Smart Learning Analytics: Student Academic Performance Data Representation, Processing and Prediction

  • Vladimir L. UskovEmail author
  • Jeffrey P. Bakken
  • Kaustubh Gayke
  • Juveriya Fatima
  • Brandon Galloway
  • Keerthi Sree Ganapathi
  • Divya Jose
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 188)


Smart education requires design, development, implementation and active use of innovative systems, technologies, teaching and learning strategies and approaches that are based on various data sources in academia, modern mathematical methods in data statistics and data analytics, and state-of-the-art data-driven approaches and technologies. The availability of tools that measure, collect, clean, organize, analyze, process, store, visualize and report data about student academic performance in an academic course and/or student overall academic progress in the selected program of study has given rise to the field of learning analytics for student academic success. Student data representation, processing and prediction, as a central part of learning analytics system, are crucial topics for researchers and practitioners in academia. Our vision for the engineering of smart learning analytics—the next generation of learning analytics—is based on the concept that this technology should strongly support (a) various “smartness” levels of smart education such as adaptivity, sensing, inferring, anticipation, self-learning and self-organization, and (b) main types of data analytics of smart education such as descriptive, diagnostic, predictive and prescriptive analytics. This paper presents the up-to-date findings and outcomes of the research, design and development project at the InterLabs Research Institute at Bradley University (USA) aimed at application of a quantitative approach to student academic performance data representation, hierarchical levels of data processing, multiple quality evaluation criteria to be selected and used, and high-quality student academic performance data prediction in smart learning analytics systems.


Smart learning analytics Student academic performance Data representation Data processing Quality of data prediction Machine learning algorithms and models Evaluation criteria for data prediction 


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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Vladimir L. Uskov
    • 1
    Email author
  • Jeffrey P. Bakken
    • 2
  • Kaustubh Gayke
    • 1
  • Juveriya Fatima
    • 1
  • Brandon Galloway
    • 1
  • Keerthi Sree Ganapathi
    • 1
  • Divya Jose
    • 1
  1. 1.Department of Computer Science and Information Systems and InterLabs Research InstituteBradley UniversityPeoriaUSA
  2. 2.The Graduate SchoolBradley UniversityPeoriaUSA

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