learning from and with multiple representations: lessons from science classrooms peggy van meter...

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

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

Learning From and With Multiple Representations:

Lessons from Science Classrooms

Peggy Van Meter

Educational Psychology ProgramCollege of Education

Page 2: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Objectives

1. Provide a sense of how an interdisciplinary educational research program could be carried out.

2. Share practical ideas that can be applied in your classrooms

How could some of these research methods be applied to answer questions about my students’ learning?

How could these applications be applied to support student learning in my classrooms?

Page 3: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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

Page 4: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Nonverbal representations are common in STEM courses.

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Page 5: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Theoretical Framework: Cognitive Theory of Multimedia Learning

Verbal Concepts Selected

Verbal Concepts Organized

Recognize verbal-nonverbal

correspondence

Mental Model constructed by integration of verbal/ nonverbal representations and prior knowledge.

Verbal Text Nonverbal Representation

Page 6: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Students have great difficulty constructing integrated mental models.

1. Integration is highly demanding. • can easily overload students’ cognitive resources

2. Students rely on surface feature similarities to determine the match between representations.• miss the deep conceptual relationship between

representations

3. Students find it challenging to move between representations.

Page 7: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Theoretical Framework: Self-regulated Learning

Task Cues Set Goals Tactics &

StrategiesProducts

Monitoring

Page 8: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Two main hypotheses guide my thinking.

1. Student learning and problem solving with nonverbal representations improves when students use strategies.

2. 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.

Page 9: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Students in the statics course learn to solve analysis problems.

Page 10: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Our research in statics has involved several different research studies.

1. Pilot verbal protocol study • Identify major cognitive processes of modeling

2. Cluster analysis • Explored effects of individual difference variables

3. Verbal protocol study • Compared self-regulated learning processes of

strong and weak students

4. Design experiments • Design and testing of an intervention

Page 11: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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.

Page 12: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Students must map conceptual, verbal knowledge onto diagrams.

Page 13: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Students memorize the Tables of Connections.

Page 14: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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

Page 15: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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

Page 16: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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

Page 17: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Strong problem solvers evaluate more frequently.

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Page 18: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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

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Page 19: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Our intervention used 3 main pedagogical tools.

1.Repetition of concepts across intervention problems• Embed connection types in different surface features

2.Prompt use of self-explanation strategy• Require causal explanation for resultant forces

3.Provide instructor explanations• Followed self-explanation prompts• Grounded in restriction of motion reasoning

Page 20: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Contents of the Intervention

Problems

Instructor Explanation

Page 21: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Students completed a posttest

Page 22: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Students who complete the intervention score higher on the posttest.

MANOVA F(3, 213) = 5.94, p < .001, 2= .08Multiple-choice F(1, 215) = 13.34, p < .001, 2= .06 Correct Selections F(1, 215) = 16.64, p < .001, 2= .07Incorrect Selections F(1, 215) = 6.30, p < .01, 2= .03

Intervention Control

Multiple-choice 7.39(.23)

6.20(.23)

# Correct Selections

17.43(.66)

13.65(.65)

# Incorrect Selections

13.25(1.17)

17.35(1.16)

Page 23: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

What do we learn from the engineering studies?

1. Strategies affect students’ ability to integrate verbal, conceptual knowledge with nonverbal problem representations.• Verbal protocol analyses provided descriptive evidence • The intervention provided causal evidence

2.  Strategy instruction can be delivered through relatively simple online environments. •  Intervention does not

•  require instructional time•  instructor expertise in strategy instruction

Page 24: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Biology studies were guided by the same hypotheses.

1. Learning with nonverbal representations can be improved when students use effective strategies.

2. Students can be taught to apply effective strategies to nonverbal representations.

Page 25: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Tutorial design is similar to common science materials.

Individual muscle cells, which can also be called muscle fibers, are bundled together to form groups of cells.

These muscle fibers are surrounded by a cell membrane called the sarcolemma. Within the sarcolemma is sarcoplasm, or the cytoplasm of a uscle cell. The sarcoplasm contains two sets of structures that play important roles in muscle movement. These two are the sarcoplasmic reticulum and sarcomere. The sarcoplasmic reticulum is a complex network of membrane sacs and tubes; sarcomere are made up of bundles of protein called myofibrils.

• > 2500 words• 25 diagrams• 27 pages

Page 26: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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)

Page 27: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Posttest used 3 types of multiple-choice items.

Text Questions

Tested knowledge only from Text

Diagram Questions

Tested knowledge only from Diagrams

Text-Diagram Questions

Tested knowledge required Text-Diagram integration

Page 28: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Students who self-explained while studying complex diagrams learned more.

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Page 29: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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

 The thin filaments are made from different proteins.  The long string of bead-like molecules is called F actin.  Each individual bead is G actin.  Each molecule of G actin has an active site where the myosin heads can bind during muscle cell contraction.  When the muscle is relaxed, the myosin heads of the thick filament cannot bind to the active sites of the thin filament, because long chains of tropomyosin cover the active sites.  Small proteins called troponin are attached along the length of the tropomyosin chains of the thin filament.

Page 30: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Metacognitive instructions and color coding exerted independent effects.

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Page 31: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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 Only• Provided only verbal text from tutorial

Text-Diagram• Provided full tutorial

Page 32: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Students who generated drawings scored higher on correspondent items.

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Page 33: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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

Page 34: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

Conclusions from the body of work

Objective 1: Classroom Applications

1. 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

  2. This instruction can be embedded within instructional materials.

• Does not require instructional time nor expertise

Objective 2: Development of a Research Program

3. A systematic program of research is important

• There are qualifications for conditions under which these instructions are effective

Page 35: Learning From and With Multiple Representations: Lessons from Science Classrooms Peggy Van Meter Educational Psychology Program College of Education

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.