Name
Abstract | ||
|---|---|---|
The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a positive or negative way from the perspective of MIL. Computational experiments compare our proposal with the most popular techniques of Multiple Instance Learning (MIL). Results show that G3P-MI achieves better performance with more accurate models and a better trade-off between such contradictory metrics as sensitivity and specificity. Moreover, it adds comprehensibility to the knowledge discovered and finds interesting relationships that correlate certain tasks and the time devoted to solving exercises with the final marks obtained in the course. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISDA.2009.108 | ISDA |
Keywords | Field | DocType |
different way,accurate model,certain task,multiple instance genetic programming,predicting academic achievement,better trade-off,contradictory metrics,multiple instance learning,university-level learning,certain course,computational experiment,better performance,genetic algorithms,machine learning,data mining,evolutionary computation,computer experiment,supervised learning,sensitivity,genetic programming | Computer aided instruction,Pattern recognition,Computer science,Evolutionary computation,Grammar,Genetic programming,Supervised learning,Artificial intelligence,Academic achievement,Genetic algorithm,Machine learning | Conference |
ISSN | Citations | PageRank |
2164-7143 | 1 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amelia Zafra | 1 | 432 | 22.64 |
| Cristóbal Romero | 2 | 2226 | 148.97 |
| Sebatián Ventura | 3 | 1 | 0.34 |