A summary of the VR study experimental design.

Quantitative methods workshop for the PhD program at MGH Institute of Health Professions. http://mghihp.edu/phdhped

MGHIHP, HE942. 8:30-11:45 a.m. on Oct 6 2020.

This is a quick guide put together by a non-expert. It is not comprehensive in any way. You can copy and paste the code to make your own flowcharts quickly in R.

For course HE930 at MGHIHP. Use selected regression and classification techniques to make predictions.

For HE930 at MGH Institute of Health Professions.

Distance matrix, k-means clustering, and assignment.

Learning goals: 1. Identify the types of questions that predictive analytics methods can help us answer in education. 2. Examine real results from a predictive analytic method and brainstorm about how we would use the results. 3. Build intuition about how machine learning algorithms can help us predict group membership using systematically-organized data.

Practice the following skills: A) Practice using decision tree and logistic regression models to predict which students are going to pass or fail, using data on student learning outcomes. B) Fine-tune predictive models. C) Compare the results of different predictive models and choose the best one. D) Brainstorm about how the predictions would be used in an educational setting.

By the end of this presentation, our goals are to: 1) Identify the types of questions that predictive analytics methods can help us answer in education. 2) Build intuition about how machine learning algorithms can help us predict group membership using systematically-organized data (also known as classification).

Created by Anshul Kumar for some special new parents