Abstract
Understanding student sentiment plays a vital role in understanding the changes that could or should be made in curriculum design at university. Learning Analytics (LA) has shown potential for improving student learning experiences and supporting teacher inquiry. Yet, there is limited research that reports on the adoption and actual use of LA to support teacher inquiry. This four-year longitudinal study captures sentiment of postgraduate students at a university in Ireland, by integrating LA with the steps of teacher inquiry. This study makes three important contributions to teaching and learning literature. First, it reports on the use of LA to support teacher inquiry over four one-year cycles of a Master of Science in Business Analytics programme between 2016 and 2020. Second, it provides evidence-based recommendations on how to optimise LA to support teacher inquiry, with specific attention as to how these can improve the assimilation of LA into the curriculum design and delivery. Third, the paper concludes with a research agenda to help improve the adoption and integration of LA in the future.
Highlights
The value of capturing student sentiment has received increasing attention by researchers in higher education (Knight et al, 2020; Linnenbrink-Garcia & Pekrun, 2011)
The specific case was purposefully chosen because, (i) monitoring student sentiment was critical as the programme underwent significant growth each year, (ii) ensuring the programme was designed for inclusive teaching as the student population was diverse, and (iii) the programme director was keen that students had a positive student experience
The 2016-17 end of year programme review was the starting point of our empirical analysis as (i) this was the first programme review conducted since the programme commenced in 2015, (ii) the programme review was conducted by the incoming and newly appointed programme director, and (iii) this dataset provided a baseline from which to compare student sentiment in subsequent academic years
Summary
The value of capturing student sentiment has received increasing attention by researchers in higher education (Knight et al, 2020; Linnenbrink-Garcia & Pekrun, 2011). LA has emerged as an area with high potential for improving student learning experiences and curriculum design (Henritius et al, 2019; Ferguson, 2012) It belongs to a suite of ‘smart technologies’ (e.g., big data), referred to as ‘intelligent technologies’ that are increasingly being promoted as the solution to ‘smart education’ (Zhu et al, 2016) as their use focuses on how learning data can be utilised to improve teaching and learning (Mayer-Schönberger & Cukier 2013; Picciano 2012). Programme learning outcomes The learning outcomes are intended to equip students with the required industry-standard skills and knowledge: (A) understand and be able to use specific IT which is used in developing business analytics. The programme commences in September and consists of three terms; September to Decembers (Term 1), January to April (Term 2), and April to August (Term 3)
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