Disaggregating Student Assessment Data to Help Inform Teachers’ Instructional Decisions

Mark GAN
Centre for Development of Teaching & Learning (CDTL)

Mark talks about ‘drilling down’ into student assessment data in order to uncover underlying patterns which enable teachers to hone in on and address their students’ most pressing learning needs.

Gan, M. J. S. (2022, August 29). Disaggregating student assessment data to help inform teachers’ instructional decisions. Teaching Connections. https://blog.nus.edu.sg/teachingconnections/2022/08/29/evidencing-teaching-analysing-student-assessment-data/ 

In this August issue, we try to make sense of student assessment data by exploring different ways of analysing the information collected.

Analysing student assessment data can provide valuable information to help teachers gain a deeper understanding of where their students are in their learning, as well as the impact of their teaching. Teachers should interrogate students’ assessment data by asking:

  • In what ways can you sort and analyse student assessment data to better understand student learning needs?
  • Why and how do you use patterns and trends in assessment data to inform your next steps in teaching?
  • What kinds of patterns should you be looking for, and what could these patterns suggest?

One way of doing this is to disaggregate the data in different ways to uncover the underlying patterns or trends, which helps teachers to focus their attention on aspects of learning needs that matter the most to their students.

We usually report student achievement data as aggregated data, in the form of whole populations (for example: cohorts, year levels, modules, whole class). For example, the mean score for a cohort of students taking a particular module. This value is a useful summary of how well students in the module perform, but may conceal important differences about the subgroup of students within the cohort, which could lead to faulty decisions.

Disaggregating data simply means looking at achievement results by specific subgroups of students. It involves ‘drilling down’ or delving more deeply into a set of results to highlight issues that pertain to individual subsets of results and/or outcomes of aggregated data. Aggregate data can be disaggregated by: gender, Year level, age groups, discipline, socio-cultural or ethnic background, nationality, on-campus/ off-campus accommodations, or any variable of interest that would help inform instructional decisions (e.g. different learning approaches such as game-based learning and flipped-classroom learning).

Analysis of data by disaggregation need not be an individual pursuit; collaboration with  colleagues and other teachers may bring different perspectives and fresh ideas to the discussion and interpretation of the findings. For example, one teacher may be excellent at analysing data, while another in the group may have deep understanding of the students’ prior knowledge and another may be a content expert. These strengths can complement and work together to inform teaching and learning in very powerful and meaningful ways.

You can use an Excel spreadsheet to carry out disaggregating data using pivot tables. See the example shown in the video in Figure 1:

Figure 1. Example of student data disaggregated by ethnicity and gender (click on the image to check out the video).


mark gan

Mark GAN is an Associate Director of the Centre for Development of Teaching and Learning (CDTL) in NUS. He has been involved in a wide variety of higher educational initiatives and programmes to enhance professional development of staff, such as courses for developing a Teaching Portfolio and writing of teaching inquiry grants. His research interests include feedback and assessment, and the impact of academic development work on teaching and learning. Mark has a PhD in Education from the University of Auckland, supervised by Professor John Hattie.

Mark can be reached at mark.gan@nus.edu.sg.


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