This research aimed to develop the prototype of information management systems for achievement evaluation and grade submission of Ubon Ratchathani Rajabhat University. The research process begins to study the need for the development of the prototype of information management system for achievement evaluation and grade submission. The research instruments were interview and questionnaires. This research found that lecturers needed to have a developed model for this system. It was found that the system including achievement evaluation grade submission and storing data were validity tested by grade information. Moreover the prototype model developed could be used as a model for the present education evaluation and grade submission at the university.

The purpose of this research is to develop learning package for an operating system course on process status for undergraduate students majoring in computer engineering. The technique of creating the cartoon story for difficult content is used to compare the process status and the steps of buying meals to enhance students’ understanding of process management. The participants are divided into two groups; the control group learning with the traditional method the experiment group learning with the learning package. The research result was that the achievement of the experimental group was higher than the control group. In addition the students were interested in learning and the students’ satisfaction is at the high level.

Advertisement and admission of target applicants who will be new undergraduate studentswill help to reduce costs promote or recruit off- campus as well. The decision tree Technique can be used to develop models to forecast numbers of new students in the classification rules for accuracy. It helps to decide on the form and place in admission of new students to meet the target even more. The seven models developed by using the rules of classification techniques decision trees were built and tested with different sampling methods: 3 models of k-fold cross-validation 3 models of percentage split and a model of training set and test set. The study result for forecasting new students via model developed by training set and test set method is higher performance than model developed by other methods. It was shown that the efficiency was 94% the precision was 94.3% the recall was 94% and the F-measure was 93.7%. Thus this model is accurate in forecasting numbers of new students using the classification rules with decision tree technique.