Mobile learning is increasingly popular due to its flexibility in timing and location. However, challenges such as small screen sizes and poor user interface design can elevate learners' cognitive load, especially extraneous cognitive load, which hinders learning. Extraneous cognitive load, stemming from user interface design complexity, must be minimized to enhance learning focus. Currently, there is no dedicated instrument for measuring extraneous cognitive load specific to mobile learning user interface design. This study aims to develop and evaluate a subjective instrument for measuring extraneous cognitive load caused by user interface design in mobile learning applications. Two sets of experiments were conducted: pretesting to establish the instrument's foundation with a small participant group, followed by pilot experiments to validate the instruments and refine experimental procedures. The NASA-TLX score was used to analyze the relationship between overall cognitive load and extraneous load across various user interface criteria. Understanding these relationships can guide user interface improvements to reduce extraneous cognitive load. Challenges encountered during pretesting and pilot experiments included participant fatigue, scale reliability issues, and incomplete data collection. To enhance reliability, adjustments were made: tasks were reduced, the scale was expanded from a 4-point to a 10-point format, and facilitators thoroughly verified data before participants concluded sessions. Creating a tool to measure how user interface design impacts users' extraneous load is important because it is the UI design, not the mobile app's content that affects extraneous load. However, general methods for measuring cognitive load may not accurately identify problems with user interface design. Therefore, an extraneous load-based method is needed. This will also eventually improve usability.

Online learning has replaced traditional face-to-face classroom instruction in the educational system. Learning via mobile, or mobile learning, is one of the solutions that most learners use since it is less expensive and easier to adopt on the go. However, in addition to hindering information transfer, issues such as small screen size and bad interface design can also make learning more cognitively demanding. This paper presents a systematic literature review on the user interface design of mobile learning applications based on the preferred reporting items for systematic reviews and meta-analyses approach. Articles selected for this study were published after the COVID-19 outbreak, between 2020 and 2022. The goal of this research was to outline the current user interface design criteria and guidelines applied when designing a mobile learning application and explore how these factors affect the learner’s cognitive load. It also aimed to identify potential research gaps and future opportunities in the creation of a UID guideline/framework for mobile learning. The findings of this study may be used as a guideline for designers, developers, educators, instructors, and others who are interested in creating a mobile learning application that provides learners with an effective knowledge and mobile learning experience.

Value-based requirements engineering plays a vital role in the development of value-based software (VBS). Stakeholders are the key players in the requirements engineering process, and the selection of critical stakeholders for the VBS systems is highly desirable. Based on the stakeholder requirements, the innovative or value-based idea is realized. The quality of the VBS system is associated with the concrete set of valuable requirements, and the valuable requirements can only be obtained if all the relevant valuable stakeholders participate in the requirements elicitation phase. The existing value-based approaches focus on the design of the VBS systems. However, the focus on the valuable stakeholders and requirements is inadequate. The current stakeholder identification and quantification (SIQ) approaches are neither state-of-the-art nor systematic for the VBS systems. The existing approaches are time-consuming, complex and inconsistent which makes the initiation process difficult. Moreover, the main motivation of this research is that the existing SIQ approaches do not provide the low level implementation details for SIQ initiation and stakeholder metrics for quantification. Hence, keeping in view the existing SIQ problems, this research contributes in the form of a new SIQ framework called ‘StakeMeter’. The StakeMeter framework is verified and validated through case studies. The proposed framework provides low-level implementation guidelines, attributes, metrics, quantification criteria and application procedure as compared to the other methods. The proposed framework solves the issues of stakeholder quantification or prioritization, higher time consumption, complexity, and process initiation. The framework helps in the selection of highly critical stakeholders for the VBS systems with less judgmental error.

The rapid development of the internet and smartphones has enabled people to access numerous information systems and large volumes of data. User-profiling technology can meet the dual challenge of analyzing user characteristics, interests, or preferences and recommending corresponding resources. Nevertheless, the insufficiency and isolation of data in traditional information systems limit the effect of user profiling, and social networks can compensate for this deficiency. With massive quantities of data in the form of text, images, videos, and relationships in social networks, user profiling can achieve highly accurate analytical results. This review comprehensively discusses user profiling for social networks, defines its criteria, and expounds on the entire process, from data collection to the profiling model and performance evaluation. It includes algorithms, application scenarios, and the advantages and disadvantages of these algorithms. Additionally, considering that technology serves humans, this review provides users with various applications in industry for user profiling based on social networks. Furthermore, it discusses the ethical and legal issues associated with user profiling. Finally, this review highlights possible future research directions in this field. Overall, this review can help researchers enhance their understanding of the current state of research in the field of user profiling and gain ideas for further study.

The increasing accessibility of mobile technology makes it easier for users to use health related applications to combat health issues. In addition to this, gamification has been widely used to increase the engagement between user and application. This study provides an insight into how gamification affects user engagement when using stress management application. Two versions of stress management application were developed, one with gamification element, and the other one without. These two applications were then evaluated by 20 participants using the UES-SF questionnaire, where data on the engagement level of the participants were collected and analysed. The results showed that all participants (100%) prefer the application with gamification to help them manage their stress level better. Statistical tests done revealed that the stress management application with gamification was also found to be significantly more engaging compared to stress management application without gamification (given p ≤ .05). A Wilcoxon Signed-Ranks test indicated that gamified stress management application was rated more favourably than the non-gamified stress management application, with Z score = -3.92, and p = 0.00. The results demonstrated the significance of gamification in increasing user engagement among users when using stress management application.