The study was to develop a computerized adaptive diagnostic testing and computerized adaptive remedial instruction system based on the probabilistic reasoning of Bayesian Network and ordering theory（OT） into Weather And Climate In Taiwan in first-grade geography course to induce the error types, sub-skills and unit target and to validate the effect of the system. The results of this study are as follow. First, Bayesian Network was employed to diagnose accuracy of the error types, sub-skills and unit target in Weather And Climate In Taiwan. The results had the 97.23% correspondence to the expert judgment, which show excellent diagnostic effects. Second, the computerized adaptive diagnostic testing system could save 30% of test items. The reaction of the test answers could be predicted and the accuracy achieved 95%, which showed the predictive ability of this system was similar to the complete test. The accuracy rate of prediction on the error types, sub-skills and unit purposes was over 95%, showing that the computerized adaptive diagnostic testing highly corresponded to the diagnosis in error types, sub-skills and unit target of the complete test. Apparently, the effect of the system was quite satisfying. Third, with the design, almost 95% of the students progressed in studies. The error types were decreased and the sub-skills were increased. All students’ average achievements on pre-tests and the post-tests had significant differences. The significant differences were also shown among the high-score, medium-score and low-score groups in the computerized adaptive diagnostic testing. The testers could receive the proper adaptive remedial instruction in time and enhance their learning.
