Designing Learning Outcomes of Chulalongkorn University’s Bachelor of Education Program: A Curriculum Research using Text Mining and Machine Learning

Abstract

The objectives of this research were threefold: (1) to develop the initial framework of teacher graduate competencies for the Faculty of Education, Chulalongkorn University, derived from Thai professional teaching standards, the university’s mission, and the faculty’s mission and values, using semantic text clustering; (2) to refine and validate the teacher graduate competency framework using semi-supervised topic modeling based on the initial framework; and (3) to define the learning objectives of the Bachelor of Education program by synthesizing the Faculty of Education, Chulalongkorn University teacher graduate competency framework. The research data sources consisted of 21 documents related to policies, teacher professional standards, global competency frameworks, and stakeholder opinions. The primary data for analysis comprised 1,627 keywords and phrases regarding desirable teacher attributes, extracted from these documents using large language models (LLMs). Data analysis was divided into two stages: the first stage involved constructing the initial teacher graduate competency framework using text clustering based on embedding vectors, combined with expert synthesis, while the second stage expanded and refined the initial framework into a complete competency framework using semi-supervised topic modeling. Thereafter, a critical review was conducted on the learning objectives that were established by the stakeholders involved in the administration of the Bachelor of Education curriculum at Chulalongkorn University. The results yielded a Faculty of Education, Chulalongkorn University teacher graduate competency framework comprising four main domains: (1) Technology and Data-Driven Learner-Centered Instructional Design; (2) Educational Management and Decision Making; (3) Self-Development and Collaboration; and (4) Ethics, Equity, and Global Citizenship. This framework demonstrated content completeness and empirical validity based on statistical content analysis at both word and document levels. It was subsequently used as a basis for defining the learning objectives of the Bachelor of Education program, enabling graduates to integrate data, technology, and ethics in designing, deciding, and developing learning sustainably. The resulting learning outcomes reflect the new role of 21st-century teachers as ‘learning engineers’ who possess systemic capabilities, cognitive flexibility, and responsibility toward society and the digital world.