Predictions in Ask Your Data❓📈

[GergelyTar]

Hey everyone,

We, as the product team of Intelligent Insights are super excited to start building prediction capabilities in Ask Your Data — and we’d love to get your input! To make these predictions as useful as possible, we want to focus on the questions and use cases that matter most to you.

So, what would you like to predict? What kind of insights would help you plan, prepare, or make decisions?

Drop your ideas in the comments below, and thanks for helping us make this feature awesome!

[Jennifer_Mayes]

Hello! I asked around at my institution and here is what we came up with:

1. Student Success Indicators
• Predict Course Completion Risk: Identify students likely to struggle or drop out of courses based on patterns of engagement, grades, assignment completion rates, and historical success factors.
• At-Risk Student Alerts: Predict which students are likely to need early intervention based on engagement with course materials, attendance, and communication frequency. Also, predict which students are "quiet quitting" the university.
• Retention Risks by Program or by Custom Lists: Forecast the likelihood of student retention in specific programs, allowing departments to adjust resources or support strategies accordingly.
2. Canvas-course Engagement and Participation Patterns
• Predictive Engagement Insights: Identify which Canvas calendar weeks or modules are prone to lower engagement, allowing instructors to plan additional support or adjust course materials.
• Forum and Discussion Insights: Help faculty predict which Canvas discussion threads or topics are likely to generate the most engagement, helping them design future course content that resonates with students.
• Multi-section Insights: Predict course performance among many sections. Will one succeed over the other?
3. Curriculum and Content Optimization
• Content Relevance and Efficacy: Predict or generate a score for which Canvas course content is likely to be most effective or needs updating based on usage and student feedback.
• Module Completion Rates: Forecast Canvas completion rates for specific modules or units, allowing faculty to adjust where students struggle or drop off.
4. Learning Behavior Insights
• Learning Style Prediction: Identify learning style preferences across student populations to offer personalized recommendations for Canvas course content delivery.
• Assignment Completion Patterns: Predict common Canvas assignment completion rates or deadlines to optimize due dates and improve student time management.
• Time-on-Task Insights: Estimate how much time students are spending on specific Canvas course elements, which can guide future adjustments to course design for better pacing.
5. Faculty Development Needs
• Instructional Adjustment Needs: Identify instructors who might benefit from targeted professional development in Canvas topics like digital teaching, engagement strategies, or course design.
• Pedagogical Insights through Canvas use: Forecast which instructional methods are most effective across courses and departments, supporting Canvas + evidence-based faculty development initiatives.

[GergelyTar]

This is golden, thanks so much Jennifer!