Honest Expert Solutions Towards Cognitive Apprenticeship

Sean Moseley; Rachel McCord

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Conference: 2014 ASEE Annual Conference & Exposition
Location: Indianapolis, Indiana
Publication Date: June 15, 2014
Start Date: June 15, 2014
End Date: June 18, 2014
ISSN: 2153-5965
Conference Session: Student Learning, Problem Solving, & Critical Thinking 1
Tagged Division: Educational Research and Methods
Page Count: 16
Page Numbers: 24.671.1 - 24.671.16
DOI: 10.18260/1-2--20562
Permanent URL: https://peer.asee.org/20562
Download Count: 358

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Paper Authors

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Sean Moseley Rose-Hulman Institute of Technology

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Sean Moseley is an Assistant Professor of Mechanical Engineering at Rose-Hulman Institute of Technology.
He received a B.S. from The Georgia Institute of Technology and an M.S. and Ph.D. from the University of California, Berkeley.

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Rachel McCord Virginia Tech orcid.org/0000-0001-5163-7675

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Rachel McCord is a graduate student in the Department of Engineering Education at Virginia Tech. She holds a B.S. and M.S. in Mechanical Engineering from the University of Tennessee. Her research interests include engineering students use of metacognitive practices while studying in groups. Her advisor is Dr. Holly Matusovich.

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Abstract

Honest Expert Solutions Towards Cognitive ApprenticeshipThe instructional model of cognitive apprenticeship presented by Collins, Brown, and Hollumsuggests that “making thinking visible” to students will improve their learning because itemphasizes how experts solve problems—both the reasoning and strategies used. The firstteaching component of cognitive apprenticeship is modeling—allowing students to observe anexpert’s approach to a task so they can develop an appropriate conceptual model. This researchpaper seeks to answer the following research question: What is the impact of viewing “honest”screencasts of expert solutions on a student’s problem solving process in a second year staticsand mechanics of materials course? The new development in this paper is that the screencastsshow the solution to a problem that the expert has never seen before, not just a completelypracticed and polished presentation of a known solution.The hypothesis underlying this study is that students will better learn the process of engineeringanalysis and can improve their own problem-solving strategies if they see solutions to typicalproblems being developed by an expert in an authentic context. This instruction by examplebecomes modeling if the expert explains the rationale behind particular analysis steps,assumptions, and equations being used—the strategic knowledge that enables them to apply ageneral concept to the specific problem at hand.The expert problem solving process is documented through the use of “honest expertscreencasts”. These screencasts are developed by capturing a subject-matter expert’s firstattempt to work through a never-before-seen problem. The expert uses a think aloud method asthey write, describing their thought processes on how they analyze the problem for importantfeatures, formulate and then execute a plan of action, and check for errors in analysis orcalculations. Document annotation and screencasting software is used to capture the handwrittenanalysis and audio, but editing out mistakes and missteps is specifically not done.Two specific incarnations of the honest expert screencast are presented to students: the “asrecorded” screencast and the “warning” screencast. The “as recorded” screencast is presented tothe students exactly as it was recorded—no editing for content (beyond noise filtering the audiotrack), no fixing mistakes, and no pop-up annotations. The “warning” screencast is the “asrecorded” screencast with the addition of warning pop-up annotations that appear when amistake has been made to warn the student viewer. The mistake is not edited out, only noted.Tracking student access of the screencasts is done through the course management software.Assessment is done by analyzing the students’ analysis process on an exam practice problem,usually given the day before the exam. This ungraded assignment is assessed using a rubric thatmeasures the performance of key problem-solving tasks in the subject—picking out key featuresof the problem, deciding on an analytical approach to use, correctly applying that approach, andchecking the reasonableness of the result.Results are currently being collected and will be presented in the full paper.

Citation
Format

Moseley, S., & McCord, R. (2014, June), Honest Expert Solutions Towards Cognitive Apprenticeship Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20562

TY  - CPAPER
AB  -            Honest Expert Solutions Towards Cognitive ApprenticeshipThe instructional model of cognitive apprenticeship presented by Collins, Brown, and Hollumsuggests that “making thinking visible” to students will improve their learning because itemphasizes how experts solve problems—both the reasoning and strategies used. The firstteaching component of cognitive apprenticeship is modeling—allowing students to observe anexpert’s approach to a task so they can develop an appropriate conceptual model. This researchpaper seeks to answer the following research question: What is the impact of viewing “honest”screencasts of expert solutions on a student’s problem solving process in a second year staticsand mechanics of materials course? The new development in this paper is that the screencastsshow the solution to a problem that the expert has never seen before, not just a completelypracticed and polished presentation of a known solution.The hypothesis underlying this study is that students will better learn the process of engineeringanalysis and can improve their own problem-solving strategies if they see solutions to typicalproblems being developed by an expert in an authentic context. This instruction by examplebecomes modeling if the expert explains the rationale behind particular analysis steps,assumptions, and equations being used—the strategic knowledge that enables them to apply ageneral concept to the specific problem at hand.The expert problem solving process is documented through the use of “honest expertscreencasts”. These screencasts are developed by capturing a subject-matter expert’s firstattempt to work through a never-before-seen problem. The expert uses a think aloud method asthey write, describing their thought processes on how they analyze the problem for importantfeatures, formulate and then execute a plan of action, and check for errors in analysis orcalculations. Document annotation and screencasting software is used to capture the handwrittenanalysis and audio, but editing out mistakes and missteps is specifically not done.Two specific incarnations of the honest expert screencast are presented to students: the “asrecorded” screencast and the “warning” screencast. The “as recorded” screencast is presented tothe students exactly as it was recorded—no editing for content (beyond noise filtering the audiotrack), no fixing mistakes, and no pop-up annotations. The “warning” screencast is the “asrecorded” screencast with the addition of warning pop-up annotations that appear when amistake has been made to warn the student viewer. The mistake is not edited out, only noted.Tracking student access of the screencasts is done through the course management software.Assessment is done by analyzing the students’ analysis process on an exam practice problem,usually given the day before the exam. This ungraded assignment is assessed using a rubric thatmeasures the performance of key problem-solving tasks in the subject—picking out key featuresof the problem, deciding on an analytical approach to use, correctly applying that approach, andchecking the reasonableness of the result.Results are currently being collected and will be presented in the full paper.
AU  - Sean Moseley
AU  - Rachel McCord
CY  - Indianapolis, Indiana
DA  - 2014/06/15
PB  - ASEE Conferences
TI  - Honest Expert Solutions Towards Cognitive Apprenticeship
UR  - https://peer.asee.org/20562
DO  - 10.18260/1-2--20562
ER  -