The UTS Connected Intelligence Centre continues to play a key role in catalysing and validating the use of analytics by faculties across the university in their learning and teaching programs. We are delighted to announce that in the most recent round of 2017 Vice-Chancellor’s Learning and Teaching Grants, CIC secured three small grants for collaborative projects with UTS teams from the Faculty of Science; the School of Mathematical and Physical Sciences; and the Faculty of Law. The projects are described below:
CIC & the Faculty of Science
Project: Investigating the diagnostic potential of a science benchmarking task
The project will investigate student outcomes of a flipped learning ‘benchmarking’ activity in which students are asked to grade and give feedback on sections of assignment that they subsequently write their own versions of. Prior investigation highlights the enormous benefits of this kind of task, but rarely are outcomes quantitatively evaluated to inform future practice. This project aims to use a large dataset from Biocomplexity at UTS to inform ongoing development of this learning strategy, particularly in supporting students and tutors in giving high quality feedback, targeting feedback to students and providing diagnostic insight to instructors to support their understanding of the students’ areas of strength and weakness.
Team members:
- Dr Simon Knight, Connected Intelligence Centre
- Dr Yvonne Davila, Faculty of Science
- Associate Professor Andrea Leigh, Faculty of Science
- Dr Leigh Martin, Faculty of Science
CIC & the School of Mathematical and Physical Sciences
Project: Noisy Sheets: A Practical Approach to Scalable, Authentic Assessment for Quantitative Literacy
The project will develop an application to support authentic data analytics assessment as a flipped-learning activity. We will develop an application that enables us to share an individual copy of a dataset to all students via google sheets. The dataset will be modified such that each student receives the data with some random variance built in; so you can work with the same data, but answers to calculations will be distinct to each individual. Sharing authentic data builds student engagement, while encouraging student to complete the work (and avoiding concerns of plagiarism in quizzes based on a shared dataset).
Team members:
- Dr Simon Knight, Connected Intelligence Centre
- Dr Mary Coupland, School of Mathematical and Physical Sciences
- Coral Connor, School of Mathematical and Physical Sciences
CIC & the Faculty of Law
Project: Assessing the impact of automated writing feedback on student revisions in Civil Law
The project aims to understand the impact of automated feedback (from a Writing Analytics tool) on student writing. We are investigating this, in the context of UTS Civil Law, by setting a pedagogically meaningful activity integrated into the subject, namely, to improve a relatively poor sample essay. Students’ revisions to this essay will be logged and analysed to see if students who are given access to the Writing Analytics tool perform differently from control conditions. The funding provided will enable us to grade the students’ revisions to the text and provide them with feedback, as well as giving us an outcome measure for analysis.
Team members:
- Dr Simon Knight, Connected Intelligence Centre
- Dr Philippa Ryan, Faculty of Law