Promoting Collaboration in Teaching and Learning: A Data-Centric Approach

Introduction

The focus of this chapter is primarily on the application of data analytics to the International Baccalaureate (IB) diploma results. Schools usually focus on what can be called the traditional data analytics—such as the use of averages, standard deviation, correlations, median, etcetera—to analyse and present their students’ performances, especially after terminal examinations like the IB diploma. This analytics is helpful to some extent, but it has significant limitations. In broad strokes, it employs key statistical data to inform existing stakeholders, and to help prospective ones in making informed decisions, about the status of learning in the respective school. When it provides fine details, the data can still be relatively isolated and individualised to the extent that they minimise or miss altogether important links to other data points. However, adapting data analytics to provide detailed and meaningful feedback on the critical task of teaching and learning depends on the objectives of the analysis and identification of relevant data points.

The source school of this chapter reviews their IB diploma results yearly. Based on the above-mentioned metrics, the analysis has been showing objectively promising details about teaching and learning in the school. This chapter believes that a different approach could yield more meaningful and insightful results about the status of “connectedness and concurrency of learning” (CCL) in the school with potential impact on both its curriculum and instructional systems. Simply, CCL is the idea that no learning is isolated. The diploma curriculum is built on the notion that students can apply learning in one discipline to solve problems or motivate inquiries in another discipline and attain learning objectives from multiple disciplines at the same time. To facilitate CCL, the International Baccalaureate Organization (IBO) requires schools to demonstrate evidence of practising collaboration, including collaborative planning and reflection (CPR)—where teachers across multiple disciplines (i) plan teaching and learning activities collectively (ii) implement the plan accordingly, and (iii) reflect on the processes in like manner (IBO, 2022, Practice 0401-02; 0403-03, 04). As a whole school approach, the IBO also prescribes specific practices to school (0401-01, 03; 0404-02) and students (0402-01, 02, 03).¹

The chapter concurs with the IBO on the centrality of the CCL to the implementation of the diploma programme. As such, the relevant analytics must identify relevant data points, and extract granular details from those, to reveal multiple actionable links across the entire teaching and learning landscape.

With its defence of the notion of CCL in the diploma programme literature, the IB offers some of the most robust foundations for collaboration in teaching and learning. But, again, the issue here is not the merit or otherwise of collaboration or CCL. To that extent, there is little gain in moralising to schools or proffering extensive theoretical arguments in defence of the instructional practice. A better approach is to empower schools to self-appraise their current implementation of the CCL practices based on critical analysis of relevant data.

This is where the chapter hopes to make an impact on knowledge. It believes that meaningful feedback on diploma students’ results must capture the status of the CCL or collaboration in a school. Toward that end, it identifies relevant data points in the diploma results to associate with the notion of connectedness and concurrency of learning along with original data analytics to measure the status of such learning in a school.