A comparative analysis of the effects of instructional design factors on student success in e-learning: multiple-regression versus neural networks

This study explores the relationship between the student performance and instructional design. The research was conducted at the E-Learning School at a university in Turkey. A list of design factors that had potential influence on student success was created through a review of the literature and interviews with relevant experts. From this, the five most import design factors were chosen. The experts scored 25 university courses on the extent to which they demonstrated the chosen design factors. Multiple regression and supervised artificial neural network (ANN) models were used to examine the relationship between student grade point averages and the scores on the five design factors. The results indicated that there is no statistical difference between the two models. Both models identified the use of examples and applications as the most influential factor. The ANN model provided more information and was used to predict the course-specific factor values required for a desired level of success

EFFECTIVE CLUSTER-FIRST ROUTE-SECOND APPROACHES USING METAHEURISTIC ALGORITHMS FOR THE CAPACITATED VEHICLE ROUTING PROBLEM

In this paper, three cluster-first route-second approaches are proposed to solve the capacitated vehicle routing problem (CVRP) that extends a traveling salesman problem (TSP). In the first phase, a giant tour covering all customers is built using three different metaheuristic algorithms as an ACO, a GA, and an ABCA. Then, the giant tour is split with respecting the vehicle capacity, and vehicles are loaded. In the second phase, we transform our problem into a small TSP after completing the clustering process, and a routing problem is solved based on a Branch-and-Bound algorithm. We evaluate the performance of these approaches on the benchmark problems. The computational results show that these approaches achieve high-quality results and gain an advantage in terms of CPU time. Besides, these approaches are also applied to a real-life case study related to a distribution CVRP meeting the weekly demands of a supermarket chain and provide a better routing solution