日本語フィールド
著者：T. Kakeshita, M. Murata, N. Kato, Y. Nakayama題名：Analysis of Student’s Learning Log Data in Fill-in-the-Blank Programming Questions発表情報：International Journal of Learning Technologies and Learning Environments 巻: 5 号: 1 ページ: 17 pagesキーワード：Computer programming education, e-learning, fill-in-the-blank question, Learning Analytics (LA), Moodle概要：抄録：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 stu-dent 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 fre-quently observed errors using association analysis. Furthermore, we analyze the an-swering 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 re-sults help to improve programming education through feedback to the class and the teacher.英語フィールド
Author：T. Kakeshita, M. Murata, N. Kato, Y. NakayamaTitle：Analysis of Student’s Learning Log Data in Fill-in-the-Blank Programming QuestionsAnnouncement information:International Journal of Learning Technologies and Learning Environments Vol: 5 Issue: 1 Page: 17 pagesKeyword：Computer programming education, e-learning, fill-in-the-blank question, Learning Analytics (LA), MoodleAn 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 stu-dent 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 fre-quently observed errors using association analysis. Furthermore, we analyze the an-swering 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 re-sults help to improve programming education through feedback to the class and the teacher.