Improving Prediction of Student Performance in a Blended Course

Sergey Sosnovsky*, Almed Hamzah

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Traditionally, systems supporting blended learning focus only on one portion of the course by tracing students’ interaction with learning content at home. In this paper, we argue that in-class activity can be also instrumental in eliciting the true state of students’ knowledge and can lead to more accurate models of their performance. Quizitor is an online platform that delivers both the at-home and the in-class assessment. We show that a combination of the two streams of data that Quizitor collects from students can help build more accurate models of students’ mastery that help predict their course performance better than models separately trained on either of these two types of activity.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I
EditorsMaria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
Place of PublicationCham
Number of pages6
ISBN (Electronic)978-3-031-11644-5
ISBN (Print)978-3-031-11643-8
Publication statusPublished - 27 Jul 2022
Event23rd International Conference on Artificial Intelligence in Education, AIED 2022 - Durham, United Kingdom
Duration: 27 Jul 202231 Jul 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Artificial Intelligence in Education, AIED 2022
Country/TerritoryUnited Kingdom


  • Adaptive learning support
  • Blended learning
  • Self-assessment
  • Student modelling
  • Voting tool


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