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http://dx.doi.org/10.14352/jkaie.2018.22.1.151

A Study on Development of Collaborative Problem Solving Prediction System Based on Deep Learning: Focusing on ICT Factors  

Lee, Youngho (Seoul Youngdo Elementary School)
Publication Information
Journal of The Korean Association of Information Education / v.22, no.1, 2018 , pp. 151-158 More about this Journal
Abstract
The purpose of this study is to develop a system for predicting students' collaborative problem solving ability based on the ICT factors of PISA 2015 that affect collaborative problem solving ability. The PISA 2015 computer-based collaborative problem-solving capability evaluation included 5,581 students in Korea. As a research method, correlation analysis was used to select meaningful variables. And the collaborative problem solving ability prediction model was created by using the deep learning method. As a result of the model generation, we were able to predict collaborative problem solving ability with about 95% accuracy for the test data set. Based on this model, a collaborative problem solving ability prediction system was designed and implemented. This research is expected to provide a new perspective on applying big data and artificial intelligence in decision making for ICT input and use in education.
Keywords
Computer based collaborative problem solving ability; ICT factor; PISA 2015; Deep learning; Correlation analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Park, Hye-Young & Rim, Haemee (2014). Analyzing features of collaborative problem solving competencies in PISA and ATC 21S: Implications for instruction and assessment in Korea. Journal of Learner-Centered Curriculum and Instruction, 14(9), 439-462.
2 Rim, Haemee (2012). PISA 2012 computer-based problem-solving ability evaluation framework and open questions. Proceedings of the KSME Fall Conference on Math. Edu, 61-66.
3 Robert, C. (2014). Machine learning, a probabilistic perspective.
4 Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.   DOI
5 Oecd.org. (2018). 2015 Database - PISA. Retrieved January 5, 2018, from http://www.oecd.org/pisa/data/2015database.
6 Oecd.org. (2018). 2015 Technical Report - PISA. Retrieved January 5, 2018, from http://www.oecd.org/pisa/data/2015-technical-report.
7 Pisa.ets.org. (2018). PISA. Retrieved January 6, 2018, from https://pisa.ets.org/PISA_ReleasedUnits/platform/index.html?user=&domain=CPS&unit=C100-Xandar&lang=eng-ZZZ.
8 Financial Security Agency (2016). Artificial intelligence overview and technology trends.
9 Hesse, F., Care, E., Buder, J., Sassenberg, K., & Griffin, P. (2015). A framework for teachable collaborative problem solving skills. In Assessment and teaching of 21st century skills, 37-56.
10 Kim, Sung-Sook & Han, Jung-A (2016). Comparative Analysis of the Effects of Students and School Factors on PISA 2012 Problem Solving Results in Korea, Singapore, and Japan. Korean Journal of Educational Research, 54(3), 225-247.
11 Korea Creative Content Agency (2017). Development Strategy of Intelligent Contents Technology.
12 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.   DOI
13 Lee, Young-Ho, Koo, Duk-Hoi, & Lim, Hyo Jin (2017). Effects of Student- and School-level ICT-related Factors on Computer-based Problem Solving: Focusing on Korea and Japan. Journal of The Korean Association of Information Education, 21(4), 425-435.   DOI
14 Nam, Chang-Woo & Shin, Su-Yeong (2014). The Effects of Students' ICT related Variables on Their Attitude toward ICT Use and Problem solving Abilities. Journal of Educational Evaluation, 27, 1265-1286.
15 OECD (2012). assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy. OECD: Paris.
16 OECD (2013). PISA 2015 Draft collaborative problem solving framework. OECD: Paris.
17 OECD (2016). PISA 2015 Assessment and Analytical Framework. OECD: Paris.