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2 references
Banihashem and colleagues systematically review 93 studies published from 2011 to 2023 to examine how learning analytics supports formative assessment.
Their coding framework combines three actors—teachers, peers, and learners as self-assessors—with three questions: where learning is going, where it is now, and how to move forward. The review finds that analytics can clarify goals, diagnose current understanding, and support feedback or instructional action, but coverage across the framework is uneven. A central concern is alignment: dashboards or predictions are useful only when their outputs connect to a defensible model of formative assessment and can be interpreted and acted upon.
For educators and designers, the study recommends building analytics around learning intentions, evidence elicitation, feedback processes, and concrete decisions rather than treating data visualisation as formative assessment by itself.
Black and Wiliam develop a theoretical account that places formative assessment inside a broader model of pedagogy.
They define practice as formative when evidence about student achievement is elicited, interpreted, and used by teachers, learners, or peers to make better-grounded decisions about next steps than they could make without that evidence. The article connects classroom interaction with theories of instruction, feedback, self-regulation, and the regulation of learning. It identifies key strategies: clarifying intentions and criteria, engineering discussions and tasks that reveal understanding, providing feedback that moves learning forward, activating students as resources for one another, and activating them as owners of learning.
For educators, the theory shifts attention from assessment instruments to decision processes: who needs evidence, what inference it supports, and how the resulting action changes the course of learning.
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