Grima, G; Golding, J; (2018) Teaching and assessing for handling large data sets within Pearson Edexcel A-level Mathematics. Presented at: IAEA Annual Conference 2018, Oxford, UK.

Slideshow Golding Grima IAEA Large data set Alevel Ma final.pptx - Accepted Version Download (2MB)

Abstract

Underpinning intelligent and purposeful harnessing of the affordances of ‘big data’ is a robust conceptual understanding of data sets of more moderate size – those which are susceptible to analysis through for example Excel or other traditional data processing application software. Standard routes through English 5-18 education currently provide very limited opportunities to develop concepts related to these small and large data sets and the associated data handling, representation and interpretation skills, yet those will be widely needed by many, if not most, adults, and require the development of both quantitative and qualitative thinking. We report on a small study which evaluates some emerging opportunities to support and assess such learning within the Pearson A-level (pre-university) Mathematics course in England. From 2017 (for examination from Summer 2018), students of A-level Mathematics are required to engage with a given ‘large data set’ using appropriate software, and their learning is summatively assessed via pre-release material. Here, ‘large data set’ is interpreted as meaning ‘not susceptible to manual handling’. This area of teaching and learning, and the mode of summative assessment for this high-stakes qualification, is new to most of the teachers and students involved. We report on the challenges they face, and their perceptions of the nature and extent of the support provided, during the first year of operation. We show that for both teachers and students there are tensions between education for appropriate use of software and a focus on the underpinning conceptual understanding. There are also challenges for both teachers and students in formative assessment of the related learning. We consider the implications for teaching, learning and assessment in relation to big data sets.

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