Inferred Study Habits of Students from E-Learning – Focusing on Increasing Test Scores in Pre-Enrollment Education

Inferred Study Habits of Students from E-Learning – Focusing on Increasing Test Scores in Pre-Enrollment Education



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In this study, we used the placement test scores, e-learning history, and posttest scores obtained in the pre-enrollment education at University A conducted in 2015, 2016, and 2017 to exploratorily classify seven types of students according to learning habits (types). It was revealed that more than 80% (with the exception of English in 2016) of the highest placement test scores (Japanese ≥ 90, English ≥ 74 in this study) were of learners with learning habits (types) of long-term completion (LTrf) or mid-term completion (MTrf). Although the percentage of learners with LTrf or MTrf learning habits (types) gradually decreased as placement test scores declined, there was no significant change in learning habit trends and learning habits (types) other than for LTrf or MTrf. In addition, learners with large increases in posttest scores (posttest score - placement score) after completing e-learning were classified with learning habits (types) of LTrf or MTrf. However, it is not only the learners who improved their test scores that were progressing without bias during the learning period (LTrf or MTrf). It was also found that more than 65% progressed in their learning (logged in) without being biased during the learning period. These findings suggest that there is no significant difference between learners' learning habits (types), placement test scores, and test score growth.

Keywords: Learning Characteristics Learning Habit Online Learning Step by Step Systems Approach
  1. Advanced Learning Infrastructure Consortium. (2003). Near-future education created by e-learning —Collection of latest e-learning practice cases. Ohmsha, Ltd.  |   Google Scholar
  2. Bassett, D. S., Wymbs, N. F., Porter, M, A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2010). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences, 108(18), 7641-7646. https://doi.org/10.1073/pnas.1018985108  |   Google Scholar
  3. Duman, B. (2010). The effects of brain-based learning on the academic achievement of students with different learning styles. Educational Sciences: Theory and Practice, 10(4), 2077-2103.  |   Google Scholar
  4. Gagne R. M. (1985a). The Conditions of Learning and Theory of Instruction. Wadsworth Pub Co.  |   Google Scholar
  5. Gagne R. M. (1985b). The Conditions of Learning and Theory of Instruction (Subsequent Edition). Wadsworth Pub Co.  |   Google Scholar
  6. Gagne R. M. & Driscoll M. (1988). Essentials of Learning for Instruction. Pearson College Div.  |   Google Scholar
  7. Gagne R. M., Wager W. W., Golas K. C., & Keller J. M. (2004). Principles of Instructional Design (5th ed.). Wadsworth Pub Co.  |   Google Scholar
  8. Handayani, B. S. & Corebima, A. D. (2017) Model brain based learning (BBL) and whole brain teaching (WBT) in learning. International Journal of Science and Applied Science, 1(2), 153-161.  |   Google Scholar
  9. Kizilceca, R. F., MarPérez-Sanagustínb, & Maldonado J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104, 18-33.  |   Google Scholar
  10. Li, P., Legault J. & Litcofsky, K. A. (2014). Neuroplasticity as a function of second language learning: Anatomical changes in the human brain. Cortex, 58, 301-324.  |   Google Scholar
  11. Seger, C. A. & Miller E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33, 203–219. https:// doi.org/10.1146/annurev.neuro.051508.135546.  |   Google Scholar
  12. Sugawara, R. (2017a). Academic achievement trends and study trends of applicants passing AO / recommendation entrance exams —Statistical analysis of placement tests and completion tests in pre-enrollment education. Research Bulletin of Meisei University, Meisei Kyoiku Center, 7, 49-56.  |   Google Scholar
  13. Sugawara, R. (2017b). Discussion of learning and study type aptitude treatment interaction in e-learning: Response to “Why e-learning cannot satisfy the act of learning?'' Journal of International ICT Application Research Society, 1(1), 16-22.  |   Google Scholar
  14. Sugawara, R. (2018). Change of the scholastic ability and the tendency of the learning about admissions office examination and examination for selected candidates passer —Statistical analysis of time series data for placement test and completion tests in pre-enrollment education. Research Bulletin of Meisei University, Meisei Kyoiku Center, 8, 17-25.  |   Google Scholar
  15. Sugawara, R. (2019). Change of the scholastic ability and the tendency of the learning about admissions office examination and examination for selected candidates passer in the 2018 Japanese calendar —Based on statistical analysis of time series data for placement test and posttest of e-learning in pre-enrollment education. Research Bulletin of Meisei University, Meisei Kyoiku Center, 9, 49-59.  |   Google Scholar
  16. Sugawara, R. (2020). Reliability of placement tests in pre-enrollment education and changes in academic ability of successful applicants in AO / recommendation entrance exams —From statistical analysis of test scores of 2015-2018 enrollees. Research Bulletin of Meisei University Meisei Kyoiku Center, 10, 45-52  |   Google Scholar
  17. Sugawara, R., Enomoto, T., Ochiai, K., Ota, M., Suzuki, H., Takahashi, N., Hiratsuka, D., Minami, A., Momoki, H., Niimura, S., Mikuriya M., Amaoka, T. & Kikuchi, S. (2017). Utilization of e-learning to improve study habits and improve academic ability of applicants passing AO / recommendation entrance exams. Research Report of JSET Conferences, 17(1), 403-406.  |   Google Scholar
  18. Sugawara, R., Okuhara, S., Fukuyama, Y., & Sato, K. (2019). Exploratory study on learning habits and test scores of students in e-learning. Research Report of JSET Conferences, 19(1), pp. 143-146.  |   Google Scholar
  19. Sugawara, R., Okuhara, S., & Sato, Y. (2020a). Exploratory study on correlations between students’ characteristics and effects in the case of online learning on university students. The 15th International Conference on Knowledge, Information and Creativity Support Systems, Proceedings of 15th International Conference on Knowledge, Information and Creativity Support Systems, 81-83.  |   Google Scholar
  20. Sugawara, R., Okuhara, S., & Sato, Y. (2020b). Study about the aptitude-treatment interaction between learning using the e-learning system and learning type of learner. International Journal of Information and Education Technology, 10(7), 488-493.  |   Google Scholar

How to Cite

Sugawara, R., & Okuhara, S. (2023). Inferred Study Habits of Students from E-Learning – Focusing on Increasing Test Scores in Pre-Enrollment Education. European Journal of Education and Pedagogy, 4(3), 31–36. https://doi.org/10.24018/ejedu.2023.4.3.659

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 Ryo Sugawara
 Google Scholar |   EJEDU Journal
 Shun Okuhara
 Google Scholar |   EJEDU Journal