Title: Teaching data in a University Transition Project to Final Year secondary school students. (Online only)

Author: Kay Douglas, Alice Smith & Peter Tormey

Theme: Teaching data skills, data ethics and AI

This poster explores and expands on the challenges experienced by the team teaching data in the school-university transition and may be of interest to a wider audience. One challenge is students’ self-perception of confidence, or lack of, with data literacy and how this was expressed in class and in assessments. Another challenge is the disparity between the experience that Scottish school students actually have of data and the expectations of data literacy by the university community. What is a fair expectation considering the school curriculum? Additionally, operating in an online environment necessitated a stripping back of material and there were fewer opportunities for in-person one-to-one discussions with students. What did we lose or gain in this environment? The speakers will also demonstrate their pragmatic approach to teaching the content on this transition course: how content of common interest and elements of student choice and personalisation were employed to enhance student engagement. As the Transition Course expands and covid measures recede, how will we continue to teach these data sessions in the future? Do we continue to be generalist in approach, or offer more specialist sessions? Do we separate STEM and non-STEM students? Do we continue to offer ‘data-confident’ and ‘data-less confident’ splits in class? How do we encourage student self-perceptions of competence in data literacy? How do we assess data skills on the transition course? Do we measure the use of data in assessment as evidence of student self-confidence? Finally, as the course expands, the course team will accrue more data themselves on how best to approach the teaching of data literacy!