, 29(4), 1377–1386.
Villagrá-Arnedo, C.-J., Gallego-Durán, F. J., Compañ, P., Llorens Largo, F., Molina-Carmona, R., et al. (2016). Predicting academic performance from behavioural and learning data.
Walstad, W. B., & Miller, L. A. (2016). What’s in a grade? grading policies and practices in principles of economics. The Journal of Economic Education, 47(4), 338–350.
Wedell, D. H., Parducci, A., & Roman, D. (1989). Student perceptions of fair grading: A range- frequency analysis. American Journal of Psychology, 102(2), 233–248.
Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in moocs: Reaching the low hanging fruit through stacking generalization. Computers in human behavior, 58, 119–129.
Xing, W., & Du, D. (2018). Dropout prediction in moocs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 0735633118757015.
Yang, S. J., Huang, J. C., & Huang, A. Y. (2017). Moocs in taiwan: The movement and expe- riences. In Open education: From oers to moocs (pp. 101–116). Springer.

指導教授

楊鎮華(Stephen J.H. Yang)

審核日期

2019-7-10

推文