Comparing classification methods for predicting distance students’ performance

Diego Garcia-Saiz, Marta Zorrilla
Proceedings of the Second Workshop on Applications of Pattern Analysis, PMLR 17:26-32, 2011.

Abstract

Virtual teaching is constantly growing and, with it, the necessity of instructors to predict the performance of their students. In response to this necessity, different machine learning techniques can be used. Although there are so many benchmarks comparing their performance and accuracy, there are still very few experiments carried out on educational datasets which have very special features which make them different from other datasets. Therefore, in this work we compare the performance and interpretation level of the output of the different classification techniques applied on educational datasets and propose a meta-algorithm to preprocess the datasets and improve the accuracy of the model, which will be used by virtual instructors for their decision making through the ElWM tool.

Cite this Paper

BibTeX
@InProceedings{pmlr-v17-garcia-saiz11a, title = {Comparing classification methods for predicting distance students' performance}, author = {Garcia-Saiz, Diego and Zorrilla, Marta}, booktitle = {Proceedings of the Second Workshop on Applications of Pattern Analysis}, pages = {26--32}, year = {2011}, editor = {Diethe, Tom and Balcazar, Jose and Shawe-Taylor, John and Tirnauca, Cristina}, volume = {17}, series = {Proceedings of Machine Learning Research}, address = {CIEM, Castro Urdiales, Spain}, month = {19--21 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v17/garcia-saiz11a/garcia-saiz11a.pdf}, url = {https://proceedings.mlr.press/v17/garcia-saiz11a.html}, abstract = {Virtual teaching is constantly growing and, with it, the necessity of instructors to predict the performance of their students. In response to this necessity, different machine learning techniques can be used. Although there are so many benchmarks comparing their performance and accuracy, there are still very few experiments carried out on educational datasets which have very special features which make them different from other datasets. Therefore, in this work we compare the performance and interpretation level of the output of the different classification techniques applied on educational datasets and propose a meta-algorithm to preprocess the datasets and improve the accuracy of the model, which will be used by virtual instructors for their decision making through the ElWM tool.} }
Endnote
%0 Conference Paper %T Comparing classification methods for predicting distance students’ performance %A Diego Garcia-Saiz %A Marta Zorrilla %B Proceedings of the Second Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2011 %E Tom Diethe %E Jose Balcazar %E John Shawe-Taylor %E Cristina Tirnauca %F pmlr-v17-garcia-saiz11a %I PMLR %P 26--32 %U https://proceedings.mlr.press/v17/garcia-saiz11a.html %V 17 %X Virtual teaching is constantly growing and, with it, the necessity of instructors to predict the performance of their students. In response to this necessity, different machine learning techniques can be used. Although there are so many benchmarks comparing their performance and accuracy, there are still very few experiments carried out on educational datasets which have very special features which make them different from other datasets. Therefore, in this work we compare the performance and interpretation level of the output of the different classification techniques applied on educational datasets and propose a meta-algorithm to preprocess the datasets and improve the accuracy of the model, which will be used by virtual instructors for their decision making through the ElWM tool.
RIS
TY - CPAPER TI - Comparing classification methods for predicting distance students’ performance AU - Diego Garcia-Saiz AU - Marta Zorrilla BT - Proceedings of the Second Workshop on Applications of Pattern Analysis DA - 2011/10/21 ED - Tom Diethe ED - Jose Balcazar ED - John Shawe-Taylor ED - Cristina Tirnauca ID - pmlr-v17-garcia-saiz11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 17 SP - 26 EP - 32 L1 - http://proceedings.mlr.press/v17/garcia-saiz11a/garcia-saiz11a.pdf UR - https://proceedings.mlr.press/v17/garcia-saiz11a.html AB - Virtual teaching is constantly growing and, with it, the necessity of instructors to predict the performance of their students. In response to this necessity, different machine learning techniques can be used. Although there are so many benchmarks comparing their performance and accuracy, there are still very few experiments carried out on educational datasets which have very special features which make them different from other datasets. Therefore, in this work we compare the performance and interpretation level of the output of the different classification techniques applied on educational datasets and propose a meta-algorithm to preprocess the datasets and improve the accuracy of the model, which will be used by virtual instructors for their decision making through the ElWM tool. ER -
APA
Garcia-Saiz, D. & Zorrilla, M.. (2011). Comparing classification methods for predicting distance students’ performance. Proceedings of the Second Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 17:26-32 Available from https://proceedings.mlr.press/v17/garcia-saiz11a.html.

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