Article 2020

Learning analytics for educational innovation: A systematic mapping study of early indicators and success factors

International Journal of Computer Information Systems and Industrial Management Applications
Journal · Vol. 12 · pp. 138-154
Abstract

Today, theoretical and practical advancements in information and communication technologies (ICT) have proven to be indispensable towards achieving the goals of modern educational institutions including the underlying process models. There is evidence that existing technologies such as learning analytics (LA) can not only be used to understand the users (e.g. learners) and the context in which learning takes place, but also can take educators further in achieving different learning goals and innovation. On the one hand, there is a need for educators to adopt digital technologies in support of different activities that constitute educational processes; ranging from the changing higher institutional labour market to the rapid renovation of information systems and tools used to support learners. Moreover, such a requirement also relates to an educational community that is expected to include more proactive and creative learning strategies and experiences for the said stakeholders (e.g. teachers and students). On the other hand, this study shows that to meet those needs, learning analytics which implies measurement, collection, analysis, and reporting of data about the progress of stakeholders and learning contexts; is of importance. To this end, this paper conducts a systematic mapping study of current literature to determine trends in learning analytical methods and its application over the past decade. We look at how learning analytics has been used to support improved process monitoring and management (e.g. educational process innovation) within different organizational settings and case studies application. Consequently, this paper proposes a Learning Analytics Educational Process Innovation (LAEPI) model that leverages the ever-increasing amount of data that are recorded and stored about different learning activities or digital footprints of users within the educational domain to provide a method that proves to be useful towards maintaining continuous improvement and monitoring of different educational platforms. Thus, the notion of learning analytics for Educational Process Innovation in this paper. Technically, this work illustrates the implication of the method using dataset about online learning activities of university students for its experimentations and analysis. © 2020 MIR Labs.

Keywords

Author Keywords

Higher education lifelong learning educational innovation Learning Analytics Learning activities Process modelling

Index Keywords

Author Affiliations
Writing Lab, Tecnológico de Monterrey, Monterrey, NLE, Mexico
Writing Lab, Tecnológico de Monterrey, Monterrey, NLE, Mexico, School of Engineering Sciences, Tecnológico de Monterrey, Monterrey, NLE, Mexico
Department of Occupational Science and Occupational Therapy, University of Toronto Faculty of Medicine, Toronto, ON, Canada
Funding & Acknowledgements
No funding information
References 10 References
1 Ferguson, Rebecca, Learning analytics: Drivers, developments and challenges, International Journal of Technology Enhanced Learning, 4, 5-6, pp. 304-317, (2012)
2 Learning Analytics and Educational Data Mining in Practice A Systemic Literature Review of Empirical Evidence, (2014)
3 Daniel, Ben Kei, Big Data and analytics in higher education: Opportunities and challenges, British Journal of Educational Technology, 46, 5, pp. 904-920, (2015)
4 Competency Based Education Learning Portal Planning Education for Improved Learning Outcome, (2015)
5 Bogarín, Alejandro, Clustering for improving Educational process mining, ACM International Conference Proceeding Series, pp. 11-15, (2014)
6 Trčka, Nikola, Process mining from educational data, Handbook of Educational Data Mining, pp. 123-142, (2010)
7 Bogarín, Alejandro, A survey on educational process mining, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8, 1, (2018)
8 van der Aalst, Wil M.P., Process mining: Data science in action, Process Mining: Data Science in Action, pp. 1-467, (2016)
9 Cairns, Awatef Hicheur, Using semantic lifting for improving educational process models discovery and analysis, CEUR Workshop Proceedings, 1293, pp. 150-161, (2014)
10 Okoye, Kingsley, The application of a semantic-based process mining framework on a learning process domain, Advances in Intelligent Systems and Computing, 868, pp. 1381-1403, (2018)
Quick Actions
Citation Metrics
18
Times Cited (Scopus)

References 10
Document Identifiers
  • EID 2-s2.0-85087957356
  • Language English