Although researchers have investigated technical adequacy and usability of written-expression curriculum-based measures (WE-CBM), the economic implications of different scoring approaches have largely been ignored. The absence of such knowledge can undermine the effective allocation of resources and lead to the adoption of suboptimal measures for the identification of students at risk for poor writing outcomes. Therefore, we used the Ingredients method to compare implementation costs and cost-effectiveness of hand-calculated and automated scoring approaches. Data analyses were conducted on secondary data from a study that evaluated predictive validity and diagnostic accuracy of quantitative approaches for scoring WE-CBM samples. Findings showed that automated approaches offered more economic solutions than hand-calculated methods; for automated scores, the effects were stronger when the free writeAlizer R package was employed, whereas, for hand-calculated scores, simpler WE-CBM metrics were less costly than more complex metrics. Sensitivity analyses confirmed the relative advantage of automated scores when the number of classrooms, students, and assessment occasions per school year increased; again, writeAlizer was less sensitive to the changes in the ingredients than the other approaches. Finally, the visualization of the cost-effectiveness ratio illustrated that writeAlizer offered the optimal balance between implementation costs and diagnostic accuracy, followed by complex hand-calculated metrics and a proprietary automated program. Implications for the use of hand-calculated and automated scores for the universal screening of written expression with elementary students are discussed.

Written expression curriculum-based measurement (WE-CBM) is a formative assessment approach for screening and progress monitoring. To extend evaluation of WE-CBM, we compared hand calculated and automated scoring approaches in relation to the number of screening samples needed per student for valid scores, the long-term predictive validity and diagnostic accuracy of scores, and predictive and diagnostic bias for underrepresented student groups. Second- to fifth-grade students (n = 609) completed five WE-CBM tasks during one academic year and a standardised writing test in fourth and seventh grade. Averaging WE-CBM scores across multiple samples improved validity. Complex hand-calculated metrics and automated tools outperformed simpler metrics for the long-term prediction of writing performance. No evidence of bias was observed between African American and Hispanic students. The study will illustrate the absence of test bias as necessary condition for fair and equitable screening procedures and the importance of future research to include comparisons with majority groups.

Existing approaches to measuring writing performance are insufficient in terms of both technical adequacy as well as feasibility for use as a screening measure. This study examined the validity and diagnostic accuracy of several approaches to automated text evaluation as well as written expression curriculum-based measurement (WE-CBM) to determine whether an automated approach improves technical adequacy. A sample of 140 fourth grade students generated writing samples that were then scored using traditional and automated approaches and examined in relation to the statewide measure of writing performance. Results indicated that the validity and diagnostic accuracy for the best performing WE-CBM metric, correct minus incorrect word sequences, and the automated approaches to scoring were comparable, with automated approaches offering potentially improved feasibility for use in screening. Averaging scores across three time points was necessary, however, in order to achieve improved validity and adequate levels of diagnostic accuracy across the scoring approaches. Limitations, implications, and directions for future research regarding the use of automated scoring approaches for screening are discussed.