Predict: Staff ask youth to make predictions, conjectures, or hypotheses (e.g., “if you …, then what will happen?”)
Model: Staff support youth in using a simulation, experiment, or model to answer questions, explore solutions, or test hypotheses (e.g., Youth run a robotics program to determine whetherit does what they expect it to; Youth try an alternate way to solve an equation and test their results against another example, etc.)
Analyze: Staff support youth in analyzing data to draw conclusions (e.g., after an experiement, youth are asked to use results to make a generalization like “Your heartbeat increases when you exercise”, etc.)
Measure: Staff support youth in collecting data or measuring (e.g., Youth use rulers or yardsticks to measure length; Youth count the number of different species of birds observed in a specific location, etc.)
Tools: Staff support youth in using tools of the field (e.g., youth use calculators for mathematics; ph-tests for biology; woodworking tools for building, etc.)
Precision: Staff highlight value of precision and accuracy in measuring, observing, recording, or calculating (e.g., measurement error can impact an experiment or conclusion; measure twice, cut once; scientist always need to double-check their claculations before drawing conclusions; you must observe carefully to see the difference between various species of sparrows, etc.)
Vocabulary: Staff model use of STEM vocabulary terms (e.g., SCIENCE - chlorophyll, density, atomic, nuclear, geologic, light year; ENGINEERING - torgue, currents, force; MATH - rate of change, slope, percent, etc.)
Classification: Staff support youth in using classification and abstraction, linking concrete examples to principles, laws, categories, and formulas (e.g., Mice, porcupines, and squirrels are all rodents, rodents are all mammals; The pool ball moved because for every action, there is an equal and opposite reaction; etc.)
Symbols: Staff support youth in conveying STEM concepts through symbols, models, or other nonverbal language (e,g, youth use diagrams, equations, flowcharts, outlines, mock-ups, desgin software, dioramas, physical models, prototypes, graphs, charts, tables, equations, etc.)
Asking questions or defining problems: Discussing topics to investigate and pose questions.
Constructing measures of phenomena: Figuring out how or why to inscribe as data an observation about phenomena and generating coding frames or measurement tools.
Collecting data: Recording observations as data and accessing already-created data.
Data modeling: Understanding and explaining phenomena using models of the data.
Interpreting and communicating findings: Discussing and sharing and presenting findings.
## Numeric Variables
## # A tibble: 9 x 13
## var type missing complete n mean sd min
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 analyze integer 0 236 236 0.1483051 0.3561575 0
## 2 classification integer 0 236 236 0.2584746 0.4387266 0
## 3 measure integer 0 236 236 0.2245763 0.4181899 0
## 4 model integer 0 236 236 0.2881356 0.4538571 0
## 5 precision integer 0 236 236 0.3771186 0.4856951 0
## 6 predict integer 0 236 236 0.3898305 0.4887483 0
## 7 symbols integer 0 236 236 0.4279661 0.4958355 0
## 8 tools integer 0 236 236 0.3516949 0.4785142 0
## 9 vocabulary integer 0 236 236 0.6822034 0.4666096 0
## # ... with 5 more variables: `25% quantile` <dbl>, median <dbl>, `75%
## # quantile` <dbl>, max <dbl>, hist <chr>
Asking questions or defining problems and Predict
Constructing measures of phenomena and Classification and Precision
Collecting data and Measure and Tools (maybe)
Data modeling and Model and Symbols
Interpreting and communicating findings and Symbols and Analyze
One way I can combine my coding frame (focused on what students are doing) and the PQA codes that align with the aspects of working with data in my coding frame is to consider the PQA codes as moderators for each of the codes, whereby each of the work with data activities can be considered in terms of whether students are doing them and whether they are doing them with support. I think the modeling is a bit more complex but I really like this idea. I will think through how to write up the data analysis section in my dissertation proposal, if this sounds good.