Specific User Research Articles

Tailored Investments Needed to Support Weather, Water, Ice, and Climate Services in the Polar Regions

Abstract The Polar Prediction Project (PPP), one of the flagship programs of the World Meteorological Organization’s (WMO) World Weather Research Programme (WWRP), has come to an end after a decade of intensive and coordinated international observing, modeling, verification, user engagement, and education activities. While PPP facilitated many advancements in modeling and forecasting, critical investment is now required to turn prediction science into salient environmental services for the polar regions. In this commentary, the members of the Societal and Economic Research and Applications task team of PPP, a group of social scientists and service delivery specialists, identify a number of insights and lessons that are critical for the implementation of the follow-up program Polar Coupled Analysis and Prediction for Services (PCAPS). We argue that in order to raise the societal value of polar environmental services, we need to better understand the diversity of highly specific user contexts; to tailor the actionability of weather, water, ice, and climate (WWIC) service development in the polar regions through inclusive transdisciplinary approaches to coproduction; to assess the societal impact of improved environmental services in the polar regions; and to invest and provide dedicated funding for involving the social sciences in research and tailoring processes across all the polar regions.

Video quality adaptation using CNN and RNN models for cost-effective and scalable video streaming Services

Video streaming services require adaptive bit rate strategies that optimize video quality based on network conditions and user preferences to provide a cost-effective and scalable solution. In this manuscript, we present a novel architecture that utilizes a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features from the video stream and predict optimal bit rates for each frame. The CNN feature extractor extracts relevant features from the input video stream, which are then passed on to the RNN predictor, where the optimal bit rate is predicted to ensure a personalized viewing experience tailored to the current network conditions and specific user preferences. Our experimental results demonstrate that the proposed architecture achieves significant improvements in video quality while minimizing bandwidth usage and providing a better user experience. Specifically, our results show a 37.1% improvement in average bit rate, indicating that the proposed architecture optimizes the video quality and reduces bandwidth usage by 37.1% on average. We also observed a 16.6% improvement in Quality of Experience (QoE), meaning that users will perceive the video quality as better. Additionally, rebuffering was reduced by 87.5%, indicating that users enjoy smoother video playback without interruptions. Importantly, our architecture is optimized for Dynamic Adaptive Streaming over HTTP (DASH), addressing the need for efficient streaming over the most widely used protocol in the industry.

Your Device May Know You Better Than You Know Yourself-Continuous Authentication on Novel Dataset using Machine Learning

This research aims to further understanding in the field of continuous authentication using behavioural biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch dynamics can effectively distinguish users. However, further studies are needed to make it viable option for authentication systems. You can access our dataset at the following link:https://github.com/AuthenTech2023/authentech-repo.

The ‘overall protection’ of temporary agency workers, the role of social partners, and the case of Denmark

The article analyses the CJEU ruling C-311/21 TimePartner, where the Court interpreted the option provided in the Temporary Agency Work Directive for social partners to derogate from the principle of equal treatment. The CJEU concluded that the term ‘while respecting the overall protection of temporary agency workers’ is a criterion for using the option to derogate, and that a derogating collective agreement must be subject to legal review of fulfilling this criterion, even in Member States where collective agreements are presumed to be correct. Furthermore, the CJEU ruled that in order to respect the overall protection, any disadvantages in basic working conditions, i.e., working time and pay, should be set off against other advantages relating to the basic working conditions for the temporary agency workers. Finally, that the assessment is an in concreto comparison of disadvantages and advantages in terms of the basic working conditions applicable at the specific user undertaking. The article argues that the CJEU, with this approach, at the same time restricts the room for negotiation of national social partners concluding derogating agreements, and directs the legal review of the national judiciaries. This approach may interfere in the national industrial relations systems in some Member States more than others. The article then discusses how the Danish legal framework respects the overall protection of temporary agency workers. Temporary agency workers are addressed in many collective agreements in force at user undertakings with a view to counteracting circumvention of the collective agreement, and likewise, many temporary work agencies are covered by collective agreements. The transposition process in 2013 of the option to derogate is presented. The Danish Supreme Court in 2019 ruled, that the term ‘respecting the overall protection’ in the derogation provision in the Danish Temporary Agency Workers Act constitutes a legal criterion, which can be subject to judicial review, and is not an automatic consequence of a collective agreement. The article discusses how an assessment of the level of protection would be carried out in Danish industrial judiciaries, taking inspiration from earlier industrial case law comparing the overall protection of competing collective agreements. The article finally asserts that the approach of the CJEU in the TimePartner ruling protects temporary agency workers rather than pursuing flexibility in the labour markets, and as such favours one of the dual aims of the Directive. In that light, the approach taken by the CJEU ensures that derogating collective agreements meet certain standards in terms of the basic working conditions of temporary agency workers. At the same time, the ruling does not interfere with other negotiated or legislated working conditions, where the TAW Directive does not in itself require equal treatment or ‘overall protection’. The limitation of the TAW Directive to concern equal treatment only for basic working conditions, and not all working conditions, thus continues to allow a degree of flexibility for employers as well as for temporary agency workers.

"Cyclist at 12 o'clock!": a systematic review of in-vehicle advanced driver assistance systems (ADAS) for preventing car-rider crashes.

While Advanced Driver Assistance Systems (ADAS) have become a prominent topic in road safety research, there has been relatively little discussion about their effectiveness in preventing car collisions involving specific vulnerable road users, such as cyclists. Therefore, the primary objective of this systematic literature review is to analyze the available evidence regarding the effectiveness of in-vehicle ADAS in preventing vehicle collisions with cyclists. To achieve this goal, this systematic review analyzed a selection of original research papers that examined the effectiveness of ADAS systems in preventing car-cyclist collisions. The review followed the PRISMA protocol, which led to the extraction of 21 eligible studies from an initial pool of 289 sources indexed in the primary scientific literature databases. Additionally, word community-based content analyses were used to examine the research topics and their links within the current scientific literature on the matter. Although the current number of studies available is still scarce (most sources focus on car-motorcyclist or car-pedestrian crashes), the overall quality of the available studies has been reasonably good, as determined by the selected evaluation methods. In terms of studies' outcomes, the literature supports the value of in-vehicle ADAS for preventing car-cyclist crashes. However, threatful side effects such as unrealistic expectations of these systems and users' overconfidence or desensitization are also highlighted, as well as the need to increase driver training and road user awareness. The results of this study suggest that Advanced Driver Assistance Systems have significant potential to contribute to the prevention of driving crashes involving cyclists. However, the literature emphasizes the importance of concurrently enhancing user-related skills in both ADAS use and road-user interaction through educational and training initiatives. Future research should also address emerging issues, such as ADAS-related behavioral ergonomics, and conduct long-term effectiveness assessments of ADAS in preventing car-cycling crashes and their subsequent injuries. PROSPERO, unique identifier CRD42024505492, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=505492.

Handover algorithm based on Bayesian-optimized LSTM and multi-attribute decision making for heterogeneous networks

Heterogeneous networks (HetNets) which can satisfy the diverse requirements of future terminal tasks through providing different network access services for terminals, have emerged as a vital technology for the development of next-generation networks. However, frequent access to different networks often leads to high handover delay and handover failure, resulting in a degradation of user experience. Therefore, maintaining seamless network connectivity has become a challenging problem for applications with high real-time and reliability requirements in HetNets. To address this problem, we propose a handover algorithm based on Bayesian-optimized Long Short-Term Memory (BO-LSTM) and multiple attribute decision making (HO-BLM) to reduce handover latency and failure, ensuring seamless network connectivity. To reduce handover delay, we designed a handover decision prediction algorithm based on BO-LSTM to trigger handover in advance by predicting the network state of users in the future. Additionally, we propose a preselection mechanism based on current network status to further reduce the calculation delay. Moreover, a multi-attribute decision making (MADM)-based selection approach is designed to select the best alternative network for a specific user, where the utility value of the best alternative network is calculated by weighing the users’ preferences for network parameters of different business types and the actual status of the candidate networks. Simulation results show that the proposed algorithm decreases the handover failure by up to 42.1% and the handover delay by up to 68% compared with traditional handover approaches.

Analysing environmental opinion using highly customisable visualisation tools to understand citizens’ attitudes and barriers

Knowledge of public opinion is key to understanding citizens' attitudes towards environmental policies. However, large polls and surveys generate complex datasets from which it is not always easy to draw conclusions. In addition, tailor-made solutions for analysing public opinion face the challenge of handling too many layers of information, which can easily lead to an overwhelming user experience and impair decision-making. Service design methodologies can support the design of ad hoc visualisation tools focused on user needs. We present Op-e-nion, a case study of a visualisation tool for the analysis of public opinion regarding environmental issues, aimed at administrations and public institutions. The involvement of experts from different fields allowed for the identification of the main metrics necessary to target the least engaged socio-demographic groups as well as the barriers that limited their environmental actions. Experts also highlighted useful aspects of the design process and the final prototype to help them define more effective campaigns and policies to address social challenges and promote citizen action. An innovative step was introduced in the methodology by involving non-state actors in the evaluation of the tool, ensuring problem detection and enhancing the sustainability of the final product. Important aspects for the visualisation of multi-categorical data included simplifying the interaction with the tool while prioritising relevant information, and using highly customizable visualisations to answer specific user requirements and changing needs (i.e. analytical vs. managerial tasks). Improved visualisations of public opinion data will, in turn, better support the development of policies shaped by citizens’ concerns.

Constraint-based high utility mobile trajectory pattern mining for security systems

In security system, high utility pattern mining of a large number of users mobile trajectories is helpful to analyze user behavior patterns, and enhance the internal prevention of the security system.Currently, the frequent pattern mining for mobile trajectory in security systems do not take into account the differences in the safety levels between staying points and the frequency of their occurrence. Existing high utility mobile trajectory pattern mining methods are unable to discover staying points that meet specific user requirements. To solve these problems, a constraint-based high utility mobile trajectory mining algorithm is proposed (names as HUIM-ILC-ACO). It takes into account the user’s stay time, stay frequency, and the safety level of each stopping point and calculates the utility value of each staying point by incorporating these factors through weighted computation. Based on this, the algorithm combines ant colony optimization with length constraints and item constraints to construct a method for mining high utility mobile trajectory patterns that better align with user interests. Experimental results on real datasets and a target mobile trajectory RFID dataset show that proposed algorithm is efficient in terms of runtime and pattern quantity, and it can effectively mine a pattern set that is closer to user interests.

Improving Supply Chain Human-Machine Systems by the Analysis of Departmental-Level User Characteristics

In this study, the authors aimed to enhance supply chain efficiency at Company A, a key player in China's manufacturing sector, by focusing on user-centric system design. The approach involved analyzing departmental-level user characteristics to inform the development of an optimized human-machine interface. To achieve this, they conducted focus groups and card sorting exercises, identifying specific user needs across departments like planning, purchasing, receiving, warehousing, production, and quality control. The application of these insights into system design led to significant operational improvements. Notably, time savings were observed in departments such as receiving (74.44%) and quality control (41.86%). This tailored approach underscored the importance of understanding and integrating user characteristics in supply chain systems, leading to enhanced productivity and a competitive edge. The study demonstrates the impact of user-focused system design in improving both efficiency and user satisfaction in supply chain management.

Reliability of river fleet vessels hull elements

Improving the process of fault detection of metal ship hull elements by using a probabilistic approach is considered in the paper. The most labor-intensive part of the defect detection report, namely, measurement and assessment of hull structures individual elements wear, accounting for more than 90 % of the total measurements, is reviewed. Defect detection of a ship hull means the examination, measurement and assessment of defects in each element of the ship hull in order to establish method and volumes of repairs that ensure its reliable operation until the next survey under specified operating conditions. The necessity of carrying out a preliminary calculation stage for defect detection is substantiated. It allows you to significantly reduce the number of measurements of hull structures elements, which will have a positive effect on reducing the cost of repair both due to a smaller number of measurements and the duration of repairs. It has been proved that using the probability approach to predict the residual thickness of individual elements also takes into account the effect on the total strength, which guarantees the reliability of the ship hull in operation. Tables to assess the technical condition of individual hull elements based on their level of wear have been developed, and a method for filling them has improved. The tables are made in two versions, namely, paper and electronic, the most convenient for a specific user — the designer of the defect detection report tables. A test example of using the electronic tables for assessing technical condition by wear level, which proves that the predicted residual thicknesses obtained by calculation will in all cases, be less, than similar ones obtained from measurements results, is carried out using a representative vessel. This proves the reliability and feasibility of using the preliminary defect detection stage, which is essentially an analogue of the concept of the zero stage in ship repair. Using the probability approach to determine the residual thicknesses will allow you not only to reduce the costs of defect detection of the hull itself, but also at early stages (before placing the ship on a slip or dock) to reduce the volume of hull structures repairs, and to choose the most appropriate and effective method of repair.

Zero-shot test time adaptation via knowledge distillation for personalized speech denoising and dereverberation.

A personalization framework to adapt compact models to test time environments and improve their speech enhancement (SE) performance in noisy and reverberant conditions is proposed. The use-cases are when the end-user device encounters only one or a few speakers and noise types that tend to reoccur in the specific acoustic environment. Hence, a small personalized model that is sufficient to handle this focused subset of the original universal SE problem is postulated. The study addresses a major data shortage issue: although the goal is to learn from a specific user's speech signals and the test time environment, the target clean speech is unavailable for model training due to privacy-related concerns and technical difficulty of recording noise and reverberation-free voice signals. The proposed zero-shot personalization method uses no clean speech target. Instead, it employs the knowledge distillation framework, where the more advanced denoising results from an overly large teacher work as pseudo targets to train a small student model. Evaluation on various test time conditions suggests that the proposed personalization approach can significantly enhance the compact student model's test time performance. Personalized models outperform larger non-personalized baseline models, demonstrating that personalization achieves model compression with no loss in dereverberation and denoising performance.

Towards a general user model to develop intelligent user interfaces

The way end-users interact with a system plays a crucial role in the high acceptance of software. Related to this, the concept of Intelligent User Interfaces has emerged as a solution to learn from user interactions with the system and adapt interfaces to the user’s characteristics and preferences. However, existing approaches to designing intelligent user interfaces are limited by their user models, which are not capable of representing each and every user characteristic valid for any context. This work aims to address this limitation by presenting a user model that can abstractly represent a wide set of user characteristics in any context of interaction. The model is based on a synthesis of previous works that have proposed specific user models. After the analysis of these works, a more sophisticated user model has been defined, including some required characteristics not existing in previous works. This model has been validated with 62 real end-users who have expressed the users’ characteristics that they consider as relevant to adapt the interaction. The results show that most of these characteristics can be represented by the proposed user model. This user model is the first step towards creating intelligent user interfaces that can adapt interactions to users with similar characteristics and preferences in similar contexts.

Using solar-assisted biogas for space heating in rural China: An optimization case study

While biomass feedstock is readily available in rural areas of China, offering considerable potential for biogas utilization, the predominant use of biogas systems is limited to cooking rather than heating. This limitation arises from the mismatch between the high demand for heating in winter and the relatively low biogas production rates, creating a supply–demand disparity. The primary objective of this study is to enhance the heating reliability of the biogas system while simultaneously reducing the overall heating system cost. To achieve this goal, we propose an optimized operational strategy and design method centred around the concept of supply–demand matching. Our approach involves implementing a passive solar greenhouse-assisted centralized biogas production and decentralized heat supply system. The size of the biogas plant is tailored to meet specific user demands, with user demand regulation achieved through biogas storage capacity. Optimization techniques include feed time and insulation quilt control, leveraging waste heat recovery, as well as biogas storage optimization through adjustments in hydraulic residence time. The optimized design of the representative project demonstrates a significant reduction in the construction cost of the digester and storage tanks, which was 1/3 less compared to the pre-optimization period. Considering government subsidies, the price of biogas was only 54% of the local natural gas price, substantially reducing the overall cost of heating.

Research on Leased Space of Urban Villages in Large Cities Based on Fuzzy Kano Model Evaluation and Building Performance Simulation: A Case Study of Laojuntang Village, Chaoyang District, Beijing

China is in the transition period of urbanization, and the imbalance of land use generated during the original rapid development of megacities still exists, resulting in the preservation of a large number of urban villages in the peripheral areas of cities. Unlike formal housing, these villages are heavily influenced by the confrontation and interweaving between top-down policies of local governments and bottom-up spontaneous construction by local villagers, resulting in a diverse and complex status quo. At the same time, self-built housing in urban villages has become a gathering place for a large number of low-income migrants due to its relatively low rental prices. In terms of the research of urban villages on the current situation of housing, the real needs of users and housing performance are mostly subjective judgments and conclusions. Therefore, the entry point of this paper is the leased space and the leasers, taking Laojuntang Village in Chaoyang District of Beijing as the research object and analyzing its current problems and the real needs of users. This paper adopts an integrated approach of fuzzy Kano model evaluation and building performance simulation. After analyzing the data and discussing the influencing factors, this paper identifies the specific problems and user needs that exist in leased spaces in Laojuntang Village: (1) The architectural performance of leased space is a Must-be Quality, which is the bottom-line requirement and must be improved. (2) The quality of the basic facilities and equipment of the leased space is mostly a One-dimensional Quality, Attractive Quality, and the degree of importance is lower than that of the building performance. (3) The external activity facilities of the leased space are mostly undifferentiated needs, for which the local users are not particularly prominent. (4) The scale and plot ratio of local housing is relatively high, and users are dissatisfied and complaining, which falls under the category of Reverse Quality.

Enhanced blood glucose levels prediction with a smartwatch.

Ensuring stable blood glucose (BG) levels within the norm is crucial for potential long-term health complications prevention when managing a chronic disease like Type 1 diabetes (T1D), as well as body weight. Therefore, accurately forecasting blood sugar levels holds significant importance for clinicians and specific users, such as type one diabetic patients. In recent years, Continuous Glucose Monitoring (CGM) devices have been developed and are now in use. However, the ability to forecast future blood glucose values is essential for better management. Previous studies proposed the use of food intake documentation in order to enhance the forecasting accuracy. Unfortunately, these methods require the participants to manually record their daily activities such as food intake, drink and exercise, which creates somewhat inaccurate data, and is hard to maintain along time. To reduce the burden on participants and improve the accuracy of BG level predictions, as well as optimize training and prediction times, this study proposes a framework that continuously tracks participants' movements using a smartwatch. The framework analyzes sensor data and allows users to document their activities. We developed a model incorporating BG data, smartwatch sensor data, and user-documented activities. This model was applied to a dataset we collected from a dozen participants. Our study's results indicate that documented activities did not enhance BG level predictions. However, using smartwatch sensors, such as heart rate and step detector data, in addition to blood glucose measurements from the last sixty minutes, significantly improved the predictions.

Phishing Detection Using Machine Learning: A Model Development and Integration

This study aimed to develop a robust machine learning-based phishing detection system using algorithms such as K-nearest neighbour (KNN), artificial neural network (ANN), and random forest (RF). It utilised datasets from Ariyadasa et al. (2021) and UNB (2016) to discern patterns distinguishing legitimate from phishing websites. Furthermore, an objective was to integrate the optimal model into a Django-based web application, facilitating real-time phishing detection. A comprehensive literature review on phishing detection techniques was also undertaken. Datasets chosen underwent rigorous pre-processing to address missing values and imbalance. Feature selection was achieved manually and automatically using mutual information classification. Three machine learning algorithms, RF, KNN, and ANN, were explored. Their hyper-parameters were optimised using GridSearchCV. Performance results highlighted RF's accuracy at 99.78%, KNN's at 99.67%, and ANN's at 99.11%. While RF and KNN models perfectly identified legitimate websites, ANN showcased an impeccable detection of phishing websites. The RF model, with the highest accuracy, was integrated into a Django application, providing a user interface for real-time phishing detection. All models exhibited high accuracy rates, demonstrating their efficacy in phishing detection. While RF was integrated into the web application for this study, the choice between models depends on specific user or business requirements and priorities. Feedback mechanisms within the Django application further promise refinement in future recommendations. The study provides a foundational step toward enhancing web safety through effective phishing detection.

МОДЕЛЬ АРХИТЕКТУРНО-ПРОСТРАНСТВЕННЫХ ПРИНЦИПОВ ДЛЯ ОБЕСПЕЧЕНИЯ АДАПТАЦИИ ЛЮДЕЙ С АУТИЗМОМ

Current architectural environment mostly accounts for the interests of its neurotypical users, which do not correspond with the needs of people with neurodiversity. The relevance of using certain architectural solutions for autistic people is based on their sensitivity to external stimuli, as well as statistics with increased rates of autism diagnoses. After analyzing theoretical works of foreign researchers and architects, and taking into account modern architectural solutions for people with autism spectrum disorder, we developed a model of architectural and spatial guidelines. It was based on five types of interaction between the autistic person and space: sense of self in personal space; sense of self in public space; perception of space around self; sensory sense of space; and sensory overload. The model considers the relationship between the person and the space, and prioritizes approaches to autism-friendly design such as differentiation, zoning, gradation, linking the external and internal environment, and specific architectural solutions. This design approach will adapt autistic people to interact with the environment in future, and may have a positive impact on the other user groups through "the curb-cut effect" (a phenomenon when solutions aimed at transforming the living conditions of a specific vulnerable user group help society as a whole). The model can be applied as a guide in architectural design and can also be used for future theoretical research.

Implementation and performance estimation of new archive system for the TLS control system

The TLS (Taiwan Light Source) is a third generation of synchrotron light source that has been operated for more than 25 years, and its control system is a proprietary designed system. The TLS control system maintenance is difficult because of component outage problems. Some parts of the control system are being rejuvenated with the help of the EPICS framework used in the TPS (Taiwan Photon Source) control system to ensure that TLS continues to operate normally, saving both manpower and money. A new EPICS archive system was needed to efficiently record various machine parameters and status information during routine operations. As a result, the EPICS Archiver Appliance has been evaluated as suitable for deploying to archive TLS machine data which encapsulated the PV (Process Variable) channel access. Specific graphical user interfaces and API packages have been supported for quickly retrieving archived data, as well as a plotting function for easy diagnosis. Furthermore, the performance of this new TLS archive system has been estimated, and related system resources will be manually adjusted for better service. The efforts will be summarized in this paper.

Unraveling Knowledge-Based Chatbot Adoption Intention in Enhancing Species Literacy

Aim/Purpose: This research investigated the determinant factors influencing the adoption intentions of Chatsicum, a Knowledge-Based Chatbot (KBC) aimed at enhancing the species literacy of biodiversity students. Background: This research was conducted to bridge the gap between technology, education, and biodiversity conservation. Innovative solutions are needed to empower individuals with knowledge, particularly species knowledge, in preserving the natural world. Methodology: The study employed a quantitative approach using the Partial Least Square Structural Equation Modeling (PLS-SEM) and sampled 145 university students as respondents. The research model combined the Task-Technology Fit (TTF) framework with elements from the Diffusion of Innovation (DOI), including relative advantage, compatibility, complexity, and observability. Also, the model introduced perceived trust as an independent variable. The primary dependent variable under examination was the intention to use the KBC. Contribution: The findings of this research contribute to a deeper understanding of the critical factors affecting the adoption of the KBC in biodiversity education and outreach, as studies in this context are limited. This study provides valuable insights for developers, educators, and policymakers interested in promoting species literacy and leveraging innovative technologies by analyzing the interplay of TTF and DOI constructs alongside perceived trust. Ultimately, this research aims to foster more effective and accessible biodiversity education strategies. Findings: TTF influenced all DOI variables, such as relative advantage, compatibility, observability, and trust positively and complexity negatively. In conclusion, TTF strongly affected usage intention indirectly. However, relative advantage, complexity, and observability insignificantly influenced the intention to use. Meanwhile, compatibility and trust strongly affected the use intention. Recommendations for Practitioners: Developers should prioritize building and maintaining chatbots that are aligned with the tasks, needs, and goals of the target users, as well as establishing trust through the assurance of information accuracy. Educators could develop tailored educational interventions that resonate with the values and preferences of diverse learners and are aligned closely with students’ learning needs, preferences, and curriculum while ensuring seamless integration with the existing educational context. Conservation organizations and policymakers could also utilize the findings of this study to enhance their outreach strategies, as the KBC is intended for students and biodiversity laypeople. Recommendation for Researchers: Researchers should explore the nuances of relationships between TTF and DOI, as well as trust, and consider the potential influence of mediating and moderating variables to advance the field of technology adoption in educational contexts. Researchers could also explore why relative advantage, complexity, and observability did not significantly impact the usage intention and whether specific user segments or contextual factors influence these relationships. Impact on Society: This research has significant societal impacts by improving species literacy, advancing technology in education, and promoting conservation efforts. Species knowledge could raise awareness regarding biodiversity and the importance of conservation, thereby leading to more informed and responsible citizens. Future Research: Future works should address the challenges and opportunities presented by KBCs in the context of species literacy enhancement, for example, interventions or experiments to influence the non-significant factors. Furthermore, longitudinal studies should investigate whether user behavior evolves. Ultimately, examining the correlation between species literacy, specifically when augmented by chatbots, and tangible conservation practices is an imperative domain in the future. It may entail evaluating the extent to which enhanced knowledge leads to concrete measures promoting biodiversity preservation.

Accessibility Evaluation of Interactive Learning Mobile Applications for Individuals with Intellectual Disabilities

In today’s education sector, mobile technologies, including tablets and smartphones, have become integral tools, enabling the implementation of mobile learning in pedagogical activities. However, accessibility issues in mobile applications pose challenges, particularly for individuals with disabilities, contradicting the goals of Sustainable Development and the Convention on the Rights of Persons with Disabilities. This paper aims to investigate the extent to which the industry considers accessibility in designing mobile learning applications for this specific user group. We evaluate the accessibility of interactive mobile applications designed for reading and alphabet learning, employing two different guidelines, by conducting heuristic evaluation based on research-based guideline and a design standard introduced by The World Wide Web Consortium. Three experts, each bringing a unique perspective and background, were engaged in the evaluation process. The heuristic evaluations were executed based on the criteria outlined in the selected guidelines. This involved identifying violations and employing a scoring system to quantify the findings. Subsequently, the experts engaged in discussions to achieve consensus and establish final scores for each checkpoint. The results provided a quantitative basis for our analysis. The results of the evaluation performed in this study expose a prevalent disregard for guideline checkpoints among these interactive mobile learning apps. Only two applications could be considered highly inclusive, indicating the need of improvement. Based on the results, the accessibility criteria were also categorized into four primary groups, highlighting the requirements that were least and most addressed by the applications.