Data Skills and Artificial Intelligence (DS&AI) Data Skills Literacy for Educators
Luisa Cutillo (School of Mathematics and Leeds Institute for Data Analytics (LIDA))
The research activities of this project aim to:
- Identify which programming languages, tools and practices are currently being taught
- Identify gaps between existing provisions and current state of the art
- Identify gaps between our offerings and competitor institutions
- How to adapt and build on the resources shared by the ATI pilot DS&AI Educators' Programme to define training programs for our educators with the aim of offering the most cutting-edge teaching approaches in DS&AI to our students
- Highlight existing areas of excellence at Leeds
- Establish current challenges, shortcomings, and excellence in our pedagogical and technical approaches to teaching DS&AI
The research approach
Some methods will be:
- Survey past, current and prospective UG and PG students’ experiences and expectations concerning skills taught in our current DS&AI oriented modules
- Create a task-and-finish group comprised of DS&AI educators and tools developers, selected UG and PG students, the local DS&AI in education interest group and industry representatives
- Survey module and program leaders to create a taxonomy of data skills taught in existing programs
- Ideas and best practice exchanged at mini-symposia for educators
- Feedback collected at students’ and educators ‘Show&Tell’ events to demonstrate learning outcomes due to the new pedagogical teaching strategies
The fields of Data Science and Artificial Intelligence (DS&AI) are evolving at an unprecedented rate. This increases the risk that knowledge and working practices become outdated, even for recently trained educators. Our syllabi need to be continuously updated and there is a widening gap between workforce requirements, skills of educators and, therefore the skills we teach students. This project will close this gap for colleagues in STEM and non-STEM subjects.
The concept of data is very wide. Different disciplines use data that are not directly comparable, but which can be described by similar Machine Learning (ML) models. For example, images, text, or audio recordings, can be quantified into a table of numbers or matrix and processed with ML tools for dimensionality reductions or networks inference. Such variety of data and disciplines supports the wide applicability of the proposed research.
I will create an infrastructure in the University of Leeds to train and upskill colleagues from both STEM (Science, Technology, Engineering and Math) and non-STEM disciplines involved in DS&AI education involved in DS&AI education. My activities will build on the Alan Turing Institute’s (ATI) pilot DS&AI Educators' Programme that I am attending as ATI Fellow. My project will contribute to Leeds’ Curriculum Redefined activities, leading to improved courses and ultimately benefit the students by providing them the skills needed to be successful in the modern DS&AI workforce.
The implementation of my project will support LITE’s commitment to excellence and innovation in education.
If you would like to find out more about the project contact Luisa (L.Cutillo@leeds.ac.uk).
Each fellowship has a project sponsor that helps the fellows achieve impact across the institution. The sponsor for this fellowship is Paul Baxter on behalf of Leeds Institute for Data Analytics (LIDA).
Project start date: September 2022