Predicting Factors That Influence Students' Learning Outcomes Using Learning Analytics in Online Learning Environment

被引：0

作者：

Ulfa S. ^{[1
]}

Fatawi I. ^{[2
]}

机构：

[1] State University of Malang, Malang

[2] Islamic Institute of Nurul Hakim, Lombok

来源：

International Journal of Emerging Technologies in Learning | 2020年 / 16卷 / 01期

关键词：

Concept mapping; learning analytics; learning outcomes; nonhuman interaction; student success in online learning;

D O I：

10.3991/IJET.V16I01.16325

中图分类号：

学科分类号：

摘要：

The application of online learning has increased significantly, recently. One of the key successes in online learning is student interactions. An active learning strategy would engage the students to interact with the course or to get involved in the learning process. The objective of this research was to predict which one of the student activities that would improve the learning outcome of the students? All the activities are related to non-human interaction. One of the activities is concept mapping. All the students' activities in online learning were stored in LMS and the data generated as a learning analytics. A linear regression method was used to analyze the data. This research confirmed that working on exercises by using concept mapping yields significant results in improving the learning outcome of students. © 2020. All rights reserved.

引用

页码：4 / 17

页数：13

共 46 条

[1]

Ghilay Y., Effectiveness of Learning Management Systems in Higher Education: Views of Lecturers with Different Levels of Activity in LMSs, Journal of Online Higher Education, 3, 2, pp. 29-50, (2019)

[2]

Lee L.- K., Cheung S. K. S., Kwok L.- F., Learning Analytics: Current Trends and Innovative Practices, Journal of Computers in Education, 7, pp. 1-6, (2020)

[3]

Rayens W., Ellis A., Creating a Student-Centered Learning Environment Online, Journal of Statistics Education, 26, 2, pp. 92-102, (2018)

[4]

Lopes A. P., Learning Management System in Higher Education, Proceedings of Edulearn14 Conference, (2014)

[5]

Reischl V., Toro J. T. M., Learning Management System, Igniting Your Teaching with Educational Technology, CA, Innovate Learning, pp. 13-22, (2017)

[6]

Aldowah H., Al-Samarraie H., Fauzy M. W., Educational Data Mining and Learning Analytics for 21st Century Higher Education: A Review and Synthesis, Telematics and Informatics, 37, pp. 13-49, (2019)

[7]

Jagadish H. V., Gehrke J., Labrinidis A., Papakonstantinou Y., Patel J. M., Ramakrishnan R., Sahabi C., Big Data and Its Challenge, Communication of The ACM, 7, 7, pp. 86-94, (2014)

[8]

Papamitsiou Z. K., Economides A. A., Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence, Educational Technology & Society, 17, 4, pp. 49-64, (2014)

[9]

Greller W., Drachsler H., Translating Learning into Numbers: A Generic Framework for Learning Analytics, Educational Technology & Society, 15, 42, pp. 42-57, (2012)

[10]

Lu O., Huang J., Huang A., Yang S., Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course, Interactive Learning Environments, 25, 2, pp. 1-15, (2017)

← 1 2 3 4 5 →