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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.
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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)
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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 Record] [SCOPUS Times Cited 1]
 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 127] [SCOPUS Times Cited 137]
 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 1035] [SCOPUS Times Cited 1161]
 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 60]
 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 965] [SCOPUS Times Cited 1174]
 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 65] [SCOPUS Times Cited 86]
 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 267] [SCOPUS Times Cited 384]
 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 536] [SCOPUS Times Cited 643]
 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 8] [SCOPUS Times Cited 9]
 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 1]
 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 207] [SCOPUS Times Cited 240]
 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 1285] [SCOPUS Times Cited 1641]
 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 249] [SCOPUS Times Cited 440]
 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.
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