10.23641/asha.8204786.v1
Priya Kucheria
Priya
Kucheria
McKay Moore Sohlberg
McKay Moore
Sohlberg
Jason Prideaux
Jason
Prideaux
Stephen Fickas
Stephen
Fickas
Development and testing of an ARSD algorithm: Read, Understand, Learn, & Excel (Kucheria et al., 2019)
ASHA journals
2019
reading
literacy
postsecondary
students
Read, Understand, Learn, & Excel
development
testing
automated
strategy
detection
algorithm
predictor
success
comprehension
skills
text
academic
behavioral
process
learning
education
reader
tool
accuracy
digital
expository
iterative
design
computer
undergraduate
college
data
proof of concept
approach
objective
automatic
clinical
Educational Technology and Computing
2019-06-06 23:23:50
Online resource
https://asha.figshare.com/articles/online_resource/Development_and_testing_of_an_ARSD_algorithm_Read_Understand_Learn_Excel_Kucheria_et_al_2019_/8204786
<div><b>Purpose:</b> An important predictor of postsecondary academic success is an individual’s reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader’s use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.</div><div><b>Method:</b> An iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).</div><div><b>Results:</b> Agreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.</div><div><b>Conclusion: </b>Read, Understand, Learn, & Excel provides proof of concept that a reader’s approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.</div><div><br></div><div><b>Supplemental Material S1.</b> Research assistant training protocol.</div><div><br></div><div><b>Supplemental Material S2.</b> Visual guide: This guide highlights the steps involved in matching coded behaviors to strategies. </div><div><br></div><div>Kucheria, P., Sohlberg, M. M., Prideaux, J., & Fickas, S. (2019). Read, Understand, Learn, & Excel: Development and testing of an automated reading strategy detection algorithm for postsecondary students. <i>American Journal of Speech-Language Pathology, 28, </i>1257–1267<i>.</i> https://doi.org/10.1044/2019_AJSLP-18-0181</div>