|2/2018 - 4|
Generic Approach for Interpretation of PCA Results - Use Case on Learner's Activity in Social Media ToolsMIHAESCU, M. C. , POPESCU, P. S. , MOCANU, M. L.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,304 KB) | Citation | Downloads: 709 | Views: 2,252|
data engineering, knowledge representation, machine learning, social network services, social computing
analysis(13), principal(11), component(10), learning(5), education(5), student(4), social(4), review(4), research(4), advanced(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 27 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02004
Web of Science Accession Number: 000434245000004
SCOPUS ID: 85047847074
Intensive usage of social media tools for educational purposes transformed many previously tackled issues from classical e-Learning systems. Among the most general challenging issues reside in building classification models having the performed activities set as independent variables and final grade as dependent variable. A critical step in data analysis process regards building interpretable models in terms of explaining feature values and ranges along with their influence on target class. We asked whether dimensionality reduction techniques may be effectively used such that high quality interpretable models are obtained. Principal component analysis (PCA) dimensionality reduction technique, scaling and several classical classification techniques were used to create a data analysis pipeline that produces classification models with similar accuracy of initial classification models built on raw available data. Experimental results show that features that characterize the activity performed on each social tool and on all tools are highly interpretable in our classification context. The proposed approach is flexible and can be adapted to similar practical use cases in which a large number of features is difficult to be interpreted and a digest is required as being more useful for bringing a better insight on data.
|References|||||Cited By «-- Click to see who has cited this paper|
| M. C. Mihaescu, P. S. Popescu, E. Popescu, "Data analysis on social media traces for detection of "spam" and "don't care" learners", The Journal of Supercomputing, vol. 73, no. 10, pp. 1-22, 2017. |
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 4]
 A. Herve, J. W. Lynne, "Principal component analysis", Wiley interdisciplinary reviews: computational statistics vol. 2, no. 4 pp. 433-459, 2010.
 W. K. Lim, K. Wang, C. Lefebvre, A. Califano, "Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks", Bioinformatics vol. 23, no. 13, pp. i282-i288, 2007.
[CrossRef] [Web of Science Times Cited 134] [SCOPUS Times Cited 143]
 V. René, Y. Ma, S. S. Sastry. "Principal component analysis." Generalized Principal Component Analysis. Springer, New York, vol. 40, pp. 25-62, 2016.
 R. C. Radhakrishna. "The use and interpretation of principal component analysis in applied research", Sankhya: The Indian Journal of Statistics, Series A, pp. 329-358, 1964.
 I. T. Jolliffe, J. Cadima. "Principal component analysis: a review and recent developments." Phil. Trans. R. Soc. A. 374, no. 2065, 2016: 20150202.
[CrossRef] [Web of Science Times Cited 3466] [SCOPUS Times Cited 3799]
 T. J. Webster, "A principal component analysis of the US News & World Report tier rankings of colleges and universities", Economics of Education Review vol. 20 no. 3 pp. 235-244, 2001.
[CrossRef] [Web of Science Times Cited 65]
 M. E. Tipping and C. M. Bishop. "Mixtures of probabilistic principal component analyzers", Neural computation, vol. 11, no. 2, pp. 443-482, 1999.
[CrossRef] [Web of Science Times Cited 1113] [SCOPUS Times Cited 1357]
 X. Moke, Y. Liang, W. Wu, "Predicting Honors Student Performance Using RBFNN and PCA Method", International Conference on Database Systems for Advanced Applications, Springer, pp. 364-375, 2017.
 D. Z. Dumpit and C. J. Fernandez, "Analysis of the use of social media in Higher Education Institutions (HEIs) using the Technology Acceptance Model", International Journal of Educational Technology in Higher Education vol. 14, no. 1, pp. 5, 2017.
[CrossRef] [Web of Science Times Cited 128] [SCOPUS Times Cited 177]
 N. Marangunic, A. Granic. "Technology acceptance model: a literature review from 1986 to 2013." Universal Access in the Information Society vol. 14, no. 1, pp. 81-95, 2015.
[CrossRef] [Web of Science Times Cited 664] [SCOPUS Times Cited 915]
 P. B. Lowry, J. Gaskin. "Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it." IEEE transactions on professional communication vol. 57, no. 2, pp. 123-146, 2014.
[CrossRef] [Web of Science Times Cited 955] [SCOPUS Times Cited 1130]
 C. Giovannella, F. Scaccia, E. Popescu, "A PCA study of student performance indicators in a Web 2.0-based learning environment", Advanced Learning Technologies (ICALT, 2013 IEEE 13th International Conference on Advanced Learning Technologies, pp. 33-35, 2013.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 11]
 S. C. Nsizwana, K. D. Ige, N. G. Tshabalala, "Social Media Use and Academic Performance of Undergraduate Students in South African Higher Institutions: The Case of the University of Zululand." Journal of Social Sciences vol. 50, no. 1-3, pp. 141-152, 2017.
[CrossRef] [SCOPUS Times Cited 5]
 M. H. Marks, "Student engagement in instructional activity: Patterns in the elementary, middle, and high school years." American educational research journal vol. 37, no. 1 pp. 153-184, 2000.
 T. M. Paivi, T. K. Markku, M. T. Salla, "Effects of educational background on students' attitudes, activity levels, and knowledge concerning the environment." The journal of environmental education vol. 31, no. 3, pp. 12-19, 2000.
 A. Alireza, R. Lister, H. Haapala, A. Vihavainen. "Exploring machine learning methods to automatically identify students in need of assistance." Proceedings of the eleventh annual International Conference on International Computing Education Research. ACM, pp. 121-130, 2015.
 C-L. Lee, P.S. Yashwan, "Student modeling using principal component analysis of SOM clusters", In Advanced Learning Technologies, Proceedings. IEEE International Conference, pp. 480-484, 2004.
 P. Mangiameli, S. K. Shaw, D. West, "A comparison of SOM neural network and hierarchical clustering methods", European Journal of Operational Research vol. 93, no. 2 pp. 402-417, 1996.
[CrossRef] [Web of Science Times Cited 228] [SCOPUS Times Cited 272]
 C. Girish, F. Sahin. "A survey on feature selection methods." Computers & Electrical Engineering vol. 40, No. 1. pp. 16-28, 2014.
[CrossRef] [Web of Science Times Cited 2488] [SCOPUS Times Cited 3092]
 J. E. Jackson, "A user's guide to principal components", John Wiley & Sons, vol. 587, 2005.
 I. T. Jolliffe, "Principal Component Analysis and Factor Analysis", Principal component analysis. Springer New York, pp. 115-128, 1986.
 L. J. Cao, K.S. Chua, W.K. Chon, H.P. Lee, Q.M. Gu, "A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine", Neurocomputing vol. 55, no. 1, pp. 321-336, 2003.
[CrossRef] [Web of Science Times Cited 272] [SCOPUS Times Cited 472]
 H. Shi, B. Yin, Y. Kang, C. Shao, J. Gui, "Robust L-Isomap with a Novel Landmark Selection Method." Mathematical Problems in Engineering 2017. Vol. 2017, Article ID 3930957, pp. 12, 2017.
 L. van der Maaten, E. Postma, J. van den Herik. Dimensionality Reduction: A Comparative Review", TiCC, Tilburg University, vol. 10, pp. 66-71, 2009.
Web of Science® Citations for all references: 9,524 TCR
SCOPUS® Citations for all references: 11,377 TCR
Web of Science® Average Citations per reference: 366 ACR
SCOPUS® Average Citations per reference: 438 ACR
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2023-09-30 01:05 in 75 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.