Classification of Natural Language Descriptions for Bayesian Knowledge Tracing in Minecraft

Samuel Hum, Frank Stinar, Hae Jin Lee, Jeffrey Ginger, H. Chad Lane

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Application of Bayesian Knowledge Tracing (BKT) has primarily occurred in formal learning settings. This paper presents an integration of BKT in an informal learning context to assess the structure and skill level of learner scientific observations. We compare different approaches to text classification in a Minecraft science simulation. Our models were trained on data collected from two separate middle schools with students of different backgrounds. Experimental results demonstrate the effectiveness of several machine learning models to automatically label observations.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022, Proceedings
EditorsMaria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
Number of pages4
ISBN (Print)9783031116469
StatePublished - 2022
Event23rd International Conference on Artificial Intelligence in Education, AIED 2022 - Durham, United Kingdom
Duration: Jul 27 2022Jul 31 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13356 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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


  • Bayesian Knowledge Tracing
  • Informal learning
  • Minecraft

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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