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Using Learning Data to Improve the Design of Learning Programs |
Tuesday, May 29, 2007 |
10:30 AM–11:50 AM |
Madeleine AB |
Area: EDC/AUT; Domain: Service Delivery |
Chair: Guy S. Bruce (Appealing Solutions, LLC) |
CE Instructor: Guy S. Bruce, Ed.D. |
Abstract: Behavior Analysis is defined by its pragmatic approach to understanding and changing human behavior. The application of behavior analysis to the design of learning programs means that designers collect data on the learning produced by their programs and use those data to develop programs that improve that learning. Three case studies describing how learning data were collected and used to improve the design of a learning program will be presented. |
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Teaching Parents of Children with Autism about Behavioral Intervention via On-Line Instruction: Using Learning Data to Evaluate and Improve Course Design. |
RICHARD K. FLEMING (University of Massachusetts Medical School), Cheryl Gray (Praxis, Inc.), Charles Hamad (University of Massachusetts Medical School), Carol Curtin (University of Massachusetts Medical School) |
Abstract: Parents of children newly diagnosed with an autism spectrum disorder (ASD) need clear and accurate information in areas that include behavioral intervention. Frequently the internet proves an efficient means to that end, but that depends on the quality and presentation (instructional design) of the information. This paper describes the development and evaluation of an online course designed to introduce parents of children with ASD to the nature and types of research-supported behavioral interventions (Educating Parents: Behavioral Intervention in Autism, NIMH, 1R41MH071130-01, R. Fleming, PI). Focus groups with parents (n=16) and professionals (n=8), combined with other needs analysis procedures, provided useful advance information on content and design. Written content was developed, supported by brief video clips of behavioral instruction, among other design features. Twenty-one (21) parents then participated in a field evaluation of the course, providing us with demographic, pre-/post-test (learning) and satisfaction data. These data, particularly learning data, were analyzed to revise the course and guide a subsequent Phase II grant proposal. Field test results, evaluation procedures and revisions are presented and discussed. |
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Computer-Aided Personalized System of Instruction: Teaching and Research. |
JOSEPH J. PEAR (University of Manitoba), Kirsten M. Wirth (University of Manitoba) |
Abstract: Computer-Aided Personalized System of Instruction (CAPSI) is an online teaching method that emphasizes written answers to questions. Students in CAPSI-taught courses write more and receive more feedback than possible in traditional courses. A unique archiving feature facilitates research on a number of issues of central importance to education. This presentation will discuss CAPSI-research findings with regard to developing higher-order thinking, the effects of feedback on student performance, the effects of peer reviewing (a central and unique feature of CAPSI) on the learning of the reviewers, improving the accuracy and quality of peer reviewing, the effectiveness of feedback on student performance as learners and as peer reviewers, procrastination and ways to reduce it. |
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Using Learning Efficiency Data to Improve the Design and Implementation of Learning Programs for Children with Autism. |
GUY S. BRUCE (Appealing Solutions, LLC), Donald J. McCary (St. Louis Special School District), James Keefe (Warren Achievement Center) |
Abstract: Teachers of children with Autism collected data to evaluate the learning efficiency of their existing programs for teaching functional communication skills such as manding. Learning efficiency is a measure of the amount of improvement in a targeted skill produced in the amount of time the learner has spent interacting with a learning program. These data were graphed on a standard learning efficiency chart, allowing the teachers and their supervisors to evaluate current learning efficiencies by comparing their slopes to the slope of the learning efficiency criterion line. The teachers and their supervisors then made changes in the design and implementation of their learning programs that were not producing the desired learning efficiencies and collected additional learning efficiency data to evaluate whether changes in the learning program improved learning efficiencies. |
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