Education | Insight Maker

Summary and Causal loop diagram of Paulo Freire's 1968 book . See also Youtube video esp Chomsky, Freire 1998 Cultural Action for Freedom publication and Zhong Yaying ubc 2018 thesis

Pedagogy of the Oppressed

Geoff McDonnell

6 months ago

This diagram shows the impact of government policy and funding on a student's access to post-secondary education and the institution's health and wellness services.

P1: Systemigram

Chieng Luphuyong

Predator-prey models are the building masses of the bio-and environments as bio masses are become out of their asset masses. Species contend, advance and scatter essentially to look for assets to support their battle for their very presence. Contingent upon their particular settings of uses, they can take the types of asset resource-consumer, plant-herbivore, parasite-have, tumor cells- immune structure, vulnerable irresistible collaborations, and so on. They manage the general misfortune win connections and thus may have applications outside of biological systems. At the point when focused connections are painstakingly inspected, they are regularly in actuality a few types of predator-prey communication in simulation.

Looking at Lotka-Volterra Model:

The well known Italian mathematician Vito Volterra proposed a differential condition model to clarify the watched increment in predator fish in the Adriatic Sea during World War I. Simultaneously in the United States, the conditions contemplated by Volterra were determined freely by Alfred Lotka (1925) to portray a theoretical synthetic response wherein the concoction fixations waver. The Lotka-Volterra model is the least complex model of predator-prey communications. It depends on direct per capita development rates, which are composed as f=b−py and g=rx−d.

A detailed explanation of the parameters:

The parameter b is the development rate of species x (the prey) without communication with species y (the predators). Prey numbers are reduced by these collaborations: The per capita development rate diminishes (here directly) with expanding y, conceivably getting to be negative.
The parameter p estimates the effect of predation on x˙/x.
The parameter d is the death rate of species y without connection with species x.
The term rx means the net rate of development of the predator population in light of the size of the prey population.

Reference:

http://www.scholarpedia.org/article/Predator-prey_model

Lotka-Volterra Model: Prey-Predator Simulation

Pavan Kumar Guntur

21

Strategic Planning Model

Thaddeus Hunt

Clone of Mobility_JV1

Martina Chalupová

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.

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

Yolande Tra

This model is under construction, not at all ready, don't use it for any purposes (my suggestion ☺) yet.

Clone of adazhi under construction

Evija Mirke

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.

Student Achievement

David Olsen

3

Physical meaning of the equations

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 becomes

dx/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.

Clone of Prey&Predator

reno marcello m

Grimm's ODD and Nate Osgood's ABM Modeling Process and Courses See also Pattern Oriented Modelling IM-3834

Clone of The Modelling Process

Ismael Victor Costa

WIP based on Raafat Zaini's Triple Helix and PhD Colloquium and ISDC ithink models as a starting point for health care systems science modelling growth dynamics

Clone of Startup University Model

Jim Rogers

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 becomes

dx/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.

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

John Petersen

252

This version of the CAPABILITY DEMONSTRATION model has been further calibrated (additional calibration phases will occur as better standardized data becomes available). 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 de-abstracted and built out to: higher provenance levels -- coupled with increased factorization, rigorous causal inclusion and improved parameterization.

Clone of Version 6A: Calibrated Student-Home-Teachers-Classroom

JMantilla

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.

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

JMantilla

The lack of recognition of efforts is a plague of society, an immense amount of effort goes unrecognized. Meaning there is constant input on behalf of an individual with a varying degree of output. This is a highly concerning situation for the system as a whole. For a positive feedback loop to take place, inputs must balance with outputs, respectively.

In this model, inputs balance with outputs creating a dynamic contribution.

A Comprehensive System for Information Management

Rayne Man

Crea un Bucle de Realimentación Negativa, modelando el llenado de un vaso con agua. Esta versión incluye el concepto de manejo de tabla o no liberalidad.

Universidad del Cauca.

Profesor: Miguel Angel Niño Zambrano

curso: Enlace Curso en Moodle

Videos ejemplos: Enlace a la lista de videos del curso youtube

Ejemplo 2: Llenar vaso con agua - No Linealidad - Tabla

Miguel Angel Niño Zambrano

9

The probability density function (PDF) of the normal distribution or Bell Curve of Normal or Gaussian Distribution is the mean or expectation of the distribution (and also its median and mode).

The parameter is its standard deviation with its variance then, A random variable with a Gaussian distribution is said to be normally distributed and is called a normal deviate.

However, those who enjoy upskirts are called deviants and have a variable distribution :)

A random variable with a Gaussian distribution is said to be normally distributed and is called a normal deviate.

If mu = 0 and sigma = 1

If the Higher Education Numbers Are Increased then the group decision making ability of society would be raised above that of a middle teenager as it is now

BUT

Governments can control children by using bad parenting techniques, pandering to the pleasure principle, so they will make higher education more and more difficult as they are doing

85% of the population has a qualification level equal or below a 12th grader, 17 year old ... the chance of finding someone with any sense is low (~1 in 6) and the outcome of them being chosen by those who are uneducated in the policies they are to decide is even more rare !!!

Experience means little if you don't have enough brain to analyse it

Democracy is only as good as the ability of the voters to FULLY understand the implications of the policies on which they vote., both context and the various perspectives. National voting of unqualified voters on specific policy issues is the sign of corrupt manipulation.

Democracy: Where a group allows the decision ability of a teenager to decide on a choice of mis-representatives who are unqualified to make judgement on social policies that affect the lives of millions.

The kind of children who would vote for King Kong who can hold a girl in one hand and swat fighter jets out of teh sky off the tallest building, doesn't have a brain cell or thought to call his own but has a nice smile and offers little girls sweets.

updated 16/3/2020 from 4 years 3 months ago

Clone of The probability density function (PDF) of the normal distribution or Bell Curve Gaussian Distribution by Guy Lakeman

Nilo Guimaraes

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.

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

Cristen Jones

Escape behavior often results in more stress than one escapes from.

@LinkedIn, Twitter, YouTube

Classroom Behaviour Model - Basic

Gene Bellinger

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.

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

JMantilla

Trying to use a diagram to show that a bad "early start" leads to low rates of progress/yr. With low rates of progress/year the kiddos fall much shorter than their peers and can't meet the milestones expected for the cohort. Failing to meet these, they fail and other negative long-term consequence are likely to follow.

Model of how kiddos who start behind can stay behind

Evan

Perceptual Control Theory Model of Balancing an Inverted Pendulum. See Kennaway's slides on Robotics. as well as PCT example WIP notes. Compare with IM-1831 from Z209 from Hartmut Bossel's System Zoo 1 p112-118

Balancing an Inverted Pendulum PCT Model

Geoff McDonnell

3

From "A Causal Model of Organizational Performance and Change," By Burke, W. W. & Litwin, G.H., In Journal of Management, 18, pp. 523-545.

Clone of Organizational Change

migueldelestal

Summary of Thorstein Veblen's 1916 Book The Higher Learning in America pdf

Clone of Higher Learning

Josh Hawley

Education Models