Publication details
Educational data mining for analysis of students’ solutions
| Authors | |
|---|---|
| Year of publication | 2014 |
| Type | Article in Proceedings |
| Conference | Znalosti 2014 |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | educational data mining; logic proofs; clustering; outlier detection; sequence mining |
| Description | We introduce novel methods for analysis of logic proofs constructed by undergraduate students. The methods employ 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 generalization. We showed in [8-11] that these representations allow us to find interesting subgroups of similar solutions and also to detect outlying solutions. Specifically, distribution of errors is not independent on behavioral patterns and we were able to find clusters of erroneous solutions. We also observed a significant dependence between time duration of solving the task and an appearance of the most serious error. This text brings a brief summary of four contributions [8-11] presented for presentation in the period from October 2013 until September 2014. In the second part, based on [11] we focus on a newly developed outlier detection method that helps to find unusual solutions, both correct and erroneous. |