Optimizing Educational Quality Through Effective Supervision and Data Processing
Author: Nia Rahmawati Ansari /2249100063
(Class II B / Postgraduate Student of Islamic Educational Management, UIN Sunan Gunung Djati Bandung)
Improving the quality of education is a primary focus in developing human resources. Effective supervision and data processing ensure that educational programs are implemented according to their intended goals and provide a solid foundation for evidence-based decision-making. Through systematic data analysis, various educational issues can be identified and addressed accurately, thereby continuously enhancing the quality of educational services and positively impacting students and educational institutions as a whole. Therefore, optimizing educational quality through supervision and data processing involves four key components:
First, data analysis, which is the process of organizing data into patterns or categories to identify themes and hypotheses. Data is divided into qualitative (non-numerical) and quantitative (numerical). The steps include unit processing (using emic or analytic typologies), categorization based on relevance principles, and checking validity through triangulation and peer discussion. Descriptive data analysis uses descriptive statistics (frequency, central tendency) to describe the characteristics of quantitative data or interpret the meaning of qualitative data. The goal is to understand phenomena through data reduction, category development, and systematic interpretation.
Second, tabulation of evaluation data for educational programs involves organizing data into tables with variable codes to facilitate manual or computerized analysis. This process includes coding variables, using coding sheets, and classifying both quantitative and qualitative data to generate systematic conclusions. Narrative, non-tabulated data, although individual and unique, can still be processed by grouping responses into thematic categories and calculating their frequencies. This technique ensures the validity of findings through consistent coding and field data integration, resulting in relevant, accurate, and systematically sound information.
Third, after tabulation comes data processing, which involves both quantitative and qualitative analysis. Quantitative analysis utilizes descriptive statistics (frequency, mean, standard deviation) and inferential statistics (t-tests, ANOVA, chi-square tests) to test hypotheses. Qualitative analysis follows the Miles and Huberman model (1984), consisting of data reduction (simplification of information), data presentation (in matrices or charts), and drawing and verifying conclusions through triangulation and peer discussion. This process ensures the validity of findings by integrating data systematically.
Fourth and finally, the last stage involves computer-assisted data processing . Technology aids in data input, output, storage, and distribution. The methods of processing include batch processing (group data handling) and immediate processing (real-time data entry via terminals). Applications such as SPSS assist in data processing to ensure accuracy and efficiency.
The analysis and processing of data from educational supervision and evaluation aim to enhance educational quality systematically. Qualitative and quantitative data are collected through various methods such as questionnaires, interviews, observations, and documentation. The process involves data reduction, tabulation, and interpretation to produce accurate and meaningful results.
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*) This article is summarized from the teaching materials of the course "Educational Supervision and Evaluation," Part 8: Analysis and Processing of Supervision and Evaluation Data. Course Instructor: Prof. Dr. H. Rusdiana, M.M.