Learning Analytics Explained

Sclater, Niall

doi:10.4324/9781315679563

Cited by 93 publications

(47 citation statements)

References 0 publications

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“…Data was collected through four audio-recorded focus groups conducted by the research team and transcribed verbatim. After providing written informed consent and a definition of learning analytics, participants were asked about their current knowledge of learning analytics prior to watching brief videos on learning analytics and student dashboards in higher education (Teaching with Technology, 2013; Sclater, 2015a). The videos were presented as examples of learning analytics systems.…”

Section: Methodsmentioning

confidence: 99%

Student Attitudes toward Learning Analytics in Higher Education: “The Fitbit Version of the Learning World”

et al. 2016

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Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students' knowledge, attitudes, and concerns about big data and learning analytics through four focus groups (N = 41). Thematic analysis of the focus group transcripts identified six key themes. The first theme, “Uninformed and Uncertain,” represents students' lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; “Help or Hindrance to Learning,” “More than a Number,” and “Impeding Independence”; represented students' perceptions of the likely impact of learning analytics on their learning. “Driving Inequality” and “Where Will it Stop?” represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how “personal” vs. “collective” purposes or principles can intersect with “uniform” vs. “autonomous” activity. The findings highlight the need the need to engage students in the decision making process about learning analytics.

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Section: Methodsmentioning

confidence: 99%

Student Attitudes toward Learning Analytics in Higher Education: “The Fitbit Version of the Learning World”

et al. 2016

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“…The data in itself is of little use without effective interventions. Examples of the interventions deployed as part of learning analytics implementations have included: sending an email to the student to ask how they were getting on where there were concerns about the student's progress, posting a traffic light signal on a student's page, prompts to visit further online support resources, and invitations to meet with a tutor to discuss progress (Sclater, 2017).…”

Section: Interventionsmentioning

confidence: 99%

“…This is a growing field in the UK, with a national project being developed by Jisc in collaboration with pathfinder institutions (JISC, n.d.). Applications of learning analytics include: Early Alert and Student Success, Course Recommendation, Adaptive Learning and Curriculum Design (Sclater, 2017). Many UK implementations predominantly focus on Early Alert and Student Success, with increased student retention rates being a key aim.…”

Section: Introductionmentioning

confidence: 99%

The potential and pitfalls of learning analytics as a tool for supporting student wellbeing

Ahern

2018

JLTHE

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Learning Analytics is a growing field in UK Higher Education Institutes (HEIs) with many implementations focusing on Early Alert and Student Success, but is this putting the cart before the horse? In the 2017 #StepChange report Universities UK suggested that learning analytics should be aligned to student wellbeing. With reported increases in incidents of student mental ill-health and extra demands being placed on student support services as a consequence this seems an appropriate application of this technology. However, there are a number of concerns related to student privacy and the interpretation and presentation of the analytics. Also, who will be designing and performing interventions? At a time when there is growing concerns around the wellbeing of staff, should we be adding a further burden? Do we risk being eaten by the analytics crocodile? It is clear that to utilise learning analytics in this way poses a number of challenges, but in the information age, when this data is available to us is it moral or legal to remain the caterpillar, knowing nothing of who our students are? With Institute for Public Policy Research stating that “…a majority of HEIs should take measures to ensure that the nature of course content and delivery does not result in academic rigour being sought at the expense of students’ mental health and wellbeing.” we should be doing all we can to adequately and pro-actively support our students.

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“…However, it is questionable whether such tools are available or in use at all institutions wishing to follow such a data-driven learning design process, as outlined by Beetham (2012) in a Jisc report of learning design barriers in the UK. Other barriers to the adoption of learning analytics practices around the world include capacity building and training staff members to read and understand data and visualisations (Sclater, 2017;Verbert et al, 2013). In institutions in Africa, previous work has similarly outlined ambivalence towards the use of virtual learning environments (Unwin et al, 2010).…”

Section: Considerations For the Appropriateness Of The Ould Approach mentioning

confidence: 99%

“…It was noted that lecturers in participants' respective countries were often required to take on extensive teaching loads, impinging on the time allocated for designing effective modules that address student needs, as required in the OULD method and the aforementioned suggestions. Even when such design steps were taken, it was noted by many participants that it can be difficult to find teaching staff who are experienced with collecting and analysing learning analytics data from students in order to develop learner-specific module designs, as is frequently also seen in European contexts (Beetham, 2012;Sclater, 2017). This is mainly because staff with data literacy are few within existing human resources for many universities and there is often little time, opportunity or funds allocated towards learning design and analytics.…”

Section: Suggestion 6:build Human Resources For Module Design and Datmentioning

confidence: 99%

Learning design in diverse institutional and cultural contexts: suggestions from a participatory workshop with higher education professionals in Africa

Mittelmeier

Long

Cin

et al. 2018

Open Learning: The Journal of Open, Distance and e-Learning

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Learning design approaches, such as those adopted by the Open University, provide a set of tools and resources for purposefullydesigning modules with a focus on student experiences. However, many of the current learning design strategies have been situated within specific institutions in Europe and North America. This means that there are several issues worth considering around if and how established learning design approaches make sense in diverse institutional and cultural contexts. To critically assess the relevance and appropriateness of learning design strategies in new contexts, this article describes an in-depth participatory workshop with 34 education professionals from five African countries. Altogether, 10 suggestions for learning design practices were derived from the consensus of workshop participants, which provide a foundation for the development of learning design practices moving forward.

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Learning Analytics Explained

Cited by 93 publications

References 0 publications

Student Attitudes toward Learning Analytics in Higher Education: “The Fitbit Version of the Learning World”

Student Attitudes toward Learning Analytics in Higher Education: “The Fitbit Version of the Learning World”

The potential and pitfalls of learning analytics as a tool for supporting student wellbeing

Learning design in diverse institutional and cultural contexts: suggestions from a participatory workshop with higher education professionals in Africa

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