# Difference between revisions of "Bridging Principles and Examples through Analogy and Explanation"

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===Summary Table=== | ===Summary Table=== | ||

− | == | + | |

− | ( | + | |

+ | ====Study 1 (In Vivo)==== | ||

{| border="1" cellpadding="5" cellspacing="0" style="text-align: left;" | {| border="1" cellpadding="5" cellspacing="0" style="text-align: left;" | ||

| '''PIs''' || Timothy Nokes and Kurt VanLehn | | '''PIs''' || Timothy Nokes and Kurt VanLehn | ||

|- | |- | ||

− | | '''Study Start Date''' || | + | | '''Study Start Date''' || October, 2007 |

|- | |- | ||

− | | '''Study End Date''' || | + | | '''Study End Date''' || December, 2007 |

|- | |- | ||

− | | '''LearnLab Site''' || | + | | '''LearnLab Site''' || United States Naval Academy |

|- | |- | ||

− | | '''Number of Students''' || | + | | '''Number of Students''' || 78 |

|- | |- | ||

− | | '''Total Participant Hours''' || | + | | '''Total Participant Hours''' || 312 |

|- | |- | ||

− | | '''Data Shop''' || | + | | '''Data Shop''' || Expected Spring, 2008; Analysis on-going |

|} | |} | ||

<br> | <br> | ||

− | + | ====Study 2 (Laboratory)==== | |

− | ===Study 2 ( | + | |

{| border="1" cellpadding="5" cellspacing="0" style="text-align: left;" | {| border="1" cellpadding="5" cellspacing="0" style="text-align: left;" | ||

| '''PIs''' || Timothy Nokes and Kurt VanLehn | | '''PIs''' || Timothy Nokes and Kurt VanLehn | ||

|- | |- | ||

− | | '''Study Start Date''' || | + | | '''Study Start Date''' || June, 2008 |

|- | |- | ||

− | | '''Study End Date''' || | + | | '''Study End Date''' || August, 2008 |

|- | |- | ||

− | | '''LearnLab Site''' || | + | | '''LearnLab Site''' || University of Pittsburgh |

|- | |- | ||

− | | '''Number of Students''' || | + | | '''Number of Students''' || anticipated 60 |

|- | |- | ||

− | | '''Total Participant Hours''' || | + | | '''Total Participant Hours''' || anticipated 240 |

|- | |- | ||

− | | '''Data Shop''' || Expected | + | | '''Data Shop''' || Expected Fall, 2008 |

|} | |} | ||

<br> | <br> | ||

===Abstract=== | ===Abstract=== | ||

− | The purpose of the current work is to test the hypothesis that learning the relations between principles and examples is critical to deep understanding and [[transfer]]. It is proposed that there are at least two paths to acquiring these relations. The first path is through | + | The purpose of the current work is to test the hypothesis that learning the relations between principles and examples is critical to deep understanding and [[transfer]]. It is proposed that there are at least two paths to acquiring these relations. The first path is through [[self-explanation]] of how [[worked examples]] are related to the principles. The second path is learning a schema through [[analogical comparison]] of two examples and then relating that schema to the principle. These hypotheses are tested in both a [[in vivo experiment]] in the [[Physics]] LearnLab as well as laboratory studies. |

===Research Question=== | ===Research Question=== | ||

− | The central problem addressed in this work is how to facilitate students’ deep learning of new concepts. Of particular interest is to determine what learning paths lead to a deep understanding of new concepts that enables | + | The central problem addressed in this work is how to facilitate students’ deep learning of new concepts. Of particular interest is to determine what learning paths lead to a deep understanding of new concepts that enables [[robust learning]] including [[long-term retention]], [[transfer]], and [[accelerated future learning]]. |

===Background and Significance=== | ===Background and Significance=== | ||

Line 53: | Line 53: | ||

One reason that students may rely so heavily on prior examples to solve new problems is that they lack a deep understanding for how the principles are instantiated in the examples. That is, they may lack the knowledge and skills required for relating the principle components to the problem features. Some prior research by Nisbett and colleagues (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991) has shown that when students are given brief training on an abstract rule (the statistical principle for the Law of Large Numbers) with illustrating examples they perform better than students trained on the rule or examples alone. This result was shown in a domain where the students were hypothesized to have an intuitive understanding of the principle prior to training. One plausible interpretation of this result is that the students used their intuitive understanding of the principle to relate the abstract rule to the illustrating examples. This possibility is intriguing and suggests that a training procedure designed to facilitate understanding of the relations between principles and examples may result in deep learning. | One reason that students may rely so heavily on prior examples to solve new problems is that they lack a deep understanding for how the principles are instantiated in the examples. That is, they may lack the knowledge and skills required for relating the principle components to the problem features. Some prior research by Nisbett and colleagues (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991) has shown that when students are given brief training on an abstract rule (the statistical principle for the Law of Large Numbers) with illustrating examples they perform better than students trained on the rule or examples alone. This result was shown in a domain where the students were hypothesized to have an intuitive understanding of the principle prior to training. One plausible interpretation of this result is that the students used their intuitive understanding of the principle to relate the abstract rule to the illustrating examples. This possibility is intriguing and suggests that a training procedure designed to facilitate understanding of the relations between principles and examples may result in deep learning. | ||

− | The current research builds on this result by postulating that learning activities designed to focus students on learning the relations between examples and principles should improve their conceptual understanding and lead to [[robust learning]]. We examine two learning paths to acquiring these relations: [[self-explanation]] and [[analogical comparison]]. Self-explanation has been shown to facilitate both procedural and conceptual learning and [[transfer]] of that knowledge to new contexts. Prior work by Chi, Bassok, Lewis, Reimann, and Glaser (1989) showed that good learners were more likely than poor learners to generate inferences relating the worked examples to the principles and concepts of the problem. This result suggests that ''prompting'' students to self-explain the relations between principles and worked examples will further facilitate learning. Of central interest to the current work is to understand how students learn to coordinate the knowledge representations of principles and examples through explanation. The second path is learning a schema through analogical comparison. Prior work has shown that analogical comparison can facilitate schema abstraction and [[transfer]] to new problems (Gentner, Lowenstein, & Thompson, 2003; Kurtz, Miao, & Gentner, 2001). However, this work has not examined how learning from problem comparison impacts understanding of an abstract principle. The current work examines how analogical comparison may help bridge students’ learning of the relations between principles and examples. | + | The current research builds on this result by postulating that learning activities designed to focus students on learning the relations between examples and principles should improve their conceptual understanding and lead to [[robust learning]]. We examine two learning paths to acquiring these relations: [[self-explanation]] and [[analogical comparison]]. [[Self-explanation]] has been shown to facilitate both procedural and conceptual learning and [[transfer]] of that knowledge to new contexts. Prior work by Chi, Bassok, Lewis, Reimann, and Glaser (1989) showed that good learners were more likely than poor learners to generate inferences relating the worked examples to the principles and concepts of the problem. This result suggests that ''prompting'' students to self-explain the relations between principles and [[worked examples]] will further facilitate learning. Of central interest to the current work is to understand how students learn to coordinate the knowledge representations of principles and examples through explanation. The second path is learning a schema through [[analogical comparison]]. Prior work has shown that [[analogical comparison]] can facilitate schema abstraction and [[transfer]] to new problems (Gentner, Lowenstein, & Thompson, 2003; Kurtz, Miao, & Gentner, 2001). However, this work has not examined how learning from problem comparison impacts understanding of an abstract principle. The current work examines how analogical comparison may help bridge students’ learning of the relations between principles and examples. |

===Independent Variables=== | ===Independent Variables=== | ||

'''Type of instruction''' | '''Type of instruction''' | ||

− | + | All three groups receive principle booklets providing textual descriptions of physics principles (rules) for rotational kinematics (e.g., angular velocity, angular displacement, etc.), pairs of [[worked examples]], as well as isomorphic problem solving tasks. The primary manipulation is the activity engaged in during learning. | |

− | **Participants read through | + | *Control - Reading |

− | * | + | **Participants first read through the principle booklets. Next they read through the two [[worked examples]] one at a time. Each example includes an explicit explanation/justification for each step. Next, they solve two isomorphic problems^. |

− | **Participants read the principle. Next they | + | *Self-Explain |

− | *Analogy | + | **Participants first read through the principle booklets. Next they are given the first of the [[worked examples]] and are instructed to self-explain each solution step. After self-explaining they read through explanations for each step (same as control). After completing the first example they perform the same task for the second example. Next they solve one isomorphic problem^. |

− | **Participants first read the principle | + | *Analogy |

+ | **Participants first read through the principle booklets. Next they read through the two [[worked examples]] one at a time. Each example includes an explicit explanation/justification for each step (same as control). Then they are instructed to compare each part of the examples writing a summary of the similarities and differences between the two (e.g., goals, concepts, and solution procedures). Next, they solve one isomorphic problem^. | ||

+ | |||

+ | ^The control group solves two problem isomorphs whereas the self-explanation and analogy groups only solve one to control for time on task. | ||

===Dependent Variables=== | ===Dependent Variables=== | ||

'''Learning Measures''' (manipulation check) | '''Learning Measures''' (manipulation check) | ||

*Control group: Performance on practice problems | *Control group: Performance on practice problems | ||

− | * | + | *Self-explanation group: Content of explanations |

− | *Analogy | + | *Analogy group: Comparison summaries and content of explanations |

'''Test Measures''' | '''Test Measures''' | ||

*[[Normal post-test]] | *[[Normal post-test]] | ||

− | **Problem solving | + | **Problem solving |

+ | ***Solving a problem requiring the application of the same principles, concepts, and equations but asks the student to find a different sought value (almost identical to learning problem) | ||

+ | ***Solving a problem requiring the application of the same principles, concepts, and equations but includes additional IRRELEVANT information in the problem statement. To solve this problem correctly a student must have deeper understanding of the meaning of the variables. One cannot rely on superficial surface strategies. | ||

*[[Transfer]] | *[[Transfer]] | ||

− | ** | + | **Multiple choice |

− | *** | + | ***A novel test that assesses qualitative understanding of the concepts. Students are asked to reason about concepts and principles. |

− | + | ||

− | + | ||

− | *Performance on | + | *Performance on [[Andes]] problems |

**Learning curves | **Learning curves | ||

**Solution times | **Solution times | ||

Line 84: | Line 87: | ||

*[[Long-term retention]] | *[[Long-term retention]] | ||

− | ** | + | **Homework and Final exam performance |

+ | |||

+ | *[[Accelerated future learning]] | ||

+ | **Performance on subsequent topics (e.g., rotational dynamics) as measured by [[Andes]] performance | ||

===Hypotheses=== | ===Hypotheses=== | ||

*Learning the ''relations'' between principles and examples is critical to deep understanding and [[transfer]]. | *Learning the ''relations'' between principles and examples is critical to deep understanding and [[transfer]]. | ||

− | ** | + | **[[Self-explanation]] can serve as one mechanism to facilitate this learning. |

**Problem schemas may help bridge the student's understanding between principles and examples. | **Problem schemas may help bridge the student's understanding between principles and examples. | ||

− | **Analogical comparison can serve as one mechanism to facilitate schema acquisition. | + | **[[Analogical comparison]] can serve as one mechanism to facilitate schema acquisition. |

===Expected Findings=== | ===Expected Findings=== | ||

Line 97: | Line 103: | ||

*Variety of test tasks will help identify what knowledge components are learned: | *Variety of test tasks will help identify what knowledge components are learned: | ||

− | + | **Problem solving: different sought: Analogy = Self-explanation = Control; accuracy | |

− | **Problem solving | + | **Problem solving: irrelevant info: Analogy = Self-explanation > Control; accuracy |

− | **Problem solving | + | **Multiple choice: Analogy = Self-explanation > Control; more likely to get understand the concepts facilitating qualitative reasoning. |

− | ** | + | |

− | *Andes performance: Analogy | + | *Andes performance: Analogy = Self-explanation > Control; errors rates |

===Explanation=== | ===Explanation=== |

## Latest revision as of 08:33, 29 August 2011

## Contents

- 1 Bridging Principles and Examples through Analogy and Explanation

## Bridging Principles and Examples through Analogy and Explanation

Timothy J. Nokes and Kurt VanLehn

### Summary Table

#### Study 1 (In Vivo)

PIs |
Timothy Nokes and Kurt VanLehn |

Study Start Date |
October, 2007 |

Study End Date |
December, 2007 |

LearnLab Site |
United States Naval Academy |

Number of Students |
78 |

Total Participant Hours |
312 |

Data Shop |
Expected Spring, 2008; Analysis on-going |

#### Study 2 (Laboratory)

PIs |
Timothy Nokes and Kurt VanLehn |

Study Start Date |
June, 2008 |

Study End Date |
August, 2008 |

LearnLab Site |
University of Pittsburgh |

Number of Students |
anticipated 60 |

Total Participant Hours |
anticipated 240 |

Data Shop |
Expected Fall, 2008 |

### Abstract

The purpose of the current work is to test the hypothesis that learning the relations between principles and examples is critical to deep understanding and transfer. It is proposed that there are at least two paths to acquiring these relations. The first path is through self-explanation of how worked examples are related to the principles. The second path is learning a schema through analogical comparison of two examples and then relating that schema to the principle. These hypotheses are tested in both a in vivo experiment in the Physics LearnLab as well as laboratory studies.

### Research Question

The central problem addressed in this work is how to facilitate students’ deep learning of new concepts. Of particular interest is to determine what learning paths lead to a deep understanding of new concepts that enables robust learning including long-term retention, transfer, and accelerated future learning.

### Background and Significance

Much research in cognitive science has shown that when students first learn a new domain such as statistics or physics they rely heavily on prior examples to solve new problems (Anderson, Greeno, Kline, & Neves, 1981; Ross, 1984; VanLehn, 1998). Furthermore, laboratory studies indicate that students prefer to use examples even when they have access to written instructions or principles (LeFerve & Dixon, 1986; Ross, 1987). For example, LeFerve and Dixon (1986) showed that when learning to solve induction problems, students preferred to use the solution procedure illustrated in the example over the one described in the written instructions. Although using examples enables novices to make progress when solving new problems they are often only able to apply such knowledge to near transfer problems with similar surface features (see Reeves & Weissberg, 1994 for a review). It is principally through extended practice in the domain that students begin to develop more ‘expert-like’ abilities such as being able to ‘perceive’ and use the deep structural features of the problem (Chi, Feltovich, & Glaser, 1981) or use a forwards-working problem solving strategy (Sweller, Mawer, & Ward, 1983).

One reason that students may rely so heavily on prior examples to solve new problems is that they lack a deep understanding for how the principles are instantiated in the examples. That is, they may lack the knowledge and skills required for relating the principle components to the problem features. Some prior research by Nisbett and colleagues (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991) has shown that when students are given brief training on an abstract rule (the statistical principle for the Law of Large Numbers) with illustrating examples they perform better than students trained on the rule or examples alone. This result was shown in a domain where the students were hypothesized to have an intuitive understanding of the principle prior to training. One plausible interpretation of this result is that the students used their intuitive understanding of the principle to relate the abstract rule to the illustrating examples. This possibility is intriguing and suggests that a training procedure designed to facilitate understanding of the relations between principles and examples may result in deep learning.

The current research builds on this result by postulating that learning activities designed to focus students on learning the relations between examples and principles should improve their conceptual understanding and lead to robust learning. We examine two learning paths to acquiring these relations: self-explanation and analogical comparison. Self-explanation has been shown to facilitate both procedural and conceptual learning and transfer of that knowledge to new contexts. Prior work by Chi, Bassok, Lewis, Reimann, and Glaser (1989) showed that good learners were more likely than poor learners to generate inferences relating the worked examples to the principles and concepts of the problem. This result suggests that *prompting* students to self-explain the relations between principles and worked examples will further facilitate learning. Of central interest to the current work is to understand how students learn to coordinate the knowledge representations of principles and examples through explanation. The second path is learning a schema through analogical comparison. Prior work has shown that analogical comparison can facilitate schema abstraction and transfer to new problems (Gentner, Lowenstein, & Thompson, 2003; Kurtz, Miao, & Gentner, 2001). However, this work has not examined how learning from problem comparison impacts understanding of an abstract principle. The current work examines how analogical comparison may help bridge students’ learning of the relations between principles and examples.

### Independent Variables

**Type of instruction**
All three groups receive principle booklets providing textual descriptions of physics principles (rules) for rotational kinematics (e.g., angular velocity, angular displacement, etc.), pairs of worked examples, as well as isomorphic problem solving tasks. The primary manipulation is the activity engaged in during learning.

- Control - Reading
- Participants first read through the principle booklets. Next they read through the two worked examples one at a time. Each example includes an explicit explanation/justification for each step. Next, they solve two isomorphic problems^.

- Self-Explain
- Participants first read through the principle booklets. Next they are given the first of the worked examples and are instructed to self-explain each solution step. After self-explaining they read through explanations for each step (same as control). After completing the first example they perform the same task for the second example. Next they solve one isomorphic problem^.

- Analogy
- Participants first read through the principle booklets. Next they read through the two worked examples one at a time. Each example includes an explicit explanation/justification for each step (same as control). Then they are instructed to compare each part of the examples writing a summary of the similarities and differences between the two (e.g., goals, concepts, and solution procedures). Next, they solve one isomorphic problem^.

^The control group solves two problem isomorphs whereas the self-explanation and analogy groups only solve one to control for time on task.

### Dependent Variables

**Learning Measures** (manipulation check)

- Control group: Performance on practice problems
- Self-explanation group: Content of explanations
- Analogy group: Comparison summaries and content of explanations

**Test Measures**

- Normal post-test
- Problem solving
- Solving a problem requiring the application of the same principles, concepts, and equations but asks the student to find a different sought value (almost identical to learning problem)
- Solving a problem requiring the application of the same principles, concepts, and equations but includes additional IRRELEVANT information in the problem statement. To solve this problem correctly a student must have deeper understanding of the meaning of the variables. One cannot rely on superficial surface strategies.

- Problem solving
- Transfer
- Multiple choice
- A novel test that assesses qualitative understanding of the concepts. Students are asked to reason about concepts and principles.

- Multiple choice

- Performance on Andes problems
- Learning curves
- Solution times
- Error rates

- Long-term retention
- Homework and Final exam performance

- Accelerated future learning
- Performance on subsequent topics (e.g., rotational dynamics) as measured by Andes performance

### Hypotheses

- Learning the
*relations*between principles and examples is critical to deep understanding and transfer.- Self-explanation can serve as one mechanism to facilitate this learning.
- Problem schemas may help bridge the student's understanding between principles and examples.
- Analogical comparison can serve as one mechanism to facilitate schema acquisition.

### Expected Findings

- If learning the relations is critical for deep understanding and transfer then the groups prompted to explain relations should perform better on the test tasks than the unprompted group.
- If schema acquisition helps bridge this understanding then the Analogy+explanation group should perform best.

- Variety of test tasks will help identify what knowledge components are learned:
- Problem solving: different sought: Analogy = Self-explanation = Control; accuracy
- Problem solving: irrelevant info: Analogy = Self-explanation > Control; accuracy
- Multiple choice: Analogy = Self-explanation > Control; more likely to get understand the concepts facilitating qualitative reasoning.

- Andes performance: Analogy = Self-explanation > Control; errors rates

### Explanation

Prompting students to explain how each step of a worked example is related to the principles facilitates the generation of inferences connecting the physics principles and concepts to the procedures and equations in the problem. These inferences serve to highlight the importance of the concepts in problem solving and increase the likelihood of future activation when solving novel problems. Furthermore, they serve as the critical links integrating and coordinating the principle knowledge components with the problem features.

By comparing similarities and differences of worked examples students have an opportunity to identify the important features of the problems. After having identified the important features they can be related to the principle description through explanation.

### Descendents

None

### Annotated Bibliography

- Anderson, J. R., Greeno, J. G., Kline, P. J., & Neves, D. M. (1981). Acquisition of problem-solving skill. In J. R. Anderson (Ed.),
*Cognitive skills and their acquisition*(pp. 191-230). Hillsdale, NJ: Erlbaum. - Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems.
*Cognitive Science, 13*, 145-182. - Chi, M. T. H., De Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding.
*Cognitive Science, 18*, 439-477. - Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices.
*Cognitive Science, 5*, 121-152. - Dufresne, R. J., Gerace, W. J., Hardiman, P. T., & Mestre, J. P. (1992). Constraining novices to perform expertlike analyses: effects on schema acquisition.
*Journal of the Learning Sciences, 2*, 307-331. - Fong, G. T., & Nisbett, R. E. (1991). Immediate and delayed transfer of training effects in statistical reasoning.
*Journal of Experimental Psychology: General, 120*, 34-45. - Fong, G. T., Krantz, D. H., & Nisbett, R. E. (1986). The effects of statistical training on thinking about everyday problems.
*Cognitive Psychology, 18*, 253-292. - Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding.
*Journal of Educational Psychology, 95*, 393-408. - Kurtz, K. J., Miao, C. H., & Gentner, D. (2001). Learning by analogical bootstrapping.
*Journal of the Learning Sciences, 10*, 417-446. - LeFerve, J., & Dixon, P. (1986). Do written instructions need examples? Cognition and Instruction, 3, 1-30.
- Mestre, J. P. (2002). Probing adults’ conceptual understanding and transfer of learning via problem posing.
*Applied Developmental Psychology, 23*, 9-50. - Reeves, L. M., & Weissberg, W. R. (1994). The role of content and abstract information in analogical transfer.
*Psychological Bulletin, 115*, 381-400. - Ross, B. H. (1984). Remindings and their effects in learning a cognitive skill.
*Cognitive Psychology, 16*, 371-416. - Sweller, Mawer, & Ward (1983). Development of expertise in mathematical problem solving.
*Journal of Experimental Psychological: General, 112*, 639-661. - VanLehn, K. (1998). Analogy events: How examples are used during problem solving.
*Cognitive Science, 22*, 347-388.