Factors Influencing YouTube as a Learning Tool and Its Influence on Academic Achievement in a Bilingual Environment Using Extended Information Adoption Model (IAM) with ML Prediction—Jordan Case Study

Comparing educational and dissemination videos in a STEM YouTube channel: A six-year data analysis

Didactic videos have proven to be particularly beneficial for Science, Technology, Engineering and Mathematics (STEM) education challenges. This is due to their contribution to the reduction of the intrinsic cognitive load, as well as fostering connections among subjects to promote generative processing. This fact, together with the wide presence of STEM dissemination videos on YouTube, creates an opportunity both for teachers to focus on dissemination channels to acquire quality materials, and for content creators to develop specific videos aiming to serve as pedagogical aid. Aligned with such reasoning, this paper intends to evaluate the use and performance of a STEM YouTube channel that already publishes didactic videos categorized by intentionality into dissemination or educational. Using owners' data, the channel has been analyzed, considering a six-year extension period, from 2017 to 2022, and distinguishing between the two categories of contents. This dual intention of the channel allows to evaluate the educational use of dissemination channels, compared to their use for curiosity satisfaction. Through Mann-Whitney U and correlation analyses, the main channels' metrics have been analyzed in terms of awareness (i.e., impressions, views, or subscribers), use (view duration) and interaction (comments, shares, likes or dislikes). Significant differences have been found in the performance of educational and dissemination contents, such as in views (p < 0.001), average view duration (p = 0.044) and likes (p < 0.001). Additionally, video length optimization has been found as a determining parameter influencing video performance, being educational videos more sensitive to this metric than dissemination videos. This study has shown that a STEM YouTube channel of these characteristics can benefit from publishing videos aiming to a pedagogical purpose, increasing its use compared with other STEM dissemination channels only aiming to an entertainment purpose. Additionally, this study supports previous findings that video length optimization is a determinant characteristic for audience retention rate.

Analysis of factors affecting the effectiveness of face-to-face marketing learning via TikTok, YouTube and video conferencing

After the COVID-19 pandemic, the use of virtual platforms, social networks and online applications has been totally modified by returning to face-to-face systems. The mandatory use of these tools in the period of social distancing has led to their extensive development. This paper analyzes the effects of the use of ICTs in a purely face-to-face environment. Therefore, it analyzes the factors that affect student satisfaction and effectiveness in marketing learning through the use of digital tools and online applications in face-to-face classes, and this analysis is from a dual perspective of user and learning dissatisfaction. Using a learner-centered approach in university classes, the use of the digital platform Blackboard, the digital tool YouTube and the social network TikTok in university marketing teaching is analyzed. The study is carried out on a sample of 327 university students of different levels. The analysis is performed through a mixed methodology using a symmetric (PLS-SEM) and non-symmetric (fsQCA) approach, allowing a better generalization of the results. In the measurement of effectiveness, both user assessment and student learning assessment are significative, the two being affected in a similar way. However, they are not related to each other, acting independently. Attitude is the construct that has the greatest impact on both types of satisfaction. Perceived enjoyment also exerts a notable influence, especially on learning satisfaction. The study presents one of the first post-pandemic approaches to the analysis of the effectiveness of technological tools (ICT tools) in the face-to-face setting of university marketing classes.

Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM

Previous research has found support for depression and anxiety associated with social networks. However, little research has explored parents' depression and anxiety constructs as mediators that may account for children's depression and anxiety. The purpose of this paper is to test the influence of different factors on children's depression and anxiety, extending from parents' anxiety and depression in Jordan. The authors recruited 857 parents to complete relevant web survey measures with constructs and items and a model based on different research models TAM and extended with trust, analyzed using SEM, CFA with SPSS and AMOS, and ML methods, using the triangulation method to validate the results and help predict future applications. The authors found support for the structural model whereby behavioral intention to use social media influences the parent's anxiety and depression which correlate to their offspring's anxiety and depression. Behavioral intention to use social media can be enticed by enjoyment, trust, ease of use, usefulness, and social influences. This study is unique in exploring rumination in the context of the relationship between parent-child anxiety and depression due to the use of social networks.

Predictors for E-Government Adoption of SANAD App Services Integrating UTAUT, TPB, TAM, Trust, and Perceived Risk

Using mobile applications in e-government for the purpose of health protection is a new idea during COVID-19 epidemic. Hence, the goal of this study is to examine the various factors that influence the use of SANAD App As a health protection tool. The factors were adopted from well-established models like UTAUT, TAM, and extended PBT. Using survey data from 442 SANAD App from Jordan, the model was empirically validated using AMOS 20 confirmatory factor analysis, structural equation modeling (SEM) and machine learning (ML) methods were performed to assess the study hypotheses. The ML methods used are ANN, SMO, the bagging reduced error pruning tree (RepTree), and random forest. The results suggested several key findings: the respondents’ performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, trust, and perceived service quality of this digital technology were significant antecedents for their attitude to using it. The strength of these relationships is affected by the moderating variables, including age, gender, educational level, and internet experience on behavioral intention. Yet, perceived risk did not have a significant effect on attitude towards SANAD App The study adds to literature by empirically testing and theorizing the effects of SANAD App on public health protection.