Abstract
Investment in technological subsystems to support the activity of teaching and learning and the various areas of the life of Higher Education Institutions (HEI) is of increasing importance in the implementation of the policy and strategy of these organizations. Each of these subsystems collects a huge amount of data that, if properly organized, can provide useful information for decision making and informed action, which results in the need to articulate the strategic vision with Information Systems. This study aims to identify the technological requirements and understand the technical difficulties in accessing data sources of different technological subsystems in order to facilitate dialogue between the departments which hold the data to build a future Academic Analytics in a HEI. To achieve these goals a prototype was conceived which involved the aggregation, cleansing and standardization of data sources and resulted in a database that integrates records from three data sources—the LCMS (Learning Content Management System), academic management services and quality management services. This prototype allows reports and analysis to be made. Future studies for the development and implementation of Academic Analytics that can produce information to support decision making and control of the HEI’s activity are also possible.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bach, C. (2010). Learning Analytics: Targeting instruction, curricula and student support. In Office of the Provost, 2012-08-01. Drexel University. http://www.iiis.org/CDs2010/CD2010SCI/EISTA_2010/PapersPdf/EA655ES.pdf Accessed 15 October 2013.
Barneveld, A., Arnold, K., & Campbell, J. (2012). Analytics in higher education: Establishing a common language. EDUCAUSE Learning Initiative (ELI) White Paper.
Campbell, J., DeBlois, P., & Oblinger, D. (2007a). Academic analytics: a new tool for a new era. Educause Review Online, 42, 40–57.
Campbell, J., & Oblinger, D. (2007). Academic analytics. Educause White Paper, 1–24.
Eckerson, W. (2007). Predictive analytics: Extending the value of your data warehousing investment. TDWI Best Practices Report, First Quarter 2007. Renton, WA, USA: The Data Warehousing Institute.
Ferguson, R. (2012). The state of learning analytics in 2012: A review andfuture challenges. Technical Report KMI-12-01. Milton Keynes, UK: Knowledge Media Institute, The Open University.
Ferreira, S. A., & Andrade, A. (2012a). Ambientes de aprendizagem ricos em tecnologia - arquitetura e contributos para a gestão. Revista Portuguesa de Investigação Educacional, 12, 241–272.
Ferreira, S. A., & Andrade, A. (2012b). Conception of a management tool of Technology Enhanced Learning Environments. International Journal of Advanced Computer Science and Applications (IJACSA), 3(2), 42–47.
Goldstein, P. J. (2005). Academic analytics: The use of management information and technology in higher education. (Vol. Educase Center for Applied Research). ECAR Key Findings.
Hevner, A., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems. MIS Quarterly, 28(1), 75–105.
Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Ludgate, H. (2013). NMC Horizon Report: 2013 Higher Education Edition. Austin, Texas.
Long, P., & Siemens, G. (2011). Penetrating the fog: analytcs in learning and education. Educause Review Online, 46, 31–40.
Lucas, H. C. (1987). Information systems, concepts for managements (3rd ed.). Singapura: McGraw Hill, International Editions.
Norris, D., Baer, L., & Offerman, M. (2009). A national agenda for action analytics - White paper. Paper presented at the National Symposium on Action Analytics, St. Paul, Minnesota.
OLAP.COM (2010). OLAP software and education wiki. http://olap.com/w/index.php/OLAP_Education_Wiki. Accessed Web Page 2010.
Peffers, K., Tuunanen, T., Gengler, C. E., Rossi, M., Hui, W., Virtanen, V., et al. The design science research process: a model for producing and presenting information systems research. In C. S., & H. A. (Eds.), First International Conference on Design Science Research in Information Systems and Technology (DESRIST 2006) Claremont, California, 2006 (pp. 83–106)
Picciano, A. G. (2012). The evolution of big data and learning analytics in American Higher Education. Journal of Asynchronous Learning Networks, 16(3), 9–20.
Sampiere, R., Collado, C., & Lucio, P. (2006). Metodologia de Pesquisa (Vol. 3). São Paulo: McGrawHill.
Shum, S. B. (2012). Policiy brief - Learning Analytics. MoscowFederation: UNESCO.
Siemens, G. (2012). The data-intesive university. In American Association of State Colleges and Universities Conference, San Francisco, California.
SoLAR (2013). Third Conference on Learning Analytics and Knowledge. http://lakconference2013.wordpress.com/. Accessed 06–24 2013.
Acknowledgments
Foundation for Science and Technology of the Portuguese Republic (PhD Grant SFRH/BD/75815/2011), Regional Department of Education and Formation of the Azores.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Ferreira, S.A., Andrade, A. Academic analytics: Anatomy of an exploratory essay. Educ Inf Technol 21, 229–243 (2016). https://doi.org/10.1007/s10639-014-9317-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-014-9317-9