Search

Intensive mode delivery (IMD) of subjects and courses offer a flexible option for today's diverse student body, many of whom juggle work and carer responsibilities alongside study. However, little focus has been placed on the detailed design of IMD for different teaching roles. Responding to a call for science specific data, we investigated students' and staff perceptions of learning and teaching in IMD undergraduate science subjects. Using data collected via student surveys and teacher interviews, we present our findings through the transition pedagogy framework which will help learning designers and teachers make quality design decisions. We found students and staff identified positive aspects of IMD, such as accelerating progression, high engagement, and smaller class size. The challenges with IMD include the intensity associated with workload. In most subjects, student attainment was higher in IMD compared to standard delivery over a semester. By comparing the perceptions to achievements, we examine the implications for designing positive and effective student learning experiences for IMD in science subjects.

In calibration tasks students assess exemplar texts using criteria against which their own work will be assessed. Typically, these tasks are used in the context of training for peer assessment. Little research has been conducted on the benefits of calibration tasks, such as benchmarking, as learning opportunities in their own right. This paper examines a dataset from a long-running benchmarking task (~500 students per semester, for four semesters). We investigate the relationship of benchmarking performance to other student outcomes, including ability to self-assess accurately. We show that students who complete the benchmarking perform better, that there is a relationship between benchmarking performance and self-assessment performance, and that students appreciate the support for learning that benchmarking tasks provide. We discuss implications for teaching and learning flagging the potential of calibration tasks as an under-explored tool.