Abstract—The purposes of this research were: 1) to analyze predictive factors for professional experience transfer and 2) to develop a professional experience transfer model from the prediction of an intelligent portfolio using service agents. This article first presents an analysis of factors predicting the transfer of professional experience, which consists of 5 main components: 1) Qualification, 2) Conditions, 3) Knowledge, 4) Assessment method, and 5) Professional Standards. The results of an assessment of the quality of professional experience transfer by a sample of 11 experts showed that the average total score for all aspects was high. In the second part of this article, a professional experience transfer model was derived from the prediction of an intelligent portfolio using service agents. This was developed by integrating intelligent portfolio predictions with the service agent into the model. This model consisted of 3 main components: 1) Import Data, 2) Process, and 3) Results. The intelligent service agent filtered and searched for information by following the criteria for professional experience transfer. The results can be applied to higher education diploma levels to enhance professional skills in advanced vocational training according to the curriculum of the Vocational Education Commission. Evaluation of the professional experience transfer model showed that the average score for all aspects was extremely high.
Index Terms—Intelligent portfolio, service agent, professional experience transfer, prediction.
The authors are with the King Mongkut's University of Technology North Bangkok, Thailand (e-mail: firstname.lastname@example.org, Prachyanun.email@example.com, firstname.lastname@example.org).
Cite:S. Kittiviriyakarn, P. Nilsook, and P. Wannapiroon, "The Professional Experience Transfer Model from the Prediction of an Intelligent Portfolio Using Service Agents," International Journal of Information and Education Technology vol. 10, no. 6, pp. 428-434, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).