Validating an Automated System of Evaluation for English Drafting Skill: A Case of Large-scale,High-stakes Selection of Entry-level Managerial Positions

Abstract

The effectiveness of using a constructed response measure for assessing drafting ability is proven and has been used extensively in managerial selection in state-owned organizations. With the advent of online recruitment trends and technology-enhanced assessments, automated scoring has been conceived as a replacement for human scoring with the purpose of emulating the human scoring system. In the context of large-scale writing assessments, automated scoring could provide superior results to human scoring in terms of validity and reliability. In the present study, an attempt has been made to validate an automated essay scoring (AES) algorithm. The study was conducted on a sample of 11,497 randomly selected from a population of 54,392 shortlisted candidates for a national-level examination for the selection of entry-level executives in managerial positions in state-owned banks and a state-owned insurance company. The evaluation of the descriptive (constructed response) component of the examination (English composition) was carried out parallelly by four expert human raters and the AES algorithm. The parameters for evaluation were devised and made available to raters and the algorithm. Data were analysed using mean SD and Pearson correlation coefficient. Results show that the mean scores of human expert raters (M = 14.648) and automated algorithm method (M = 15.804) were similar. Further analysis was also undertaken to check the convergent validity of the features used in the algorithm by examining the relation of algorithm scores with sub scores from the objective test of the same construct. Results indicate a significant correlation. It can thus be said that the algorithm scoring method developed can be considered a replacement in that it can complement human expert raters in the evaluation of descriptive papers with consistent scoring and fairness, without inherent biases of inter-rater and intra-rater variation, in addition to practical benefits of speed and cost.