All Publications


  • Variation in Respondent Speed and its Implications: Evidence from an Adaptive Testing Scenario JOURNAL OF EDUCATIONAL MEASUREMENT Domingue, B. W., Kanopka, K., Stenhaug, B., Soland, J., Kuhfeld, M., Wise, S., Piech, C. 2021

    View details for DOI 10.1111/jedm.12291

    View details for Web of Science ID 000664347600001

  • Rapid online assessment of reading ability. Scientific reports Yeatman, J. D., Tang, K. A., Donnelly, P. M., Yablonski, M., Ramamurthy, M., Karipidis, I. I., Caffarra, S., Takada, M. E., Kanopka, K., Ben-Shachar, M., Domingue, B. W. 2021; 11 (1): 6396

    Abstract

    An accurate model of the factors that contribute to individual differences in reading ability depends on data collection in large, diverse and representative samples of research participants. However, that is rarely feasible due to the constraints imposed by standardized measures of reading ability which require test administration by trained clinicians or researchers. Here we explore whether a simple, two-alternative forced choice, time limited lexical decision task (LDT), self-delivered through the web-browser, can serve as an accurate and reliable measure of reading ability. We found that performance on the LDT is highly correlated with scores on standardized measures of reading ability such as the Woodcock-Johnson Letter Word Identification test (r=0.91, disattenuated r=0.94). Importantly, the LDT reading ability measure is highly reliable (r=0.97). After optimizing the list of words and pseudowords based on item response theory, we found that a short experiment with 76 trials (2-3min) provides a reliable (r=0.95) measure of reading ability. Thus, the self-administered, Rapid Online Assessment of Reading ability (ROAR) developed here overcomes the constraints of resource-intensive, in-person reading assessment, and provides an efficient and automated tool for effective online research into the mechanisms of reading (dis)ability.

    View details for DOI 10.1038/s41598-021-85907-x

    View details for PubMedID 33737729

  • Heteroscedastic regression modeling elucidates gene-by-environment interaction Domingue, B. W., Kanopka, K., Trejo, S., Tucker-Drob, E. M. SPRINGER. 2020: 451–52
  • Deep Knowledge Tracing and Engagement with MOOCs Mongkhonvanit, K., Kanopka, K., Lang, D., Azcona, D., Chung, R. ASSOC COMPUTING MACHINERY. 2019: 340–42