Analysis of Student’s Learning Log Data in Fill-in-the-Blank Programming Questions

Tetsuro Kakeshita; Miyuki Murata; Naoko Kato; Youhei Nakayama

doi:10.52731/ijltle.v5.i1.565

Tetsuro Kakeshita Saga University
Miyuki Murata National Institute of Technology, Kumamoto College
Naoko Kato National Institute of Technology, Ariake College
Youhei Nakayama Saga University

DOI: https://doi.org/10.52731/ijltle.v5.i1.565

Keywords: Computer programming education, e-learning, fill-in-the-blank question, Learning Analytics (LA), Moodle

Abstract

We have developed a programming education support tool pgtracer which provides fill-in-the-blank questions containing a C++ program and a trace table. In this paper, we analyze the study log and the answer log collected by pgtracer. We analyze student activities and incorrect answers to find the tendency and frequent mistakes of the students. We next classify the type of incorrect answers in the log data for 18 fill-in-the-blank questions with 127 blanks. We then identify the patterns of frequently observed errors using association analysis. Furthermore, we analyze the answering process to fill the blanks of the students and find that the right answer ratio affects the answering process. We expect that these analysis techniques and the results help to improve programming education through feedback to the class and the teacher.

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