Enhancing face-to-face evaluation using alternative optical mark recognition: A case study from the University of Cabuyao’s college of education

Abstract

Technology is defined as the use of scientific knowledge to solve practical problems. However, educators’ initiatives to integrate technology have been mostly prohibitively expensive. In this context, researchers proposed the automation of one of the most important processes but highly repetitive tasks among educators, the processing of student test results. The aim was to determine the alignment with evaluation standards and the acceptability of a cost-effective alternative. This study utilized a mixed-method approach, specifically concurrent triangulation. Quantitative and qualitative data were gathered concurrently, and then compared and combined the results to get a comprehensive understanding of the topic. Quantitatively, it involved the use of mean, standard deviation, t-test, and Cohen’s d to evaluate Alternative Optical Mark Recognition (AOMR) according to the required educators’ evaluation standards and its impact on reducing educators’ clerical workload. Qualitatively, semi-structured interviews and thematic analysis were employed to elucidate educators’ perspectives regarding the use of AOMR and the broader integration of technology as a whole. Results showed a one-hundred-thirty times efficiency compared to the manual process without losing the accuracy and reliability of data. Participants underscored the positive effect of AOMR in diminishing the labor-intensive nature of a crucial yet arduous clerical task for educators. Additionally, participants also emphasized unexpected benefits, including email results distribution, backup e-copies of sheets, ease of data management, class record integration, and automated student ranking. These findings offer valuable insights into the challenges surrounding the integration of technology in educational contexts in general, shedding light on the advantages of AOMR in the evaluation of student test results, in particular.

Keywords

• alternative optical mark recognition
• evalbee
• integrating automation
• student test evaluation
• technology in education