Developing a Learning Model to Enhance Data Analyst Competencies Based on Professional Qualification Standards

Abstract

The purposes of this study were to 1) develop of a learning model to enhance the competencies of data analyst in according with professional qualification standards, 2) develop a competency-based curriculum structure for data analysts, and 3) examine the results of implementing the develop learning model. This study employed a mixed methods research design. The population consisted of 235 undergraduate students. The sample group comprised 26 second-year students majoring in Information Technology, Faculty of Social Technology, Rajamangala University of Technology Tawan-ok, who voluntarily participated in the experimental phase and were selected through purposive sampling. The research instruments included a questionnaire, an achievement test, and competency assessment form (with a reliability coefficient of 0.89). The data were analyzed using percentage, mean, standard deviation, and the Wilcoxon Signed-Rank Test as a non-parametric statistic. The data analysis employed both descriptive and inductive methods.
The results of the research found that:
1. The MB Model learning framework, based on competency-based learning, was developed. It consisted of four components: 1) Input, 2) Process, 3) Output, and 4) Feedback. The learning process comprised six instructional steps: goals setting, stimulating interest, knowledge sharing, creative production, presentation, and evaluation. Additionally, the professional standards for data analysts were developed base on the qualifications framework from Thailand’s Professional Qualification Instituted and six international standards.
2. A competency-base curriculum structure was created, comprising six core competency units and fifteen sub-units. Each unit included defined learning outcomes, content, learning activities, and assessment evidence. 
3. The implementation of the model demonstrated that the learning package achieved and effectiveness index of 80.77/86.51 according to the KW#2 criteria. Students’ post-learning competency scores were significantly higher than their pre-learning scores at the .05 level. Furthermore, the learning model was evaluated by expert as being effective and appropriate for enhancing data analyst competencies.