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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Mechanistic Models: Overview of Compartment Models01:21

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118
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Improving models for student retention and graduation using Markov chains.

Mason N Tedeschi1, Tiana M Hose2, Emily K Mehlman3

  • 1New College of Florida, Sarasota, Florida, United States of America.

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|June 26, 2023
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Summary

A Markov model accurately estimates graduation rates for underrepresented students. Learning Assistants in science courses increased six-year graduation rates by 9%, with larger gains for minority and first-generation students.

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Area of Science:

  • Educational research
  • Higher education studies
  • Academic intervention analysis

Background:

  • Graduation rates are critical for evaluating academic interventions.
  • Traditional methods struggle with small sample sizes and data demands for underrepresented groups.
  • Estimating graduation rates for minority and first-generation students presents unique challenges.

Purpose of the Study:

  • To introduce a Markov model for more reliable graduation rate estimation.
  • To assess the impact of Learning Assistants on student graduation.
  • To address inequalities in academic success for underrepresented students.

Main Methods:

  • Utilized a Markov model to analyze graduation rate data.
  • Employed a Learning Assistant program as a case study.
  • Compared outcomes for underrepresented minority and first-generation students.

Main Results:

  • The Markov model enhances confidence and reduces bias in graduation rate estimates.
  • Learning Assistants correlated with a 9% increase in six-year graduation rates.
  • Gains were more significant for underrepresented minority (21%) and first-generation students (18%).

Conclusions:

  • Learning Assistants effectively improve overall graduation rates.
  • The Markov model provides a robust tool for intervention assessment.
  • Academic interventions can successfully reduce graduation rate disparities.