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Educational data mining is a specific data mining field applied to data originating from educational environments, it relies on different approaches to discover hidden knowledge from the available data. Among these approaches are machine learning techniques which are used to build a system that acquires hidden knowledge from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems. In our research, we propose a "Student Advisory Framework" that utilizes classification and clustering. This system can be used to guide the first year university students to the more suitable educational track. The classification phase will predict the department which is most likely to be chosen by a student and the clustering phase will recommend a department to student by showing his expected rate of success for each department, this recommendation aims to decrease the high rate of academic failure for first year students. Our approach is tested using a real case study from

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Symbiotic organisms search algorithm (SOS) is a new meta-heuristic algorithm based on the symbiotic relationship between the biological, which was proposed in recent years. In this paper, we propose new robust and powerful metaheuristic algorithm called an improved version of symbiotic organisms search algorithm integrated with construction algorithm and mutation operators (ISOS) to solve quadratic assignment problems. The quadratic assignment problem (QAP) considers NP-hard combinatorial optimisation problem and has various practical applications. A set of benchmarks from QAPLIB Library are employed to evaluate the performance of proposed algorithm. ISOS is compared to several algorithms in the literature. The comparisons show that the proposed algorithm shows reliable, efficient and promising results.