Difference between revisions of "Craig observing"
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=== Abstract ===
=== Abstract ===
This research project investigates why students learn from [[collaboratively observing]] worked examples. Previous laboratory research has shown that learners
This research project investigates why students learn from [[collaboratively observing]] worked examples. Previous laboratory research has shown that learners watch a video the with a partner learn significantly more than a learner that alone (Chi, Roy, & Hausmann, in press). We will test the [[robustness]] of this effect by seeing if it transfers into the Physics learnlab.
will either collaboratively or individually observe videos on the principles of rotational kinematics. The presented either tutoring session or worked example. The tutoring human undergraduates taking . worked example videos of the expert tutor the steps and reasoningthe problems .
=== Glossary ===
=== Glossary ===
Revision as of 15:27, 15 February 2007
--Scotty 12:53, 19 September 2006 (EDT)
Learning from Problem Solving while Observing Worked Examples
Scotty Craig, Soniya Gadgil, Kurt VanLehn, and Micki Chi
This research project investigates why students learn from collaboratively observing worked examples. Previous laboratory research has shown that learners who watch a video of a problem solving tutoring session while collaboratively solving the same problems with a partner learn significantly more than a learner that observes the tutoring session and solves the problems alone (Chi, Roy, & Hausmann, in press). We will test the robustness of this effect by seeing if it transfers into the Physics learnlab.
Students will either collaboratively or individually observe videos on the principles of rotational kinematics. The videos presented either a tutoring session or a worked example. The tutoring videos showed an expert human tutor working with undergraduates taking an introductory physics course. The worked example videos consisted of the expert tutor solving the rotational kinematics problems while orally describing the steps and reasoning. In both videos, the work is done on Andes, and in both conditions, the students solve problems on Andes.
- Collaboratively observe: two learners watch a video and work together to solve the problem in this case with the Andes system.
- Individually observe: a solo learners watches a video and attempts to solve the problem in this case with the Andes system.
How is robust learning affected by collaboratively versus individually observing worked examples?
The current study varied both number of observers and type of tape observed. The multiple-observer variable consisted of two participants observing a video while problem solving or an individual participant watching a video while problem solving. Information presentation format was used to manipulate the example type variable. Participants either watched an expert worked example of Andes problems that provided steps to solve the Andes problems and information on why the steps where needed or they watched a tutoring session where a human tutor works with a tutee to help with solve the problems. Since this study will be conduced in the learnlab, the condition where an individual observed the tutoring session was eliminated because previous lab studies have not shown this contrast to be effective.
A dialogue hypothesis for collaboratively observing while problem solving from worked examples would be that viewing the expert tutoring session would produce more learning (normal or robust) than viewing a content equivalent condition of expert problem solving. However, an alternative (Content equivalency hypothesis) would be that since the expert tutoring session and the expert worked example both provide good learning conditions with the same content they should both produce mastery of the material (Klahr & Nigam, 2004).
- Transfer, immediate: After exposure to the treatment, students will complete three transfer problems in Andes. These problems will test the same concepts from training in new situations that require implementation of the problems in new ways.
- Near transfer, retention: Students will be given a 12 item multiple choice pretest and posttest that taps into their ability to transfer the principles of rotational kinematics to new situations. This will serve as a measure of immediate learning for the study.
- 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 will be analyzed.
- Acceleration of future learning: The training was on Rotational kinematics, and it was followed in the course by a unit on torque. Log data from the torque homework will be analyzed as a measure of acceleration of future learning.
Some preliminary analyses have been conducted on our transfer multiple choice questionnaires. So far, an analysis of the pretest and posttest multiple choice questionnaires have yielded significant learning gains between pretest to posttest, F (1,65) = 14.987, p <.001 with a proportional M =.56 and M = .66 respectively. However, so far there have been no significant differences found among conditions on the questionnaires.
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 collaboration conditions could comprise the learning-by-doing paths where learners can work together to complete the Andes problems or the paired learners could reply on the video as their information providing agent and simply copy the steps. Alternatively the participants in the solo condition would have to rely exclusively on the video for information and thus reply on more direct copying of steps thus allowing another agent (the video) to do most of the work.
- The student takes the learning-by-doing path unless it becomes too difficult. This study attempts to control the student’s path choice by presenting them with the tutorial dialogue that could encourage communication or an expert worked example that gives a walk through of the problem without the dialogue interaction. So, when students take the learning-by-doing path, they learn more than when they take the alternative path.
- 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.
- 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. 
- 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.