Computer-Aided Grouping of Students with Reading Disabilities for Effective Response-to-Intervention.

Our research work focuses on computer-aided grouping of students based on questions answered in an assessment for effective reading intervention in early education. The work can facilitate placement of students with similar reading disabilities in the same intervention group to optimize corrective actions. We collected ELA (English Language Arts) assessment data from two different schools in USA, involving 365 students. Each student performed three mock assessments. We formulated the problem as a matching problem—an assessment should be matched to other assessments performed by the same student in the feature space. In this paper, we present a study on a number of matching schemes with low-level features gauging the grade-level readability of a piece of writing. The matching criterion for assessments is the consistency measure of matched questions based on the students’ answers of the questions. An assessment is matched to other assessments using K-Nearest-Neighbor. The best result is achieved by the matching scheme that considers the best match for each question, and the success rate is 17.6%, for a highly imbalanced data of only about 5% belonging to the true class.