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Purpose This study aimed to examine the impact of ICT and digital knowledge on students’ thoughts and beliefs. Using Information and Communication Technology (ICT) in learning and teaching processes can improve the interpretation of knowledge, not only in the learning process but also for thoughts and beliefs. Beliefs and thoughts as propositional content are understood to be a subjective manner of knowing and becoming a focal point of education process. In addition, ICT plays a vital role in enhancing the efficiency of the teaching process which can change the thoughts of learners. So, in this paper, the usage of ICT in education was considered as a key factor for improving students’ thoughts and beliefs. In addition, a conceptual model was proposed to evaluate this impact. Design/methodology/approach Data were collected from 384 students from secondary schools in Iran. For assessing the elements of the model, a complete questionnaire was designed. For statistical analysis of questionnaires, SPSS 22 and SMART-PLS 3.2 software package was used. Findings The obtained results showed the high strength of the proposed model. The outcomes indicated that digital technology acceptance positively affects students’ thoughts and beliefs. In addition, the findings showed that the role of digital knowledge, digital training facilities and digital education content on students’ thoughts and beliefs was significant. Research limitations/implications The authors deal with one experiment and so the results cannot be generalized. The trail should be repeated with many groups and in diverse contexts. Originality/value Despite the importance of the investigating the impact of ICT and digital knowledge on the students’ thoughts and beliefs, the relationship among these factors was not examined well in previous research. Thus, the investigation of the impact of ICT and digital knowledge on the students’ thoughts and beliefs is the main originality of this research. For this goal, a new conceptual model is proposed, which has 11 sub-indicators within four variables: digital technology acceptance, digital knowledge, digital training facilities and digital education content.

Purpose Despite the importance of investigating the impact of cloud computing on the e-learning process, the relationship development between the two is not examined well. Thus, the main goal of this research is to assess how to improve the e-learning process by using cloud services. This paper aims to attempt to investigate the impact of cloud computing on e-learning development. Design/methodology/approach The paper is based on both quantitative and qualitative methodologies. For review-related work and statistical analysis of questionnaires, the SPSS 22 and SMART-PLS 3.2 software package are used. Findings The results from the study show that e-learning development is significantly influenced by the quality of services, cloud features, university readiness and users’ readiness. Originality/value The adoption of cloud technology within an instructional environment has the capacity of offering new opportunities for improvement and innovation for gaining knowledge of the process.

Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta-heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non-dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta-heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution.Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms. KEYWORDScloud computing, multi-objective optimization, particle swarm optimization algorithm, task scheduling INTRODUCTIONCloud computing as an innovative distributed computing can provide dynamic resources, which are highly virtualized. 1,2 In addition, cloud providers deliver their services (such as software, hardware, platform, and storage) over the Internet. 3,4 Therefore, there are no shrink-wrapped boxes containing discs or hardware for users to buy. The cloud providers typically charge monthly recurring fees based on usage. 5-7 Many benefits, including the cost reduction and flexibility, make cloud computing to be superior technology in computing. 8 Due to the numerous benefits of cloud computing, many users have been attracted to use cloud-based services. 9,10 There are some issues in the cloud computing that must be addressed, such as resource discovery, 11,12 trust evaluation, 13-15 task, 16-18 resource allocation, 19-21 and so on. 22 Among them, task scheduling has a primary role in reaching the efficiency of distributed computing systems such as clouds and grids. [23][24][25] In the cloud systems, a task scheduler allocates the submitted tasks to proper resources for performing. 26-28 From the creation of cloud computing, task scheduling has gained significant attention, and many researchers have investigated many methods based on artificial intelligence-based algorithms to solve it. 29 It is responsible for finding potential cloud resources for satisfying the users' requests in the cloud environment. The main aim of task scheduler is decreasing the total cost, completion time and the computational complexity. 30,31 However, most of the existing research has focused on a single objective task scheduling in cloud systems. However, in real cases, more than one factor should be considered. 29 Since task scheduling is an NP-hard issue, many meta-heuristic algo...