TY - GEN
T1 - An Analysis of Learning Analytics Approaches for Course Evaluation
AU - Wong, Billy T.M.
AU - Li, Kam Cheong
AU - Liu, Mengjin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Course evaluation plays a crucial role in analysing the effectiveness of a course. Despite the emerging trend of applying learning analytics approaches to course evaluation, only limited research has been conducted on reviewing and examining the features of relevant practices. This study analysed the learning analytics approaches used for supporting course evaluation. It covered 27 empirical studies collected from Scopus that were published between 2013 and 2022. The results show the purposes of course evaluation based on learning analytics, including the enhancement of learning experience, effectiveness in learning and teaching, and learning performance and engagement. They also highlight the popular types of data for the learning analytics approaches, such as student performance, feedback, and online learning behaviours, as well as the analytical methods frequently applied, such as statistical tests, content analysis, and descriptive statistics. Additionally, the data visualisation methods most frequently used are also identified, such as tables, bar charts, and line charts. These findings inform the use of learning analytics in course evaluation and provide practical references for its implementation.
AB - Course evaluation plays a crucial role in analysing the effectiveness of a course. Despite the emerging trend of applying learning analytics approaches to course evaluation, only limited research has been conducted on reviewing and examining the features of relevant practices. This study analysed the learning analytics approaches used for supporting course evaluation. It covered 27 empirical studies collected from Scopus that were published between 2013 and 2022. The results show the purposes of course evaluation based on learning analytics, including the enhancement of learning experience, effectiveness in learning and teaching, and learning performance and engagement. They also highlight the popular types of data for the learning analytics approaches, such as student performance, feedback, and online learning behaviours, as well as the analytical methods frequently applied, such as statistical tests, content analysis, and descriptive statistics. Additionally, the data visualisation methods most frequently used are also identified, such as tables, bar charts, and line charts. These findings inform the use of learning analytics in course evaluation and provide practical references for its implementation.
KW - course evaluation
KW - data visualisation
KW - learning analytics
KW - learning behaviour
UR - https://www.scopus.com/pages/publications/85199552006
U2 - 10.1007/978-981-97-4442-8_17
DO - 10.1007/978-981-97-4442-8_17
M3 - Conference contribution
AN - SCOPUS:85199552006
SN - 9789819744411
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 229
EP - 237
BT - Blended Learning. Intelligent Computing in Education - 17th International Conference on Blended Learning, ICBL 2024, Proceedings
A2 - Ma, Will W. K.
A2 - Li, Chen
A2 - Fan, Chun Wai
A2 - U, Leong Hou
A2 - Lu, Angel
T2 - 17th International Conference on Blended Learning, ICBL 2024
Y2 - 29 July 2024 through 1 August 2024
ER -