{"id":2351,"date":"2016-04-20T11:28:21","date_gmt":"2016-04-20T10:28:21","guid":{"rendered":"https:\/\/blogs.ncl.ac.uk\/ltdev\/?p=2351"},"modified":"2021-09-17T14:53:20","modified_gmt":"2021-09-17T13:53:20","slug":"ucisa-learning-analytics-conference","status":"publish","type":"post","link":"https:\/\/blogs.ncl.ac.uk\/ltdev\/2016\/04\/20\/ucisa-learning-analytics-conference\/","title":{"rendered":"UCISA Learning Analytics Conference"},"content":{"rendered":"

On the 12th of April, I attended the UCISA event on learning analytics. This was a single day event held in Birmingham. Due to travel issues, we unfortunately missed the first speaker, Sarah Porter,\u00a0Co-Chair of the National Inquiry into data in HE, High Education Commission\u00a0– the author of the HEA report, “from bricks to clicks<\/a>”<\/p>\n

OU Analyse (ppt)<\/a><\/h4>\n

Zdenek Zdrahal, Knowledge Media Institute, The Open University<\/p>\n

The second speaker was Zdenek Zdrahal from Media Unit at the Open University. He described the use of predictive analytics to attempt to identify at-risk students even before the first assignment had taken place, as 98.6% of students who failed the first assignment did not complete the programme. They used a combination of static data including gender and educational history, and fluid data notably VLE interactions. The accuracy of the predictions was increased greatly even when only using the count of “no of clicks” in the VLE.<\/p>\n

In practice, each item in the VLE was given a label and different paths were mapped to view the successful students possible paths and the typical failing student path. The \u00a0data analysis involved the use of 4 different data modelling techniques. If the student failed two of those techniques they were classified as at risk and intervention was put in place by the School. Spreadsheets were sent to the School highlighting the risk levels of each student. A dashboard has been created that highlights at risk students and the data used.<\/p>\n

A student dashboard has been created that shows the student their “nearest neighbours”. These are students showing similar engagement behaviours and the predicted outcome for those students. It also predicts whether the student will submit the next assignment. To improve the students predicted outcomes, various activities are recommended.\u00a0Currently, students cannot access this dashboard but\u00a0it is now a priority to release this. I think the extra step of showing suggested activity to students has a real benefit to the learning experience. There is an intervention strategy automatically suggested to the student. The key would be to make sure these suggestions are accurate and relevant.<\/p>\n

\"OULA\"
\nThey have released an anonymised open source dataset. –\u00a0https:\/\/analyse.kmi.open.ac.uk\/open_dataset<\/a><\/p>\n

Newcastle University does not have a retention issue overall but there may be areas where the use of data in this way may be beneficial – for example distance learning programmes.<\/p>\n

JISC Learning Analytics (ppt)<\/a><\/h4>\n

Michael Webb, the Director of Technology and Analytics at JISC talked about the history of learning analytics , then through some of the work that they are carrying out in the field of learning analytics.<\/p>\n

Michael described their work with learning analytics as “the application of big data techniques such as machine-based learning and data mining to help learners and institutions meet their goals.”<\/p>\n

Predictive learning analytics as seen in the Open University presentation was defined as the “statistical analysis of historical and current data derived from the learning process to create models that allow for predictions that can be used to improve learning outcomes. Models are developed by ‘mining’ large amounts of data to find hidden patterns that correlate to specific outcomes.” JISC are confident that predictive learning models would be ‘fairly’ transferable between institutions.<\/p>\n

JISC currently have a learning analytics project that has three core strands:<\/p>\n