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The results reveal the low performance of the models exploiting static analysis warnings alone, while we observe significant improvements when combining the warnings with additional code metrics. Nonetheless, we still find that the best model does not perform better than a random model, hence leaving open the challenges related to the definition of ad-hoc features for code smell prediction.Motivated by the need for greater understanding of systems that involve interfaces between a nematic liquid crystal, a solid substrate and a passive gas that include nematic-substrate-gas three-phase contact lines, we analyse a two-dimensional static ridge of nematic resting on a solid substrate in an atmosphere of passive gas. Specifically, we obtain the first complete theoretical description for this system, including nematic Young and Young-Laplace equations, and then, making the assumption that anchoring breaking occurs in regions adjacent to the contact lines, we use the nematic Young equations to determine the continuous and discontinuous transitions that occur between the equilibrium states of complete wetting, partial wetting and complete dewetting. In particular, in addition to continuous transitions analogous to those that occur in the classical case of an isotropic liquid, we find a variety of discontinuous transitions, as well as contact-angle hysteresis, and regions of parameter space in which there exist multiple partial wetting states that do not occur in the classical case.The successful integration of technology in teaching is a key component of education. Although prior research highlighted factors fostering the use of technology by teachers, few studies focused on whether these factors vary among teachers of different grade levels and subjects. Moreover, no studies have investigated personal experiences related to distance education among a large sample of teachers. To address these gaps, the present mixed-method study sought to examine whether factors promoting distance education varied among Italian teachers of different grade levels and subjects. A further aim was to explore experiences of teachers using distance education. The sample involved 357 Italian teachers and preservice teachers who completed an online questionnaire during the COVID-19 pandemic that also contained open-ended questions. Findings indicated that teaching self-efficacy was greater in pre-service and primary teachers, while facilitating conditions were greater in humanities and science secondary teachers. The perceived ease of use of technology and technology for pedagogy skills were more pronounced among science secondary teachers. Advanced technology skills were lower in humanities secondary teachers while the behavioural intention to use technology was greatest among pre-service teachers. Four themes emerged from the qualitative study of teachers’ insights. These included positive and negative aspects of using technology, the relationship with students, the versatility of distance education, and the quality of lessons. This study underscores the need to address training based on different teachers’ grade levels and subjects, and to focus on the emerging themes to better integrate the use of technology in schools.The purpose of this study was to investigate the perceptions of users about using digital detox applications and to display relationships among personality traits and technology-related variables. This study was designed using survey approach and employed Generalized Structured Component Analysis (GSCA). As such, 11 hypotheses were constructed and tested. The study recruited 263 participants who utilize detox applications to avoid social media distractions. Data were collected through Google Form and analyzed using GSCA Pro 1.1 to better understand whether the proposed conceptual model fits the data. The results of the study indicated that behavioral intention predicted usage behavior significantly; performance expectancy, effort expectancy, and social influence positively affected behavioral intention; in turn, agreeableness and extroversion positively influenced performance expectancy, and extroversion affected effort expectancy; finally, neuroticism had a statistically significant and negatively associated with effort expectancy of using social media detox apps. The significant exceptions were that facilitating conditions were not predictive of behavioral intention, openness to experience did not influence performance expectancy, and conscientiousness was not linked to effort expectancy. The proposed conceptual model explained 56.68% of the amount of variation, indicating that instructors, policy makers and software designers should consider personal factors for preparing practical intervention approaches to mitigate learning issues related to social media distraction.Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees’ interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees’ learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.Digital learning environments have been gaining prominence during the last few years. In particular, the rising usage of mobile devices, including smartphones and tabs, has invited researchers to design and develop learning applications and games for such platforms. Mobile applications and games have been developed for learning languages like many other domains. However, most of these games are fun-based and lack a holistic design and development approach. Therefore, as a principal contribution, this research presents a theoretical model for designing language learning games in a cultural context. The proposed model combines the elements of sociocultural theory with the concepts and elements of gamification, keeping in view the requirements and educational settings, including level and mode of education, etc., to ensure the effectiveness and usability of the developed game. Subsequently, based on the proposed model, a Language Learning Game (LLG) has been designed and developed through a systematic process that involves game design, low-fidelity, and high-fidelity prototyping and its validation. The LLG has been evaluated comprehensively at different stages by incorporating standard methods. Whereby this research augments the existing set of heuristics by proposing a number of specialized heuristics for the evaluation of serious games to gauge their conformance to the cultural context. The evaluation results show that the game has overall usability scores of 90%. While the quasi-experiment-based pre-test and post-test have been conducted, the results reveal that the results obtained by LLG are statistically significantly better than adopted mobile application and traditional group.In connection with the situation with COVID-19 almost all universities in the world were transferred to e-learning format, therefore new factors started to influence academic engagement and performance. Psychological security is one of these factors. Many researches have studied the importance of psychological security level among students, some of them proposed the methodology of assessing the indicator. Nevertheless, there are few studies that demonstrate the relationship between psychological security level of students and their academic engagement and performance. The aim of the current study is to close this scientific gap. For the assessment the Trustworthiness Factors survey, Academic Engagement Scale and academic performance results were used. A total of 351 students aged between 19 and 21 (M = 19.57, SD = 0.59), mainly female (57%), were integrated in the sample. Online surveys were conducted to reveal the level of students’ psychological security, their academic engagement and performance in the process of e-learning and analyze the associations between these variables. The female students analyzed showed higher levels of psychological security, and especially in the communication of own ideas in webinar rooms. SecinH3 supplier The same tendency was found in the levels of academic engagement and performance. The findings obtained by using the linear regression analysis technique indicated that psychological security predicted academic performance positively. In contrast to earlier studies, student safety is considered not only as an aspect of personal data security, but more as a psychological one. It was possible to conclude that the influence of psychological security on students’ engagement and academic performance is particularly visible in the online educational environment.E-learning system success factors identification is of major interest in higher education. Understanding the role of students’ aspirations factor affecting the success of the e-learning system is a challenge for most educational institutions. The present study aims to analyze the effects of students’ aspirations factors in ensuring the success of the e-learning system through a developed research model extended from the integrated updated Unified Theory of Acceptance and Use of Technology and the DeLone and McLean Information System Success Model. The study participants who made up the model data sample were collected from 379 students engaged in the e-learning system at universities across the Kingdom of Saudi Arabia. Students’ aspirations are resumed in Motivation, Expectation, and Enjoyment factors. The structural equation model was used to analyze the main causes and effects that would guide students towards the use and success of the e-learning system. The study results showed the strong relationship between the students’ aspirations factors (Motivation, Expectation, and Enjoyment) and the adoption factors (Intention to Use and Perceived Usefulness) that lead to increased students’ confidence that e-learning adds value to their educational experience. In addition, results revealed the determining role of the effect of the Enjoyment factor on the benefits expected from the e-learning system process. Therefore, higher education institutions that aspire to benefit the most from the e-learning system should pay close attention to the aspirations of their students and enhance their enjoyment, and then redefine the “e” in e-learning as enjoy rather than simply electronic.