Episode 91

What If Your Personalization Engine Is Solving the Wrong Problem?

Guest name: Kelly Philbin, PhD
Guest title: Director of Core Adoptions
Guest company: IXL Learning
Host: Olivia Lara-Gresty

Brief description of the episode

How can core curriculum support personalized learning without creating more work for teachers? In this episode of Tech in EdTech, Olivia Lara-Gresty is joined by Kelly Philbin, PhD, Director of Core Adoptions at IXL Learning, to discuss connecting instruction, assessment, differentiation, and practice. They explore how assessment can help teachers understand student needs and decide what to do next, while keeping students connected to grade-level learning. The conversation also examines support for multilingual learners, the balance between curriculum coherence and teacher flexibility, and what product teams can learn from educator feedback.

Key Takeaways:

- Look beyond right or wrong answers to understand why the student struggled.
- Recognize that two students can miss the same question for completely different reasons.
- Use in-the-moment assessment to surface misconceptions, missing readiness skills, or difficulty explaining reasoning.
- Connect the insight to an appropriate next step instead of leaving teachers with a score to interpret.
- Keep teachers in control of the final instructional decision.
- Avoid separate tools, resources, and data points that teachers have to connect on their own.
- Link instruction, assessment, differentiation, and practice so one informs the next.
- Show clearly who needs support, who is on track, and who is ready to move ahead.
- Pair learner data with a useful next step teachers can act on.
- Build personalization into the core experience instead of layering it on later.
- Do not treat English proficiency as a measure of cognitive ability.
- Separate language barriers from actual learning gaps before adjusting difficulty.
- Use scaffolds like visuals, modeling, discourse, and translation without lowering rigor.
- Build those supports into the product instead of leaving teachers to create them.
- Make sure personalization does not send students backward because it diagnosed the wrong need.
- Look for patterns across teachers, schools, and districts.
- Ask what problem sits behind the feature request.
- Check whether the issue is really about pacing, workflow, or implementation.
- Separate recurring instructional friction from local preference.
- Use follow-up questions before deciding a request belongs in the product.
- Notice fewer misconceptions and better transfer of learning.
- Watch for stronger confidence, persistence, and participation.
- Check whether students can explain the thinking behind their answer.
- See whether teachers can identify and respond to needs faster.
- Notice less time spent searching for resources and more time spent teaching.

What If Your Personalization Engine Is Solving the Wrong Problem?

Brief description of the episode

Key Takeaways:

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