No description available.

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There is a need for effective cost efficient training programs. Individual differences have been shown to be the most important variable in many training programs and they should be paid special attention in the design of training programs. Compared in this experiment is computer-controlled (lockstep) training, adaptive training, and learner-centered training. Learner-centered and adaptive training are geared to the individual. Instead of lockstep training, learner-centered training allows the trainee to determine the amount or sequence of training at different levels of proficiency. Adaptive training is training based on the participant's performance. As the participant's performance improves he or she is graduated to a harder level of the training program. In this experiment the dependent variable was the average number of crashes in the transfer trials. The ANOVA indicated there was a significant difference of type of training, F(2, 27) = 4.20, p=0.0251. Planned comparisons were performed to verify the hypotheses such that learner-centered would have the least number of crashes in transfer followed by adaptive and computer-controlled group having the most errors in transfer. As predicted the computer-controlled training group had significantly more crashes than adaptive and learner-centered in the transfer, F(1,27)=8015, p=0.0040, and F(1,27)=3.48, p=0.0348, respectively. Contrary to the hypotheses there was no significant difference between the adapted training group and the learner-centered training group, F(1,27)=0.9764, p=0.3336. As there was no significant difference between adaptive and learner-centered training groups this research suggests that as long as the trainee has some input into his or her training whether adaptively or self-paced, the learning will be superior to learning in a pre-programmed manner.

No description available.

published_or_final_version / Education / Master / Master of Science in Information Technology in Education

published_or_final_version / Education / Master / Master of Science in Information Technology in Education

published_or_final_version / Education / Master / Master of Science in Information Technology in Education

A pretest-treatment-posttest experimental comparison of three individual study support environments for an introductory Management Information Systems course indicated a positive trend in student achievement on structured examinations. The three environments studied include a textbook and class notes presentation method, and two Computer Instructional Support Environments (CISE), drill and practice, and tutorial. Of these, the drill and practice individual study support method displayed a consistently positive effect on examination achievement within each of seven MIS topics. Each topic examined could be classified on the basis of the level of learning objective, and further statistical analysis results indicate a strong correlation between the CISE drill and practice method and attaining a learning objective level of knowledge. Previous research in CISE implementation has been limited by the assumptions of single learning objective levels and single presentation methods, when in fact, the environment is highly complex. A series of experimental observations for introductory MIS topics, in which the learning objective level for each is identified, compares three support environments for individual study. Separate statistical analyses, performed on individual topics, indicate a higher level of achievement by student participants in the CISE Drill and Practice environment for score improvement and improvement in the time required to complete the posttest activity. Two ANOVA models examined the relationships between individual study support methods and topics within two classifications of learning objectives. These results indicate a strong relationship between study support method and learning objective level for text score improvement. The pretest-treatment-posttest experimental design used in this analysis may be replicated across additional topics within the Introductory MIS course to increase the number of topic observations in each learning objective classification. In addition, the experiment can be performed using the same topics to increase the sample size and further clarify the statistical results.

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