A Generative Student Model for Scoring Word Reading Skills

—This paper presents a novel student model intended to automate word-list-based reading assessments in a classroom setting, specifically for a student population that includes both native and nonnative speakers of English. As a Bayesian Network, the model is meant to conceive of student reading skills as a conscientious teacher would, incorporating cues based on expert knowledge of pronunciation variants and their cognitive or phonological sources, as well as prior knowledge of the student and the test itself. Alongside a hypothesized structure of conditional dependencies, we also propose an automatic method for refining the Bayes Net to eliminate unnecessary arcs. Reading assessment baselines that use strict pronunciation scoring alone (without other prior knowledge) achieve 0.7 correlation of their automatic scores with human assessments on the TBALL dataset. Our proposed structure significantly outperforms this baseline, and a simpler data-driven structure achieves 0.87 correlat...

• Type: Journal
• Year: 2011
• Where: TASLP
• Authors: Joseph Tepperman, Sungbok Lee, Shrikanth Narayanan, Abeer Alwan
• Keywords: Assessment, Model, Prior Knowledge, Software Engineering, TASLP 2011