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This study was designed to investigate the factors that affect the success rate of Information Technology Education students which composed of Computer Science and Information Technology. Several variables such as years of gr aduation, entrance exams, and other variables have been used for the investigation. Several data mining techniques such as linear regression, neural network, and decision tree have employed to determine the valid predictors and acceptability of the data mining technique. The results show that the best predictor taken from the entrance exams is non-verbal ability while the best forecasting using data mining is decision tree analysis with 99.19 percent accuracy. If the results taken from the system will be incorporated in entrance examinations results, admission office will be able to identify students that can graduate on-time and whose students should be taken as probationary in the program. It can also identify students not to be taken in the program to avoid waste of time in studying at the University.

Vocabularies, the core of any language, is probably the most challenging and time consuming part of learning a foreign language in a diverse and disperse community of learners. This study proposes an approach that can help a learner build up his/her English vocabulary volume by intensive article reading, the inclusion of Google Cloud Natural Language API and Glosbe Dictionary API, the use of review value calculation computing technique. The review vale calculation were able to determine the number of days were the new words should be reviewed and be part of the long-term memory. Result shows that students were able to increase their words acquisition skills by applying technology and computing. Students were able to retain words fast and understand better, by employing an interactive monitoring process. If the system will be implemented carefully, it is hypothetically produce a faster technique in acquiring new vocabularies for foreign students.

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