Students’ Expectations of Learning Analytics [1842] Home : Annual Conference 2017

Students’ Expectations of Learning Analytics [1842]

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Thu, Sep 7 2017, 1:00pm – 2:40pm
Authors: Alexander Whitelock-Wainwright, Dragan Gasevic, and Ricardo Tejeiro
Room: Mountford Hall (G)
Theme: At the forefront of innovation
Type: 20-minute session

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Description

Higher education institutions are beginning to recognise the benefits that big data can bring to student learning. This recognition can be attributed to the growing interest in learning analytics (LA). The possibilities that LA can bring in revolutionising learning at higher education are endless. Through the development of advanced feedback systems (Park and Jo, 2015) and early warning systems to detect at-risk students (Arnold and Pistilli, 2012), LA is benefiting many stakeholders in higher education. These advances made in LA, whilst advantageous for student learning, are initiatives led predominately by researchers or practitioners in the field. In other words, the expectations of students towards what they would like from a system have not been fully recognised. As the main beneficiaries from a service designed to support learning (Clow, 2012), students cannot continue to be passive recipients of LA services. Instead, it is pertinent that students’ beliefs are accommodated into any future LA policy. There are many considerations to make in designing an LA system, from the type of service that will be offered, to the ethical and legal standards that must be adhered to (Prinsloo and Slade, 2016; Sclater, 2016). Developing a system, therefore, cannot be undertaken without an exploration into students’ expectations as they will hold different beliefs towards the service they want to receive. Failing to do so may result in students becoming dissatisfied with the service, which in turn may reduce their intentions to utilise the LA service altogether. Taking the importance of stakeholder expectations in LA service development, we sought to create an instrument to measure student beliefs towards LA, which will be focus of our session.

The session will provide a background on LA, but framed in terms of being a service offered to students in order to support their learning. Framing the LA this way allows for an exploration into how the concept of service quality can provide an ideal perspective for understanding how the disconfirmation of expectations can affect behavioural intentions in the future. We will then present the development of our instrument designed to measure students’ expectations about LA. By this point, the instrument will have undergone peer review, a pilot study, and roll-outs across European universities. We will provide results from the data collected and future research directions that should then be undertaken.

References

Arnold, K.E., Pistilli, M.D., 2012. Course signals at Purdue: using learning analytics to increase student success, in: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, pp. 267–270.

Clow, D., 2012. The learning analytics cycle, in: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge – LAK ’12. ACM Press, New York, New York, USA, p. 134.

Park, Y., Jo, I.-H., 2015. Development of the Learning Analytics Dashboard to Support Students’ Learning Performance. J UCS 21, 110–133.

Prinsloo, P., Slade, S., 2016. Student vulnerability, agency, and learning analytics: An exploration. J. Learn. Anal. 159–182.

Sclater, N., 2016. Developing a code of practice for learning analytics. J. Learn. Anal. 16–42.

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