Achieving peak performance in summative assessment

In the world of sport, athletes structure their training so that they peak for competition. Many different factors are taken into account as part of this peaking for competition known as periodisation, but chief among them is the volume and intensity of training. Volume represents the total amount of work performed during training and the intensity represents how ‘hard’ the athlete works during training sessions. In the basic sense, a periodised programme begins with a high training volume, and low training intensity as the athlete builds their base levels of fitness. However as training progresses and competition edges closer, the volume reduces whilst the intensity increases (Figure 1). Hence as competition approaches, the intensity of training will be at or beyond the effort level required for competition to support achievement of peak performance. As such, the closer competition is the more competition like training should be.

Figure 1. Basic hypothetical periodisation model

Programmes of learning don’t generally have competitions to peak for, but they do have assessments which students must be in the best possible shape to do well in. As such, I’ve often wondered about the extent to which the broad principles of periodisation could be applied to the design of modules and programmes to allow students to peak for their assessments. In the learning environment a measurement of ‘volume’ is fairly straightforward since it can be represented by the total number of tasks we get the students to ‘do’ in classroom and self-directed learning environments and thus represents the ‘how much’ aspects of study. ‘Intensity’ in the learning sense may be represented by how challenging study tasks are. A measure of ‘challenge’ could be gauged by where tasks sit on the revised version of bloom’s taxonomy (Krathwohl & Anderson, 2009) (Table 2) which possesses both knowledge and cognitive dimensions. Hence, tasks that are low in both the knowledge and cognitive dimensions would represent low challenge, with those that are high in each dimension representing high challenge tasks.

Table 1. 3 Dimensional model of Bloom’s taxonomy including knowledge and cognitive dimensions (Krathwohl & Anderson, 2009)

Adhering to Petty’s (2014) advice that student success can be supported by setting ladders of tasks that climb Bloom’s taxonomy, in this proposed model, the climbing of Bloom’s taxonomy is done in periodised fashion, following the model presented in Figure 1, allowing students to peak for their assessments. For example, in the early phases of a module or programme, several relatively low challenge study tasks would be set to allow the development of base level knowledge and cognitive abilities. As assessment approaches, the total number of tasks would be reduced, with the level of challenge increased via the setting of tasks that are higher in both knowledge and cognitive domains of the taxonomy. In essence, the closer assessment is, the more assessment like study tasks should be, with students required to undertake formative assessment tasks at or above the level of challenge found in their summative assessments, stretching and challenging their use of content. All study tasks of course should reflect the modular or programme learning outcomes, which themselves should be reflected in the summative assessment in constructively aligned fashion (Biggs & Tang, 2011), with opportunities for triangulated feedback (self, peer and teacher). As such, students get the opportunity to regularly and progressively practice the knowledge and cognitive skills outlined in the learning outcomes, and receive and provide formative feedback. Subsequently, students are supported to ‘peak’ for their summative assessments. Or at least that is the idea…I would love to know what people think…

References

Biggs, J. and Tang, C. (2011) Teaching for quality learning at university. Maidenhead: McGraw-Hill Education.

Krathwohl, D.R. and Anderson, L.W. (2009) A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman.

Petty, G. (2004) Teaching today: A practical guide. Cheltenham: Nelson Thornes.


Dr Kevin L. Merry is the lead for academic development at De Montfort University. As a trained exercise physiologist, Kevin has always been interested in how the principles underpinning sport and exercise training can be applied to the design of student learning. An award winning teacher, Kevin has been instrumental in embedding Universal Design for Learning (UDL) as DMU’s principal approach to learning, teaching and assessment, developing new approaches to course design underpinned by the UDL principles.

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