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.
Acknowledgements
The authors acknowledge the extensive contributions by Amy Johnson and Haojun Pei on this project, and also thank Matt Jacovina, Laura Allen, Renu Balyan, Kathleen Corley, Aaron Likens, Tricia Guerrero, Kevin Kent, Cecile Perret, Melissa Stone, Joseph Aubele, Carson Flood, Ashleigh Horowitz, Gary Ma, Amber Poteet, and our teacher-partners for their contributions to iSTART.
Notes
1 We did, however, build a stand-alone module, StairStepper, in which the difficulty of the text is not just overt, but central to the game mechanics (Perret, Johnson, McCarthy, Guerrero, & McNamara, Citation2017).