Performance Mapping of Optical Mark Recognition (OMR) Machines in the Division of Batangas City: Basis for Program Improvement

Abstract

INTRODUCTION

Gathering data from summative and formative assessments is important in education to assess student knowledge and skills. Because of this, the Division of Batangas Cityacquired 10 Optical Mark Recognition (OMR) technology which intelligently reads multiple-choice questions, checks boxes or filled-in bubbles on documents and converts the results into meaningful data. With OMR automation tools, extracting data from exams and surveys helps get schools well on their way to accurate data analysis. This study aimed to describe the performance mapping of OMR machines in the Division of Batangas City and compared these machine-generated results with those done manually by teachers.

METHODS

The study used qualitative design using descriptive-thematic synthesis and quantitative using correlation of statistics results. Purposive sampling was utilized in choosing 7 participants from 7 schools with OMR machines in the Division of Batangas City. A total of 7 OMR machines were used to map the performance, seven (7) teacher-operators were interviewed in gathering data.

RESULTS

OMR machine is useful in data scanning scannable answer sheets of students quarter examinations, it gives quick and accurate assessment results but enables to process thin paper and improper shading, and sometimes experienced technical problem just like any other machines. Further results show that there is no significant difference between the results taken from the OMR machines and the teacher's computed results. Thus, the proposed program may be used to enhance the utilization of OMR machines in the division and uplift students achievement.

DISCUSSIONS

The optical mark reader (OMR) machines are useful for checking scannable answer sheets during quarter examinations. On the other hand, the disadvantages of the OMR machines include its inability to process thin paper and improper shading. All six machines tested generated the same results for a specific examination sample in mathematics. Thus all of these machines have a parallel performance which indicates that all of them produce accurate results. When the OMR average summary of statistics and teachers computed summary of statistics were compared, the tc=0.0332 was less than the tt=2.101 at 18 degrees of freedom. Therefore the null hypothesis was accepted which means that there is no significant difference in the OMR average summary of statistics and the teacher's computed summary of statistics.