DATA-ADAPTIVE INSIGHT AND ACTION PLATFORM FOR HIGHER EDUCATION
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.
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.