Date(s) - 06/08/2018
9:30 am - 3:30 pm
SICSA Education is pleased to be sponsoring the Learning Analytics for Improving Evidence-Based Teaching which is taking place on Monday 6 August 2018 at the University of St Andrews.
The purpose of this SICSA-sponsored workshop is to encourage an evidence-based approach to teaching by leveraging quantitative and qualitative data available to CS schools. Most importantly, we plan to organise a multi-institution study on using machine learning and AI-based techniques on existing data to improve learning outcomes across multiple universities. The workshop will serve to formulate the goals of such a study and forge the necessary collaborations to make this happen.
We are very happy to announce that the chief regulatory adviser at Jisc Technologies Andrew Cormack will give an invited talk about the legal and ethical framework for learning analytics. In addition to the invited talk, the workshop will consist of a set of breakout sessions and a final discussion dedicated to preparing a follow-up study. The breakout sessions will involve discussions about existing quantitative and qualitative data available to educators, how these data influence teaching, what (statistical and other) data processing is useful for driving decisions, and which algorithmic approaches could be applied across institutions.
Evidence-based teaching is of particular importance in fast-moving fields like Computer Science, and is therefore of interest to many higher education institutions. We have more data on students and courses than ever before including grades, entry requirements, qualitative and quantitative feedback, and career paths after leaving the university, and as computer scientists we are well equipped to process such data. It is important to measure the positive and negative impact of changes to the delivery (e.g. lecture capture, different lecturers) and content (slides, supporting material, organisation) in order to maintain and hopefully improve learning outcomes over time.
However, measuring how teaching approaches affect learning outcomes can be challenging because of issues such as data protection, small numbers of students, changes in the curriculum, or changes in admission procedures. Measuring differences between institutions is even harder because of differences in course structure, class sizes and marking scales. We believe that computer science techniques such as data mining, machine learning and artificial intelligence will become increasingly important in this field, and would like to set up an ambitious study across several universities based on the findings of this workshop. Such a study is only possible if coordinated well across institutions and this workshop aims to provide the basis for such collaboration.
The workshop will involve 24 academics, mainly from SICSA-affiliated institutions, aiming to foster an exchange of ideas and best practice. While the central topic is CS education, we hope to also appeal to CS academics engaged in data ethics, machine learning, and artificial intelligence (e.g. for processing data in natural text form) because the topic provides an important application of CS, and has great potential for impact.