Like all robust development processes, our learning design approach includes a number of opportunities to learn from feedback. One is our curriculum design student panel (aimed at capturing students’ views on learning design ideas before they’re live). Another is real-time student feedback (RTSF), which gathers feedback from students as they’re studying with the aim of ensuring they get the support they need. Short questionnaires focusing on recently studied topics are embedded into students’ online study planners so they can reflect and comment on their experiences and receive extra guidance based on their comments.
Our module teams can learn from RTSF too. In this post, we take a look at how students and module teams can benefit from RTSF and some of the impacts it’s making.
In the Open University, we have developed a suite of LA (Learning Analytics) visualisations called ‘Action for Analytics’ (A4A: slides from a presentation giving more detail) designed to help those responsible for producing modules to see the effects of their designs. For example, it’s possible to track just how much use videos we produce for the module get watched and therefore see whether doing more would be a good investment.
This has been very successful with our colleagues outside the Learning Design team (mostly academics) being able to track what is going on with their modules real time and also see the effects of changes as they are bought in.
However, the tool is limited to a set of ‘baked in’ dashboards so its not possible to split the above data into students who ended up failing the module from those who passed and compare the two graphs. This could give useful insight into the value of individual parts of a module and also if students are accessing it or not.
Drilling down into the data: A4A isn’t the only route to exploring statistics about students on modules. There are a number of databases underlying the visualisations and these can be accessed directly by specialist staff. Using our access rights, we have been experimenting with producing bespoke visualisations not in the current suite that we think could help those writing and designing modules. These are currently prototypes but show some promise:
In this visualisation, individual students are shown one per row at the top. If they have accessed any element of the course (one section per column) the corresponding cell is blue. If they have never accessed it, it’s shown white. At the bottom, students are grouped (e.g. ‘withdrawers’ and ‘registered’ – not withdrawn) and cells are now coloured with hot colours showing low usage and cool colours showing high usage.
Example Interpretation: As an example of its use, the last column is the block assignment. It can clearly be seen that section 18 (column 2nd from right, expanded up left) is attracting a high percentage of students visiting it at least once. Section 17 (3rd from right) is attracting considerably lower numbers of students, especially amongst withdrawers. This is a factor of inclusion of section 18 in the assignment, whereas 17 is not and, as a result, students are choosing to skip it. From a design point of view, should it be included at all?
More granularity:In our work investigating this graphic, we think it will become even more useful when there are improvements in the granularity, at present we can only see that students have accessed a whole section. For example, it will be much more useful to see how far they got within a section itself – did they give up half way through? Improvements in the learning analytics the VLE records should help with this.
Next Steps: This is a work in progress, already we are making the patchwork quilt visualisation more sophisticated and have plans for other experiments.