No abstract

The increasing popularity of e-learning has created a need for accurate student achievement prediction mechanisms, allowing instructors to improve the efficiency of their courses by addressing specific needs of their students at an early stage. In this paper, a student achievement prediction method applied to a 10-week introductory level e-learning course is presented. The proposed method uses multiple feed-forward neural networks to dynamically predict students' final achievement and to cluster them in two virtual groups, according to their performance. Multiple-choice test grades were used as the input data set of the networks.This form of test was preferred for its objectivity. Results showed that accurate prediction is possible at an early stage, more specifically at the third week of the 10-week course. In addition, when students were clustered, low misplacement rates demonstrated the adequacy of the approach. The results of the proposed method were compared against those of linear regression and the neural-network approach was found to be more effective in all prediction stages. The proposed methodology is expected to support instructors in providing better educational services as well as customized assistance according to students' predicted level of performance.

No abstract

No abstract

Abstract. The development of high-quaUty e-leaming products is one of the most demanding areas in the field of educational research. Reliability of the students' grading mechanisms especially in the case of virtual classrooms, which lack in physical student-instructor interaction, is extremely important. In this paper, based on real data, we utilize two reliability estimation methods to calculate several multiple choice tests' reUability. Moreover, since multiple choice tests are an imperfect measure of students' knowledge, we also estimate the students' true ability of scoring using the tests' standard error of measurement. Concluding this study embeds reliability assessment methods in the e-leaming process and then carefiilly analyzes the produced data to provide the strengths and weaknesses of the analyzed course's multiple choice tests.