Episode 21

Assessments for Personalized Instruction and Learning

Brief description of the episode

In conversation with Paul Grudnitski, CEO of AmpUp IO, as he shares what the future of assessments holds, and how assessments are being refined for personalized instruction and learning at scale.

Key Takeaways:

- To measure the learning and progress of students in the beginning, middle, and end to personalize instruction.
- To determine what content is to be taught next.
- To plan resources that will help facilitate the acquisition of learning.
- By estimating a student’s ongoing ability depending on how they answer the question, it goes up or down, and then gives easier or harder items depending on the ability estimate.
- Ability estimation with the help of a sequence of various levels of linear tests
- Through adaptive items, score in the context of a single item, and get an idea of how the student is answering questions to provide hints accordingly.
- Use QTI 3 as the new standard for building adaptive items to improve student achievement and student outcomes in the realm of mathematics.
- Cognitive Diagnostic modeling computes probabilities very rapidly, and this accelerates the movement to personalized learning and instruction.
- Machine learning tech like Bayesian networks tells educators where student interventions need to be made and improves their probability of skill mastery.
- By measuring their students’ progress and further deciding the content they will teach.
- By deciding the way through which the student will demonstrate his/her learning
- Creating a plan to facilitate the acquisition of learning.

Assessments for Personalized Instruction and Learning

Brief description of the episode

Key Takeaways:

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