The Astrophysical Fluency Project A Framework for Generating

The Data • 353 multiple-choice questions • used in Think-Pair-Share (TPS) or Peer Instruction

Research Progression: • initially: determine the characteristics of the questions in the data set

Representations: ways that information is conveyed 1. words a. b. written spoken 2. pictures

Intellectual Tasks: cognitive exercises the learner engages in 1. 2. 3. 4. 5. 6.

Question Complexity Rubric (QCR): richness of the conversation among learners defending the answer

Research Progression: • progression: recognize a lack of high-complexity questions overall and that some,

Research Progression: • currently: our framework goes much deeper than the levels we’ve seen

From simpler to more complex n o i t s ue q g n

From simpler to more complex Fluency-inspiring questions • require learners to extract information from

Conclusions Immediate implications: use the framework like a tool. • Systematic way to characterize

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The Astrophysical Fluency Project: A Framework for Generating Fluency-Inspiring Opportunities Rica Sirbaugh French (Mira. Costa College + Center for Astronomy Education) Edward Prather (University of Arizona + Center for Astronomy Education)

The Data • 353 multiple-choice questions • used in Think-Pair-Share (TPS) or Peer Instruction (PI) • created by faculty in professional development workshops • Center for Astronomy Education Teaching Excellence Workshops • 41 workshops from 2005 -2015 • 293 questions Earth-Sun-Moon Renaissance Astronomy Solar System Light Stars Exoplanets & Life in the Universe • Galaxies & Cosmology • • •

The Data • 353 multiple-choice questions • used in Think-Pair-Share (TPS) or Peer Instruction (PI) • created by faculty in professional development workshops • Center for Astronomy Education Teaching Excellence Workshops • 41 workshops from 2005 -2015 • 293 questions • AAPT/APS/AAS Workshops for New Faculty in Physics and Astronomy • 4 workshops from 2015 -2017 • 60 questions Earth-Sun-Moon Renaissance Astronomy Solar System Light Stars Exoplanets & Life in the Universe • Galaxies & Cosmology • • •

Research Progression: • initially: determine the characteristics of the questions in the data set 1. What kinds and how many different ways of conveying information are used in the question? ü “representations” 2. What kinds and how many different cognitive exercises must the learner engage in to answer the question? ü “intellectual tasks” 3. How rich of a conversation would this question promote among learners attempting to explain and defend their answers? ü “Question Complexity Rubric (QCR)” Ø Develop framework to systematically characterize and catalog the diversity of representations and intellectual tasks and identify levels of complexity.

Representations: ways that information is conveyed 1. words a. b. written spoken 2. pictures & diagrams a. b. c. d. 3. 4. 5. 6. photographs static images figures sketches graphs & charts tables mathematical formalism numbers 7. animations & simulations a. b. moving pictures and/or diagrams with no user interaction simulators with user interaction mechanism(s) 8. recordings of reality a. b. video audio 9. gestures (real-time) a. b. facial expressions body movements

Intellectual Tasks: cognitive exercises the learner engages in 1. 2. 3. 4. 5. 6. 7. 8. visualize draw/sketch model compare identify predict extrapolate count 9. 10. 11. 12. 13. 14. 15. rank sort match quantitative reasoning calculate apply/analyze write

Question Complexity Rubric (QCR): richness of the conversation among learners defending the answer

Research Progression: • progression: recognize a lack of high-complexity questions overall and that some, but not all topics are missing particular representations and/or intellectual tasks Ø Identify variables missing from different topics. Ø Are we even using the representations that offer the right affordances for developing fluency in that topic? Ø Generate new questions to fill gaps. Ø Generate questions that combine representations and tasks in ways not seen in the data.

Research Progression: • currently: our framework goes much deeper than the levels we’ve seen and used so far • The framework has helped us learn how to create high-level QCR questions for different topics. Ø Exploit the framework to systematically do all of the previous things and help us create new pathways of intellectual engagement for moving learners toward fluency. Ø Explore potential for more pedagogically powerful questions.

From simpler to more complex

From simpler to more complex n o i t s ue q g n ri i p s n -i y c n Flue

From simpler to more complex Fluency-inspiring questions • require learners to extract information from and transfer it between multiple different representations, • engage learners in multiple intellectually rigorous tasks, and • promote complex discourse to justify an answer.

From simpler to more complex n o i t s ue q g n ri i p s n -i y c n Flue

Conclusions Immediate implications: use the framework like a tool. • Systematic way to characterize and catalog the representations, tasks, and levels of complexity in multiple-choice TPS/PI questions. • For particular topics, identify gaps in usage of particular representations and tasks. • Generate new questions to fill those gaps. • Generate fluency-inspiring questions Deeper implications: the framework is not simply a tool for making new questions! It is truly generative in that it § informs how we think about our disciplines and § provides pathways for exploring and creating more pedagogically interesting and powerful opportunities ( fluencyinspiring opportunities) opportunities for learners to unpack complex concepts, practice critical discernment, and develop fluency. It is easily extended to other types of instructional materials, methods, and disciplines.

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