Name
Abstract | ||
|---|---|---|
A principal component analysis is carried out on the undergraduate level “Stochastic Models” course. We determine that the first principal component has a positive correlation with the score of the final written cumulative exam. This could possible mean that the final exam could be eliminated from engineering curricula, but the variability is significant as measured by the correlation R statistic. We gathered a much larger sample and found that the variability increased, indicating changes in the course and students emphasis in learning activities. Therefore we concluded, that the evidence presented does not justify eliminating written cumulative final exams. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.procs.2016.05.465 | ICCS |
Keywords | Field | DocType |
Principal Component Analysis, Course Design, Learning Activities, Evaluation, Learning Analytics | Data mining,Statistic,Computer science,Engineering curricula,Engineering education,Positive correlation,Artificial intelligence,Principal component analysis,Machine learning | Conference |
Volume | Issue | ISSN |
80 | C | 1877-0509 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| raul ramirezvelarde | 1 | 22 | 4.26 |
| Nia Alexandrov | 2 | 11 | 4.36 |
| Miguel Sanhueza-Olave | 3 | 0 | 1.35 |
| Raul Perez-Cazares | 4 | 8 | 1.17 |