Telematics and Informatics Reports最新文献

Goal setting is vital in learning sciences, but the scientific evaluation of optimal learning goals is underexplored. This study proposes a novel methodological approach to determine optimal learning goals. The data in this study comes from a gamified learning app implemented in an undergraduate accounting course at a large German university. With a combination of decision trees and regression analyses, the goals connected to the badges implemented in the app are evaluated. The results show that the initial badge set already motivated learning strategies that led to better grades on the exam. However, the results indicate that the levels of the goals could be improved, and additional badges could be implemented. In addition to new goal levels, new goal types are also discussed. The findings show that learning goals initially determined by the instructors need to be evaluated to offer an optimal motivational effect. The new methodological approach used in this study can be easily transferred to other learning data sets to provide further insights.

Current trends in keyboard design show that QWERTY-similarity is a key factor for high user-adaptability keyboard design. This design approach has the challenge that the higher the similarity the lower the text-entry rate for the optimized keyboard. This article reports on the findings of an empirical study which we conducted on QWERTY-users to measure the effect of using an entry-level keyboard in improving user-ability to adapt to a new keyboard. The study used two Central-Bantu physical keyboards (entry-level: with high QWERTY-similarity, and advanced-level: with low similarity) which we had designed in an earlier study. The empirical study obtained learning-curves of the Advanced-level keyboard, of a 12-participant group which was first introduced to an entry-level keyboard, against a control-group of similar size, in a longitudinal study design. A two-sample t-test on the empirical results showed that the entry-level approach caused a marginally significant text-entry-rate improvement of 9.4% with p < .09. A two-sample U-test on word-error rates indicated a non-significant improvement of 8.4%. Our study has shown that the use of entry keyboards is an effective strategy in improving keyboard user-adaptability.

Deep learning and machine learning techniques present unmatched opportunities to improve healthcare in sub-Saharan Africa (SSA). However, there is a paucity of literature on AI-based applications deployed to improve care in SSA, which makes it challenging to organise the research contributions in the present and to highlight obstacles and emerging research areas that need to be explored in the future. This study applied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model to conduct a comprehensive review of deep learning and machine learning models deployed in SSA to improve access to care while exploring emerging opportunities, trends and implications for integrating AI-based models in SSA healthcare. This study reveals that AI models can analyse and derive inferences from massive health data for early detection, diagnosis, monitoring for chronic disorders, prediction of diseases, monitoring large-scale public health patterns and help limit exposure in contagious environments. AI can facilitate the development of targeted health interventions and improve patient outcomes in all stages of diagnosis, treatment, drug development and monitoring, personalised medicine, patient control and care. Integrating AI models with health applications can tremendously assist health professionals and policymakers in disease diagnosis and making informed decisions. AI algorithms bias, poor access to health data and formats, and lack of policies and frameworks supporting the integration of data-driven AI-based solutions into health systems hinder the integration of AI-based models into health systems. There is a need for transparency and ethical use of AI and crafting policies that support the use of AI in SSA health systems. Utilising AI-based models in healthcare can also assist researchers and healthcare workers to move towards smart care and better comprehend future research needs of AI in smart care.

Snapchat offers a unique function, the Snapchat Streak, which is a gamified function within the app that motivates users to participate in daily interactions. This feature of the application can aid users in building a friendship with their peers. Given the requirement of interacting on the platform every 24 hours, our exploratory study aims to investigate how Snapchat streaks are associated with Fear of Missing Out (FOMO), problematic smartphone use and social media self-control. We conducted a study among a final sample of 2483 early adolescents (M_age = 13.46 years old; SD = 0.894) in the Dutch-speaking community of Belgium. The results indicate that the girls were more likely than boys to engage in a Snapchat streak and were more likely to engage in streaks for a longer period of time. Problematic smartphone use was associated with the engagement in Snapchat streaks. Lastly, FOMO, problematic smartphone use, and social media self-control were correlated with the number of people and the number of days adolescents maintained Snapchat streaks with, albeit it being a weak relationship. Implications of the findings for understanding adolescent Snapchat use are provided.

A lot of research has been conducted to explore the high amount of daily usage time on social networking sites (SNSs). We suggest a two-dimensional frame of reference for investigating the usage of SNSs, which incorporates both four motives for usage and mode of usage in terms of active/passive usage. A special feature of the item construction was to combine the motive facet and the active/passive facet as integral dimensions in the formulation of items. The advantage of this approach is the comprehensive assessment of SNS use with high content validity. Till now, such a measure, which is based on the two-dimensional frame of reference, does not exist. To fill the gap the aim of our study was to develop the Motives to use SNSs Scale (MOTUS) assessing: (1) active as well as passive use and (2) underlying motives by conducting two studies (N₁ = 597, N₂ = 437). Study 1 focused on item development, item reduction, and identification of factor structure. The analysis resulted in an eight-factor structure that represented active and passive domains of each motive. Study 2 confirmed this factor structure by showing good values for model fit. The results confirmed the construct validity of the MOTUS. Results are discussed with respect to applications and limitations of the new measure.

As esports continues to grow in popularity, it is important to evaluate esports viewers and the fan communities that form around esports. The present study surveyed esports fans (N = 174) to examine motives for watching esports, how those motives may relate to engagement behaviors within the esports community, and if the strength of fan identity mediated the relationship between motives and engagement. The three categories of motives assessed were skill-based, entertainment-based, and relationship-based. Results suggest that the strength of fan identity predicts both viewing frequency and engagement online within esports communities. Skill-based motives predicted greater viewing frequency, while relationship-based motives predicted both viewing frequency and engagement online. Mediation analysis revealed that fan identity partially mediated skill-based motives and fully mediated relationship-based motives. Entertainment-based motives did not predict viewing or engagement. Together, our results provide additional insight into esports fandoms, and the significance of relationships formed and sustained.

The prolonged COVID-19 pandemic reduced the performance of the software developer team in the Indian IT industry because of numerous psychosocial challenges while working in a non-dedicated workspace. Therefore, using the lens of self-determination theory (SDT), the present study investigated the indirect effect of mindfulness on team performance via psychological safety and active remote engagement under the bounded condition of adhocracy culture. The web-based responses of 604 team members nested in 99 teams from 10 Indian IT companies were used to perform a multilevel analysis. The analyses were conducted using Mplus 8.0 version to test the hypothesis. The results showed a significant serial mediation role of psychological safety and remote engagement in the relationship between the mindfulness of software developers and team performance. The relationship between mindfulness and psychological safety becomes stronger when the adhocracy culture is high. Similarly, the relationship between mindfulness and team performance via the serial mediation of psychological safety and active remote engagement becomes stronger when the adhocracy culture is high. Similar to many psychological approaches, this study supports the postulates advocated by SDT, while emphasizing the central role played by mindfulness.

This paper makes a contribution to the literature on the relationship between undernourishment and ICT. There are very few studies on this topic, and existing studies have not yet looked at the channels through which ICT affect undernourishment. We use a stochastic impact model extended to the population, affluence, and technology (STIRPAT) regression model to estimate both the effect and transmission of ICT on undernourishment in 45 African countries from 2000 to 2017. The empirical results show that ICT use measured by internet and mobile phone penetration reduces malnutrition. Furthermore, the mediation analysis reveals that ICT not only have a direct positive effect on undernourishment but also a positive indirect effect through their impact on per capita income, education, health, and access to clean water. However, the total effect is positive and indicates that ICT are useful in reducing undernourishment in Africa. In addition, these results have also shown us that variables such as education, public spending on health and access to drinking water are useful in reducing malnutrition in Africa. However, per capita income and the level of inflation exacerbate malnutrition on the continent. To enhance the beneficial effects of ICT on malnutrition, governments should design policies to improve access to water, education, and health care; better distribution of national wealth.

This systematic review aimed to display the trajectory of research on the chosen topics, conduct a methodical analysis of the relevant literature by evaluating the journal parameters, and suggest potential avenues for research on disruptive innovation (DI) in agriculture. A systematic literature review (SLR) method was used to conduct the review research. A total number of 61 articles on DI in agriculture were reviewed. The PRISMA flowchart completed the overall process to confirm the final selection of the review. The review outcomes provided that most DI in agriculture research was based on food supply, technology adoption, digital risk management, and agriculture modernization from the evidence of some selected developed and developing countries. Only a few DI research were found from the evidence of transition economies. However, the review found a lack of research on the government's inclusion of DI in agriculture. More investigations are required for the sub-sector of the agriculture industry for the actual development of the overall agriculture industry. So far, the researchers' knowledge; the review is possibly the very first attempt by any researchers to work on SLR research regarding DI in agriculture. This paper contributes to agriculture, a vital process in the marketplace that currently lacks a strong foundation of innovation theory in this sector. This paper broadens the understanding of this subject by comprehensively analyzing this study area.

In our paper, we used factor analysis and artificial intelligence-based modeling to reveal how video gamers living in an economically peripheric European Union member state can be segmented based on gaming time. We performed a causal analysis of two groups—occasional and frequent players—based on the data of a 2022 representative survey. The results show that in a semi-peripheral country, the adult population plays video games in a similar proportion as in the central countries (every second person), but at the same time, a high degree of polarization can be demonstrated in the time spent gaming. Based on our results, players can be predicted with high reliability based on player preferences, that is, game types, motivations, and shopping habits. However, it is worth supplementing these categories with players' socio-demographic characteristics. Our research contributes to a deeper understanding of the factors and causal relationships that determine video gamers in semi-peripheral countries; moreover, it sheds light on the distinguishing variables that function as group builders.