Innovative Analytical Assessment: The Relationship Between English E-assessment CBT Scores and Traditional Methods of Assessment

Abstract

King Saud University’s (KSU) Preparatory Year (PY) examines around 12,000 students per year. The sole objective of the PY is to enable students to progress to the main campus of KSU and complete their degrees; and the main focus of its program is English language acquisition, through the English Language Skills Department (ELSD). Examining such a large number of students every year puts a strain on the resources of the faculty as a whole. As a consequence, maximizing the efficiency of the examination process is of paramount concern. Computer-based testing (CBT) is an extremely effective way of monitoring the knowledge acquisition of a large body of students. Alongside the CBT the PY also implements a speaking and writing examination for the ELSD. This study finds, through a quantitative analysis of statistical data collected from the results of ELSD Humanities students’ PY examinations, and multiple regression analysis, a high degree of correlation between the demonstration of proficiencies in the speaking and writing examinations and that of the CBT. This study proposes to measure students’ English proficiency through the exclusive use of CBT assessment. Schema theory is utilized to illustrate how the acquisition and organization of knowledge by students can be analyzed effectively. The study takes account of research to date in the field of Instructional Design and Technology to identify the need for learning solution through analysis. The study calls for further research to identify elements of ESL examinations that are overly tedious and inefficient in determining linguistic competencies, using a regression statistical model. It also calls for a more sophisticated statistical analysis, to explore the interrelations in language skills in more detail in order to provide the most rigorous and relevant evaluative framework.

Keywords: Instructional Design, Learning Analytics, Educational Data Mining, Learner Characteristics, e-Assessment.