오프라인 강좌에서 대학생의 학업성취에 따른 학습관리시스템 활동의 차이 분석
An Analysis on the Difference of LMS Activities according to Academic Achievement of College Students in Off-line Courses
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본 연구는 대학의 일반적인 강좌 형태인 오프라인 강좌의 학습관리시스템에서 학습자의 활동과 학업성취 간의 관계를 살펴봄으로써 오프라인 강좌에서 학습분석의 적용 가능성을 탐색하고자 하였다. 이를 위해 A대학의 오프라인 강좌 중 교수-학습 활동이 매주 등록된 44강좌의 수강생 2,119명의 학업성취도와 Moodle 기반의 학습관리시스템에서 활동한 로그 데이터를 분석하였다. 행동 로그 데이터는 학습상태 확인, 읽기, 쓰기로 구분하고, 학업성취도는 A그룹(평점 4.0이상/4.5), B그룹(평점 4.0 미만, 3.0 이상), C그룹(평점 3.0 미만)으로 구분하여, 다변량분산분석과 일원분산분석을 실시하였다. 연구결과는 다음과 같다. 첫째, 학업성취 수준에 따라 학습상태 확인, 읽기, 쓰기 활동 모두에서 유의미한 차이가 있는 것으로 나타났다. 둘째, 주차별로 학업성취 수준에 따른 활동의 차이를 분석한 결과, 읽기와 학습상태 확인 활동은 1주차부터 유의미한 차이가 있었고, 쓰기 활동은 3주차부터 유의미한 차이가 나타났다. 학습상태 확인, 읽기, 쓰기 활동 모두에서 A그룹> B그룹> C그룹 순으로 많은 것으로 나타나 학업성취도가 높은 학생이 학습관리시스템 내에서 많은 활동을 하는 것을 확인하였다. 셋째, 1주차에서 13주차까지는 읽기, 학습상태 확인, 쓰기 활동의 순으로 높게 나타났으나 16주차에서는 쓰기가 학습상태 확인 활동보다 많은 것으로 나타났다. 이를 통해 향후 이러닝 강좌뿐만 아니라 오프라인 강좌에서도 학습분석 연구가 활성화될 수 있음을 확인하였다. 이 연구결과를 바탕으로 오프라인 강좌에서의 학습분석학적 예측 모형 개발을 위한 시사점을 도출하고 후속연구에 대한 제언을 하였다.
Abstract
The study aims to investigate the potential of learning analytics in off-line courses by examining the relationship between learner's academic achievement and activities in the learning management systems. For this purpose, we analyzed academic achievement and the behavioral log of 2,119 students from 44 off-line courses whose instructor posted learning activities on the Moodle based LMS weekly in A university. The behavioral log data were clustered into learning status check, reading, and writing, and students were classified as group A (rated 4.0 or more / 4.5), group B (less than 4.0, 3.0 or more), and group C (less than 3.0) according to their academic achievement. The results of the study are as follows. First, there were significant differences in learning status check, reading, and writing activities of students according to their academic achievement. Second, as a result of analyzing the difference of activities according to academic achievement weekly, there was a significant difference in the activity of reading and learning status check from the first week, and the writing activity showed a significant difference from the third week. In order of A, B, and C group, all of learning status check, reading, and writing activities were more, indicating that students with higher academic achievement perform more activities within the learning management system. Third, from the first to 13th week, reading and learning status check activities were more than writing activities. However, in the 16th week, it was found that writing activities were more than learning status check activities. This study confirmed that learning analytics research can be expanded to off-line courses. Based on the results, implications for the development of predictive models in off-line courses and for follow-up research were suggested.
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