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Bethany Rittle-Johnson Jon Star
It Pays to Compare: Effectively Using Comparison to Support Student Learning of Algebra Bethany Rittle-Johnson Jon Star
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Our approach to improving students’ mathematics learning
Identify instructional practices used in exemplary and typical classrooms Use cognitive science literature to focus on practices most likely to help student learning Experimentally evaluate impact of the instructional practice on student learning and develop instructional guidelines IES Conference 2008
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Potential of comparison
Mathematics Education: Central tenet of reform efforts; used by teachers Cognitive Science: A fundamental learning mechanism IES Conference 2008
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Central tenet of math reforms
Students benefit from sharing and comparing solution methods “nearly axiomatic”, “with broad general endorsement” (Silver et al., 2005) Noted feature of ‘expert’ math instruction Present in classrooms in high performing countries such as Japan and Hong Kong (Ball, 1993; Fraivillig, Murphy, & Fuson, 1999; Huffred-Ackles, Fuson, & Sherin Gamoran, 2004; Lampert, 1990; Silver et al., 2005; NCTM, 1989, 2000; Richland et al 2007; Stigler & Hiebert, 1999) IES Conference 2008
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Used in some Algebra textbooks
Sobel, M.A., Maletsky, E. M., Lerner, N., & Cohen, L.S. (1985) Algebra One, Harper and Row Inc. IES Conference 2008
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But does comparison improve student learning?
No evidence that comparison improves student learning in mathematics Cognitive science research suggests that it should… IES Conference 2008
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Comparison in cognitive science
“The simple, ubiquitous act of comparing two things is often highly informative to human learners…. Comparison is a general learning process that can promote deep relational learning and the development of theory-level explanations” (Gentner, 2005, pp. 247, 251)
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Fundamental learning mechanism
Lots of evidence from cognitive science Identifying similarities and differences in multiple examples is an important pathway to flexible, transferable knowledge Mostly laboratory studies Rarely done with school-age children or in mathematics (Catrambone & Holyoak, 1989; Gentner, Loewenstein, & Thompson, 2003; Gick & Holyoak, 1983; Kurtz, Miao, & Gentner, 2001; Loewenstein & Gentner, 2001; Namy & Gentner, 2002; Oakes & Ribar, 2005; Schwartz & Bransford, 1998) IES Conference 2008
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Does comparison support math learning?
Goal of our IES grant Investigate whether comparison can support conceptual and procedural knowledge of equation solving (and estimation) Explore what types of comparison are most effective Experimental studies in intact classrooms IES Conference 2008
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Why equation solving? Often students’ first exposure to abstraction and symbolism of mathematics Area of weakness for US students (Blume & Heckman, 1997; Schmidt et al., 1999) According to NCTM and National Math Panel Report, linear equation solving should be a focal point of math instruction in middle school Although real-world contexts and informal solution methods are powerful for simple problems, equations and equation solving are more effective for complex problems (Koedinger, Alibali & Nathan, 2008) IES Conference 2008
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Multiple methods for solving equations
Some are better than others Students tend to memorize only one method Example: Solving 3(x + 1) = 15 Method #1: 3(x + 1) = 15 3x + 3 = 15 3x = 12 x = 4 Method #2: 3(x + 1) = 15 x + 1 = 5 x = 4 IES Conference 2008
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Study 1 Research question: Does comparing solution methods improve equation solving knowledge? Research design: Randomly assigned to: Comparison condition Compare and contrast alternative solution methods Sequential condition Study same solution methods sequentially Rittle-Johnson, B. & Star, J.R. (2007). Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. Journal of Educational Psychology. IES Conference 2008
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Translation to the classroom
Students study and explain worked examples with a partner Based on core findings in cognitive science -- the advantages of: Worked examples (e.g. Sweller, 1988) Generating explanations (e.g. Chi et al, 1989; Rittle-Johnson, 2006) Peer collaboration (e.g. Fuchs & Fuchs, 2000) IES Conference 2008
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Comparison condition IES Conference 2008
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Sequential condition next page IES Conference 2008
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Predicted outcomes Students in comparison condition will make greater gains in: Procedural knowledge, including success on novel problems Procedural flexibility (e.g. use more efficient methods; evaluate when to use a procedure) Conceptual knowledge (e.g. equivalence) IES Conference 2008
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Study 1 Method Participants: 70 7th-grade students and their math teacher Design: Pretest - Intervention - Posttest Replaced 2 lessons in textbook Intervention occurred in partner work during 2 1/2 math classes Intervention: Randomly assigned to Compare or Sequential condition Studied worked examples with partner Solved practice problems on own IES Conference 2008
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Procedural knowledge assessment
Equation Solving Intervention: /3 (x + 1) = 15 Posttest Familiar: -1/4 (x – 3) = 10 Posttest Novel: (t + 3) = 0.5 IES Conference 2008
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Procedural flexibility
Use of more efficient solution methods on procedural knowledge assessment Knowledge of multiple methods Solve each equation in two different ways Evaluate methods: Looking at the problem shown above, do you think that this way of starting to do this problem is a good idea? An ok step to make? Circle your answer below and explain your reasoning. (a) Very good way Ok to do, but not a very good way (c) Not OK to do IES Conference 2008
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Conceptual knowledge assessment
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Gains in procedural knowledge
Novel - procedural transfer F(1, 31) =4.49, p < .05 IES Conference 2008
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Flexible use of procedures
Comparison students more likely to use more efficient method and somewhat less likely to use the conventional method Solution Method at Posttest (Proportion of problems) Solution Method Comparison Sequential Conventional .61~ .66 Demonstrated efficient .17* .10 ~ p = .06; * p < .05 IES Conference 2008
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Gains in flexible knowledge of procedures
Most pronounced on Evaluating non-standard items IES Conference 2008 F(1,31) = 7.73, p < .01
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Gains in conceptual knowledge
No Difference IES Conference 2008
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Summary of Study 1 Comparing alternative solution methods is more effective than sequential sharing of multiple methods Improves procedural transfer and flexibility In mathematics, in classrooms IES Conference 2008
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Comparison can help: Now what?
Replicated findings for fifth graders learning computational estimation Goal: Develop guidelines for using comparison to support mathematics learning Starting Point: Standard classroom practices IES Conference 2008
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What are teachers doing?
US teachers use comparison in 8th grade math lessons (average 4 per lesson) Types of comparisons (used with equal frequency): Compare two similar problems with same basic solution Compare two moderately similar problems or solutions Compare a problem to a mathematical rule or principle Compare a problem to a non-mathematical situation (Richland , Holyoak & Stigler, 2004 analysis of TIMSS videos) IES Conference 2008
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What are teachers doing?
May not be using comparison well Teachers, rather than students, initiate comparisons and make links between examples When they present multiple solutions, rarely provide support for or discuss comparisons Don’t know which types of comparison support learning e.g. Comparisons to contexts from different domains rarely support learning in laboratory studies. (Richland , Holyoak & Stigler, 2004; Richaland, Zur & Holyoak, 2007; Chazan & Ball, 1999) IES Conference 2008
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What about Algebra I textbooks?
Compare two similar problems with same basic solution method (Equivalent Equations) Bellman,A.E., Bragg,S.C., Charles, R.I., Hall,B., Handlin, W.G., & Kennedy, D. (2007) Algebra 1, Pearson Education Inc, Pearson Prentice Hall IES Conference 2008
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Algebra I textbooks Compare problems with different structures (Different Problem Types) Hollowell, K.A., Ellis, W., & Schultz, J.E. (1997). HRW Algebra. Holt, Rinehart, & Winston. IES Conference 2008
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Algebra I textbooks Compare different solution methods to same problem (Solution Methods) Sobel, M.A., Maletsky, E. M., Lerner, N., & Cohen, L.S. (1985) Algebra One, Harper and Row Inc. IES Conference 2008 31
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Comparison in Algebra 1 textbooks
Type of Comparison Percent of worked examples Equivalent equations (similar problems; same method) 33% Different problem types (diff probs, solved same way) 1% Solution methods (one problem solved in two ways) 19% None - single worked examples 47% This is number of individual worked examples - so in CI, these are members of pairs or triplets Informal analysis of 10 Algebra I textbooks - chapter on multi-step linear equations IES Conference 2008
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What should be compared?
Variety of comparisons are being used in math classrooms What are benefits and drawbacks to different types of comparisons? Study 1 confirms that comparing solution methods aids learning, as suggested by expert teaching practices Cognitive science literature suggests that comparing two problems solved with the same solution method should benefit learning IES Conference 2008
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Study 2 Research question: What are the relative merits of comparing solution methods vs. comparing problems? Research design: Randomly assigned to: Compare solution methods Compare problems that: Are very similar (Equivalent) Have different problem features (Different problem types) IES Conference 2008
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Types of comparison Solution Methods Problem Types Equivalent
(one problem solved in 2 ways) Problem Types (2 different problems, solved in same way) Equivalent (two similar problems, solved in same way) Take a vote - Make this slide pretty! IES Conference 2008
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Study 2 Method Participants: 161 7th & 8th grade students from 3 schools (more diverse sample) Design: Pretest - Intervention - Posttest - 2 week Retention Replaced 3 lessons in textbook Randomly assigned to Compare Solution Methods Compare Problem Types Compare Equivalent Intervention occurred in partner work Assessment adapted from Study 1 Won’t present retention results today IES Conference 2008
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Conceptual knowledge results
* F (2, 153) = 5.76, p = .004, 2 = .07 IES Conference 2008
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Procedural knowledge results
No differences, even on novel problem types IES Conference 2008
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Flexible use of procedures
* These are estimated marginal means F (2, 153) = 4.96, p = .008, 2 = .06 IES Conference 2008
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Flexible knowledge of procedures
* Students who compared solution methods scored higher than students who compared equivalent equations, p < .002, and marginally higher than students who compared problem types, p < Students who compared problem types had greater flexibility knowledge than those who compared equivalent equations, but this difference did not reach significance (p = .158). F (2, 153) = 5.01, p = .008, 2 = .07 IES Conference 2008
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Summary Across studies, Comparing Solution Methods often supported the largest gains in conceptual knowledge, procedural knowledge and procedural flexibility Supported attention to multiple methods and their relative efficiency, which both predicted learning IES Conference 2008
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Guidelines for using comparison
Provide a written record of examples Leverage current use of worked examples in textbooks Contrast important dimensions in the examples, such as problem features or solution methods Contrasting correct and incorrect solution methods can help too (Kelley Durkin, IES pre-doc research) Have students compare a familiar method to an unfamiliar method Invite comparisons by using common labels and prompting for specific comparisons, including efficiency of the methods Be sure students, not just teachers, are comparing and explaining Incorporate some direct instruction Using worked examples allows you to implement key features of effective comparison, rather than building them spontaneously during instruction - e.g. ensure contrast meaningful, not trivial differences IES Conference 2008
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What’s next? Teacher Professional Development for using comparison in Algebra I courses Type of comparison matched to prior knowledge and sequencing different types of comparison
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Acknowledgements For slides, papers or more information, contact: Funded by a grant from the Institute for Education Sciences, US Department of Education Thanks to research assistants at Vanderbilt: Holly Harris, Shanelle Chambers, Jennifer Samson, Anna Krueger, Heena Ali, Kelley Durkin, Kelly Cashen, Calie Traver, Sallie Baxter, Amy Goodman, Adam Porter, John Murphy, Rose Vick, Alexander Kmicikewycz, Jacquelyn Beckley and Jacquelyn Jones And at Michigan State: Kosze Lee, Kuo-Liang Chang, Howard Glasser, Andrea Francis, Tharanga Wijetunge, Beste Gucler, and Mustafa Demir And at Harvard: Martina Olzog, Jennifer Rabb, Christine Yang, Nira Gautam, Natasha Perova, and Theodora Chang IES Conference 2008
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