Difference between revisions of "Self-explanation: Meta-cognitive vs. justification prompts"
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Revision as of 15:17, 1 February 2008
- 1 The Effects of Interaction on Robust Learning
- 1.1 Summary Table
- 1.2 Abstract
- 1.3 Background and Significance
- 1.4 Glossary
- 1.5 Research question
- 1.6 Independent variables
- 1.7 Hypothesis
- 1.8 Dependent variables
- 1.9 Results
- 1.10 Explanation
- 1.11 Further Information
The Effects of Interaction on Robust Learning
Robert G.M. Hausmann, Brett van de Sande, Sophia Gershman, & Kurt VanLehn
|PIs||Robert G.M. Hausmann (Pitt), Brett van de Sande (Pitt), Sophia Gershman (WHRHS), & Kurt VanLehn (Pitt)|
|Other Contributers||Tim Nokes (Pitt)|
|Study Start Date||Sept. 1, 2007|
|Study End Date||Aug. 31, 2008|
|LearnLab Site||Watchung Hills Regional High School (WHRHS)|
|Number of Students||N = 75|
|Total Participant Hours||150 hrs.|
|DataShop||Loaded: data not collect|
The literature on studying examples and text in general shows that students learn more when they are prompted to self-explain the text as they read it. Experimenters have generally used two types of prompts: meta-cognitive and justification. An example of a meta-cognitive prompt would be, "What did this sentence tell you that you didn't already know?" and an example of a justification prompt would be, "What reasoning or principles justifies this sentence's claim?" To date, no study has included both types of prompts, and yet there are good theoretical reasons to expect them to have differential impacts on student learning. This study will directly compare them in a single experiment using high schools physics students.
Background and Significance
The self-explanation effect has been empirically demonstrated to be an effective learning strategy, both in the laboratory and in the classroom. The effect sizes range from d = .74 – 1.12 in the lab for difficult problems (Chi, DeLeeuw, Chiu, & LaVancher, 1994; McNamara, 2004) to d = .44 – .92 in the classroom (Hausmann & VanLehn, 2007). However, both the amount and quality of spontaneously produced self-explanations is highly variable (Renkl, 1997). To increase both the likelihood and quality, different prompting procedures have been designed to solicit student-generated explanations. However, an open question is how to structure the learning environment to maximally support learning from self-explanation. One method to support robust learning is to design instructional prompts that increase the probability that students will frequently generate high-quality self-explanations. What counts as a “high-quality self-explanation?”
The answer to that question may depend on the type of knowledge to be learned. Knowledge can be categorized into two types, either procedural or declarative knowledge. In physics, students often learn the procedural skill of solving problems by studying examples. An example is a solution to a problem, which is derived in a series of steps. An example step contains either an application of a physics principle or mathematical operator. The transition from one step to the next can be justified by a reason consisting of the applicable principle or operator. Therefore, an effective prompt for procedural learning asks the student to justify each step of an example with a domain principle or operator. For the purposes of this proposal, we shall call this type of prompt “justification prompts.”
Contrast a high-quality self-explanation from problem solving in physics with an explanation from a declarative domain (i.e., the human circulatory system). In this domain, the student’s task is to develop a robust mental model of a physical system. Instead of a solution example broken down by steps, the information is presented as a text, with each sentence presented separately. When each sentence is read, the student’s goal is to revise or augment his or her initial mental model (Chi, 2000). A high-quality explanation in this domain requires the student to consider the relationship between the structure, behavior, and function of the various anatomical features of the circulatory system. When a student begins reading about the heart, she rarely (if ever) comes to the task as a blank slate. More likely, the student has an initial mental model that is flawed in some way. Therefore, the student must revise her initial mental model to align itself with the content of the text. This is generally not an easy task because the reader is required to use her prior knowledge to comprehend the text, while simultaneously revise that same knowledge. In this case, a high-quality self-explanation may consist of reflecting on one’s own understanding, comparing it to the target material, explaining the discrepancies between the two, and revising the mental model. Therefore, we shall refer to prompts that encourage this type of behavior as “meta-cognitive prompts.”
Thus, different types of self-explanation prompts may lead to different learning outcomes. Justification-based prompts may inspire more gap-filling activities, while meta-cognitive prompts may evoke more mental model repair. While both types of prompting techniques have been used in prior research on self-explaining, what remains to be explored, however, is a systematic exploration of the differential impact prompting for justifications or meta-cognitive activities on robust learning.
How is robust learning affected by justification-based vs. meta-cognitive prompts for self-explanation?
Only one independent variable, with three levels, was used:
- Prompt Type: meta-cognitive prompts vs. justification-based prompts vs. attention-focusing prompts
Prompting for an explanation was intended to increase the probability that the individual or dyad will traverse a useful learning-event path. Meta-cognitive prompts were designed to increase the students' awareness of their developing knowledge. The justification-based prompts were designed to motivate students to explicitly articulate the principle needed to solve a particular problem. Finally, the attention-focusing prompts were designed as a set of control prompts. The purpose was to steer their attention to the examples that they are studying, without motivating any particular type of active cognitive processing.
- Normal post-test
- Near transfer, immediate: During training, worked examples alternated with problems, and the problems were solved using Andes. Each problem was similar to the example that preceded it, so performance on it is a measure of normal learning (near transfer, immediate testing). The log data were analyzed and assistance scores (sum of errors and help requests, normalized by the number of transactions) were calculated.
- Robust learning
- Long-term retention: On the student’s regular mid-term exam, one problem was similar to the training. Since this exam occurred a week after the training, and the training took place in just under 2 hours, the student’s performance on this problem is considered a test of long-term retention.
- Near and far transfer: After training, students did their regular homework problems using Andes. Students did them whenever they wanted, but most completed them just before the exam. The homework problems were divided based on similarity to the training problems, and assistance scores were calculated.
- Accelerated future learning: The training was on electrical fields, and it was followed in the course by a unit on magnetic fields. Log data from the magnetic field homework was analyzed as a measure of acceleration of future learning.
Procedure Participants were randomly assigned to condition. The first activity was to train the participants in their respective explanation activities. They read the instructions to the experiment, presented on a webpage, followed by the prompts used after each step of the example.
All of the participants were enrolled in a year-long, high-school physics course. The task domain, electrodynamics, was taught at the beginning of the Spring semester. Therefore, all of the students were familiar with the Andes physics tutor. They did not need any training in the interface. Unlike our previous lab experiment, they did not solve a warm-up problem. Instead, they started the experiment with a fairly complex problem.
Once they finished, participants then watched a video solving an isomorphic problem. Note that this procedure is slightly different from previous research, which used examples presented before solving problems (e.g., Sweller & Cooper, 1985; Exper. 2). The videos decomposed into steps, and students were prompted to explain each step. The cycle of explaining examples and solving problems repeated until either 4 problems were solved or 2 hours elapsed. The first problem was used as a warm-up exercise, and the problems became progressively more complex.
- Chi, M. T. H. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology (pp. 161-238). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
- Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477.
- Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417-424). Amsterdam: IOS Press.
- McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38(1), 1-30.
- Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1-29.