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What is it about?
Making Good Progress is an in depth exploration of the two main purposes of assessment:
- Formative assessment should give us information about how students can make further progress, ‘identifying consequences’ so that teachers and students can adjust to learn even more
- Summative assessment should allow us to make meaningful comparisons between a student’s and a larger cohort’s performance against a set of standards. It’s about sampling a knowledge domain without assessment itself becoming the primary focus.
Christodoulou explores the information that assessments of different kinds give us. She discusses the nature of formative and summative assessment and, with references to the findings from cognitive psychology, argues that the types of assessment we use for the two purposes above should be quite different.
She references important contributions to educational thinking in her examination of the flaws of descriptor-based assessments, which are widely used, and of the different inferences we can draw from different types of assessment. It’s helpful to understand these between a quality and a difficulty model:
- The quality model covers situations were teachers judge the quality of students’ work or performance against a set of standards or criteria, for example in essays or compositions
- The difficulty model is where the level of challenge is inherent in the questions that are set and students’ success in answering them determines the standards.
Each type of assessment has different implications for our sense of achievement, progress and standards, and the validity and reliability of the data that we can capture.
What are the main messages for teachers?
- Good formative assessments should be high frequency and low stakes, focusing on specific elements of the curriculum. They should allow for repetition and practice, keeping the results in raw form to track the details of what has been learned and what the next steps might be
- Good summative assessments need to be sat in standard conditions to be reliable, scan a large knowledge domain and ideally use scaled scores rather than grades to allow for meaningful comparisons. These do not need to be very frequent.