Open Learning Analytics: A Systematic Review of Benchmark Studies using Open University Learning Analytics Dataset (OULAD)

Author:

Alhakbani Haya A.1,Alnassar Fatema M.2

Affiliation:

1. Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia

2. Computing, Goldsmiths, University of London, United Kingdom

Publisher

ACM

Reference27 articles.

1. Rami A Sa’di , Ahmad Abdelraziq , and Talha A Sharadgah . 2021. E-Assessment at Jordan’s Universities in the Time of the COVID-19 Lockdown: Challenges and Solutions. Arab World English Journal (AWEJ) Special Issue on Covid 19 ( 2021 ). Rami A Sa’di, Ahmad Abdelraziq, and Talha A Sharadgah. 2021. E-Assessment at Jordan’s Universities in the Time of the COVID-19 Lockdown: Challenges and Solutions. Arab World English Journal (AWEJ) Special Issue on Covid 19 (2021).

2. Muhammad Sammy Ahmad , Ahmed  H Asad , and Ammar Mohammed . 2021. A Machine Learning Based Approach for Student Performance Evaluation in Educational Data Mining. In 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) . IEEE , 187–192. Muhammad Sammy Ahmad, Ahmed H Asad, and Ammar Mohammed. 2021. A Machine Learning Based Approach for Student Performance Evaluation in Educational Data Mining. In 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 187–192.

3. Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment

4. Fatema Alnassar , Tim Blackwell , Elaheh Homayounvala , and Matthew Yee-king. 2021 . How Well a Student Performed? A Machine Learning Approach to Classify Students’ Performance on Virtual Learning Environment. In 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 1–6. Fatema Alnassar, Tim Blackwell, Elaheh Homayounvala, and Matthew Yee-king. 2021. How Well a Student Performed? A Machine Learning Approach to Classify Students’ Performance on Virtual Learning Environment. In 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 1–6.

5. The Application of Gaussian Mixture Models for the Identification of At-Risk Learners in Massive Open Online Courses

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