Training experience satisfaction prediction based on trainees' general information

dc.contributor.advisorGhosh, Joydeepen
dc.contributor.committeeMemberGraser, Thomasen
dc.creatorHuang, Hsiu-Min Chang, 1958-en
dc.date.accessioned2011-01-04T23:03:18Zen
dc.date.available2011-01-04T23:03:18Zen
dc.date.available2011-01-04T23:03:23Zen
dc.date.issued2010-08en
dc.date.submittedAugust 2010en
dc.date.updated2011-01-04T23:03:23Zen
dc.descriptiontexten
dc.description.abstractTraining is a powerful and required method to equip human resources with tools to keep their organizations competitive in the markets. Typically at the end of class, trainees are asked to give their feelings about or satisfaction with the training. Although there are various reasons for conducting training evaluations, the common theme is the need to continuously improve a training program in the future. Among training evaluation methods, post-training surveys or questionnaires are the most commonly used way to get trainees’ reaction about the training program and “the forms will tell you to what extent you’ve been successful.” (Kirkpatrick 2006) A higher satisfaction score means more trainees were satisfied with the training. A total of 40 prediction models grouped into 10-GIQs prediction models and 6-GIQs prediction models were built in this work to predict the total training satisfaction based on trainees’ general information which included a trainee’s desire to take training, a trainee’s attitude in training class and other information related to the trainee’s work environment and other characteristics. The best models selected from 10-GIQs and 6-GIQs prediction models performed the prediction work with the prediction quality of PRED (0.15) >= 99% and PRED (0.15) >= 98%, separately. An interesting observation discovered in this work is that the training satisfaction could be predicted based on trainees information that was not related to any training experience at all. The dominant factors on training satisfaction were the trainee’s attitude in training class and the trainee’s desire to take the training which was found in 10-GIQs prediction models and 6-GIQs prediction models, separately.en
dc.description.departmentElectrical and Computer Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-08-1656en
dc.language.isoengen
dc.subjectData miningen
dc.subjectLeast squares regressionen
dc.subjectTraining satisfaction predictionen
dc.subjectPrediction qualityen
dc.subjectTraining satisfaction factorsen
dc.subjectSoftware measurementen
dc.subjectModelsen
dc.subjectTraining evaluation methodsen
dc.titleTraining experience satisfaction prediction based on trainees' general informationen
dc.type.genrethesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen

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