Learning Analytics in Big Data Era. Exploration, Validation and Predictive Models Development

  • Ioannis C. DrivasEmail author
  • Georgios A. Giannakopoulos
  • Damianos P. Sakas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12149)


The untamed big data era raises opportunities in learning analytics sector for the provision of enhanced educational material to learners. Nevertheless, big data analytics, brings big troubles in exploration, validation and predictive model development. In this paper, the authors present a data-driven methodology for greater utilization of learning analytics datasets, with the purpose to improve the knowledge of instructors about learners performance and provide better personalization with optimized intelligent tutoring systems. The proposed methodology is unfolded in three stages. First, the learning analytics summarization for initial exploratory purposes of learners experience and their behavior in e-learning environments. Subsequently, the exploration of possible interrelationships between metrics and the validation of the proposed learning analytics schemas, takes place. Lastly, the development of predictive models and simulation both on an aggregated and micro-level perspective through agent-based modeling is recommended, with the purpose to reinforce the feedback for instructors and intelligent tutoring systems. The study contributes to the knowledge expansion both for researchers and practitioners with the purpose to optimize the provided online learning experience.


Learning analytics Big data Methods e-learning Intelligent tutoring systems Online learning platforms Learning management systems 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department Archival, Library and Information Systems, Lab of Information ManagementUniversity of West AtticaEgaleoGreece
  2. 2.School of Applied Economics and Social SciencesAgricultural University of AthensAthensGreece

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