Jisc micro-projects: supporting innovation in learning analytics

The UK learning analytics network is about to select some micro-projects to push forward learning analytics in new and innovative directions. At the next meeting of the network in Nottingham on 24th June, the community will decide which of the proposals will receive funding from Jisc of up to £5,000 each.

Nottingham Trent University

Some interesting suggestions have already been proposed via ideascale and been voted on publicly. The seven most popular ideas will now go forward in Nottingham for a panel to decide which will receive funding. Here’s a quick summary of the top contenders:

  1. The call of duty visualised – workload planning tool – Jitse van Ameijde, Lisette Toetenel, Vicky Marsh, Nai Li & David Cook, the Open University
    The problem: Providing learning material to students that’s of interest, at the right level, and stretches their learning, is challenging. Student workload contributes to students’ decisions around withdrawing from study and is among the most important course-related factors influencing student drop-out.
    The solution: the project will develop a tool to enable staff to record course directed workload using a taxonomy of seven activity types (assimilative, finding and handling information, communication, productive, experiential, interactive/adaptive and assessment). By linking this information to student outcome information, insight will be provided into the impact of workload on student outcomes, e.g. relating the amount and type of workload to student satisfaction and pass rates.
  1. Automated system for cognitive presence coding – Vitomir Kovanović, Srećko Joksimović & Dragan Gašević, University of Edinburgh
    The problem: A widely used theoretical model that describes different dimensions of online learning is the Community of Inquiry (CoI) framework. The central component of the model is cognitive presence, which is associated with the development of students’ critical thinking and construction of meaning through the collaborative inquiry process. This is normally analysed post-course by surveys or manual coding – not allowing any analysis of the social learning as it unfolds.
    The solution: The project will enable the use of the CoI framework for continuous monitoring of student discourse. Using text mining and text classification techniques the team will develop an automated content analysis system to label each student discussion message in accordance with CoI coding scheme. This will then provide novel visualisations and dashboards which give insights into student learning processes.
  1. Learner-led curriculum development – Jon Cole, Morley College
    The problem: In Adult Education, institutions develop large numbers of new courses each year to meet the needs of potential learners. However, development takes time and resources, and there’s limited local market information available – so courses are often developed with uncertainty as to the likely demand. Consistently, cancellation rates for new courses are far higher than for established courses.
    The solution: In order to deliver what learners actually want this project will tap into the huge volumes of data available through social media, potentially providing localised evidence of new trends in personal interests which could lead to course development.  Meanwhile a ‘testing’ area would be set up for trying out new course ideas and subsequent analysis of sentiment towards them.  A third objective is to link social media data to existing college management information systems to better understand existing learner satisfaction and potential course interests.
  1. What is the role of emotions in learning analytics? – Bart Rienties, Denise Whitelock (Open University) & Steven Warburton (University of Surrey)
    The problem: An increasing body of research has found that emotions are key “drivers” for learning. Emotions play a critical role in the learning and teaching process because learners’ feelings impact motivation, self-regulation and academic achievement. In blended and online contexts limited research is available on how emotions impact learning. Measuring emotions in learning analytics brings significant epistemological, ontological, theoretical and practical challenges.
    The solution: The project will provide and test Garrison’s (2011) adjusted Community of Inquiry framework for learning analytics to unpack and understand the role of emotional presence in blended and online learning. In the University of Surrey students will be given fitness trackers, while psychometric instruments will measure emotional responses in/outside the classroom, linked to VLE tracking behaviour. In the Open University, OpenMentor will be used to investigate the role of emotions arising from feedback. By measuring and conceptualising the impact of emotions on students’ attitudes, behaviour and cognition, the aim is to unpack how emotions can be tracked in learning analytics with and without smart devices.
  1. Can survey data be used as an indicator of learning outcomes? – Karl Molden, University of Greenwich
    The problem: The University of Greenwich currently collects a large volume of survey data from a number of different sources; as well as the National Student Survey the University also runs many other surveys of different groups of students – postgraduates, new entrants, overseas students etc. Some of these surveys are also conducted by other institutions which means that it is possible to make comparisons and perform benchmarking exercises. However, very little work has been done in trying to understand if further value could be leveraged from the data by matching it to other information sources.
    The solution: The project will take data from the ‘University Student Survey’ and match it to end of session progression and completion information. The first stage of the analysis of this merged dataset would be to investigate if a correlation exists between responses to sets of questions which are indicative of positive engagement with the University and better learning outcomes. If such a correlation can be shown, the second stage would be to apply a similar analysis to in-session, course level survey responses which could potentially be used to help understand which students may require support to achieve the best learning outcomes.
  1. Transparent analytics to support co-creation – Steph Comley, Falmouth University
    The problem: At Falmouth academic staff and students frequently work together to co-create learning resources. Feedback from students studying a very practical, experiential learning course has suggested that those from a non-academic background have struggled with the sections of the course that deliver large chunks of theoretical information. Learning analytics could support retention of these students, assessing this approach of co-creation for positive impact on student engagement. The intention is to introduce transparency and informed discourse regarding learning analytics, enabling students to be active participants in the curation of the learning experience.
    The solution: This project will use learning analytics to aid students in understanding the impact of co-created resources. It is hoped that this will encourage greater engagement with learning resources, and in doing so also support student retention. Through the triangulation of methods to collect learning analytics, including Google Analytics and the Experience API, the project will track the paths students are taking to build their theoretical knowledge. It will also look at how students work through the resources and activities available and will observe developing trends as to the type of resources that are accessed heavily and those that are not, how students work through the material and how this translates into knowledge building.
  1. Revolutionising teaching and learning – Amanda Parker, City of Liverpool College
    The problem: We have the data – we collect it from many sources and it tells us lots of different things. But what about if we were to provide innovative teaching and learning, and a classroom space that incorporates new technologies at its very heart, to track the impact on our students?
    The solution: Flipped learning excites and engages people, and builds learning communities. The project will use learning analytics to follow students’ progress through their course in a specially modified classroom. From their engagement with the VLE, to their use of the library resources, to attendance, retention and achievement, their learning journey will be charted. The College will also use data and feedback from tutors on how the project has affected their teaching practice in order to obtain a 360 degree view on how learners can best be engaged and inspired in the digital age.

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