An automation analytics system and method for building analytical models for an education application uses data-availability segments of students, which are clustered into segment clusters, to create the analytical models for the segment clusters using a machine learning process. The analytical models can be used to identify at least at least actionable insights.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for building analytical models for an education application, the method comprising: extracting features from data of students; segmenting the students into data-availability segments; for each data-availability segment, determining a subset of features based on model performance; clustering the students within each data-availability segment into segment clusters using one or more features in the subset of features; for each segment cluster, determining another subset of features based on model performance; and creating the analytical models for the segment clusters using a machine learning process, the analytical models providing at least actionable insights. 2. The method of claim 1, wherein the creating the analytical models includes combining predictive models with propensity-score matching. 3. The method of claim 2, wherein the combining the predictive models with the propensity-score matching includes identifying key success features from a predictive model building process and constructing propensity-score models using one or more of the key success features to enable matching in highly predictive propensity-score domain. 4. The method of claim 1, further comprising performing statistical hypothesis testing with Bonferroni correction as a function of time and various segments to explain what interventions work for which segments of the students under what context. 5. The method of claim 1, further comprising predicting initial course success for guidance using at least one of course/student similarity analyses, collaborative filtering, clustering of the students based on a predictive feature subset for course success and identifying similar courses similar students have taken, and dynamic feature-based prediction. 6. The method of claim 1, further comprising estimating inherent course difficulties adjusted for student skills to identify gatekeeper courses, and toxic or synergistic course combinations using representations of concurrent-course combinations and their grades along with key student attributes for success. 7. The method of claim 1, further comprising producing a heat map of a particular student that includes faculty-student interactions, student-student interactions, student performance and predicted scores to provide an understanding of how these variables interact with one another. 8. The method of claim 1, further comprising producing a table of effective faculty-student and faculty features as a function of student segments/clusters using student success measures and changes in student behavior post faculty engagement. 9. A computer-readable storage medium containing program instructions for method for building analytical models for an education application, wherein execution of the program instructions by one or more processors of a computer system causes the one or more processors to perform steps comprising: extracting features from data of students; segmenting the students into data-availability segments; for each data-availability segment, determining a subset of features based on model performance; clustering the students within each data-availability segment into segment clusters using one or more features in the subset of features; for each segment cluster, determining another subset of features based on model performance; and creating the analytical models for the segment clusters using a machine learning process, the analytical models providing at least actionable insights. 10. The computer-readable storage medium of claim 9, wherein the creating the analytical models includes combining predictive models with propensity-score matching. 11. The computer-readable storage medium of claim 10, wherein the combining the predictive models with the propensity-score matching includes identifying key success features from a predictive model building process and constructing propensity-score models using one or more of the key success features to enable matching in highly predictive propensity-score domain. 12. The computer-readable storage medium of claim 9, wherein the steps further comprises performing statistical hypothesis testing with Bonferroni correction as a function of time and various segments to explain what interventions work for which segments of the students under what context. 13. The computer-readable storage medium of claim 9, wherein the steps further comprises predicting initial course success for guidance using at least one of course/student similarity analyses, collaborative filtering, clustering of the students based on a predictive feature subset for course success and identifying similar courses similar students have taken, and dynamic feature-based prediction. 14. The computer-readable storage medium of claim 9, wherein the steps further comprises estimating inherent course difficulties adjusted for student skills to identify gatekeeper courses, and toxic or synergistic course combinations using representations of concurrent-course combinations and their grades along with key student attributes for success. 15. The computer-readable storage medium of claim 9, wherein the steps further comprises producing a heat map of a particular student that includes faculty-student interactions, student-student interactions, student performance and predicted scores to provide an understanding of how these variables interact with one another. 16. The computer-readable storage medium of claim 9, wherein the steps further comprises producing a table of effective faculty-student and faculty features as a function of student segments/clusters using student success measures and changes in student behavior post faculty engagement. 17. An automation analytics system comprising: a feature extraction module configured to extract features from data of students; a segmentation module configured to segment the students into data-availability segments; a segment feature optimizing module configured to determine a subset of features based on model performance for each data-availability segment; a clustering module configured to cluster the students within each data-availability segment into segment clusters using one or more features in the subset of features; a cluster feature optimizing module configured to determine another subset of features based on model performance for each segment cluster; and a model building module configured to create analytical models for the segment clusters using a machine learning process, the analytical models providing at least actionable insights. 18. The automation analytics system of claim 17, wherein the model building module is configured to combine predictive models with propensity-score matching to create the analytical models. 19. The automation analytics system of claim 18, wherein the model building module is configured to identify key success features from a predictive model building process and to construct propensity-score models using one or more of the key success features to enable matching in highly predictive propensity-score domain. 20. The automation analytics system of claim 17, wherein the model building module is configured to perform statistical hypothesis testing with Bonferroni correction as a function of time and various segments to explain what interventions work for which segments of the students under what context. 21. The automation analytics system of claim 17, wherein the model building module is configured to predict initial course success for guidance using at least one of course/student similarity analyses, collaborative filtering, clustering of the students based on a predictive feature subset for course success and identifying similar courses similar students have taken, and dynamic feature-based prediction. 22. The automation analytics system of claim 17, wherein the model building module is configured to estimate inherent course difficulties adjusted for student skills to identify gatekeeper courses, and toxic or synergistic course combinations using representations of concurrent-course combinations and their grades along with key student attributes for success.