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Educational knowledge generation from administrative data

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Abstract

Most universities use Information Systems (IS) to perform their daily administrative activities (student enrollment, data files, accountancy, etc.), and an integrated Learning Management System (LMS) to support teaching and learning. However, although a lot of effort has been put into deploying these computerized systems, the data that they provide are not fully exploited from an educational perspective. In this paper we describe a case in which these data have been used to identify relevant relations between the general use of the LMS, the existence of teaching innovation programs and the quality of education. The method used can be easily generalized and employed in other different contexts, to derive meaningful information on the impact that some variables may have over others.

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References

  • Alonso, F., López, G., Marique, D., & Viñes, J. M. (2008). Learning objet, learning objectives and learning design. Innovations in Education and Teaching International, 45(4), 389–400.

    Article  Google Scholar 

  • Caruso, J. B., & Salaway, G. (2008). The ECAR study of undergraduate students and information technology, 2008—key findings. Tech. Rep., Boulder, CO: Educause Center of Applied Research.

  • Cerverón, V., Moreno, P., Cubero, S., Roig, D., & Roca, S. (2007). Universitat de València’s Aula Virtual: A single integrated LMS for a university. In IADIS e-learning 2007 conference proceedings (pp. 43–50), Lisbon.

  • Chung, W. T., Stump, G., Hilpert, J., Husman, J., Kim, W., & Lee, J. E. (2008). Addressing engineering educators concerns: Collaborative learning and achievement. In: Proceedings 38th annual conference frontiers in education (FIE) (pp. T3A-3–T3A-7). Saratoga Springs: NY, USA.

  • Dimokas, N., Mittas, N., Nanopoulos, A., & Angelis, L. (2008). A prototype system for educational data warehousing and mining. In: Panhellenic Conference on Informatics (PCI) (pp. 199–203). Samos, Greece.

  • Estes, C. A. (2004). Promoting student-centered learning in experiential education. Journal of Experiential Education, 27(2), 141–160.

    Google Scholar 

  • Gibbs, G., & Simpson, C. (2005). Conditions under which assessment supports students’ learning. Learning and Teaching in Higher Education, 1(1), 2–31.

    Google Scholar 

  • Holbert, K., & Karady, G. (2009). Strategies, challenges and prospects for active learning in the computer-based classroom. IEEE Transactions on Education, 52(1), 31–38.

    Article  Google Scholar 

  • Iiyoshi, T., Hannafin, M., & Wang, F. (2005). Cognitive tools and student centred learning: Rethinking tools, functions and applications. Educational Media International, 42(4), 281–296.

    Article  Google Scholar 

  • Kember, D. (2009). Promoting student-centred forms of learning across an entire university. Higher Education, 58(1), 1–13.

    Article  Google Scholar 

  • Lei, J., & Zhao, Y. (2007), Technology uses and student achievement: A longitudinal study. Computers and Education, 49(2), 284–296.

    Article  Google Scholar 

  • Levene, H. (2005). Contributions to probability and statistics: Essays in honor of Harold Hotelling (pp. 278–292). Stanford: Stanford University Press.

    Google Scholar 

  • McDonald, J., & Gibbons, A. (2009). Technology I, II, and III: Criteria for understanding and improving the practice of instructional technology. Educational Technology Research and Development, 57(3), 377–392.

    Article  Google Scholar 

  • Moskal, P., Dziuban, C. D., Upchurch, R., Hartman, J., & Truman, B. (2006). Assessing online learning: What one university learned about student success, persistence, and satisfaction. Peer Review: Emerging Trends and Key Debates in Undergraduate Education: Learning and Technology, 8(4), 26–29.

  • Motschnig-Pitrik, R., & Holzinger, A. (2002). Student-centered teaching meets new media: Concept and case study. Educational Technology and Society, 5(4), 160–172.

    Google Scholar 

  • Perera, I. (2009). Student participation for blended learning activities: A case study. In eLearning and software for education (pp. 33–38). Bucharest, Rumania

  • Pirnay-Dummer, P., Ifenthaler, D., & Spector, M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research and Development, 58(1), 3–18.

    Article  Google Scholar 

  • R Development Core Team. (2009). R: A language and environment for statistical computing (ISBN 3-900051-07-0). Vienna, Austria: R Foundation for Statistical Computing.

  • Rodriguez, D., & Santiago, N. (2005). Integrating novel methodologies, tools, and it resources for graduate level courses in high performance computing and advanced signal processing algorithms. In 6th international conference on information technology based higher education and training (pp. F3D/15–F3D/18). Santo Domingo, Dominican Republic.

  • Schmidt, H. G. (1993). Foundations of problem-based learning: Some explanatory notes. Medical Education, 27(5), 422–432.

    Article  Google Scholar 

  • Snedecor, G. W., & Cochran, W. G. (1989). Statistical methods (8th edn). Iowa: Iowa State University Press.

    Google Scholar 

  • Sørebø, Ø., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The role of self-determination theory in explaining teachers’ motivation to continue to use e-learning technology. Computers and Education, 53(4), 1177–1187.

    Article  Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2006). Using multivariate statistics (5th edn). Pearson, NJ, USA: Pearson Education

    Google Scholar 

  • Tolley, H., & Shulruf, B. (2009). From data to knowledge: The interaction between data management systems in educational institutions and the delivery of quality education. Computers and Education, 53(4), 1199–1206.

    Article  Google Scholar 

  • Traphagan, T., Kucsera, J., & Kishi, K. (2010). Impact of class lecture webcasting on attendance and learning. Educational Technology Research and Development, 58(1), 19–37.

    Article  Google Scholar 

  • Wurst, C., Smarkola, C., & Gaffney, M. A. (2008). Ubiquitous laptop usage in higher education: Effects on student achievement, student satisfaction, and constructivist measures in honors and traditional classrooms. Computers and Education, 51(4), 1766–1783.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the personnel at the Computer Services and the European Convergence Office of the University of Valencia for their collaboration and help. This work has been partially funded by FEDER and the Spanish Ministry of Education, through projects Consolider Ingenio 2010 CSD2007-00018 and DIN2009-14205-C04-03; and by the University of Valencia, through projects DocenTIC and Finestra Oberta 08/DT/04/2009, 18/DT/05/2010 and 47/FO/35/2010.

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Correspondence to Miguel Arevalillo-Herráez.

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Arevalillo-Herráez, M., Moreno-Clari, P. & Cerverón-Lleó, V. Educational knowledge generation from administrative data. Education Tech Research Dev 59, 511–527 (2011). https://doi.org/10.1007/s11423-010-9185-y

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