Distance learning has spread nowadays on a large scale across the world, which has led to many challenges in education such as invigilation and learning coordination. These challenges have attracted the attention of many researchers aiming at providing high quality and credibility monitoring of students. Distance learning has offered an effective education alternative to traditional learning in higher education. The lecturers in universities face difficulties in understanding students’ emotions and abnormal behaviors during educational sessions and e-exams. The purpose of this study is to use computer vision algorithms and deep learning algorithms to develop a new system that supports lecturers in monitoring and managing students during online learning sessions and e-exams. To achieve the proposed objective, the system employs software methods, computer vision algorithms, and deep learning algorithms. Semi-structural interviews were also used as feedback to enhance the system. The findings showed that the system achieved high accuracy for student identification in real time, student follow-up during the online session, and cheating detection. Future work can focus on developing additional tools to assist students with special needs and speech recognition to improve the follow-up facial recognition system’s ability to detect cheating during e-exams in distance learning.

As the COVID-19 epidemic caused new requirements in education, the use of various technologies and materials in science education has gained more importance for sustainability. Among other objectives, the subject of science aims to help students gain skills such as identifying problems, doing research, forming hypotheses, completing experiments, conducting analyses, and reporting the findings. Some of the problems experienced in science education are caused by the lack of tools and equipment. Through augmented reality (AR), a developing technology that is also used in the field of education, a digital layer is superimposed over authentic world images. The main aim of this study is to determine the views of students and teachers regarding augmented reality content developed for science education. The study group consists of 80 seventh-grade students and 4 science teachers. The study adopted a qualitative data collection method so the researchers developed and used semi-structured interview forms for the students and the teachers during the interviews. Both the students and the teachers reported the positive effects of AR practices on improving the understanding of science topics, offering a visual topic introduction, and contributing to the in-class interaction during class hours.

As one of the developing technologies and increased use in the field of ed-ucation, Augmented Reality (AR) technology adds a digital (audio, video, graphic and GPS location) layer to authentic world images. The aim of this study is to determine students’ attitudes regarding AR contents developed for science education. The study group consists of 77 7th grade students studying in a private college during 2018-2019 academic year spring se-mester. The study adopted quantitative data collection method and imple-mented Attitude Scale for Augmented Reality Applications. The collected data were analyzed through paired samples statistics, mean and standard deviation. The data analysis showed that students were satisfied with AR applications, found it interesting and easy to use and would like to use it in the future.

Education was one of the many day-to-day activities affected by the novel coronavirus pandemic (COVID-19). When countries began to shut down in April 2020, nationwide lockdowns, self-isolation, or quarantine became the new normal for everyone. The education sector was kept alive by smart learning environments. Now, more than ever, online learning and tools were implemented. This study aims to systematically review the literature on the impact of the pandemic on smart learning environments. The method adopted in this paper is a systematic literature review, and it will use the PRISMA technique. A qualitative approach was applied in the data collection process to achieve the aim. The essential advantage was that smart learning environments were convenient and easily adapted by students during the pandemic. The main challenge was connectivity issues and failure to adapt to non-traditional methods. The paper concluded a rise in the usage of smart learning environments, and educators and students adapted quickly to the shift.

As cyber security attack rates increase, so do cyber security concerns. Cyber security is the collection of tools, policies, security concepts, security measures, risk management approaches, actions, education, applications, security and technologies that can be used in order to protect the cyber environment and user rights. The aim of this study is to determine the behaviours of pre-service teachers as this study’s objective is to determine the behaviours of pre-service teachers regarding cyber security. This study is designed according to the quantitative research method and the Personal Cyber Security Ensuring Scale has been used. 144 pre-service teachers of two different universities who took up instructional technologies and material design courses in the 2019–2020 spring semester participated in the study. It has been observed that the participants frequently took measures when they noticed unreliable people and situations in the internet environment but took occasional measures in terms of not leaving any trace, taking precautions and protecting personal privacy. Keywords: Cyber attack, cyber security, personal cyber security, pre-service teachers