Training experience satisfaction prediction based on trainees' general information

Repository

Training experience satisfaction prediction based on trainees' general information

Show full record

Title: Training experience satisfaction prediction based on trainees' general information
Author: Huang, Hsiu-Min Chang, 1958-
Abstract: Training 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.
Department: Electrical and Computer Engineering
Subject: Data mining Least squares regression Training satisfaction prediction Prediction quality Training satisfaction factors Software measurement Models Training evaluation methods
URI: http://hdl.handle.net/2152/ETD-UT-2010-08-1656
Date: 2010-08

Files in this work

Download File: HUANG-MASTERS-REPORT.pdf
Size: 1.640Mb
Format: application/pdf

This work appears in the following Collection(s)

Show full record


Advanced Search

Browse

My Account

Statistics

Information