The use of software is an essential part of our modern lives. Hence, this increases the importance of studying Software Engineering (SE) course. In general, the software engineering graduates usually lack the necessary skills, expertise, abilities, and sufficient knowledge when beginning their careers in the software industry. Due to that, a majority of students find it difficult to find relevant jobs. This paper proposes novel methods to teach SE course to improve the teaching and enhance knowledge and skills of students. It is proposed to include: identifying the course learning objectives (CLOs) and the required skills of the SE course, combining cooperative learning and mastery learning strategies to teaching software engineering, using social media to teach SE course, and establish the OSES in an educational institution. The goal of this paper is to improve the quality of SE teaching and facilitate students learning to prepare them for their future careers. Qualitative technique is used as a research design to evaluate the proposed solution. The results indicate that this proposal is supported by the majority of professionals working in the academia and industry. IndexTerms-Software engineering, education, cooperative mastery, social media, industry experts, methods of teaching.The Proposed Methods to Improve Teaching of Software Engineering

Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers.