Investigating the robustness of vicarious learning: Sense Making with Deep-level reasoning questions
Scotty Craig, Kurt VanLehn, and Micki Chi
Craig and colleagues had participants watch information on computer hardware over a series of studies in an effort to determine ways improve learning while observing material. These studies pointed toward a deep level reasoning question effect for improving learning while observing. This effect states that if you insert a series of relevant deep level questions into observed material learning will be improved. A series of studies have shown that it is this series of deep level questions that is important. The same improvement is not seen if They found that participants exposed to dialogs both increased deep level question asking and in another series of studies improved learning. However, this was only if deep level questions were used. Further investigations found that simply observing a presentation with deep-level questions improves learning (regardless of monolog/dialog format) over various controls. However, it is not know why this method works over observing other methods of learning. It is also not known if this effect can be useful for learning outside the lab setting. The current in vivo experiment will present the identical core content on magnetism using the examples problems from the Andes tutoring system in three different ways. The material will be presented as a worked example. The content will be divided into knowledge components. The knowledge components will be preceded by a deep question (e.g. What are the implications of having the magnetic field close to an electrified wire?), a prompt for learners to reflection on the material or a self explanation prompt (e.g. Please begin your self-explanation). Measures of short term retention, Andes transfer, and long term robust learning will be measured. A pretest and posttest will be implemented to measure short term retention. These tests will consist of deep-multiple choice questions that tap the materials essential knowledge components. The pretest and posttest will be counterbalanced to prevent an order effect. The learners’ interaction with Andes will be observed for differences on completion time, within task behavior, and the completion rates of the Andes homework. Long term classroom transfer between the conditions will come from tracking learners’ performance on in class tests on the experimental material.
Forthcoming, but will probably include
- Vicarious learning
- Deep-level reasoning question
How is robust learning affected by observing multimedia displays with narrative deep-level reasoning questions?
The current study varied the level of guidance provided. The level of guidance was varied by presented students with a deep-level reasoning condition, a self-explanation condition and a reflection condition. The deep-level reasoning questions provided a step-by-step guide that scaffolded the learner during the learning process. The self-explanation conditions asked that students build the links of these scaffolds by self-explaining the steps. The reflection condition presented the participants with the steps and asked them to reflect on the material as it was presented.
A guided learning hypothesis would predict that since the deep-level questions provided greater provided a constant cognitive guide the deep-level question condition would improve learning over the reflection condition and possibly the self-explanation condition if the students could not produce the guidance why producing the self-explanations. Alternatively, a content equivalency hypothesis would be that since all three conditions provide the same content they should all produce learning of the material (Klahr & Nigam, 2004).
- Near transfer, immediate: During training, 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) were calculated. This measure showed self-explanation was more effective than instructional explanation.
- Near transfer, 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 over 2 hours, the student’s performance on this problem is considered a test of retention. Results on the measure were mixed.
- Homework: After training, students did their regular homework problems using Andes. Students can do them whenever they want, but most normally complete them just before the exam. The homework problems were divided based on similarity to the training problems. Homework for both similar (near transfer) and dissimilar (far transfer) problems were analyzed.
This study is part of the Interactive Communication cluster, and its hypothesis is a specialization of the IC cluster’s central hypothesis. The IC cluster’s hypothesis is that robust learning occurs when two conditions are met:
- The learning event space should have paths that are mostly learning-by-doing along with alternative paths were a second agent does most of the work. In this study, the deep-level question condition and the self-explanation condition could comprise the “learning-by-doing paths” in that learners are guided to produce clearer mental models of the material. Alternatively the participants in the reflection condition would not be guided to produce better mental models, but would reply more on the video to provide all relevant links for them.
- The student should take the learning-by-doing path unless it becomes too difficult. This study attempts to control the student’s path choice by presenting them with deep-level questions that guide them to building better mental models. However, the self-explanation and reflection conditions require the students to produce the learning by doing path. In these conditions, if the production becomes to difficult for the students then they will not learn. The study is testing that when students take the learning-by-doing path, they learned more than when they take the alternative path. Since none of the students attempted more than a few self-explanations, it appears that the students in the self-explanation conditions took this path.
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- Chi, M. T. H., Roy, M., & Hausmann, R. G. M. (in press). Learning from observing tutoring collaboratively: Insights about tutoring effectiveness from vicarious learning. Cognitive Science.
- 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 Leew, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477.
- Craig, S. D., Driscoll, D., & Gholson, B. (2004). Constructing knowledge from dialog in an intelligent tutoring system: Interactive learning, vicarious learning, and pedagogical agents. Journal of Educational Multimedia and Hypermedia, 13, 163-183. 
- Craig, S. D., Sullins, J., Witherspoon, A. & Gholson, B. (in press/2006). Deep-Level Reasoning Questions effect: The Role of Dialog and Deep-Level Reasoning Questions during Vicarious Learning. Cognition and Instruction, 24(4), 563-589.
- Gholson, B. & Craig, S. D. (in press/2006). Promoting constructive activities that support vicarious learning during computer-based instruction. Educational Psychology Review, 18, 1XX-1XX. 
- Klahr, D. & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15, 661-667.
--Scotty 12:01, 19 September 2006 (EDT)