Personalized learning in iSTART: Past modifications and future design

Kathryn S. McCarthy, Micah Watanabe, Jianmin Dai, Danielle S. McNamara

Research output: Contribution to journalArticle

Abstract

Computer-based learning environments (CBLEs) provide unprecedented opportunities for personalized learning at scale. One such system, iSTART (Interactive Strategy Training for Active Reading and Thinking) is an adaptive, game-based tutoring system for reading comprehension. This paper describes how efforts to increase personalized learning have improved the system. It also provides results of a recent implementation of an adaptive logic that increases or decreases text difficulty based on students’ performance rather than presenting texts randomly. High school students who received adaptive text selection showed increased sense of learning. Adaptive text selection also resulted in greater pre-training to post-training comprehension test gains, especially for less-skilled readers. The findings demonstrate that system-driven, just-in-time support consistent with the goals of personalized learning benefit the efficacy of computer-based learning environments.

Original languageEnglish (US)
Pages (from-to)301-321
Number of pages21
JournalJournal of Research on Technology in Education
Volume52
Issue number3
DOIs
StatePublished - Jul 2 2020

Keywords

  • Personalized learning
  • adaptive text selection
  • reading comprehension
  • text difficulty

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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