Data-driven educators analyze patterns in student responses to identify misconceptions or alternative conceptions and, using those analyses, consider possible next steps for instruction.
When analyzing work for student misconceptions, data-literate educators look for higher-level (NOT item- or task-specific) errors that can be generalized for individual students, groups of students, or the whole class. In so doing, educators do the following things:
Through this process, data-driven educators identify and plan interventions to improve student understanding.
To earn the micro-credential for Analyzing Student Misconceptions, you must submit an example of how you systematically catalogue misconceptions, your analysis of those mistakes, and reflections on the reasons for the misconceptions. You may also provide optional context.
Your submission will be assessed according to the rubric below. You must earn a (3) Proficient or (4) Exemplary score in order to earn the micro-credential.
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