The present study concludes that fuzzy logic systems enable one to validly predict a distance student’s year-end academic performance on the basis of the first eight-week data. His/her year-end academic performance can be predicted in accordance with the data on how many days pass before he/she logs on to a class after it has been uploaded to the system, how often he/she logs on to the class, how long he/she stays online in the class, how well he/she scores in the online quiz taken in Week 4, and how well he/she scores in the midterm exam taken in the classroom in Week 8. In this respect, the lowest result is provided by the classic fuzzy model. More accurate results are obtained from the fuzzy model that is based on expert opinion as well as the gene-fuzzy model, which is based on the optimization of the intervals for membership functions using genetic algorithm.