For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Learners' Self Checking and Its Effectiveness in Conceptual Data Modeling Exercises
Takafumi TANAKA Hiroaki HASHIURA Atsuo HAZEYAMA Seiichi KOMIYA Yuki HIRAI Keiichi KANEKO
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/07/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section PAPER (Special Section on Knowledge-Based Software Engineering)
Conceptual data modeling, self checking, artifact-making process, UML,
Full Text: PDF(1.8MB)
>>Buy this Article
Conceptual data modeling is an important activity in database design. However, it is difficult for novice learners to master its skills. In the conceptual data modeling, learners are required to detect and correct errors of their artifacts by themselves because modeling tools do not assist these activities. We call such activities self checking, which is also an important process. However, the previous research did not focus on it and/or the data collection of self checks. The data collection of self checks is difficult because self checking is an internal activity and self checks are not usually expressed. Therefore, we developed a method to help learners express their self checks by reflecting on their artifact making processes. In addition, we developed a system, KIfU3, which implements this method. We conducted an evaluation experiment and showed the effectiveness of the method. From the experimental results, we found out that (1) the novice learners conduct self checks during their conceptual data modeling tasks; (2) it is difficult for them to detect errors in their artifacts; (3) they cannot necessarily correct the errors even if they could identify them; and (4) there is no relationship between the numbers of self checks by the learners and the quality of their artifacts.