Data Mining for Discovering Effective Time-Series Transition of Learning Strategies on Mutual Viewing-Based Learning
Yuto Omae*1, Tatsuro Furuya*2, Kazutaka Mizukoshi*3, Takayuki Oshima*4, Norihisa Sakakibara*4, Yoshiaki Mizuochi*4, Kazuhiro Yatsushiro*5, and Hirotaka Takahashi*6
*1National Institute of Technology, Tokyo College
1220-2 Kunugida, Hachioji, Tokyo 193-0942, Japan
*2Yamanashi Municipal Tekisen Elementary School
1200 Kubodaira, Makioka, Yamanashi 404-0013, Japan
*3Digital Alliance Co., Ltd.
2-12-1 Kitaguchi, Kofu, Yamanashi 400-0024, Japan
*4Joetsu University of Education
1 Yamayashiki, Joetsu, Niigata, 943-8512, Japan
*5Yamanashi Prefectural University
5-11-1 Iida, Kofu, Yamanashi 400-0035, Japan
*6Nagaoka University of Technology
1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, Japan
We aim to develop a real-time feedback system of learning strategies during lesson time to improve academic achievement. It has been known that mutual viewing-based learning is an effective educational method. However, even though mutual viewing is an effective lesson style, there are effective or ineffective learning strategies in the learners’ individual activities. In general, the method of evaluating learning strategies is a questionnaire survey. However, the questionnaire cannot measure the learning strategies in real time. Thus, it is difficult to detect the students who use ineffective learning strategies during lesson time in real time. Recently, a system that can measure the learning strategies in real time has been developed. Using this system, it is possible to detect students who use ineffective learning strategies during lesson time on the mutual viewing-based learning. From this point of view, we aim to develop a recommendation system for real-time learning strategies for teachers and students to achieve a highly educational effect. For this purpose, we must know the features of effective or ineffective learning strategies via a system that can measure learning strategies. In this paper, we report the discovery of features of effective or ineffective learning strategies based on the data-mining approach using the k-means method, transition diagram, and random forest. We classified the time-series learning strategies over 40 min into 216 strategies and surveyed the improvement probability of academic achievement via a random-forest-based classification model. By embedding our results into the system, we may be able to automatically detect students who use ineffective learning strategies and recommend effective learning strategies.
-  T. Umemoto, “The Effects of Metacognitive and Motivational Regulation Strategies on the Use of Cognitive Strategies and Persistence in Learning,” Japan J. of Educational Technology, Vol.37, No.1, pp. 79-87, 2013.
-  Y. Omae, T. Mitsui, and H. Takahashi, “Effect on Satisfaction through Super Science High School’s Education,” 2015 IEEE/SICE Int. Symp. on System Integration, pp. 146-150, 2015.
-  C. A. Wolters and M. Hussain, “Investigating Grit and Its Relations with College Students’ Self-Regulated Learning and Academic Achievement,” Metacognition and Learning, Vol.10, No.3, pp. 293-311, 2015.
-  A. U. Chamot, “Language Learning Strategy Instruction: Current Issues and Research,” Annual Review of Applied Linguistics, Vol.25, pp. 112-130, 2005.
-  K. Cho and C. D. Schunn, “Scaffolded Writing and Rewriting in the Discipline: A Web-based Reciprocal Peer Review System,” Computers & Education, Vol.48, No.3, pp. 409-426, 2007.
-  K. Mizukoshi, T. Furuya, T. Oshima, N. Sakakibara, Y. Mizuochi, Y. Omae, H. Takahashi, and K. Yatsushiro, “Performance Analysis of “edulog” System,” 2017 IEEE/SICE Int. Symp. on System Integration, pp. 583-588, 2017.
-  D. M. A. Sluijsmans, G. Moerkerke, J. J. G. van Merrienboer, and F. J. R. Dochy, “Peer Assessment in Problem based Learning,” Studies in Educational Evaluation, Vol.27, No.2, pp. 153-173, 2001.
-  Y. Fujihara, H. Ohnishi, and H. Kato, “A Practice of Repetition Peer Assessment in ICT Education,” J. of the Information Processing Society of Japan, Vol.49, No.10, pp. 3428-3438, 2008.
-  Y. Mizuochi, Y. Kubota, and J. Nishikawa, “A Study on the Effect of Mutual Evaluation by Digital Portfolio,” J. of Research in Science Education, Vol.46, No.3, pp. 75-83, 2006.
-  A. Kishi and Y. Mizuochi, “A Case Study on the Effects of Mutual Evaluations by Students’ Own Video Clips in the Operation Skill of a Microscope,” J. of Science Education in Japan, Vol.41, No.3, pp. 282-294, 2017.
-  N. Fang, “Correlation between Students’ Motivated Strategies for Learning and Academic Achievement in an Engineering Dynamics Course,” Global J. of Engineering Education, Vol.16, No.1, pp. 6-12, 2014.
-  O. Ahmed, M. K. Uddin, and M. Khanam, “Motivation and Learning Strategies as Strong Predictors of Academic Achievement,” Indian J. of Psychology, Vol.6, No.1, pp. 120-132, 2016.
-  Stem Learning and Research Center, “Instruments: Motivated Strategies for Learning Questionnaire,” http://stelar.edc.org/instruments/motivated-strategies-learning-questionnaire-mslq [accessed December 23, 2017]
-  Y. Omae, K. Nakahira, H. Takahashi, Y. Tsuchiya, R. Shukuin, T. Mitsui, and Y. Fukumura, “Survey of Relation between Learning Behavior and Test Score,” Proc. of the 2015 IEICE General Conf., p.209, 2015.
-  H. Matsukawa, S. Kitamura, Y. Nagamori, S. Hisamatsu, Y. Yamauchi, M. Nakano, Y. Kanamori, and N. Miyashita, “Development of a Feedback System of Learning Strategy to Students Utilizing Data Mining Technology,” Japan J. of Educational Technology, Vol.31, No.3, pp. 307-316, 2007.
-  H. F. Golino, C. M. A. Gomes, and D. Andrade, “Predicting Academic Achievement of High-School Students Using Machine Learning,” Psychology, Vol.5, No.18, pp. 2046-2057, 2014.
-  M. Ueno, “Data Mining and Text Mining Technologies for Collaborative Learning in an ILMS “Samurai”,” IEEE Int. Conf. on Advanced Learning Technologies, pp. 1052-1053, 2004.
-  U. Budiyanto, S. Hartati, S. N. Azhari, and D. Mardapi, “Intelligent System E-Learning Modeling According to Learning Styles and Level of Ability of Students,” Int. Conf. on Soft Computing in Data Science, pp. 278-290, 2017.
-  L. A. DelSignore, T. A. Wolbrink, D. Zurakowski, and J. P. Burns, “Test-enhanced E-learning Strategies in Postgraduate Medical Education: A randomized cohort study,” J. of Medical Internet Research, Vol.18, No.11, e299, 2016.
-  M. Ji, C. Michel, E. Lavoué, and S. George, “An Architecture to Combine Activity Traces and Reporting Traces to Support Self-regulation Processes,” 2013 IEEE 13th Int. Conf. on Advanced Learning Technologies, pp. 87-91, 2013.
-  C. D. Manning, P. Raghavan, and H. Schutze, “Introduction to Information Retrieval,” Cambridge University Press, 2008.
-  Oxford Dictionaries, “Markov chain,” https://en.oxforddictionaries.com/definition/us/markov_chain [accessed December 29, 2017]
-  T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” translated by M. Sugiyama, T. Ide, T. Kamishima, T. Kurita, and E. Maeda, Kyoritsu Publisher, 2014.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.