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Design Pattern for Observational Investigation | Design Pattern 2167 [ | ]
Title
Design Pattern for Observational Investigation
Overview  
This design pattern supports the writing of tasks that address scientific reasoning and process skills in observational (non-experimental) investigations. Observational investigations differ from experimental investigations. In experimental investigations, it is necessary to control or manipulate one or more of the variables of interest to test a prediction or hypothesis; in observational investigations, variables typically cannot be altered at all (e.g., objects in space) or in a short time frame (e.g., a lake ecosystem). This design pattern may be used to generate groups of tasks for any science content strand.
Use glossary
  U1.
This design pattern supports the construction of tasks that address observational investigations - that is, investigations where experimental methods are not appropriate (e.g., earth and space science, demography, paleoanthropology, physiology, ecology). In order for students to have a well-rounded understanding of the scientific method, they need to be familiar with the context and methods of observational investigations.   details
Focal knowledge, skills, and abilities glossary
Fk1.
Ability to analyze why observational investigation methods are more appropriate than experimental methods for some phenomena/situations   details
Fk2.
Ability to distinguish between observational and experimental methodology   details
Fk3.
Ability to generate or evaluate predictions or hypotheses about scientific phenomena that are appropriate for observational investigation   details
Fk4.
Ability to formulate conclusions, create models, and appropriately generalize results from observational investigations   details
Fk5.
Ability to test predictions or hypotheses using observational methods   details
Fk6.
Ability to plan a systematic collection of observational data based on a predicted relationship
Fk7.
Ability to collect, analyze, and interpret observational data with appropriate tools
Additional knowledge, skills, and abilities glossary
Ak1.
Content knowledge (may be construct relevant)   details
Ak2.
Prerequisite knowledge from earlier grades   details
Ak3.
Data collection and analysis   details
Ak4.
Representational forms (e.g., graphs, maps)   details
Potential observations glossary
Po1.
Appropriateness/strength of observational evidence to help confirm or disconfirm a prediction or hypothesis   details
Po2.
Accuracy in identifying the effects of an observed active phenomenon and how these effects are consistent with a posited cause and effect relationship   details
Po3.
Correctness of recognized pattern in data to support a prediction or hypothesis   details
Po4.
Plausibility/correctness of explanation for observed findings   details
Po5.
Accuracy in critiquing the observational investigation methods, evidence, and conclusions of others   details
Po6.
Plausibility and systematicity of the data collection plan
Po7.
Correctness of selected tools and procedures for data collection
Po8.
Systematicity and appropriateness of collected data
Po9.
Appropriateness of measurement precision
Potential work products glossary
Pw1.
Identification or generation of a prediction or hypothesis that is appropriate to an observational investigation situation   details
Pw2.
Identification of observational settings where data could be collected to confirm or disconfirm a prediction or hypothesis   details
Pw3.
Identification of additional source of data that could confirm or disconfirm a prediction or hypothesis supported by existing data   details
Pw4.
Identification or generation of a replicable data collection process (e.g., repeated sampling over time or at several locations)   details
Pw5.
Identification of potentially disconfirming observations
Pw6.
Filling in of a representational form (e.g., a graph, chart, or map) to show the relationship among variables relevant to a prediction or hypothesis   details
Pw7.
Generation or selection of an explanation for observed findings   details
Pw8.
Critique of flawed explanation based on observations
Pw9.
Peer critique (hypothetical in a standard assessment, real in classroom work) of the observational investigation methods, evidence, and conclusions   details
Potential rubrics glossary
Characteristic features glossary
Cf1.
Scientific question not amenable to experimentation, because impractical, unethical, etc.   details
Cf2.
Presentation of a real-world situation with patterns suggesting the relationship between at least two variables that can be observed systematically (but are not amenable to experimental investigation).   details
Variable features glossary
Vf1.
Content (strand) context   details
Vf2.
Qualitative or quantitative investigations   details
Vf3.
Number of variables and the complexity of their relationships   details
Vf4.
Simple or complex investigations   details
Vf5.
Type of data representation (e.g., patterns in geographically distributed phenomena via geospatial visualizations; patterns in data; similarities in specialized representations appropriate to the scientific phenomenon)   details
Vf6.
Sufficient or insufficient data about an already established relationship   details
Vf7.
Amount of scaffolding given to student to guide the presentation or representation of data collected   details
Vf8.
Amount of observational data from which an analysis, explanation, or conclusion is to be drawn   details
Vf9.
Completeness of model given from which predictions or hypotheses can be generated   details
  Vf10.
 
  Vf11.
 
Narrative structure glossary
Cause and effect. An event, phenomenon, or system is altered by internal or external factors.
Change over time. A sequence of events is presented to highlight sequential or cyclical change in a system.
Investigation. A student or scientist completes an investigation in which one or more variables may be observed or manipulated and data are collected
Specific to general and Parts to whole. Specific characteristics of a phenomenon are presented, culminating in a description of the system or phenomenon as a whole.
National educational standards glossary
NSES 8ASI1.1. Identify questions that can be answered through scientific investigations. Students should develop the ability to refine and refocus broad and ill-defined questions. An important aspect of this ability consists of students' ability to clarify questions and inquiries and direct them toward objects and phenomena that can be described, explained, or predicted by scientific investigations. Students should develop the ability to identify their questions with scientific ideas, concepts, and quantitative relationships that guide investigation.
NSES 8ASI1.2. Design and conduct a scientific investigation. Students should develop general abilities, such as systematic observation, making accurate measurements, and identifying and controlling variables. They should also develop the ability to clarify their ideas that are influencing and guiding the inquiry, and to understand how those ideas compare with current scientific knowledge. Students can learn to formulate questions, design investigations, execute investigations, interpret data, use evidence to generate explanations, propose alternative explanations, and critique explanations and procedures.
NSES 8ASI1.3. Use appropriate tools and techniques to gather, analyze, and interpret data. The use of tools and techniques, including mathematics, will be guided by the question asked and the investigations students design. The use of computers for the collection, summary, and display of evidence is part of this standard. Students should be able to access, gather, store, retrieve, and organize data, using hardware and software designed for these purposes.
NSES 8ASI1.4. Develop descriptions, explanations, predictions, and models using evidence. Students should base their explanation on what they observed, and as they develop cognitive skills, they should be able to differentiate explanation from description, providing causes for effects and establishing relationships based on evidence and logical argument. This standards requires a subject knowledge base so the students can effectively conduct investigations, because developing explanations establishes connections between the content of science and the contexts within which students develop new knowledge.
NSES 8ASI1.5. Think critically and logically to make the relationships between evidence and explanations. Thinking critically about evidence includes deciding what evidence should be used and accounting for anomalous data. Specifically, students should be able to review data from a simple experiment, summarize the data, and form a logical argument about the cause-and-effect relationships in the experiment. Students should begin to state some explanations in terms of the relationship between two or more variables.
NSES 8ASI1.6. Recognize and analyze alternative explanations and predictions. Students should develop the ability to listen and to respect the explanations proposed by other students. They should remain open to and acknowledge different ideas and explanations, be able to accept the skepticism of others, and consider alternative explanations.
State standards glossary
State benchmarks glossary
MCA II: 6.I.A.2. The student will explain why scientists often repeat investigations to be sure of the results.
MCA II: 7.I.A.2. The student will explain natural phenomena by using appropriate physical, conceptual and mathematical models.
MCA II: 6.I.B.1. The student will identify questions that can be answered through scientific investigation and those that cannot.
MCA II: 7.I.B.1. The student will formulate a testable hypothesis based on prior knowledge.
MCA II: 6.I.B.2. The student will distinguish among observation, prediction and inference.
MCA II: 6.I.B.4. The student will present and explain data and findings from controlled experiments using multiple representations including tables, graphs, physical models and demonstrations.
MCA II: 8.I.B.1. The student will know that scientific investigations involve the common elements of systematic observations, the careful collection of relevant evidence, logical reasoning and innovation in developing hypotheses and explanations.
MCA II: 8.I.B.2. The student will describe how scientists can conduct investigations in a simple system and make generalizations to more complex systems.
MCA III: 7.1.1.1.1. Understand that prior expectations can create bias when conducting scientific investigations. For example: Students often continue to think that air is not matter, even though they have contrary evidence from investigations.
MCA III: 7.1.1.1.2. Understand that when similar investigations give different results, the challenge is to judge whether the differences are significant, and if further studies are required. For example: Use mean and range to analyze the reliability of experimental results.
MCA III: 8.1.1.1.1. Evaluate the reasoning in arguments in which fact and opinion are intermingled or when conclusions do not follow logically from the evidence given. For example: Evaluate the use of pH in advertising products related to body care and gardening.
MCA III: 7.1.1.2.1. Generate and refine a variety of scientific questions and match them with appropriate methods of investigation, such as field studies, controlled experiments, reviews of existing work and development of models.
MCA III: 7.1.1.2.3. Generate a scientific conclusion from an investigation, clearly distinguishing between results (evidence) and conclusions (explanation).
MCA III: 7.1.1.2.4. Evaluate explanations proposed by others by examining and comparing evidence, identifying faulty reasoning, and suggesting alternative explanations.
MCA III: 8.1.1.2.1. Use logical reasoning and imagination to develop descriptions, explanations, predictions and models based on evidence.
MCA III: 7.1.3.4.1. Use maps, satellite images and other data sets to describe patterns and make predictions about natural systems in a life science context. For example: Use online data sets to compare wildlife populations or water quality in regions of Minnesota.
MCA III: 8.1.3.4.1. Use maps, satellite images and other data sets to describe patterns and make predictions about local and global systems in Earth science contexts. For example: Use data or satellite images to identify locations of earthquakes and volcanoes, ages of sea floor, ocean surface temperatures and ozone concentration in the stratosphere.
I am a kind of glossary
These are kinds of me glossary
These are parts of me glossary
Templates glossary
Exemplar tasks glossary
Online resources glossary
References glossary

 

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List of Examples:

Activity    Add'l KSAs: Affective    Add'l KSAs: Cognitive    Add'l KSAs: Executive    Add'l KSAs: Language and Symbols    Add'l KSAs: Perceptual    Add'l KSAs: Skill and Fluency    Continuous Zone    Design Pattern    Educational Standard    Evaluation Phase    Evaluation Procedure (rubric)    Materials and Presentation    Measurement Model    Narrative Structure    Observable Variable    State Benchmark    State Standards    Student Model    Student Model Variable    Task Exemplar    Task Model Variable    Task Specification    Template    Variable Features: Affective    Variable Features: Cognitive    Variable Features: Executive    Variable Features: Language and Symbols    Variable Features: Perceptual    Variable Features: Skill and Fluency    Work Product   


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