The present study was conducted on distance students enrolled in Basic Computer Sciences. Further studies could focus on different courses and provide comparative results. The present study did not take the demographics of the participants into account. Further studies could build other models that also include demographics and present results in comparison with those of the present study. In addition, further studies could find intervals for fuzzy logic membership functions through clustering methods.
The model used in the present study can be adapted to the learning management system. In this way, it will be possible to predict distance education students’ academic performance early during the semester on the basis of real-time data.