A data-driven multidimensional assessment model for English listening and speaking courses in higher education

Abstract

Based on multiple assessment approach, this study used factor analysis and neural network modeling methods to build a data-driven multidimensional assessment model for English listening and speaking courses in higher education. We found that: (1) Peer assessment, student self-assessment, previous academic records, and teacher assessment were the four effective assessors of the multi-dimensional assessment of English listening and speaking courses; (2) The multidimensional assessment model based on the four effective assessors can predict the final academic performance of students in English listening and speaking courses, with previous academic records contributing the most, followed by peer assessment, teacher assessment, and student self-assessment. Therefore, a multidimensional assessment model for English listening and speaking courses in higher education was proposed: the academic performance of students (on a percentage basis) should be composed of 29% previous academic records, 28% peer assessment, 26% teacher assessment, and 17% student self-assessment. This model can guide teachers to intervene with students who need help in a timely manner, based on various assessors, thereby effectively improving their academic performance.