Task Title: Ecosystem Balancing Act: Predators, Prey, and Population Dynamics
Grade: High School
Date: 2024
SEP: Using Mathematics and Computational Thinking
DCI: LS2.A Interdependent Relationships in Ecosystems, LS2.C Ecosystem Dynamics, Functioning, and Resilience
CCC: Scale, Proportion, and Quantity, Cause and Effect
Task Purpose: To assess students’ ability to use mathematical and computational representations (a predator-prey simulation model) to explain factors affecting carrying capacity and population dynamics in ecosystems.
What was in the task, where was it, and why is this evidence?
The phenomenon of fluctuating populations and ecosystem stability is inherently interesting and observable in nature. The scenario explicitly connects abstract models to real-world concepts like habitat fragmentation (islands).
The visual, interactive nature of the simulation makes the mathematical concepts accessible. The multiple variables in the simulation provide ample data for all three parts of the task.
Features of engaging, relevant, and accessible tasks:
| Features of scenarios | Yes | Somewhat | No | Rationale |
|---|---|---|---|---|
| Scenario presents real-world observations | [x] | [ ] | [ ] | The scenario presents the real-world observable phenomenon of fluctuating predator and prey populations. |
| Scenarios are based around at least one specific instance, not a topic or generally observed occurrence | [x] | [ ] | [ ] | It focuses specifically on the interaction between wolves and rabbits in varying environments. |
| Scenarios are presented as puzzling/intriguing | [x] | [ ] | [ ] | The fluctuating balance of the ecosystem and the impact of isolation are intriguing concepts to explore. |
| Scenarios create a “need to know” | [x] | [ ] | [ ] | Students need to understand how varying factors affect the balance to predict outcomes. |
| Scenarios are explainable using grade-appropriate SEPs, CCCs, DCIs | [x] | [ ] | [ ] | The scenario uses computational models to explain cause-and-effect in ecosystems. |
| Scenarios effectively use at least 2 modalities (e.g., images, diagrams, video, simulations, textual descriptions) | [x] | [ ] | [ ] | The task uses text descriptions and a highly interactive visual simulation with graphing. |
| If data are used, scenarios present real/well-crafted data | [x] | [ ] | [ ] | The simulation generates realistic, mathematical data reflecting Lotka-Volterra dynamics. |
| The local, global, or universal relevance of the scenario is made clear to students | [x] | [ ] | [ ] | The scenario explicitly connects abstract models to real-world concepts like habitat fragmentation (islands). |
| Scenarios are comprehensible to a wide range of students at grade-level | [x] | [ ] | [ ] | The visual, interactive nature of the simulation makes the mathematical concepts accessible. |
| Scenarios use as many words as needed, no more | [x] | [ ] | [ ] | The introduction and framing are concise, relying on the simulation to deliver the bulk of the information. |
| Scenarios are sufficiently rich to drive the task | [x] | [ ] | [ ] | The multiple variables in the simulation provide ample data for all three parts of the task. |
| Evidence of quality for Criterion A: [ ] No | [ ] Inadequate | [x] Adequate | [ ] Extensive |
Suggestions for improvement of the task for Criterion A:
None.
Consider in what ways the task requires students to use reasoning to engage in sense-making and/or problem solving.
Students must use reasoning to explain the shape of the growth curve (Part 1), the phase shift between predator and prey peaks (Part 2), and the vulnerability of isolated populations (Part 3).
Evidence of SEPs (which element[s], and how does the task require students to demonstrate this element in use?)
Students actively use a computational model to simulate population dynamics and analyze the resulting mathematical representations (graphs) to support their explanations.
Evidence of CCCs (which element[s], and how does the task require students to demonstrate this element in use?)
Students must identify cause-and-effect relationships by isolating variables (e.g., changing hunting efficiency and observing the effect on ecosystem stability). They also analyze scale and quantity by observing how carrying capacity limits population size.
Evidence of DCIs (which element[s], and how does the task require students to demonstrate this element in use?)
Students demonstrate understanding of LS2.A and LS2.C by explaining how interdependent relationships (predation) and resource limits (carrying capacity) drive ecosystem dynamics and resilience (or collapse in isolated environments).
Consider in what ways the task requires students to use multiple dimensions together.
To answer Question 6, students must integrate the CCC of cause and effect with the DCI of ecosystem resilience, using evidence generated from the SEP (computational model) to explain the vulnerability of isolated populations.
Consider in what ways the task explicitly prompts students to make their thinking visible (surfaces current understanding, abilities, gaps, problematic ideas).
Questions prompt students to “Describe,” “Explain,” “Compare,” and “Justify your prediction,” requiring written explanations that surface their reasoning and understanding of the modeled phenomena.
| Evidence of quality for Criterion B: [ ] No | [ ] Inadequate | [x] Adequate | [ ] Extensive |
Suggestions for improvement of the task for Criterion B:
None.
Consider specific features of the task that enable students to make local, global, or universal connections to the phenomenon/problem and task at hand. Note: This criterion emphasizes ways for students to find meaning in the task; this does not mean “interest.” Consider whether the task is a meaningful, valuable endeavor that has real-world relevance–that some stakeholder group locally, globally, or universally would be invested in.
The concept of habitat fragmentation and island biogeography (Part 3) connects the abstract model to a major global conservation issue.
Describe what modes (written, oral, video, simulation, direct observation, peer discussion, etc.) are expected/possible.
Students interact visually and kinesthetically with the simulation, analyze visual graphs, and produce written responses.
| Features | Yes | Somewhat | No | Rationale |
|---|---|---|---|---|
| Task includes appropriate scaffolds | [x] | [ ] | [ ] | The task breaks down the complex ecosystem into isolated parts (prey only, then adding predators, then adding isolation). |
| Tasks are coherent from a student perspective | [x] | [ ] | [ ] | The progression from simple carrying capacity to complex predator-prey dynamics is logical. |
| Tasks respect and advantage students’ cultural and linguistic backgrounds | [x] | [ ] | [ ] | The language is straightforward and relies heavily on universally understood visual data. |
| Tasks provide both low- and high-achieving students with an opportunity to show what they know | [x] | [ ] | [ ] | The synthesis question (Q7) provides a high ceiling for advanced students to extrapolate. |
| Tasks use accessible language | [x] | [ ] | [ ] | Complex terms are defined in context. |
Consider how the task cultivates students interest in and confidence with science and engineering, including opportunities for students to reflect their own ideas as a meaningful part of the task; make decisions about how to approach a task; engage in peer/self-reflection; and engage with tasks that matter to students.
The interactive nature of the simulation allows students to test their own “what-if” scenarios, fostering curiosity and confidence in using computational tools.
Consider the ways in which provided information about students’ prior learning (e.g., instructional materials, storylines, assumed instructional experiences) enables or prevents students’ engagement with the task and educator interpretation of student responses.
The task assumes basic familiarity with reading line graphs, which is appropriate for high school biology. The specific ecological concepts are developed through the simulation itself.
Describe evidence of scientific inaccuracies explicitly or implicitly promoted by the task.
The logistic growth and Lotka-Volterra predator-prey dynamics modeled in the simulation are standard, scientifically accurate representations for high school level.
| Evidence of quality for Criterion C: [ ] No | [ ] Inadequate | [x] Adequate | [ ] Extensive |
Suggestions for improvement of the task for Criterion C:
None.
Before you begin:
HS-LS2-1, HS-LS2-2. Assessing the ability to use mathematical models to explain carrying capacity and factors affecting population biodiversity.
Consider the following:
The questions are directly aligned with the PEs, requiring students to use the mathematical representation (the simulation graph) to support explanations about carrying capacity and biodiversity factors.
No significant external knowledge is required; the simulation provides the necessary data.
Yes, the written answers to the structured questions serve as artifacts demonstrating the integration of the three dimensions.
Consider what student artifacts are produced and how these provide students the opportunity to make visible their 1) sense-making processes, 2) thinking across all three dimensions, and 3) ability to use multiple dimensions together [note: these artifacts should connect back to the evidence described for Criterion B].
The written answers to the structured questions serve as artifacts demonstrating the integration of the three dimensions.
Consider how well the materials support teachers and students in making sense of student responses and planning for follow up (grading, instructional moves), consistent with the purpose of and targets for the assessment. Consider in what ways rubrics include:
(Assuming standard teacher rubrics would be paired with this task in a real classroom setting, focusing on the use of evidence from the model to support ecological claims).
The step-by-step instructions ensure students generate the necessary data without getting lost.
The open-ended questions maintain cognitive demand and allow for varied student responses.
Consider any confusing prompts or directions, and evidence for too much or too little scaffolding/supports for students (relative to the target of the assessment—e.g., a task is intended to elicit student understanding of a DCI, but their response is so heavily scripted that it prevents students from actually showing their ability to apply the DCI).
The step-by-step instructions for setting the simulation parameters ensure students generate the necessary data without getting lost, while the open-ended questions maintain cognitive demand.
| Evidence of quality for Criterion D: [ ] No | [ ] Inadequate | [x] Adequate | [ ] Extensive |
Suggestions for improvement of the task for Criterion D:
None.
Consider the task purpose and the evidence you gathered for each criterion. Carefully consider the purpose and intended use of the task, your evidence, reasoning, and ratings to make a summary recommendation about using this task. While general guidance is provided below, it is important to remember that the intended use of the task plays a big role in determining whether the task is worth students’ and teachers’ time.
The “Ecosystem Balancing Act” task effectively leverages an interactive computational model to address HS-LS2-1 and HS-LS2-2. It requires students to actively engage in the SEP of using mathematical and computational thinking to observe cause-and-effect relationships (CCC) governing carrying capacity and population dynamics (DCIs). The task is scaffolded well, moving from simple single-species dynamics to complex interacting populations, culminating in a synthesis prediction. It meets all criteria for a high-quality, three-dimensional NGSS task.
Final recommendation (choose one):