QuizMaster: An Adaptive Formative Assessment System

Fuhua Lin, Raymond Morland, Hongxin Yan

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

Abstract

In this paper, we introduce QuizMaster, an innovative web-based adaptive learning system designed for conducting formative assessment on-demand anytime during students’ course study. QuizMaster reduces learner time spent on assessment and accelerates formative feedback delivery. Leveraging a Multi-Armed Bandit algorithm for question sequencing and feedback, it ensures intelligent assessment processes. Additionally, we employ Large Language Models to auto-generate questions, enhancing instructor productivity. When deployed, QuizMaster will serve to assess adaptive algorithms for formative assessment in real-world learning scenarios. Through our detailed analysis of the QuizMaster architecture, we demonstrate how to leverage reinforcement learning and generative intelligence in the development of systems for formative assessment.

Original languageEnglish
Title of host publicationGenerative Intelligence and Intelligent Tutoring Systems - 20th International Conference, ITS 2024, Proceedings
EditorsAngelo Sifaleras, Fuhua Lin
Pages55-67
Number of pages13
DOIs
Publication statusPublished - 2024
Event20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024 - Thessaloniki, Greece
Duration: 10 Jun. 202413 Jun. 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14798 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024
Country/TerritoryGreece
CityThessaloniki
Period10/06/2413/06/24

Keywords

  • Adaptive Learning Systems
  • Formative Assessment
  • Generative Intelligence
  • Intelligent Tutoring Systems
  • Multi-armed Bandits
  • Personalized Learning
  • Reinforcement Learning

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