Learning From and With Multiple Representations: Lessons from Science Classrooms

1. Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

2. Objectives Provide a sense of how an interdisciplinary educational research program could be carried out. How could some of these research methods be applied to answer questions about my students’ learning? Share practical ideas that can be applied in your classrooms How could these applications be applied to support student learning in my classrooms?

3. Overview • Theoretical Framework • Principles underlying research hypotheses • 2 strands of research • Online Physiology Tutorials • Engineering Modeling Problems John Waters, Richard Cyr Tom Litzinger, Chris Masters, Steve Turns

4. Nonverbal representations are common in STEM courses.

5. Recognize verbal-nonverbal correspondence Theoretical Framework: Cognitive Theory of Multimedia Learning Mental Model constructed by integration of verbal/ nonverbal representations and prior knowledge. Verbal Concepts Organized Verbal Concepts Selected Verbal Text Nonverbal Representation

6. Students have great difficulty constructing integrated mental models. • Integration is highly demanding. • can easily overload students’ cognitive resources • Students rely on surface feature similarities to determine the match between representations. • miss the deep conceptual relationship between representations • Students find it challenging to move between representations.

7. Theoretical Framework: Self-regulated Learning Task Cues Products Set Goals Tactics & Strategies Monitoring

8. Two main hypotheses guide my thinking. • Student learning and problem solving with nonverbal representations improves when students use strategies. • Nonverbal strategies can be taught by using the same principles that guide instruction of verbal strategies. These hypotheses predict that student learning and problem solving with nonverbal representations will improve if we direct them to strategically process these representations.

9. Students in the statics course learn to solve analysis problems.

10. Our research in statics has involved several different research studies. • Pilot verbal protocol study • Identify major cognitive processes of modeling • Cluster analysis • Explored effects of individual difference variables • Verbal protocol study • Compared self-regulated learning processes of strong and weak students • Design experiments • Design and testing of an intervention

11. Initial studies to identify reasons students struggle with analysis. • Cluster analysis • Individual difference measures • Spatial ability, gender, SAT, conceptual knowledge • Cluster membership accounted for only 12% of the variance in test performance • Verbal protocol (Pilot) • Students think out loud while completing analysis problems • Students struggled to connect verbal, conceptual knowledge with constructed diagrams.

12. Students must map conceptual, verbal knowledge onto diagrams.

13. Students memorize the Tables of Connections.

14. Study 3: Verbal Protocol Study Research Question: What SRL processes do successful students use to support mapping verbal, conceptual knowledge to diagrams? • All students completed 2 analysis problems • Compared 6 strong and 6 weak statics students • Groups determined by scores on experimental analysis problems and relevant exam items • Thought aloud while solving 2 analysis problems • Think alouds were videotaped • Coded tapes for cognitive and metacognitive strategies

15. Coding Categories: Cognitive Strategies • Self-explanation • strategy in which students generate their own explanations of a phenomenon • generate a causal inference that connects their prior knowledge with the state of the problem at hand • 3 types of explanations • Problem Representation explanations • Principle-based explanations • Anticipative explanations

16. Coding Categories: Metacognition • Monitoring • student becomes aware they face some obstacle • something they don’t know or are having difficulty with • awareness of obstacle is followed by efforts to correct • Evaluation • some product of the analysis problem is completed • diagram or equations •  stop to evaluate the quality of this product

17. Strong problem solvers evaluate more frequently.

18. Large differences in strong problem solvers use of self-explanation.

19. Our intervention used 3 main pedagogical tools. • Repetition of concepts across intervention problems • Embed connection types in different surface features • Prompt use of self-explanation strategy • Require causal explanation for resultant forces • Provide instructor explanations • Followed self-explanation prompts • Grounded in restriction of motion reasoning

20. Problems Instructor Explanation Contents of the Intervention

21. Students completed a posttest

22. Students who complete the intervention score higher on the posttest. MANOVA F(3, 213) = 5.94, p < .001, 2= .08 Multiple-choice F(1, 215) = 13.34, p < .001, 2= .06 Correct Selections F(1, 215) = 16.64, p < .001, 2= .07 Incorrect Selections F(1, 215) = 6.30, p < .01, 2= .03

23. What do we learn from the engineering studies? • Strategies affect students’ ability to integrate verbal, conceptual knowledge with nonverbal problem representations. • Verbal protocol analyses provided descriptive evidence • The intervention provided causal evidence •  Strategy instruction can be delivered through relatively simple online environments. •  Intervention does not •  require instructional time •  instructor expertise in strategy instruction

24. Biology studies were guided by the same hypotheses. • Learning with nonverbal representations can be improved when students use effective strategies. • Students can be taught to apply effective strategies to nonverbal representations.

25. Tutorial design is similar to common science materials. • > 2500 words • 25 diagrams • 27 pages

26. Study 1: Self-explanation and Diagram Complexity • Self-explanation Strategy • Students told to explain relationship between text and diagram • Explanations were typed • Diagram complexity • Complex images were full color Pastore, Van Meter, Gu, & Cook (in prep)

27. Posttest used 3 types of multiple-choice items. Text Questions Tested knowledge only from Text Tested knowledge only from Diagrams Diagram Questions Text-Diagram Questions Tested knowledge required Text-Diagram integration

28. Significant strategy X complexity interaction Students who self-explained while studying complex diagrams learned more.

29. Study 2: Metacognitive Instructions X Color Coding • Metacognitive Instructions • Pay attention to diagrams • Think of Text-Diagram relationships • Color Coding • Same color font labeled elements in both text and diagrams

30. Metacognitive instructions and color coding exerted independent effects. Main Effect of Color Coding Main Effect of Instructions

31. Study 3: Student-generated Drawing vs. Diagram Selection • Student-generated Drawing • 7 of 25 diagrams removed • Construct drawing of missing diagram • Diagram Selection • 7 of 25 diagrams removed • Select correct diagram from set of alternatives • Text-Diagram • Provided full tutorial • Text Only • Provided only verbal text from tutorial

32. Students who generated drawings scored higher on correspondent items. Text Only lower than all other groups Drawing higher than provided

33. Conclusions from Biology studies • Self-regulated learning processes do improve learning from multimedia materials • But, effects are limited • Self-explanation improves learning only from complex diagrams • Metacognitive instructions improves integration but not diagram learning • Drawing improves learning of content directly tied to drawings

34. Conclusions from the body of work • Objective 1: Classroom Applications • College students can be taught effective self-regulated learning processes to support the integration of verbal and nonverbal representations. • Without this instruction, students did not maximize the potential of nonverbal representations • This instruction can be embedded within instructional materials. • Does not require instructional time nor expertise • Objective 2: Development of a Research Program • A systematic program of research is important • There are qualifications for conditions under which these instructions are effective

35. Summary Points • Learning in STEM disciplines requires students to understand a variety of nonverbal representations. • Students’ ability to understand and use these representations is often below hoped for levels. • Our work suggests that students benefit from instruction that prompts the application of learning strategies to nonverbal representations. • Learning improved when students were told to think about the relationships between verbal text and nonverbal diagrams. • Learning improved when students were prompted to apply a self-explanation strategy toward nonverbal representations. • Learning improved when students used a drawing construction strategy. • We encourage instructors to think about the ways in which nonverbal representations are used in their classrooms and to consider how they might help students to make better use of these representations.