Name
Abstract | ||
|---|---|---|
Teamwork is acquiring a growing relevance in learning environments. In many cases, it is a useful and meaningful way to organize the learning activities and improve their outcomes. Moreover, related skills are needed for the professional life of students. This situation makes necessary the availability of support techniques to manage the different aspects of teamwork in educational settings. A key aspect is the organization of teams. Literature offers alternatives to assess students and make up teams, but they are focused on particular and isolated aspects. This work proposes a novel methodology to develop tailored student assessments from the integration of multiple pre-existent evaluation techniques. These new techniques evaluate features belonging to student's profiles, and use the results to create groups that improve their learning experience. The process is based on clustering techniques and has three-stage. In the first one, lecturers identify features they consider relevant in their context (e.g. leadership, ability to communicate, or spatial skills) and test to asses them. Then, the training stage identifies the combinations of feature values from those tests that characterize high-performance teams, i.e. teams where group learning results are over the average in its context. Finally, the classification stage uses those values to determine which students should belong to which teams, trying to replicate the distribution of student's profiles in the best teams, and thus their results. The paper reports the experiments performed so far to evaluate the method in a computer engineering school. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-32034-2_40 | Hybrid Artificial Intelligent Systems |
Keywords | Field | DocType |
Teamwork, Group learning, Collaborative learning, Student's profile, Team formation, Classification, Clustering | Teamwork,Collaborative learning,Spatial skills,Computer science,Group learning,Knowledge management,Learning experience,Artificial intelligence,Cluster analysis,Machine learning,Replicate | Conference |
Volume | ISSN | Citations |
9648 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marta Guijarro-Mata-García | 1 | 1 | 0.35 |
| María Guijarro | 2 | 49 | 5.79 |
| Rubén Fuentes-Fernández | 3 | 263 | 29.30 |