Your browser (Internet Explorer 8 or lower) is out of date. It has known security flaws and may not display all features of this and other websites. Learn how to update your browser.

X

Menu

Education

Clone of Version 10: Hattie Calibrated Education Scenario Tool Capability Demonstration

Robert L. Brown
Despite a mature field of inquiry, frustrated educational policy makers face a crisis characterized by little to no clear research-based guidance and significant budget limitations --  in the face of too often marginal or unexpectedly deleterious achievement impacts. As such, education performance has been acknowledged as a complex system and a general call in the literature for causal models has been sounded. This modeling effort represents a strident first step in the development of an evidence-based causal hypothesis: an hypothesis that captures the widely acknowledged complex interactions and multitude of cited influencing factors. This non-piecemeal, causal, reflection of extant knowledge engages a neuro-cognitive definition of students.  Through capture of complex dynamics, it enables comparison of different mixes of interventions to estimate net academic achievement impact for the lifetime of a single cohort of students. Results nominally capture counter-intuitive unintended consequences: consequences that too often render policy interventions effete. Results are indexed on Hattie Effect Sizes, but rely on research identified causal mechanisms for effect propagation. Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Relative magnitudes of impact have been  roughly adjusted to Hattie Ranking Standards (calibration): a non-causal evidence source. This is a demonstration model and seeks to exemplify content that would be engaged in a full or sufficient model development effort.  Budget & time constraints required significant simplifying assumptions. These assumptions mitigate both the completeness & accuracy of the outputs. Features serve to symbolize & illustrate the value and benefits of causal modeling as a performance tool.

Education Policy Causal Dynamic Intervention Impact

  • 3 years 5 months ago

Clone of How tutors help the learner causal loop

Steven V. Schneider, Ph.D.

This paints a broad picture for my non-profit of how tutoring helps disadvantaged youth and, with the right jump-start from a caring individual (R1 point), how learning can get learning and skill begets further skill. I appreciate any feedback to modifications because they might shape program direction.

Future iterations will show the low skilled isolated individual gets stuck in a cycle of "no-growth." I would also like to explore the dynamics of how the learner reduces dependence on the tutor.

Causal Loop Learning Education Nonprofit

  • 4 years 8 months ago

Clone of S-Curve + Delay for Bell Curve by Guy Lakeman

Ray Madachy
​S-Curve + Delay for Bell Curve Showing Erlang Distribution
Generation of Bell Curve from Initial Market through Delay in Pickup of Customers
This provides the beginning of an Erlang distribution model

The Erlang distribution is a two parameter family of continuous probability distributions with support . The two parameters are:

  • a positive integer 'shape' 
  • a positive real 'rate' ; sometimes the scale , the inverse of the rate is used.

MATHS Statistics Physics Science Ecology Climate Weather Intelligence Education Probability Density Function Normal Bell Curve Gaussian Distribution Democracy Voting Politics Policy Erlang

  • 1 year 7 months ago

Clone of Version 8B: Calibrated Student-Home-Teachers-Classroom-LEA-Spending

Yolande Tra
This version 8B of the CAPABILITY DEMONSTRATION model. A net Benefit ROI has been added. The Compare results feature allows comparison of alternative intervention portfolios.  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Relative magnitudes and durations of impact remain in need of further data & adjustment (calibration). In the interests of maintaining steady progress and respecting budget & time constraints, significant simplifying assumptions have been made: assumptions that mitigate both completeness & accuracy of the outputs.  This model meets the criteria for a Capability demonstration model, but should not be taken as complete or realistic in terms of specific magnitudes of effect or sufficient build out of causal dynamics.  Rather, the model demonstrates the interplay of a minimum set of causal forces on a net student progress construct -- as informed and extrapolated from the non-causal research literature. Provided further interest and funding, this  basic capability model may further developed and built out to: higher provenance levels -- coupled with increased factorization, rigorous causal inclusion and improved parameterization.

Education Policy Causal Dynamic Intervention Impact

  • 3 years 6 months ago

Clone of Bio103 Predator-Prey Model ("Lotka'Volterra")

Celil Ekici

Dynamic simulation modelers are particularly interested in understanding and being able to distinguish between the behavior of stocks and flows that result from internal interactions and those that result from external forces acting on a system.  For some time modelers have been particularly interested in internal interactions that result in stable oscillations in the absence of any external forces acting on a system.  The model in this last scenario was independently developed by Alfred Lotka (1924) and Vito Volterra (1926).  Lotka was interested in understanding internal dynamics that might explain oscillations in moth and butterfly populations and the parasitoids that attack them.  Volterra was interested in explaining an increase in coastal populations of predatory fish and a decrease in their prey that was observed during World War I when human fishing pressures on the predator species declined.  Both discovered that a relatively simple model is capable of producing the cyclical behaviors they observed.  Since that time, several researchers have been able to reproduce the modeling dynamics in simple experimental systems consisting of only predators and prey.  It is now generally recognized that the model world that Lotka and Volterra produced is too simple to explain the complexity of most and predator-prey dynamics in nature.  And yet, the model significantly advanced our understanding of the critical role of feedback in predator-prey interactions and in feeding relationships that result in community dynamics.The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:
1. The prey population finds ample food at all times.2. The food supply of the predator population depends entirely on the size of the prey population.3. The rate of change of population is proportional to its size.4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.5. Predators have limitless appetite.As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]
Prey
When multiplied out, the prey equation becomesdx/dt = αx - βxy The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Education Chaos Ecology Biology Population

  • 2 years 11 months ago

Education simulation

David Charles
A simple Agent based model of modes of education.The society has two categories of people: the informal and the formal people. At the same time we have formal and informal modes of education. The formal includes going to school while informal includes socialization.

Education

  • 1 year 3 months ago

Clone of Clone of Student Achievement

Evija
In this model I am trying to depict the multiple factors and interactions that impact student academic achievement.  As educators, our goal is to optimize the progression of academic achievement, or as represented in this stock flow diagram maintain the stock (academic achievement) at the highest level.  Multiple factors enhance achievement and, conversely, multiple factors interact to reduce the stock/rate of achievement.  As individual teachers, we must understand the factors and relationships that increase and decrease achievement.  In particular, teachers in training need to begin to build a mental model of these factors and relationships.  Only then can we optimize our individual learning environments to ensure each child reaches his/her academic achievement potential.

Education

  • 5 years 9 months ago

Clone of Student Achievement

Cheryl
In this model I am trying to depict the multiple factors and interactions that impact student academic achievement.  As educators, our goal is to optimize the progression of academic achievement, or as represented in this stock flow diagram maintain the stock (academic achievement) at the highest level.  Multiple factors enhance achievement and, conversely, multiple factors interact to reduce the stock/rate of achievement.  As individual teachers, we must understand the factors and relationships that increase and decrease achievement.  In particular, teachers in training need to begin to build a mental model of these factors and relationships.  Only then can we optimize our individual learning environments to ensure each child reaches his/her academic achievement potential.

Education

  • 7 years 3 months ago

Clone of Student Achievement

Te Kou Gage
In this model I am trying to depict the multiple factors and interactions that impact student academic achievement.  As educators, our goal is to optimize the progression of academic achievement, or as represented in this stock flow diagram maintain the stock (academic achievement) at the highest level.  Multiple factors enhance achievement and, conversely, multiple factors interact to reduce the stock/rate of achievement.  As individual teachers, we must understand the factors and relationships that increase and decrease achievement.  In particular, teachers in training need to begin to build a mental model of these factors and relationships.  Only then can we optimize our individual learning environments to ensure each child reaches his/her academic achievement potential.

Education

  • 7 years 3 days ago

Pages