Emerging technologies and data-driven learning in engineering education

Overview

This research topic explores the use of emerging technologies and data‑driven approaches to enhance learning and teaching in engineering education. The project investigates how diverse educational data sets can be leveraged to support evidence‑based decision making across multiple levels of the institution—from individual educators and course teams to faculty leaders and senior executives. The work sits at the intersection of engineering education, learning analytics, and strategic use of educational data to improve student engagement, experience, and success.

Research activities

The project will work with a variety of institutional and sector‑wide data sets, such as student engagement with academic and career development support services, progression and outcome data, and student survey data collected at both institutional and national levels. Projects can be scoped to individuals’ expertise and interest areas. Activities may include data cleaning and integration, exploratory and inferential quantitative analysis, development of visualisations, qualitative analysis of feedback, and further data gathering around how developed technologies can inform teaching practice and strategic decision making in engineering education.

Outcomes

The research is expected to generate actionable insights into factors that influence student engagement and success in engineering programs. Outcomes may include evidence‑based recommendations for educators and leaders, analytical frameworks for using education data more effectively, and practical tools or exemplars that support data‑informed decision making. Depending on the level of study, outputs may include scholarly publications, conference presentations, and contributions to institutional learning and teaching strategy.

Skills and experience

Applicants should have a background in engineering, education, data science, or a related discipline. For studies with a quantitative focus, experience with quantitative data analysis and/or statistics is highly desirable, along with familiarity with tools such as Excel, R, Python, SPSS, or similar. For studies with a qualitative focus, experience in performing thematic analysis on relevant data sets (e.g. surveys, interviews, focus groups) is highly desirable.

Scholarships

You may be eligible to apply for a research scholarship.

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Keywords

• Engineering education
• Learning analytics
• Higher education
• Learning and teaching

Contact

te3al@qut.edu.au