Effectiveness of Automation in Evaluating Test Results Using EvalBee as an Alternative Optical Mark Recognition (OMR): A Quantitative-Evaluative Approach from a Philippine Public School

Within this study, the authors want to address the problem of overworking of teachers in Philippine schools due to their excessive clerical responsibility, which could lead to teacher attrition. The authors propose to automate the process, particularly the evaluation of student test results since it could improve human well-being by reducing the burden of manual labor. Automation using OMR has not been widely applied in Philippine schools due to cost issues. The authors want to observe whether an alternative OMR - EvalBee - can meet the evaluation standard even though it is free of charge. The study employed a quantitative-evaluative research method. Validated questionnaires on paper and a Google Form survey were used to collect relevant data in a secondary public school in the City Schools Division of Cabuyao – Southville1 Integrated National High School. Statistical tools such as the mean, standard deviation, t-test, and Cohen's d were used in this research to determine the effectiveness of using alternative OMR in evaluating examinations. This study shows that alternative OMR is efficient, accurate, and reliable, despite the fact that it is free, and recommends it be used as part of the teachers' standard method of test evaluation.

DETEKSI LEMBAR JAWABAN KOMPUTER MENGGUNAKAN OMR (OPTICAL MARK RECOGNITION) DI MTS NURUL IMAN

Conventional exams or manual exams were implemented decades ago and are still used today. This type of test uses a writing instrument as a test medium, namely the test is carried out in the form of general stationery such as paper, pencil, and pen, the questions and answers to the test are written by hand. One way to assess the success of the teaching process in schools is to carry out exams. In the implementation of the exam at MTS Nurul Iman, he used a computer answer sheet as an entry. Meanwhile, schools are required to have certain scanners that are expensive to correct computer answer sheets. Another alternative that can be done by schools is to manually correct computer answer sheets, but this makes a lot of time wasted, and can cause errors in correcting and slow work productivity. From the problems that have been described, to detect the computer answer sheet, a method is needed. Through this research, it is hoped that a method can be developed that automatically detects the answer choices on the computer answer sheet, so that more accurate and faster results are obtained. Based on the problems of this study, the researchers used the OMR (Optical Mark Recognition) method to detect computer answer sheets automatically. From the test results, it can be concluded that the accuracy of detection of computer answer sheets using OMR is 97%.

Extracting Medical Information from Paper COVID-19 Assessment Forms

Objective This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center. Methods An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimposed on the left and an editable web form on the right to improve ease of data validation. Crowdsourced participants validated the results of the OMR system. Overall error rate and time taken to validate were calculated. A subset of forms was validated by multiple participants to calculate agreement between participants. Results The OMR/OCR tools correctly extracted data from scanned forms fields with an average accuracy of 70% and median accuracy of 78% when the OMR/OCR results were compared with the results from crowd validation. Scanned forms were crowd-validated at a mean rate of 157 seconds per document and a volume of approximately 108 documents per day. A randomly selected subset of documents was reviewed by multiple participants, producing an interobserver agreement of 97% for documents when narrative-text fields were included and 98% when only Boolean and multiple-choice fields were considered. Conclusion Due to the COVID-19 pandemic, it may be challenging for health care workers wearing personal protective equipment to interact with electronic health records. The combination of OMR/OCR technology, a novel UI, and crowdsourcing data-validation processes allowed for the efficient extraction of a large volume of paper medical documents produced during the COVID-19 pandemic.