Developing a Learning Analytics-Based Framework for ESP in Construction Management

Abstract

In the era of digital construction, effective communication, particularly in English, has become essential for professionals in construction project management. However, many English for Specific Purposes (ESP) programs still rely on generic instructional approaches that fail to address the real communication challenges and digital literacy needs of future construction managers. Moreover, the potential of learning analytics to enhance language instruction in technical fields remains largely underutilized. This study aims to develop a learning analytics-based framework for ESP instruction tailored to the digital construction management context. The research is guided by three objectives: (1) to identify the specific English language limitations in digital communication challenges faced by construction management students, (2) to explore how learning analytics can help overcome the identified language needs and communication challenges through personalized ESP instruction, and (3) To develop a learning analytics-based ESP framework that addresses the identified language needs and communication challenges by incorporating critical success factors for effective, data-driven instruction in construction education. A quantitative research design will be employed, using structured questionnaires to collect data from undergraduate and postgraduate civil engineering students specializing in construction project management. The collected data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between learners’ language needs, learning analytics application, and ESP effectiveness. The findings will inform the development of a practical, evidence-based framework that enhances language instruction through adaptive learning strategies and digital performance tracking. This research will contribute to both construction education and applied linguistics by bridging the gap between domain-specific language training and data-informed educational technologies.