The applications of ubiquitous learning is still considered to be very limited theses days, which has to do with many of the issues that have yet to be compromised involving some of the key technologies required; among which would be "positioning issues". Complications involving positioning issues can be divided into three categories: In the first category would be those of the global positioning system (GPS), since it consumes much of manpower in obtaining environmental parameters needed in order to ensure its functioning to be free of problems. The second category has its roots in the sensitivity of signals received by the wireless network equipment; units or systems would be greatly affected by whether or not the signals are being received on a reliable basis. Finally, there stills exists considerable inaccuracy in the practical usage of positioning methods, which has prevented them from being applied in reality. By integrating the Exponentially Weighted Moving Average (EWMA) and the concepts of the Sequential Positioning Method, this study has outlined the "Integrated Positioning Method" ("IPM") which, through segmentation and comparing of signal strengths, not only would decrease the degree of complexity in computing performed but would also reduce the positioning error rates. The results of this experiment shows that besides its capacity to have the environmental parameters figured out automatically so that the instability of signals received get to be improved, the IMP also simultaneously provides a location error rate of 1.3m on average, whish is of a great precision. As the effects of the impacts of the parameters used get to be more closely observed in details, even greater improvements could be achieved in the accuracy of positioning. Since the IPM that has been developed out of this research has a positioning function that is automatic in its operations, it is more of a system that is going to meet practical applications.