The ability to better personalise student learning is perhaps one of the key affordances of educational technology. In higher education, personalisation of the curriculum is such a high-value goal for most institutions that it underpins the entire HEA Flexible Pedagogies series. The ability of technology to contribute to that is often framed in terms of learning analytics. Increasingly, many institutions are looking to harness the data footprints that students leave in order to automate the process of personalisation using predictive analytics: content which is appropriate to a students’ level, based on computer analysis of their performance data, can be selected and pushed to them through a VLE. However, I expect institutional use of analytics will have been well covered by others already, and will instead focus on how certain learning analytics tools can better help students consciously personalise their own learning.

Many analytics tools are intended for the use of teaching staff, giving them a fine-grained picture of each student’s performance, and thus helping to personalise the learning journey to best suit the learner’s ability, knowledge and skills, or even spot warning signs for those at risk of dropping out. However, in Moodle, there are a range of tools which are primarily student-facing, and can help learners themselves benefit from their own data.

To take one of the simplest examples, the Progress Bar plugin is a simple extension of Moodle which allows a student to see how much of a course they have completed.

Progress Bar view in Moodle

In a teacher-centric learning context, whether completely offline, or when using a VLE without such plugins, it will be more difficult for students to navigate a course, and understand where they are in it, and must turn to the teacher for this information. However, in a course which uses the Progress Bar plugin, students themselves are able to see their own completion status easily, and so make better decisions about what to focus on.

Other, similar, Moodle extensions exist which give further data directly to students, (or which can easily be shared by a teacher to the students) that allow students see their performance in particular topic areas. Plugins like these target both of the affordance areas covered in this week’s MOOC content. They afford the student the ability to personalise their own learning, by making them more aware of their own performance, and so better able to choose what to focus on. However they also afford the learner the ability to develop metacognitive skills, as they are given the opportunity to reflect on their performance, using the data that they have generated, and make decisions about how successful their own learning strategies are.