{"id":374,"date":"2019-07-01T16:00:07","date_gmt":"2019-07-01T15:00:07","guid":{"rendered":"https:\/\/www.open.ac.uk\/blogs\/learning-design\/?p=374"},"modified":"2022-10-12T14:05:45","modified_gmt":"2022-10-12T13:05:45","slug":"workload-mapping-part-3-concurrency-and-activity-makeup","status":"publish","type":"post","link":"https:\/\/www.open.ac.uk\/blogs\/learning-design\/?p=374","title":{"rendered":"Workload mapping part 3: concurrency and activity makeup"},"content":{"rendered":"

In this series of posts, we\u2019ve been looking at student workload mapping. This final post looks at the other neat things we can do <\/span>once we\u2019ve mapped out a module.<\/span>\u00a0<\/span><\/p>\n

Our example student, Alex, has had their workload smoothed out in the previous posts. Now that we\u2019re sure the volume of learning and teaching for this module is <\/span>manageable<\/span> we can start checking that it fits in with the wider context of their studies, and that the studies themselves are suitably varied and engaging. We\u2019re able to do this with our existing mapping data through Concurrency and Activity mapping.<\/span>\u00a0<\/span><\/p>\n

Concurrency mapping<\/span><\/b>\u00a0<\/span><\/p>\n

The Open University has an increasing number of students studying FTE (Full Time Equivalent \u2013 120 credits a year). As <\/span>the majority of<\/span> modules run throughout the course of the academic year, this results in modules overlapping one another. While proactive workload mapping has smoothed both over in our examples, assessments, and small dips and spikes can be magnified to the same damaging proportions as we discussed in our first post.<\/span>\u00a0<\/span><\/p>\n

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By taking the mapped workload from both modules and laying the week-by-week workloads over one another, we can see the concurrent workload for students studying both modules. In this case, a small overrun in both modules in week 8 has generated an unwanted spike, that could lead to the same negative outcomes demonstrated with Alex, our example student, in part 1 <\/span><link><\/span>.<\/span>\u00a0<\/span><\/p>\n

We might also see this with assessments, where the likelihood of a higher student-directed workload (from a student revisiting material, researching and drafting an assignment) impacts the overall study time available for a week. This is a <\/span>particular concern<\/span> at level 1, where students are still building their time management skills \u2013 and may struggle to prioritise conflicting assessments across multiple modules.\u00a0<\/span>\u00a0<\/span><\/p>\n

Our example student Alex may opt to prioritise the assessment on the core module of the <\/span>qualification, and<\/span> devote less time to an optional one \u2013 or feel overwhelmed by the sudden influx of <\/span>self-directed<\/span> workload and perform worse on both. While part of the solution to this is scaffolding and studentship activities, which build study skills throughout the module, maintaining an awareness of potential concurrency during design allows both calendars to be nudged towards a more harmonious alignment. This is most useful when a new module is being designed, and a relationship between that and an existing module can be predicted.<\/span>\u00a0<\/span><\/p>\n

Activity makeup<\/span><\/b>\u00a0<\/span><\/p>\n

Back in part 2, we mentioned mapping directed activities<\/a>. While doing this, we divide those activities in to:<\/span>\u00a0<\/span><\/p>\n