Can Log Files Analysis Estimate Learner’s Level of Motivation?
| dc.contributor.author | Cocea, Mihaela | de_DE |
| dc.date.accessioned | 2017-11-15T14:59:46Z | |
| dc.date.available | 2017-11-15T14:59:46Z | |
| dc.date.issued | 2006 | |
| dc.description.abstract | The learners’ motivation has an impact on the quality of learning, especially in e-Learning environments. Most of these environments store data about the learner’s actions in log files. Logging the users’ interactions in educational systems gives the possibility to track their actions at a refined level of detail. Data mining and machine learning techniques can “give meaning” to these data and provide valuable information for learning improvement. An area where improvement is absolutely necessary and of great importance is motivation known to be an essential factor for preventing attrition in e-Learning. In this paper we investigate if the log files data analysis can be used to estimate the motivational level of the learner. A decision tree is build from a limited number of log files. The results suggest that time spent reading is an important factor for predicting motivation; also, performance in tests was found to be a relevant indicator of the motivational level. | |
| dc.identifier.uri | http://abis.l3s.uni-hannover.de/images/proceedings/abis2006/abis2006_cocea.pdf | de_DE |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/5032 | |
| dc.language.iso | en | de_DE |
| dc.relation.ispartof | 14. GI-Workshop "Adapitivität und Benutzermodellierung in interaktiven Softwaresystemen" | de_DE |
| dc.title | Can Log Files Analysis Estimate Learner’s Level of Motivation? | de_DE |
| dc.type | Text/Conference Paper | |
| gi.document.quality | digidoc |