Abstract
Recent attention on the potentiality of cost-effective infrastructures for capturing and processing large amounts of data, known as Big Data has received much attention from researchers and practitioners on the field of analytics. In this paper we discuss on the possible benefits that Big Data can bring on TEL by using the case of large scale comparative assessments as an example. Large scale comparative assessments can pose as an intrinsic motivational tool for enhancing the performance of both learners and teachers, as well as becoming a support tool for policy makers. We argue why data from learning processes can be characterized as Big Data from the viewpoint of data source heterogeneity (variety) and discuss some architectural issues that can be taken into account on implementing such an infrastructure on the case of comparative assessments.
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References
LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52, 21–32 (2011)
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: The next frontier for innovation, competition, and productivity. Mckinsey Glob. Inst., 1–137 (2011)
Zikopoulos, P., Eaton, C.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data (2011)
Margaret, W., Ray, A.: PISA Programme for International Student Assessment (PISA) PISA 2000 Technical Report: PISA 2000 Technical Report. OECD Publishing (2003)
Bienkowski, M., Feng, M., Means, B.: Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington Dc Office of Educ. Technology. Us Dep. Education, pp. 1–57 (2012)
Nonnecke, B., Preece, J.: Why lurkers lurk. In: Proceedings of Americas Conference on Information Systems (AMCIS), pp. 1–10 (2001)
Sicilia, M.-A., Ebner, H., Sánchez-Alonso, S., Álvarez, F., Abián, A., García-Barriocanal, E.: Navigating learning resources through linked data: A preliminary report on the re-design of Organic. In: 1st Edunet. Proc. Linked Learn. (2011)
Cechinel, C., Sicilia, M.-Á., Sánchez-Alonso, S., García-Barriocanal, E.: Evaluating collaborative filtering recommendations inside large learning object repositories. Inf. Process. Manag. 49, 34–50 (2013)
Laney, D.: 3D data management: Controlling data volume, velocity and variety. Appl. Deliv. Strat. File. 949 (2001)
Karau, S.J., Williams, K.D.: Understanding individual motivation in groups: The collective effort model. Groups Work Theory Res., 113–141 (2001)
Christiansen, B.A., Smith, G.T., Roehling, P.V., Goldman, M.S.: Using alcohol expectancies to predict adolescent drinking behavior after one year. J. Consult. Clin. Psychol. 57, 93 (1989)
Apache Hadoop, https://hadoop.apache.org/
Dean, J., Ghemawat, S.: MapReduce: Simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)
Leo, S., Anedda, P., Gaggero, M., Zanetti, G.: Using virtual clusters to decouple computation and data management in high throughput analysis applications. In: 2010 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 411–415. IEEE (2010)
Wu, P.F., Korfiatis, N.: You Scratch Someone’s Back and We’ll Scratch Yours: Collective Reciprocity in Social Q&A Communities. J. Am. Soc. Inf. Sci. Technol. (forthcoming, 2013)
Korfiatis, N., Sicilia, M.A.: Social Measures and Flexible Navigation on Online Contact Networks. In: Proceedings of the IEEE International Conference on Fuzzy Systems ( FUZZ-IEEE), pp. 1–6. Imperial College, London (2007)
Papavlasopoulos, S., Poulos, M., Korfiatis, N., Bokos, G.: A non-linear index to evaluate a journal’s scientific impact. Inf. Sci. 180, 2156–2175 (2010)
Korfiatis, N., García-Bariocanal, E., Sánchez-Alonso, S.: Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electron. Commer. Res. Appl. 11, 205–217 (2012)
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Korfiatis, N. (2013). Big Data for Enhanced Learning Analytics: A Case for Large-Scale Comparative Assessments. In: Garoufallou, E., Greenberg, J. (eds) Metadata and Semantics Research. MTSR 2013. Communications in Computer and Information Science, vol 390. Springer, Cham. https://doi.org/10.1007/978-3-319-03437-9_23
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DOI: https://doi.org/10.1007/978-3-319-03437-9_23
Publisher Name: Springer, Cham
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