Publication details
Educational data mining for analysis of students’ solutions
| Authors | |
|---|---|
| Year of publication | 2014 |
| Type | Article in Proceedings |
| Conference | Artificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-319-10554-3_14 |
| Field | Informatics |
| Keywords | educational data mining; logic proofs; clustering; outlier detection; sequence mining |
| Description | We introduce a novel method for analysis of logic proofs constructed by undergraduate students that employs sequence mining for manipulation with temporal information about all actions that a student performed, and also graph mining for finding frequent subgraphs on different levels of generalisation. We show that this representation allows to find interesting subgroups of similar solutions and also to detect outlying solutions. Specifically, distribution of errors is not independent on behavioural patterns and we are able to find clusters of erroneous solutions. We also observed significant dependence between time duration and an appearance of the most serious error. |