Paving the way for providing teaching feedback in automatic evaluation of open response assignments

Veronica Bolon-Canedo, Jorge Diez, Oscar Luaces, Antonio Bahamonde, Amparo Alonso-Betanzos
2017 2017 International Joint Conference on Neural Networks (IJCNN)  
Peer grading has been the regular procedure to use for automatic assessment of open ended assignments in Massive Open Online Courses (MOOCs). However, and although the procedure tries to overcome the rupture of the classical teachlearn-assess/feedback cycle, it does so only in the student side, and no attempt has been made as yet in giving feedback to instructors. The work described inhere aims at filling this gap, with a proposal in which the instructors are supplied with the set of words most
more » ... e set of words most used by the best and worst ranked quartiles of assignments. In order to achieve this, a Gaussian Mixture Model (GMM) fed with the bag of words supplied by a previous feature selection algorithm is presented, with the goal of identifying the clusters of words related with similar grades. The results obtained over three pilot studies, containing assignments in three different disciplines, show that our model can lead to more complete information on the teacher feedback on the results of the assignments.
doi:10.1109/ijcnn.2017.7966289 dblp:conf/ijcnn/Bolon-CanedoDLB17 fatcat:7w6qctngd5cqzk7fne5ra3yuna