@inproceedings{664a67355ef4469ca4a8d497f1d31b97,
title = "Recommender system to identify students with learning deficiencies in assessments",
abstract = "Find areas and indicators of achievement where students need to reinforce their knowledge is a difficult task for teachers in schools. This article presents a decision-making support system that allows teachers to identify students with poor academic performance. The strategy is a Matrix Based Recommender System to rate assessments and share the results using statistical graphs. To validate this proposal we used focus group and daily meetings methodologies. The proposed strategy was tested in UGEL07-Lima-Per{\'u} with 135 schools and 25491 students in evaluation process. The evaluation results show that teachers agree with the proposed strategy, because it allows them to have assessment information everywhere and at every time. The results also highlight that using the tool users can have visual information in real time. Furthermore, the information shared through the application improves decision-making on corrective actions for poor academic performance in evaluated areas.",
keywords = "Data Visualization, Decision Making, Excel VBA, Learning Assessment, Recommender System",
author = "Ibarra, {Manuel J.} and Cristhian Serrano and Navarro, {{\'A}ngel F.}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 18th International Symposium on Computers in Education, SIIE 2016 ; Conference date: 13-09-2016 Through 16-09-2016",
year = "2016",
month = nov,
day = "21",
doi = "10.1109/SIIE.2016.7751842",
language = "English",
series = "2016 International Symposium on Computers in Education, SIIE 2016: Learning Analytics Technologies",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Mendes, {Antonio Jose} and Garcia-Penalvo, {Francisco Jose}",
booktitle = "2016 International Symposium on Computers in Education, SIIE 2016",
}