<div>This is the video of my keynote presentation at the biennial EARLI conference in 2019 at Aachen Germany. Introduction is by Prof. Pat Alexander, U. Maryland.<p>Educational assessment
began in the 20<sup>th</sup> century with the development of highly reliable statistical
methods for scoring tests. Amazing refinements have been produced in statistical
models, marking methods, administration, etc. With the development of personal computing,
tests are now delivered on-screen with adjustments for student performance and an
ever-increasing verisimilitude of real-world processes. We now have a vast
array of statistical methods to model student responses for almost any test-like
product. However, this success has been achieved by neglecting complicating factors
such as schooling processes and intra- and inter-personal factors involved in
learning and instruction. </p>
<p>Assessment ‘for’
learning has drawn attention to the interactive classroom processes of question
and answer, feedback, micro-adjustment to instruction, and the involvement of
students in assessment. Unfortunately, these dynamic processes are extremely
difficult to model mathematically because they are so context dependent. Thus,
they remain largely outside the abilities of psychometric statistics. Further,
there is strong resistance in many quarters to reducing the complexity of
classroom action to something that could be expressed as a statistical model.
Thus, we know that assessment is much more than a product, but we have little
grasp of how to capture these complexities in methods suited to the evaluation
of assessment products.</p>
<p>For a long
time, the domains of educational, social, and cultural psychologies related to learning
and instruction have been neglected in assessment. We are now on the cusp of
significant and substantial development in educational assessment as greater
emphasis on the psychology of assessment is brought into the world of testing
both products and processes. Herein lies the future for our field: integration
of psychological theory and research with statistics and technology to
understand processes that work for learning, identify how well students have
learned, and what further teaching and learning is needed.</p>
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