Pattern Analysis of Learners’ Artifact Making Processes and Errors in Conceptual Modeling Exercises

Takafumi Tanaka; Kousuke Mouri; Keiichi Kaneko

doi:10.52731/iee.v4.i1.283

Takafumi Tanaka Tokyo University of Agriculture and Technology
Kousuke Mouri Tokyo University of Agriculture and Technology
Keiichi Kaneko Tokyo University of Agriculture and Technology

DOI: https://doi.org/10.52731/iee.v4.i1.283

Keywords: artifact making process, association analysis, conceptual modeling, software engineering education

Abstract

Conceptual modeling (CM) is an important technique in database design. Education of the technique is a challenge of computer science departments in higher education institutions. The aim of this study is to find patterns of learners’ errors in their artifacts and their artifact making processes in conceptual modeling exercises. This study collected the data of learners’ artifacts and the process of creating them in an exercise of CM using a system, KIfU 3.0, and conducted an association analysis with this data. As a result, this study found the association rules regarding learners’ errors in the artifacts and/or their characteristics of the artifact making processes. For example, there are some errors that have a tendency to co-occur in the learners’ artifacts, and learners who refine their artifacts frequently tend to detect attributes of concepts successfully even though they are hard to detect from a requirement specification for the exercise. This fact implies the effectiveness of our approach and a suggestion to improve teaching of the CM.

References

D. Batra and J.G. Davis, “Conceptual data modelling in database design: similarities and differences between expert and novice designers,” Int’l J. Man-Machine Studies, vol. 37, no. 1, pp. 83–101, 1992.

M. Ohki and K. Akiyama, “A propose of the conceptual modeling criteria and its validity evaluation (in Japanese),” IEICE Trans. Information and Systems, vol. J84-D1, no. 6, pp. 723–735, 2001.

V.C. Storey and R.C. Goldstein, “Knowledge-based approaches to database design,” MIS Quarterly, vol. 17, no. 1, pp. 25–46, 1993.

M.A. Chilton, R. McHaney, and B. Chae, “Data modeling education: The changing technology,” J. Information Systems Education, vol. 17, no. 1, pp. 17–20, 2006.

R. Ramakrishnan and J. Gehrke, Database Management Systems, 3rd ed., McGrawHill Inc., New York, NY, USA, 2003.

J.D. Ullman, H. Garcia-Molina, and J. Widom, Database Systems: The Complete Book, 1st ed., Prentice Hall PTR, Upper Saddle River, NJ, USA, 2001.

S.D. Ryan, B. Bordoloi, and D.A. Harrison, “Acquiring conceptual data modeling skills: The effect of cooperative learning and self-efficacy on learning outcomes,” SIGMIS Database, vol. 31, no. 4, pp. 9–24, 2000.

M. Kayama, S. Ogata, K. Masumoto, M. Hashimoto, and M. Otani, “A practical conceptual modeling teaching method based on quantitative error analyses for novices learning to create error-free simple class diagrams,” Proc. 2014 3rd Int’l Conf. Advanced Applied Informatics, pp. 616–622, 2014.

L. Kazi, B. Radulovic, I.F. Berkovi ´ c, and Z. Kazi, “Integration of conceptual data ´ modeling methods: Higher education experiences,” Proc. 2014 37th Int’l Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 845–850, 2014.

P.P. Chen, “The entity-relationship–Toward a unified view of data,” ACM Trans. Database Systems, vol. 1, no. 1, pp. 9–36, 1976.

P. Suraweera and A. Mitrovic, “An intelligent tutoring system for entity relationship modelling,” Int’l J. Artificial Intelligence in Education, vol. 14, pp. 375–417, 2004.

H. Kung, L. Kung, and A. Gardiner, “Comparing Top-down with Bottom-up Approaches: Teaching Data Modeling,” Proc. Information Systems Educators Conf., New Orleans Louisiana, vol. 29, 2012.

T. Tanaka, H. Hashiura, A. Hazeyama, S. Komiya, Y. Hirai, and K. Kaneko, “A method for collecting learners’ thinking process in ER modeling exercises and its application experiment,” Proc. 2016 Int’l IEEE Conf. Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, pp.482-488, 2016.

R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 207–216, 1993.

K. Mouri, H. Ogata, and N. Uosaki, “Ubiquitous learning analytics in the context of real-world language learning,” Proc. 5th Int’l Conf. Learning Analytics and Knowledge, pp. 378–382, 2015.

K. Mouri, H. Ogata, and N. Uosaki, “Context-aware and Personalization Method based on Ubiquitous Learning Analytics,” J. Universal Computer Science, vol. 22, no. 10, pp. 1380–1397, 2016.

H. Ogata, B. Hou, N. Uosaki, K. Mouri, and S. Liu, “Ubiquitous learning project using life-logging technology in Japan,” J. Educational Technology & Society, vol. 17, no. 2, pp. 85-100, 2014.

G. Hwang, H. Chu, and C. Yin, “Objectives, Methodologies and Research Issues of Learning Analytics,” Interactive Learning Environments, vol. 25, no. 2, pp. 143–146, 2017.

C. J. Yin, N. Uosaki, H. C. Chu, G. J. Hwang, J. J. Hwang, I. Hatono, E. Kumamoto, and Y. Tabata, “Learning Behavioral Pattern Analysis based on Students ’Logs in Reading Digital Books,” Proc. Int’l Conf. Computers in Education, pp. 549–557, 2017.

B. Minaei-Bidgoli, G. Kortemeyer, and W. Punch, “Association analysis for an online education system,” Proc. 2004 IEEE Int’l Conf. Information Reuse and Integration, pp. 504–509, 2004.