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It has long been recognized that academic success is a result of bothcognitive and non-cognitive dimensions acting together. Consequently, anyintelligent learning platform designed to improve learning outcomes (LOs) mustprovide actionable inputs to the learner in these dimensions. However,operationalizing such inputs in a production setting that is scalable is nottrivial. We develop an Embibe Score Quotient model (ESQ) to predict test scoresbased on observed academic, behavioral and test-taking features of a student.ESQ can be used to predict the future scoring potential of a student as well asoffer personalized learning nudges, both critical to improving LOs. Multiplemachine learning models are evaluated for the prediction task. In order toprovide meaningful feedback to the learner, individualized Shapley featureattributions for each feature are computed. Prediction intervals are obtainedby applying non-parametric quantile regression, in an attempt to quantify theuncertainty in the predictions. We apply the above modelling strategy on adataset consisting of more than a hundred million learner interactions on theEmbibe learning platform. We observe that the Median Absolute Error between theobserved and predicted scores is 4.58% across several user segments, and thecorrelation between predicted and observed responses is 0.93. Game-like what-ifscenarios are played out to see the changes in LOs, on counterfactual examples.We briefly discuss how a rational agent can then apply an optimal policy toaffect the learning outcomes by treating the above model like an Oracle.

A framework for predicting, interpreting, and improving Learning Outcomes