Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Academic Achievement | 1 |
| At Risk Students | 1 |
| Automation | 1 |
| College Students | 1 |
| Computer Science | 1 |
| Correlation | 1 |
| Data Analysis | 1 |
| Data Collection | 1 |
| Feedback (Response) | 1 |
| Foreign Countries | 1 |
| Grades (Scholastic) | 1 |
| More ▼ | |
Source
| International Educational… | 1 |
Publication Type
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
| Australia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textMcBroom, Jessica; Jeffries, Bryn; Koprinska, Irena; Yacef, Kalina – International Educational Data Mining Society, 2016
Effective mining of data from online submission systems offers the potential to improve educational outcomes by identifying student habits and behaviours and their relationship with levels of achievement. In particular, it may assist in identifying students at risk of performing poorly, allowing for early intervention. In this paper we investigate…
Descriptors: Data Collection, Student Behavior, Academic Achievement, Correlation
