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A considerable part of educational systems tends to be online and computer oriented. However, online examination may create some difficulties during the evaluation of student performance. Process mining which arises as a new concept presents various powerful techniques for processing and analyzing different types of data by making use of some advanced information technologies. This paper proposes a novel approach based on process mining for evaluating the performance of students that should follow certain tasks on the computer. The proposed approach is composed of two main phases which are process mining and similarity analysis. Automatic assessment is performed totally in six steps in order to obtain students’ final grades. In addition, cheat control is possible in the last step thanks to the similarity analysis. A real‐life application in an Enterprise Resource Planning (ERP) course is performed in order to present usefulness, validity and practicality of the proposed approach. Furthermore, to evaluate the performance of the assessment system, we compared the assessment mechanism against instructor. A total of 15 students’ answers belonging to computer‐aided exam are assessed by instructor and the results showed a very good agreement between the automatic assessment system and instructor.

This study presents a coalition-based parallel metaheuristic algorithm for solving the Permutation Flow Shop Scheduling Problem (PFSP). This novel approach incorporates five different single-solution-based metaheuristic algorithms (SSBMA) (Simulated Annealing Algorithm, Random Search Algorithm, Great Deluge Algorithm, Threshold Accepting Algorithm and Greedy Search Algorithm) and a population-based algorithm (Weighted Superposition Attraction–Repulsion Algorithm) (WSAR). While SSBMAs are responsible for exploring the search space, WSAR serves as a controller that handles the coalition process. SSBMAs perform their searches simultaneously through the MATLAB parallel programming tool. The proposed approach is tested on PFSP against the state-of-the-art algorithms in the literature. Moreover, the algorithm is also tested against its constituents (SSBMAS and WSAR) and its serial version. Non-parametric statistical tests are organized to compare the performances of the proposed approach statistically with the state-of-the-art algorithms, its constituents and its serial version. The statistical results prove the effectiveness of the proposed approach.

PurposeThe purpose of this study is to develop a new parallel metaheuristic algorithm for solving unconstrained continuous optimization problems.Design/methodology/approachThe proposed method brings several metaheuristic algorithms together to form a coalition under Weighted Superposition Attraction-Repulsion Algorithm (WSAR) in a parallel computing environment. The proposed approach runs different single solution based metaheuristic algorithms in parallel and employs WSAR (which is a recently developed and proposed swarm intelligence based optimizer) as controller.FindingsThe proposed approach is tested against the latest well-known unconstrained continuous optimization problems (CEC2020). The obtained results are compared with some other optimization algorithms. The results of the comparison prove the efficiency of the proposed method.Originality/valueThis study aims to combine different metaheuristic algorithms in order to provide a satisfactory performance on solving the optimization problems by benefiting their diverse characteristics. In addition, the run time is shortened by parallel execution. The proposed approach can be applied to any type of optimization problems by its problem-independent structure.

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