Abstract
This paper reports on an application of classification and regression models to identify college students at risk of failing in first year of study. Data was gathered from three student cohorts in the academic years 2010 through 2012 (n=1207). Students were sampled from fourteen academic courses in five disciplines, and were diverse in their academic backgrounds and abilities. Metrics used included noncognitive psychometric indicators that can be assessed in the early stages after enrolment, specifically factors of personality, motivation, self regulation and approaches to learning. Models were trained on students from the 2010 and 2011 cohorts, and tested on students from the 2012 cohort. Is was found that classification models identifying students at risk of failing had good predictive accuracy (> 79%) on courses that had a significant proportion of high risk students (over 30%).
| Original language | English |
|---|---|
| Pages (from-to) | 107-114 |
| Number of pages | 8 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1183 |
| Publication status | Published - 2014 |
| Event | Workshops on Educational Data Mining, WSEDM 2014 - Co-located with 7th International Conference on Educational Data Mining, EDM 2014 - London, United Kingdom Duration: 4 Jul 2014 → 7 Jul 2014 |
Keywords
- Academic performance
- Educational data mining
- Learning analytics
- Learning approach
- Learning style
- Motivation
- Non cognitive factors of learning
- Personality
- Self-regulation