TY - JOUR
T1 - Personalized learning in iSTART
T2 - Past modifications and future design
AU - McCarthy, Kathryn S.
AU - Watanabe, Micah
AU - Dai, Jianmin
AU - McNamara, Danielle S.
N1 - Funding Information:
This project was funded by the Institute of Education Sciences, U.S. Department of Education, through Grants R305A130124, R305A120707, R305A180261, R305A180144, and the Office of Naval Research, through Grant N00014140343, to Arizona State University. The opinions expressed are those of the authors and do not represent views of the Institute, the U.S. Department of Education, or the Office of Naval Research. 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.
Publisher Copyright:
© 2020, © 2020 ISTE.
PY - 2020/7/2
Y1 - 2020/7/2
N2 - 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.
AB - 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.
KW - Personalized learning
KW - adaptive text selection
KW - reading comprehension
KW - text difficulty
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U2 - 10.1080/15391523.2020.1716201
DO - 10.1080/15391523.2020.1716201
M3 - Article
AN - SCOPUS:85087482067
SN - 1539-1523
VL - 52
SP - 301
EP - 321
JO - Journal of Research on Technology in Education
JF - Journal of Research on Technology in Education
IS - 3
ER -