#### Learning Expertise and Knowledge Structures

##### Geoff McDonnell ★

The relationship between chains of practice and networks of understanding, with threshold concepts as the transforming link. Adapted from Kinchin, I.M. and Cabot, L.B. (2012) Supporting the expert student. Paper presented at the 4th Annual Conference on Higher Education Pedagogy, 8th – 10th February, Virginia Tech., Blacksburg, VA, USA. Available

- 2 months 5 days ago

#### ATLAS: Population Dynamics

##### Kai Zhuang

- 5 years 11 months ago

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

##### Robert L. Brown

*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***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.*- 3 years 9 months ago

#### Version 11: Hattie Calibrated Education Scenario Tool Capability Demonstration

##### Yolande Tra

*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.

- 3 years 8 months ago

#### Model effects on student numbers

##### Don Fernando

- 5 years 8 months ago

#### Scenario 2 Take Two

##### Meredith Seaman

- 3 years 2 months ago

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

##### Sean R Westley

**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.

PredatorsThe 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.

- 1 year 4 months ago

#### Mobility_JV1

##### Jan Voracek

- 6 months 3 weeks ago

#### Community Engagement Schools

##### Brian Dowling

- 7 years 9 months ago

#### Learning Level Two

##### Richard Turnock

- 4 years 11 months ago

#### Version 6A NULL PRACTICE Student-Home-Teachers-Classroom

##### Robert L. Brown

*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***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.*- 3 years 10 months ago

#### Launderette Story Episode 2

##### Ante Prodan

- 3 years 4 months ago

#### Impact Model - Jack

##### John Wernet

- 1 year 2 months ago

#### Behaviour management

##### Lynette Margaret Bourke

- 4 years 2 months ago

#### Education

##### Shawn Little

- 6 years 7 months ago

#### Version 9A: Hattie Calibrated Education Scenario Tool Capability Demonstration

##### Robert L. Brown

*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.

- 3 years 9 months ago

#### Ejemplo 10: Modelado de una Población v2 - Factor Limitativo

##### Miguel Angel Niño Zambrano

- 10 months 2 weeks ago

#### Learning Level Three

##### Richard Turnock

- 4 years 11 months ago

#### Insight Starting Guide for NRM 320

##### Melvin Lee Northup

- 1 year 12 months ago

#### Behaviour flow chart

##### Karen Thompson

- 4 years 2 months ago

#### Assignment Pork Consumption

##### Eduardo

- 6 years 4 months ago

#### Version 8: Calibrated Student-Home-Teachers-Classroom-LEA-Spending

##### Yolande Tra

*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***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.*- 3 years 9 months ago

#### Scenario 2 5 November

##### Meredith Seaman

- 3 years 2 months ago

#### Temp Don

##### Don Fernando

- 5 years 8 months ago