A data mining approach to determine better methods for learner's assessment in e-learning courses

Determining how to assess learners and contents of e-learning are essential activities in its processes. These activities are conducted by a professor or teaching assistant and they determine student assessment methods، such as holding an online test or periodic homework assignment. if the organizers can be aware of the effectiveness of each activity in the quality of learning،then besides saving considerable time and resources to stakeholders courses، which transfer the content useful and realistic assessment of students and will ultimately improve e-learning. In this paper، first we use unsupervised techniques of data mining for clustering and describe the present status of learners، and extract hidden rules in e-learning data using rule mining and will discover the effective contents in desired results. Next، using supervised methods we predict results of courses. Using real data of an electronic course provided and with designing four different methods for data sampling and training system، predictions were performed and the methods were validated with an accuracy rate of 92. 86%. We have shown that the methods of this study can help teachers for a better understanding of learners and impact of such training activities required، such as describe characteristics of learners based on the discovery of hidden patterns in the scores of their acquired and determine the most effective learning activities and decide about real measures of learners.