An Agricultural Harvest Knowledge Survey to Distinguish Types of Expertise Grimm, Chase
dc.contributor.department Industrial and Manufacturing Systems Engineering 2018-02-18T16:41:52.000 2020-07-07T05:11:55Z 2020-07-07T05:11:55Z 2017-04-11
dc.description.abstract <p>Gaining insight into the unique characteristics of participants during user research is a valuable tool for both recruitment and understanding differences within the target population. This work describes an agricultural harvest knowledge survey that was created for user research studies that observed experienced combine operators driving a combine simulator in virtual crop fields. Two variations of the survey were designed, utilized, and evaluated in two separate studies. Both studies found a difference between low and high knowledge operators' performance on the knowledge survey in addition to performance differences. Based on the success of this survey as a population segmentation tool, the authors recommend three criteria for the design of future knowledge surveys in other domains: 1) use real world scenarios, 2) ensure question are neither too difficult nor too easy, and 3) ask the minimum number of questions to identify operator knowledge successfully. Future research aims to create a tool that can discern between system experts (with deep understanding of the system) and practice experts (who primarily have the wisdom of experience).</p>
dc.identifier archive/
dc.identifier.articleid 1241
dc.identifier.contextkey 10452773
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath undergradresearch_symposium/2017/presentations/61
dc.relation.ispartofseries Symposium on Undergraduate Research and Creative Expression
dc.source.bitstream archive/|||Sat Jan 15 01:16:14 UTC 2022
dc.subject.disciplines Industrial Engineering
dc.title An Agricultural Harvest Knowledge Survey to Distinguish Types of Expertise
dc.type event
dc.type.genre event
dspace.entity.type Publication
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
relation.isSeriesOfPublication 6730f354-97b8-4408-bad3-7e5c3b2fca9d Industrial Engineering
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