Proposal view
| Proposal Type: | Individual Paper |
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| Domain: | Learning and Instructional Technology |
| SIG: | Learning and Instruction with Computers |
| Type | Submitted Paper |
| Equipment |
PC and projector |
| Paper Details |
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| Title | Help design in a computer-based learning environment: teaching argumentation skills through the use of examples |
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| Abstract | Learning with self-explaining examples is an effective method in well-structured domains. We analyzed this method in teaching the complex skill of argumentation. In an experiment we compared three conditions (n = 47 students of educational sciences) that differed with respect to whether and how the processing of the examples was supported by different help functions. The analysis of the video-based examples was either supported by additional examples displaying the equivalent argumentational structure or by Conceptmaps visualizing the argumentational structure. The control group received no help. We found that examples of argumentation could be successfully employed in order to teach skills of argumentation. Covariance Analysis revealed no main effect of help design on learning outcome. However there was a significant effect of learners’ help seeking activities. Learners who used the help facilities more often showed significant higher learning outcomes. Principal based help facilities (concept maps) thereby were most accepted by the learners. |
| Summary | Research has shown that learning from worked-out examples (problem, solution-steps and final solution) is of major importance for initial skill acquisition in well-structured domains such as mathematics or physics. However, learners only benefit from this learning mode if they actively explain the examples to themselves. The learners can be encouraged to do so by the structure of the examples (e.g., salient sub-goals) and by specific self-explanation prompts. Less attention has been paid so far to example-based learning as a learning method for the acquisition of cognitive skills in ill-structured domains. Worked-out examples are usually presented text-based, but examples in ill-structured domains are difficult to be presented text-based in a sensible way, because thereby relevant context information and proximity to the learners’ experiences might get lost. Thus the best way to transmit those complex learning contents may be the use of video- or real-life models displaying people who are, for example, solving a specific problem and/or explicating their cognitive processes. In the current study we developed a computer-based learning environment designed to teach basic skills of argumentation. We designed this tool as an example-based learning environment, using video-based examples as expert models. Although such examples usually refer to well-structured written problem statements and corresponding solution steps, the modeling of an ill-structured cognitive skill can also be understood as an example. In this context self-explanations could be regarded as an activity to symbolically code the key behaviors of a model's performance. In a former study, a computer-based learning environment on argumentative skills has been developed using video-based examples as an expert model. Different types of self-explanation prompts have been implemented to foster symbolic coding: (1) descriptive prompts (prompts asking for content-specific information), and (2) principle-based prompts (prompts asking for the examples' underlying principles). Results showed that self-explanation prompts foster learning of ill-structured cognitive skills (here: argumentation skills) by video-based examples and that principle-based prompts yielded the most favourable effects. However, learning results were far from optimal. As a consequence help functions have been implemented in the learning environment supporting learners when analysing the argumentation examples. In an experiment we compared three conditions (N = 60 students of educational sciences) that differed with respect to whether and how the processing of the examples was supported by different help functions. The analysis of the video-based examples was either supported by additional examples displaying the equivalent argumentational structure or by Conceptmaps visualizing the argumentational structure. The control group received no help. The participants worked in sessions of approximately three hours. First, a paper-pencil test with multiple-choice items on prior knowledge in the content areas of the video-based examples was presented. Then the program started with a short introduction about the relevance of argumentative skills in everyday life.The general introduction was followed by the pretest on argumentative skills. Afterwards the actual learning phase began. First, explanatory information about the argumentative model taught in the learning environment was given: The participants received information about the relevance of genuine evidence to support their theory and about the advantages of accounting for possible alternative theories and counterarguments. That theoretical information was supplemented by an example. The video-based examples showed two conversations in the run-up of a workshop on interdisciplinary cooperation of teachers. The dialogue content was taken from two different domains. The dialogue was divided into four parts, (a) statement of theory and evidence, (b) statement of alternative theory, (c) rebuttal of the alternative theory, and (d) counterargument against the original theory and its rebuttal. After the presentation of the video the learners received a multiple choice question about the argumentational elements of the video sequence. False replies automatically lead to the help functions. In the experimental condition with multiple examples the help function includes an additional dialogue structured equivalently to the original dialogue. The principle-based help function of the second experimental condition contained Conceptmaps displaying the different argumentational elements of the original dialogue and their relations. After the presentation of the videos the participants were asked to have a break before working on the post-test which was as well integrated in the computer-based environment. Finally the participants filled in a questionnaire (paper-pencil) on their acceptance of the program. Results showed no main effect of help design on learning outcome. However there was a significant effect of learners’ help seeking activities. Learners who used the help facilities more often showed significant higher learning outcomes. Principal based help facilities (concept maps) thereby were most accepted by the learners. |
| Keywords | Argumentation Cognitive skills Computer-supported learning environments |
| Appendices | |
| Authors | ||||||
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| Name | Surname | Institution | Country | EARLI Number | Presenting | |
| Silke | Schworm | University of Regensburg | Germany | Silke.Schworm@paedagogik.uni-regensburg.de | * | |
