GDL

Journal from gdlhub / 2020-04-08 09:45:44
By : Anwar Ali Yahya, King Saud University
Created : 2019-01-08, with 1 files

Keyword : Particle swarm classification, Rocchio Algorithm, Educational data mining, Questions classification, Bloom's taxonomy
Url : http://www.sciencedirect.com/science/article/pii/S1319157817301799
Document Source : WEB

This paper explores the effectiveness of Particle Swarm Classification (PSC) for a classification task in the field of educational data mining. More specifically, it proposes PSC to design a classification model capable of classifying questions into the six cognitive levels of Bloom's taxonomy. To this end, this paper proposes a novel specialized initialization mechanism based on Rocchio Algorithm (RA) to mitigate the adverse effects of the curse of dimensionality on the PSC performance. Furthermore, in the design of the RA-based PSC model of questions classification, several feature selection approaches are investigated. In doing so, a dataset of teachers' classroom questions was collected, annotated manually with Bloom's cognitive levels, and transformed into a vector space representation. Using this dataset, several experiments are conducted, and the results show a poor performance of the standard PSC due to the curse of dimensionality. However, when the proposed RA-based initialization mechanism is used, a significant improvement in the average performance, from 0.243 to 0.663, is obtained. In addition, the results indicate that the feature selection approaches play a role in the performance of the RA-based PSC (average performance ranges from 0.535 to 0.708). Finally, a comparison between the performance of RA-based PSC (average performance = 0.663) and seven machine learning approaches (best average performance = 0.646) confirms the effectiveness of the proposed RA-based PSC approach.

Give Comment ?#(0) | Bookmark

Property	Value
Publisher ID	gdlhub
Organization	King Saud University
Contact Name	Herti Yani, S.Kom
Address	Jln. Jenderal Sudirman
City	Jambi
Region	Jambi
Country	Indonesia
Phone	0741-35095
Fax	0741-35093
Administrator E-mail	elibrarystikom@gmail.com
CKO E-mail	elibrarystikom@gmail.com