If LMS analytics show low post-test performance for a module, what is the recommended next action?

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Multiple Choice

If LMS analytics show low post-test performance for a module, what is the recommended next action?

Explanation:
When LMS analytics show low post-test performance, the best next step is to revise the module design based on learner feedback. This approach uses the data to identify where learners struggle and why, such as unclear objectives, content that's misaligned with assessments, pacing issues, or barriers in delivery. By collecting learner input and analyzing how the content, activities, and assessments align with learning goals, you can make targeted redesigns—clarifying objectives, adjusting difficulty and sequencing, adding practice and formative checks, and improving accessibility. The goal is to create a learning experience that supports mastery and yields more accurate demonstrations of what learners have absorbed. Disabling the module, lowering exam difficulty, or ignoring the data do not address the underlying instructional issues and impede effective learning.

When LMS analytics show low post-test performance, the best next step is to revise the module design based on learner feedback. This approach uses the data to identify where learners struggle and why, such as unclear objectives, content that's misaligned with assessments, pacing issues, or barriers in delivery. By collecting learner input and analyzing how the content, activities, and assessments align with learning goals, you can make targeted redesigns—clarifying objectives, adjusting difficulty and sequencing, adding practice and formative checks, and improving accessibility. The goal is to create a learning experience that supports mastery and yields more accurate demonstrations of what learners have absorbed. Disabling the module, lowering exam difficulty, or ignoring the data do not address the underlying instructional issues and impede effective learning.

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