Markov Student Persistence Modeling for Flexible Academic Progress
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Solution Overview
Problem
Current predictive models for higher-education institutions are inadequate for flexible term structures and competency-based learning (CBL), failing to predict student progress and persistence accurately, and do not account for diverse student groups with varying academic capabilities and time constraints.
Innovation Solution
A flexible persistence modeling system using a Markov model to quantify transitions between academic states, allowing for personalized learning pathways and predictive insights, incorporating features like sliding time windows and hierarchical time-series trees to adapt to non-traditional learning programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive models based on fixed term structures are used, then model simplicity is maintained, but accuracy in predicting student progress and persistence deteriorates for flexible term structures and competency-based learning
Solution Approach 1:
The patent applies dynamics by transitioning from static, fixed-term predictive models to dynamic models that adapt to flexible term structures and competency-based learning pathways. The system dynamically adjusts prediction parameters based on individual student progress rates, allowing the model to accommodate varying academic calendars and learning paces without sacrificing accuracy or becoming overly complex.
Solution Approach 2:
The patent implements parameter changes by modifying the temporal parameters of predictive models to align with flexible term structures and competency-based education. Instead of using fixed semester or quarter parameters, the system uses variable time parameters that reflect actual student progress through competencies, thereby improving prediction accuracy for diverse learning modalities while maintaining model tractability.
2Adaptability or versatility
If fixed-term structure models are used, then ease of operation is maintained, but adaptability to diverse student groups and learning modalities deteriorates
Solution Approach 1:
The patent applies universality by designing a predictive model framework that serves multiple functions across diverse learning modalities. The same core model structure handles both traditional fixed-term programs and flexible competency-based programs, adapting to different term structures, learning paces, and student populations without requiring separate specialized models, thereby maintaining ease of operation while enhancing adaptability.
Solution Approach 2:
The patent implements segmentation by dividing the student population into distinct segments based on their learning modality (traditional vs. competency-based) and progress patterns. This segmentation allows the system to apply appropriate prediction parameters to each group while using a unified operational framework, balancing adaptability to diverse needs with operational simplicity through standardized processes.
3Loss of information
If traditional predictive models are used, then computational efficiency is maintained, but ability to provide personalized insights for competency-based learning deteriorates
Solution Approach 1:
The patent applies the extraction principle by isolating and focusing computational resources on extracting personalized insights specifically relevant to competency-based learning from the broader student data set. Rather than computing all possible predictions uniformly, the system extracts and prioritizes personalized progress predictions for students in flexible term structures, maintaining computational efficiency while reducing information loss for targeted personalized insights.
Data Source
AI summary
A flexible persistence modeling system and method for building flexible persistence models for education institutions using a Markov model based on units of academic progress of a non-traditional learning program of an education institution. The Markov model is used to quantify transitions of students between the states as parameters of state transitions so that features from the Markov model with the parameters of state transitions can be extracted that are related to the non-traditional learning program of the education institution using defined flexible persistence. The extracted features can then be used to build at least one flexible persistence model for different segments of the students.