|標題:||階層式模糊積分多屬性決策方法應用於e-learning學習績效評估 ─ 以中華電信為例|
Hierarchical MADM of Fuzzy Integral for Learning Performance of e-learning Evaluation
|關鍵字:||網路學習;多屬性決策;模糊測度;模糊積分;E-learning;multiple attribute decision making;fuzzy measure;fuzzy integral|
本研究將透過文獻探討的方式，根據克帕屈格 (Kirkpatrick, 1996)所提出的「四階層評估模型」為基礎，彙整出一套可供企業評估e-learning學習績效之架構，結合階層式模糊積分多屬性決策方法之應用，並以實例實際評估不同學習者對於不同類別之網路教學課程之學習績效，並分別探討學習者之個人特質、個人背景及其學習動機等因素對於學習績效之影響。|
The future learning’s environment will be based on education, network, information, and communication technology. For the field of human resource and education training in this electric environment, it has become the emerging and critical topic on how to make use of the advantages and functions of e-learning more efficiently to improve the performances for education trainings. Traditional ways of the education training have not caught up the enterprise requests for learning on many aspects. On the contrary, following by the blooming progress of Internet and computer network technology, the e-learning has become the best tool for enterprises to build the most competitive human resources. Only who command the network and knowledge become the winner in the future. The e-learning is a newly efficient learning method which combines IT (Information Technology) and self-learning. The learner can learn by self-access through connecting Internet to achieve learning’s purpose. It may save some cost and the trainee traffic time. It can also achieve learning objective through computer network without the space-time limitation. The learner can even get the learning achievement just only using the pieces of spare time! They can deepen the learning image through connecting to the training server once and again to get better learning effects. However, it is more difficult and important to measure the e-learning performance efficiently. This study is based on the Donald L. Kirkpatrick’s four-level model of evaluation through the literature. The main objectives are to build a set of structure of e-learning performance evaluations, to apply the Hierarchical MADM of Fuzzy Integral for evaluating the e-learning performance through the real cases which have some category e-learning lesson, and to investigate the impact of the learning performance based on the individual traits, contexts, and motivation.
|Appears in Collections:||Thesis|