Comparison of acute physiological effects between alternating current and pulsed current electrical muscle stimulation

Electrical muscle stimulation (EMS) is widely used in rehabilitation and sport training, and alternating current and pulsed current EMS are commonly used. However, no systematic comparison between alternating and pulsed current EMS has been made in the previous studies. The main aim of this research was to compare acute physiological responses between the alternating and pulsed current EMS. The secondary purpose of the research was to investigate further muscle damage induced by EMS-evoked isometric contractions. Three experimental studies were conducted in the thesis project together with literature review about EMS

The Effectiveness of Using Mobile Interactive Voice Assistant Applications in Developing Academic Self-Efficacy of Saudi University Students during the COVID-19 PandemicThe Effectiveness of Using Mobile Interactive Voice Assistant Applications in Developing Academic Self-Efficacy of Saudi University Students during the COVID-19 Pandemic

The current research aimed to investigate the effectiveness of using mobile interactive voice assistant applications (i.e. Siri and Google Assistant) in developing Saudi university students’ academic self-efficacy during the COVID-19 pandemic. The research followed the one group quasi-experimental design. It was delimited to a period of the second term during the academic year 2020-2021. Forty-eight participants completed the Arabic Academic Self-Efficacy Scale developed and validated by the author. Results indicated that the participants showed higher improvement in the mean scores of the posttest compared to the pre-test. Additionally, in the post-testing of academic self-efficacy according to grade point average variable, there were statistically significant differences between the mean scores of the participants, whereas there are no statistically significant differences between their mean scores in the post-testing of academic self-efficacy according to mobile digital skills variable. Based on the results, maximizing training courses for faculty members to optimize their digital competencies in using modern technology in teaching is highly recommended

The investigation of using wiki technology to support self-regulated learning in the academic context at Princess Nora bint Abdul Rahman University, Saudi Arabia

Technology has become a major focal point in the modern learning environment. Web 2.0 is being increasingly widely employed in university education and has the potential to improve the quality of education. For optimum benefit to students’ learning practices, web 2.0 technology needs to actively foster regulation skills among students. Self-regulated learning skills (SRL skills) potentially offer a shift from traditional teacher-centred to learner-centred approaches. Wiki technology, as a form of web 2.0 technology, has the potential in education to foster such an approach to learning. The thesis investigates how a wiki can be utilised to enhance self-regulated learning among a cohort of female students attending higher education in Saudi Arabia.The study was primarily motivated by the lack of studies investigating SRL skill enhancement in wiki–assisted learning in higher education, in Saudi Arabia, where the education system largely relies upon teacher-centred learning. This study, therefore, was an effort to potentially improve SRL skills among students attending Princess Nora University (PNU) in Saudi Arabia, with a view to the results being applicable to teaching and learning in similar contexts. The first two objectives of this study were to explore the potential of a wiki as an enhancer of executive function and evaluation skills and to explore students’ attitude towards using wiki as a learning environment. The third objective was to explore students’ perceptions of wiki learning and its contribution to the enhancement of SRL skills. A single case study was administered before and after use of a purpose-designed wiki for an Education Technology module taken by a cohort of female students at PNU. Quantitative data was collected by a questionnaire triangulated with qualitative data gathered in interviews. The findings revealed that after using wiki, students felt that six of the eight SRL sub-skills listed under executive function and evaluation skills had, on the whole, improved significantly.Students generally reported extremely positive attitudes towards learning with wiki technology. They perceived that the reflective nature and the design of the wiki tasks, together with the pages and guidance given by the tutor, may have supported the development of SRL skills, increased their overall motivation to learn and improved their independent learning processes. Overall, this study sought to discover information on a relatively new area to Saudi higher education and acts as a stepping stone to further research into students’ perceptions of wiki technology and its effect on SRL skill enhancement. There is, of course, an opportunity in the future to measure actual SRL skill levels to corroborate the promising results which may, given the reader’s discretion, be viewed as transferable to similar cultural and study contexts

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%. We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.Comment: Accepted as a full paper at CSCW 2019. Please cite the CSCW versio

Characterizing the role of bots’ in polarized stance on social media

There is a rising concern with social bots that imitate humans and manipulate opinions on social media. Current studies on assessing the overall effect of bots on social media users mainly focus on evaluating the diffusion of discussions on social networks by bots. Yet, these studies do not confirm the relationship between bots and users’ stances. This study fills in the gap by analyzing if these bots are part of the signals that formulated social media users’ stances towards controversial topics. We analyze users’ online interactions that are predictive to their stances and identify the bots within these interactions. We applied our analysis on a dataset of more than 4000 Twitter users who expressed a stance on seven different topics. We analyzed those users’ direct interactions and indirect exposures with more than 19 million accounts. We identify the bot accounts for supporting/against stances, and compare them to other types of accounts, such as the accounts of influential and famous users. Our analysis showed that bot interactions with users who had specific stances were minimal when compared to the influential accounts. Nevertheless, we found that the presence of bots was still connected to users’ stances, especially in an indirect manner, as users are exposed to the content of the bots they follow, rather than by directly interacting with them by retweeting, mentioning, or replying

Unintended Transnational Effects of Sanctions: A Global Vector Autoregression Simulation

The debate on unintended consequences of sanctions, such as their adverse effects on human rights, public health, or the economy beyond intended sectors in the target state, has become increasingly popular over the last couple of decades. Interestingly, however, this debate has mostly overlooked the transnational aspects of these unintended consequences. This study examines one such aspect, namely the economic spillover of sanctions to neighboring countries. Our global vector autoregression oil and inventory model (GOVAR) simulations on Indonesia, a medium-level oil producer, indicate sanctions may spill over to its neighbors’ domestic economy. The risk and nature of spillover varies with respect to the type of sanctions employed, timing of sanctions, and the macroeconomic indicator in the neighboring state in question. Equity markets appear especially susceptible to a contagion effect. Understanding how a sanction spills over to neighboring states can help sender states design sanctions that minimize regional disruptions.</p