EURO-Online login
- New to EURO? Create an account
- I forgot my username and/or my password.
- Help with cookies
(important for IE8 users)
3708. Application of Predictive Analytics in Assessing Student Performance: A Case Study in a Computer Supported Collaborative Learning Environment
Invited abstract in session MD-45: Emerging Trends in Decision Analysis, stream Decision Support Systems.
Monday, 14:30-16:00Room: 30 (building: 324)
Authors (first author is the speaker)
| 1. | Yannis Psaromiligkos
|
| Business Administration, University of West Attica | |
| 2. | Athanasios Spyridakos
|
| Department of Business Administration, University of West Attica | |
| 3. | Natasa Themeli
|
| University of Athens | |
| 4. | Theodoros Anagnostopoulos
|
| University of West Attica | |
| 5. | Christos Kytagias
|
| University of West Attica | |
| 6. | Dimitris Papakyriakopoulos
|
| Business administration, University of West Attica |
Abstract
In contemporary collaborative learning environments, characterized by intricate activities and numerous stakeholder interactions, there's a pressing need to gather insights from diverse sources to attain a comprehensive understanding of learning dynamics and students' behaviours. While Learning Management Systems serve as the cornerstone of these environments, their data analysis capabilities are often limited to basic metrics such as visit frequency, completion rates, and score statistics. Consequently, there's a high demand for specialized tools to support instructional designers. Decision support in the form of Learning Analytics, rooted in the domain of business intelligence, has emerged as a forefront area of research in technology-enhanced learning. In this paper, we present the application of predictive analytics within a multi-criteria dataset structure to categorize students based on their performance in collaborative activities. Our approach was deployed in a real-world scenario involving two cohorts of an undergraduate course conducted entirely online due to the pandemic. The first cohort comprised 337 students organized into 81 groups, while the second cohort consisted of 341 students divided into 76 groups. We applied various machine learning algorithms to determine the algorithm yielding optimal performance.
Keywords
- Education and Distance Learning
- Decision Support Systems
Status: accepted
Back to the list of papers