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Making the Implicit Explicit in the Teaching of Chemical Equilibrium

Making the Implicit Explicit in the Teaching of Chemical Equilibrium. David Yaron, Michael Karabinos, Jodi Davenport, Jordi Cuadros Department of Chemistry, Carnegie Mellon University Gaea Leinhardt, Jim Greeno, Karen Evans Learning Research and Development Center, University of Pittsburgh.

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Making the Implicit Explicit in the Teaching of Chemical Equilibrium

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  1. Making the Implicit Explicit in the Teaching of Chemical Equilibrium David Yaron, Michael Karabinos, Jodi Davenport, Jordi CuadrosDepartment of Chemistry, Carnegie Mellon UniversityGaea Leinhardt, Jim Greeno, Karen EvansLearning Research and Development Center, University of Pittsburgh

  2. Overview of Projects • Chemcollective (www.chemcollective.org) • NSF CCLI and NSDL • Digital library of virtual labs and scenario based learning activities • Tutors and supported problem solving • Community building and support • Open Learning Initiative (www.cmu.edu/oli) • William and Flora Hewlett Foundation • Full enactment of instruction (based on chemcollective activities) • Pittsburgh Science of Learning Center (www.learnlab.org) • NSF SLC • Fundamental studies to advance the theory of learning

  3. Analysis of the domain Supporting practice Overview Initial problem analysis and selection of procedure Implementation of computation or procedure Changing the nature of practice Use technology to provide hints and feedback. Reflection on problem solving efforts

  4. Analysis of student response for common error types hints Templated feedback

  5. Pseudotutors

  6. Pseudotutors

  7. Fading Path 3 S Determine target PH Determine target [A-]/[HA] Construct solution with target [A-]/[HA] F Path 2 Determine solutions and volumes mixed. Path 1 Schematic representation of scaffolding for design of a buffer solution. Ovals represent episodes (pseudotutors or templated feedback). Support is added/faded by switching paths.

  8. Overview Can the problem solving be more connected to underlying chemical concepts. Initial problem analysis and selection of procedure Implementation of computation or procedure Goal should be fluency with concepts, not procedures. Reflection on problem solving efforts Use technology to fundamentally change the nature of practice.

  9. Virtual laboratory as a new form of practice • Flexible simulation of aqueous chemistry • New mode of interaction with chemical concepts • Ability to “see” inside a solution removes one level of indirection in chemical problem solving

  10. Taking learners beyond means-ends analysis • Typical textbook problem • “When 10ml of 1M A was mixed with 10ml of 1M B, the temperature went up by 10 degrees. What is the heat of the reaction between A and B?” • Virtual Lab problem • Thermochemistry/Camping 1: “Construct an experiment to measure the heat of reaction between A and B?” • Original design goal • The procedure is not being triggered in response to relevant prompt • Result of student observations • 4 sections of 30-45 students working alone; 4-5 instructors/observers • The Virtual Lab format requires students to go beyond a strategy of matching words to equations

  11. Observational studies: Knowledge refinement “The virtual lab contains 1M solutions of A, B, C, and D. Construct experiments to determine the reaction between these reagents” • Intent was to give practice with determining reaction coefficients A + 2B  3C + D • Observation When A is mixed with B, some A remains, 50% of students say: A + B  C + D + A Reveals fragile understanding of limiting reagent concept (even though they could easily perform textbook limiting reagent problems)

  12. Learning in a large lecture course • Study at Carnegie Mellon • Second semester intro course, 150 students • Information used • Pretest • 9 homework activities (virtual labs with templated feedback) • 3 hour exams • 2 pop exams (practice exam given 5 days before hour exam) • Final exam

  13. Correlations

  14. Regression and structural equation model • Linear regression accounts for 48% of the variance in the final grades • Influence of homework accounts for half of the model predictions • Structural equation model supports conclusions drawn from the regression

  15. Assessment within online stoichiometry course • Study design • Treatment (20): Online course including a scenario, tutors and virtual lab homework • Control (20): Paper and pencil, worked examples and practice • Assessment was traditional problem solving of quantitative stoichiometry problems, and some qualitative questions • Preliminary results • Biggest predictor of learning in online course is number of engagements with the virtual lab

  16. Overview What overall structure are we trying to convey? Initial problem analysis and selection of procedure An important role we, as chemists, can play is re-conceptualizing the domain, i.e. what should we teach, and how. Implementation of computation or procedure Reflection on problem solving efforts Goal of a high AP score is different than goal of robust learning of chemical concepts.

  17. Results from other domains • Expert blind spot • Ability to rank difficulty of math problems is worst for teachers of that subject • Geometry • Sub-goal structure of proofs was implicit knowledge (Anderson, Koedinger, Greeno..) • Statistics • Students could carry out statistical analysis procedures, but could not select appropriate procedures (Lovett)

  18. Domain analysis • 1) Utility of the domain • Get at the conceptual knowledge that is true to the domain, and will be generally useful • 2) Knowledge structure of the domain • Concepts, strategies, and procedures • Structure may not be obvious: Knowledge may be held implicitly by the expert • 3) Psychological aspects of the knowledge • What is easy and hard to learn • Based on observing student problems solving in class, student performance data, and analysis of artifacts • Also based on student interviews (think alouds) done on students who completed the course a few months to a year earlier

  19. Domain analysis for chemical literacy • Focused only on “Utility of the domain” • Standards should go beyond expert opinions of what to teach • Evidence of the domain as practiced • Nobel prizes for past 50 years (1952-2002) • NY Times Science Times for 2002 (54 reports) • Scientific American News Bites for 2002 (32 reports) • Evidence of the domain as taught • CA state content standards • Best selling textbooks

  20. EXPLAIN ANALYZE SYNTHESIZE Hypothesis Generation Goal(What do you want to know?) Functional Motifs Hypothesis Testing Process(How to determine What you have) Structural Motifs Assembly Motifs TOOLBOX Representational Systems Quantification Systems Domain map

  21. Full domain map

  22. Domain analysis Middle school through high school: Big concepts • Structure • Relation to properties • Functional groups • Emergent properties (bonding pattern  molecular interactions - 3 d structure) • Transformation • Physical transformations and chemical reactions • Energy and motion • Heat • Molecular motion Materials themes: Water, gold and plastic

  23. Domain analysis: Chemical thermodynamics 1) Utility of domain • Heat transfer and energy flow in systems is important • “Camping” scenario, of heating meals ready-to-eat 2) Knowledge structure of the domain • Heat flow from system 1  system 2 • Three processes that generate or absorb heat • Heat/cool • Phase change • Chemical reaction 3) Psychological aspects of the knowledge • Student observations suggest difficulty is correlated with “visibility” of the heat source/drain: Hardest is heat from a chemical reaction.

  24. Chemical thermodynamics instruction • Use a structured dialogue to expose a general strategy to solving heat-exchange problems. • Traditional instruction leaves this as “implicit knowledge” • Structured dialogue for heat exchange • What is the source of the heat? • How do you describe that effect: (q=m Cv DT, q=n DH, ..) • What is the drain of the heat? • How do you describe that effect: (q=m Cv DT, q=n DH, ..)

  25. Big picture of chemical thermodynamics

  26. Chemical equilibrium / Acid-base chemistry 1) Utility of the domain • How is this knowledge used in organic chemistry and molecular biology • Compare pH to pKa to determine ionization state • Buffers used to control pH (qualitative not quantitative) • Titration as an analytical technique • Current instruction 1: Almost a footnote (in the indicators section) 2-3: Coverage may not be sufficiently qualitative

  27. Chemical equilibrium / Acid-base chemistry 2) Knowledge structure • Flexibility with “progress of reaction” is required in problem analysis • General strategy can be constructed based on • First, determine concentration of “majority species” • Second, determine concentration of “minority species” 3) Psychological aspects of the knowledge • LeChatlier (especially with addition/removal of a species) is most retained concept • Broad confusion regarding “progress of reaction” • Q vs. K • Meaning of “initial” vs. “equilibrium” state

  28. Some features of the instruction • Sequencing • LeChatlier’s principle plays role of “prior knowledge” • Human respiration is scenario to which to attach “initial” vs. “equilibrium” state • Blood entering lungs and muscles experiences a new initial state • Blood leaving lungs and muscles has reached new equilibrium • Progress of Reaction • Concept of progress of reaction (and Q) introduced before K • Visualizationsused • General strategy for equilibrium problem analysis • Majority vs. Minority Species

  29. Majority/minority species

  30. Old vs new instruction

  31. Majority vs. minority species • A general strategy for equilibrium thinking/problem analysis? • Examine state of solution and select all strong reactions (K>>1) • Acid base: OH- + H+ ; HA + OH- and A- + H+ • Solubility: M+ + X- and M+ + L • Thought experiment: Assume large K’s are infinite and do a limiting reagent calculation • All species that do not go to zero, are majority species and you now know their concentration • Determine minority species, via equilibrium expressions • Replaces “small x approximation” with a conceptual framework

  32. Big picture of acid-base chemistry

  33. Back to domain analysis • How is this knowledge used in organic chemistry and molecular biology • Compare pH to pKa to determine ionization state • Buffers used to control pH (qualitative not quantitative) • Titration as an analytical technique • How is this addressed by new instruction • 1 and 2 • 3) Virtual labs involving titrations

  34. Development status • Stoichiometry • Full set of tutorials and supported problems (virtual lab and tutors released on ChemCollective and OLI) • Thermochemistry • Supported problems, based on structured dialogues (virtual labs and tutors): Fully tested and in process of release. • Equilibrium/Acid-Base • Supported problems on buffer design and mechanism (with fading): Fully tested. • Combined instruction/supported problems implementing new strategy: In final development, most has been tested.

  35. Research status • Study of the factors influencing learning in large chemistry classrooms (J. Chem. Ed., in press) • Online homework activities contribute substantially to learning • Benefits are not correlated with pre-test • Controlled study of online stoichiometry course • Karen Evans’ thesis to be defended this summer, replicate in next academic year • Virtual lab engagement strongest predictor of learning in the course • Expert/novice comparison of problem solving in acid-base chemistry (see Davenport poster) • Results influenced instructional design described here. • Controlled studies of new instructional approaches (see Davenport poster) • Majority/minority instruction improves performance on 2 A + 3 B 4 C K = 1.4 x 1010From 22% to 58% correct. (Finer grained analysis underway.) • Studies on full instructional modules being analyzed, and further studies planned.

  36. Discussion points • How different is majority/minority strategy from traditional instruction? • What aspects of the chemistry domain most need to be re-conceptualized? • Should we shift emphasis in freshman course towards literacy?

  37. Thanks To • Erin Fried • Jason Chalecki • Greg Hamlin • Brendt Thomas • Stephen Ulrich • Jason McKesson • Aaron Rockoff • Jon Sung • Jean Vettel • Rohith Ashok • Joshua Horan LRDC, University of Pittsburgh • Gaea Leinhardt • Jim Greeno • Karen Evans • Baohui Zhang Carnegie Mellon • Michael Karabinos • Jodi Davenport • Donovan Lange • D. Jeff Milton • Jordi Cuadros • Rea Freeland • Emma Rehm • William McCue • David H. Dennis • Tim Palucka • Jef Guarent • Amani Ahmed • Giancarlo Dozzi • Katie Chang Funding • NSF: CCLI, NSDL, SLC • William and Flora Hewlett Foundation • Howard Hughes Medical Institute • Dreyfus Foundation

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