Create a Predictive Learning Analytics Model
To create a PLA model:
Decide what you are predicting. (The most common PLA models predict whether learners will be successful in current courses.)
Choose a PLA report from the Library or create your own.
Create a New Model.
Go to “Apps” tab and select “Predictive Models.”
Click “Create Model” button in the top right corner.
Fill out the form and click next. This form is step 1 of 6.
Select “Case Identifier” columns you will be making predictions about. This will be used to relate predictions to entities in the database, e.g. user id, course id. They should be fields of type "ID". You may choose multiple columns. This is step 2 of 6.
Select “Outcome” column to be the success criteria. Choose the data column containing the outcome the model will predict. This will be populated in historical data and blank in prediction data. It must contain a value of 0 or 1. This is step 3 of 6.
Select “Feature” columns. Choose columns that will be used to predict the outcome. These must be numerical and contain a range of multiple values. You may choose multiple columns. This is step 4 of 6.
Select “Model Process Criteria” column that indicates whether the row is historical data used to train the model or current data used to generate predictions. Select a column in the data set that defines whether the data in that row is for training, prediction, or ignore: 1 = prediction, 0 = training, -1 = ignore. This is step 5 of 6.
“Check Data and Create Model.” Review your data and click “Create Model” to create.
Direct link to “Predictive Model” Builder Form here.
Predictive Model Builder Form
Model name: Name your model to differentiate it from other models and make it easy to recognize.
Model Description: Description that summarizes what the model is based on.
Source Dataset: Select the Dataset that will inform the PLA from the drop down menu.
Algorithm: You have a choice of algorithms to build your model. Currently, IntelliBoard Pro supports two algorithms, Logistic Regression and Neural Network. Neural Network tends to produce more accurate predictions but can require more data. Logistic Regression produces more linear predictions.
Next Step: Once the form is complete, click to precede to the next step.
Frequently Asked Questions:
For additional support, email us at helpdesk@IntelliBoard.net
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