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Education and Information Technologies
Erschienen in: Education and Information Technologies 2/2018

05.10.2017

Application of learning analytics using clustering data Mining for Students’ disposition analysis

verfasst von: Sanyam Bharara, Sai Sabitha, Abhay Bansal

Erschienen in: Education and Information Technologies | Ausgabe 2/2018

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Abstract

Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research work is to find meaningful indicators or metrics in a learning context and to study the inter-relationships between these metrics using the concepts of Learning Analytics and Educational Data Mining, thereby, analyzing the effects of different features on student’s performance using Disposition analysis. In this project, K-means clustering data mining technique is used to obtain clusters which are further mapped to find the important features of a learning context. Relationships between these features are identified to assess the student’s performance.
Vorheriger Artikel Factors, issues and interdependencies in the incorporation of a Web 2.0 based learning environment in higher education
Nächster Artikel Ubiquitous learning environments in higher education: A scoping literature review

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Literatur
Agus, A., & bin Mohamed Makhbul, Z. K. (2002). An empirical study on academic achievement of business students in pursuing higher education: An emphasis on the influence of family backgrounds. New paradigm of borderless education: challenges, strategies, and implications for effective education through localization and, 168.
Aher, S. B., & Lobo, L. (2012, August). Applicability of data mining algorithms for recommendation system in e-learning. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (pp. 1034-1040). ACM.
Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining educational data to predict Student’s academic performance using ensemble methods. International Journal of Database Theory and Application, 9(8), 119–136.CrossRef
Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383–398.CrossRef
Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267-270). ACM.
Arora, S., Goel, M., Sabitha, A. S., & Mehrotra, D. (2017). Learner groups in massive open online courses. American Journal of Distance Education, 31(2), 80–97.CrossRef
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning analytics (pp. 61-75). Springer New York.
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1), 3–17.
Banumathi, A., & Pethalakshmi, A. (2012, January). A novel approach for upgrading Indian education by using data mining techniques. In Technology Enhanced Education (ICTEE), 2012 I.E. International Conference on (pp. 1-5). IEEE.
Bharara, S., Sabitha, A. S., & Bansal, A. (2017, January). A review on knowledge extraction for business operations using data mining. In Cloud Computing, Data Science & Engineering-Confluence, 2017 7th International Conference on (pp. 512-518). IEEE.
Bovo, A., Sanchez, S., Héguy, O., & Duthen, Y. (2013, September). Clustering moodle data as a tool for profiling students. In e-Learning and e-Technologies in Education (ICEEE), 2013 Second International Conference on (pp. 121-126). IEEE.
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5–6), 318–331.CrossRef
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., ... & Schroeder, U. (2014). Learning analytics: Challenges and future research directions. Retrieved June, 24, 2016 on, vol. 37, pp. 1349–1359, 2007.
Chellatamilan, T., Ravichandran, M., Suresh, R. M., & Kulanthaivel, G. (2011, July). Effect of mining educational data to improve adaptation of learning in e-learning system. In Sustainable Energy and Intelligent Systems (SEISCON 2011), International Conference on (pp. 922-927). IET.
Chen, C. M., Chen, Y. Y., & Liu, C. Y. (2007). Learning performance assessment approach using web-based learning portfolios for e-learning systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(6), 1349–1359.CrossRef
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
Chu, H. C., Chen, T. Y., Lin, C. J., Liao, M. J., & Chen, Y. M. (2009). Development of an adaptive learning case recommendation approach for problem-based e-learning on mathematics teaching for students with mild disabilities. Expert Systems with Applications, 36(3), 5456–5468.CrossRef
Cobo, G., García-Solórzano, D., Santamaría, E., Morán, J. A., Melenchón, J., & Monzo, C. (2010, June). Modeling students' activity in online discussion forums: A strategy based on time series and agglomerative hierarchical clustering. In Educational Data Mining 2011.
Cobo, G., García-Solórzano, D., Morán, J. A., Santamaría, E., Monzo, C., & Melenchón, J. (2012, April). Using agglomerative hierarchical clustering to model learner participation profiles in online discussion forums. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 248-251). ACM.
Cohn, D., & Hull, R. (2009). Business artifacts: A data-centric approach to modeling business operations and processes. IEEE Data Engineering Bulletin, 32(3), 3-9.
DeKalb, J. (1999). Student absence without permission (Student Truancy). ERIC Digest.
Dráždilová, P., Martinovic, J., Slaninová, K., & Snášel, V. (2008, December). Analysis of relations in eLearning. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 03 (pp. 373-376). IEEE computer society.
Duval, E. (2011, February). Attention please!: Learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 9-17). ACM.
Eranki, K. L., & Moudgalya, K. M. (2012, July). Evaluation of web based behavioral interventions using spoken tutorials. In Technology for Education (T4E), 2012 I.E. Fourth International Conference on (pp. 38-45). IEEE.
Ermisch, J., & Francesconi, M. (2001). Family matter: Impacts of family background on educational attainment. Economica, 68, 137–156.CrossRef
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37.
Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317.CrossRef
Ghorbani, F., & Montazer, G. A. (2012, February). Learners grouping improvement in e-learning environment using fuzzy inspired PSO method. In E-Learning and E-Teaching (ICELET), 2012 Third International Conference on (pp. 65-70). IEEE.
Govindarajan, K., Somasundaram, T. S., & Kumar, V. S. (2013, December). Continuous clustering in big data learning analytics. In Technology for Education (T4E), 2013 I.E. Fifth International Conference on (pp. 61-64). IEEE.
Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.
Gunuc, S., & Kuzu, A. (2015). Student engagement scale: Development, reliability and validity. Assessment & Evaluation in Higher Education, 40(4), 587–610.CrossRef
Heiner, C., Heffernan, N., & Barnes, T. (2007, July). Educational data mining. In Supplementary Proceedings of the 12th International Conference of Artificial Intelligence in Education.
Jili, C., Kebin, H., Feng, W., and Huixia, W. (2009). E-learning behavior analysis based on fuzzy clustering. In genetic and evolutionary computing, 2009. WGEC '09. 3rd International conference on, Guilin, 2009, (pp. 863-866).
Kizilcec, R. F., Piech, C., & Schneider, E. (2013, April). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In Proceedings of the third international conference on learning analytics and knowledge (pp. 170-179). ACM.
Kuhlmann, M., Shohat, D., & Schimpf, G. (2003, June). Role mining-revealing business roles for security administration using data mining technology. In Proceedings of the eighth ACM symposium on Access control models and technologies (pp. 179-186). ACM.
Lahane, S. V., Kharat, M. U., & Halgaonkar, P. S. (2012, November). Divisive approach of clustering for educational data. In Emerging Trends in Engineering and Technology (ICETET), 2012 Fifth International Conference on (pp. 191-195). IEEE.
Liao, S. H., Chen, C. M., & Wu, C. H. (2008). Mining customer knowledge for product line and brand extension in retailing. Expert Systems with Applications, 34(3), 1763–1776.CrossRef
Lias, T. E., & Elias, T. (2011). Learning Analytics.
Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71–85.CrossRef
Mandinach, E. B., & Jackson, S. S. (2012). Transforming teaching and learning through data-driven decision making. Corwin: Thousand Oaks.CrossRef
Meit, S. S., Borges, N. J., Cubic, B. A., & Seibel, H. R. (2004). Personality differences in incoming male and female medical students. Online Submission.
Ong, C. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816–829.CrossRef
Parack, S., Zahid, Z., & Merchant, F. (2012, January). Application of data mining in educational databases for predicting academic trends and patterns. In Technology Enhanced Education (ICTEE), 2012 I.E. International Conference on (pp. 1-4). IEEE.
Putrevu, S. (2001). Exploring the origins and information processing differences between men and women: Implications for advertisers. Academy of Marketing Science Review, 2001, 1.
Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), 601–618.CrossRef
Rothman, S. (2001). School absence and student background factors: A multilevel analysis. International Education Journal, 2(1), 59–68.
Sabitha, A. S., Mehrotra, D., & Bansal, A. (2016a). Delivery of learning knowledge objects using fuzzy clustering. Education and Information Technologies, 21(5), 1329–1349.CrossRef
Sabitha, A. S., Mehrotra, D., Bansal, A., & Sharma, B. K. (2016b). A naive bayes approach for converging learning objects with open educational resources. Education and Information Technologies, 21(6), 1753–1767.CrossRef
Sabitha, A. S., Mehrotra, D., & Bansal, A. (2017). An ensemble approach in converging contents of LMS and KMS. Education and Information Technologies, 22(4), 1673–1694.CrossRef
Salazar, A., Gosalbez, J., Bosch, I., Miralles, R., & Vergara, L. (2004). A case study of knowledge discovery on academic achievement, student desertion and student retention. In Information Technology: Research and Education, 2004. ITRE 2004. 2nd International Conference on (pp. 150-154). IEEE.
Scheuer, O., & McLaren, B. M. (2012). Educational data mining. In Encyclopedia of the Sciences of Learning (pp. 1075-1079). Springer US.
Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422.
Siemens, G., & d Baker, R. S. (2012, April). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 30.
Stovall, I. (2003). Engagement and online learning. UIS community of practice for e-Learning, 3, 2014.
Tian, F., Wang, S., Zheng, C., & Zheng, Q. (2008, April). Research on e-learner personality grouping based on fuzzy clustering analysis. In Computer Supported Cooperative Work in Design, 2008. CSCWD 2008. 12th International Conference on (pp. 1035-1040). IEEE.
Tie, Z., Jin, R., Zhuang, H., & Wang, Z. (2010, June). The research on teaching method of basics course of computer based on cluster analysis. In Computer and Information Technology (CIT), 2010 I.E. 10th International Conference on (pp. 2001-2004). IEEE.
Valsamidis, S., Kontogiannis, S., Kazanidis, I., Theodosiou, T., & Karakos, A. (2012). A clustering methodology of web log data for learning management systems. Educational Technology & Society, 15(2), 154–167.
Wijayanto, F. (2015, November). Indonesia education quality: Does distance to the capital matter?(a clustering approach on elementary school intakes and outputs qualities). In Science and Technology (TICST), 2015 International Conference on (pp. 318-322). IEEE.
Wook, M., Yahaya, Y. H., Wahab, N., Isa, M. R. M., Awang, N. F., & Seong, H. Y. (2009, December). Predicting NDUM student's academic performance using data mining techniques. In Computer and Electrical Engineering, 2009. ICCEE'09. Second International Conference on (Vol. 2, pp. 357-361). IEEE.
Zhao, J. W., Gu, S. M., & He, L. (2010, June). A novel approach to clustering access patterns in e-learning environment. In Education Technology and Computer (ICETC), 2010 2nd International Conference on (Vol. 1, pp. V1-393). IEEE.
Zheng, X., & Jia, Y. (2011, December). A study on educational data clustering approach based on improved particle swarm optimizer. In IT in Medicine and Education (ITME), 2011 International Symposium on (Vol. 2, pp. 442-445). IEEE.
Zheng, Q., Ding, J., Du, J., & Tian, F. (2007, April). Assessing method for e-learner clustering. In Computer Supported Cooperative Work in Design, 2007. CSCWD 2007. 11th International Conference on (pp. 979-983). IEEE.
Zorrilla, M. E., Menasalvas, E., Marin, D., Mora, E., & Segovia, J. (2005, February). Web usage mining project for improving web-based learning sites. In International Conference on Computer Aided Systems Theory (pp. 205–210). Springer Berlin Heidelberg.
Metadaten
Titel
Application of learning analytics using clustering data Mining for Students’ disposition analysis
verfasst von
Sanyam Bharara
Sai Sabitha
Abhay Bansal
Publikationsdatum
05.10.2017
Verlag
Springer US
Erschienen in
Education and Information Technologies / Ausgabe 2/2018
Print ISSN: 1360-2357
Elektronische ISSN: 1573-7608
DOI
https://doi.org/10.1007/s10639-017-9645-7

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