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Inclusive chemistry learning is possible. Three frameworks that can be considered to create inclusive chemistry learning are Technological Pedagogical and Content Knowledge (TPACK), Universal Design for Learning (UDL), and Science, Technology, Engineering, and Mathematics (STEM). The integration of these three frameworks is still not widely used by teachers and is not popular in Indonesia. Therefore, developing and validating the TPACK‐UDL‐STEM Scale to measure teacher knowledge and abilities in designing inclusive chemistry learning is crucial. The three scale development steps proposed by Morgado et al. were used as a scale development procedure. Fifty items as the first draft of the scale were designed after reviewing the TPACK‐UDL‐STEM literature. These items were divided into five aspects (S‐PCK‐Multiple Means of Representation, T‐TCK‐Multiple means of representation, E‐PCK‐Multiple means of engagement, M‐TPK‐Multiple means of representation, STEM‐TPACK‐Multiple means of action and expression). A panel of six experts tested the first draft that focused on item content and construction. A second draft was piloted on 50 chemistry teachers randomly selected and volunteered to fill out the scale. Data were analyzed using the partial least square method to assess the scale's validity and reliability. After several stages of testing, 48 items were declared valid with a Cronbach's Alpha value > 0.8, rho_(A) value > 0.8, and composite reliability > 0.9. These values indicate that the TPACK‐UDL‐STEM Scale is valid and reliable for measuring teachers' knowledge in designing inclusive chemistry lessons. Therefore, this scale is suitable for applied research and further studies to support the development of inclusive chemistry learning.

Sign language is undoubtedly a common way of communication among deaf and non-verbal people. But it is not common among hearing people to use sign language to express feelings or share information in everyday life. Therefore, a significant communication gap exists between deaf and hearing individuals, despite both groups experiencing similar emotions and sentiments. In this paper, we developed a convolutional neural network–squeeze excitation network to predict the sign language signs and developed a smartphone application to provide access to the ML model to use it. The SE block provides attention to the channel of the image, thus improving the performance of the model. On the other hand, the smartphone application brings the ML model close to people so that everyone can benefit from it. In addition, we used the Shapley additive explanation to interpret the black box nature of the ML model and understand the models working from within. Using our ML model, we achieved an accuracy of 99.86% on the KU-BdSL dataset. The SHAP analysis shows that the model primarily relies on hand-related visual cues to predict sign language signs, aligning with human communication patterns.

Virtual reality has impacted education, where progressively more educational institutionsconsider its inclusion. The research problem derives from the need to study the educational possibilitiesprovided by integrating augmented reality into the curriculum, and its effect on academic achievement ina diverse class, specifically in the chemistry subject. This study examines 60 school-age participants withandwithout special educational needs, and addresses three overarching questions: (a)Would integratingaugmented reality (AR) technology result in better academic achievement? (b)Would knowledge beretained longer by using AR? (c) Is there any relationship between academic achievement, acceptanceand motivation regarding the use of this technology? Embracing the socio-constructivist theory oflearning and collaborative and immersive learning as a framework, this study was carried out usinga quantitative approach and a pre-experimental design. The AR VR Molecules Editor applicationwas used in chemistry lessons. Main results showed significant immediate academic achievementand content retention. Despite classroom diversity, immersive technologies enhance students’ learningregardless of whether they have special educational needs (SEN) or not. They also acknowledge that ARis a suitable sustainable technology that may foster social and cognitive justice and inclusive education,and train students that are equally prepared for the dynamic future.

Specific learning disorders affect a significant portion of the population. A total of 80% of its instances are dyslexia, which causes significant difficulties in learning skills related to reading, memorizing and the exposition of concepts. Whereas great efforts have been made to diagnose dyslexia and to mitigate its effects at primary and secondary school, little has been done at the university level. This has resulted in a sensibly high rate of abandonment or even of failures to enroll. The VRAIlexia project was created to face this problem by creating and popularizing an innovative method of teaching that is inclusive for dyslexic students. The core of the project is BESPECIAL, a software platform based on artificial intelligence and virtual reality that is capable of understanding the main issues experienced by dyslexic students and to provide them with ad hoc digital support methodologies in order to ease the difficulties they face in their academic studies. The aim of this paper is to present the conceptual design of BESPECIAL, highlighting the role of each module that composes it and the potential of the whole platform to fulfil the aims of VRAIlexia. Preliminary results obtained from a sample of about 700 dyslexic students are also reported, which clearly show the main issues and needs that dyslexic students experience and these will be used as guidelines for the final implementation of BESPECIAL.

In recent years, the need to design inclusive workplaces has grown, particularly in manufacturing contexts where high cognitive demands may disadvantage neurodiverse individuals. In manufacturing environments, neurodiverse workers often experience difficulties processing standard instructions, increasing cognitive load and errors and reducing overall performance. This study proposes a methodology to assess cognitive load during assembly tasks to support workers with dyslexia. A multi-layer fuzzy logic framework was developed, integrating physiological, environmental, and task-related data. Physiological signals, including heart rate, heart rate variability, electrodermal activity, and eye-tracking data, were collected using wearable sensors. Ambient conditions were also measured. The model emphasizes the Reading dimension of cognitive load, critical for dyslexic individuals challenged by text-based instructions. A controlled laboratory study with 18 neurotypical participants simulated dyslexia scenarios with and without support, compared to a control condition. Results indicated that a lack of support increased cognitive load and reduced performance in complex tasks. In simpler tasks, control participants showed higher cognitive effort, possibly employing overcompensation strategies by exerting additional cognitive resources to maintain performance. Support mechanisms, such as audio prompts, effectively reduced cognitive load, highlighting the framework’s potential for fostering inclusive practices in industrial environments.

Technologies for pattern recognition are used in various fields. One of the most relevant and important directions is the use of pattern recognition technology, such as gesture recognition, in socially significant tasks, to develop automatic sign language interpretation systems in real time. More than 5% of the world’s population—about 430 million people, including 34 million children—are deaf-mute and not always able to use the services of a living sign language interpreter. Almost 80% of people with a disabling hearing loss live in low- and middle-income countries. The development of low-cost systems of automatic sign language interpretation, without the use of expensive sensors and unique cameras, would improve the lives of people with disabilities, contributing to their unhindered integration into society. To this end, in order to find an optimal solution to the problem, this article analyzes suitable methods of gesture recognition in the context of their use in automatic gesture recognition systems, to further determine the most optimal methods. From the analysis, an algorithm based on the palm definition model and linear models for recognizing the shapes of numbers and letters of the Kazakh sign language are proposed. The advantage of the proposed algorithm is that it fully recognizes 41 letters of the 42 in the Kazakh sign alphabet. Until this time, only Russian letters in the Kazakh alphabet have been recognized. In addition, a unified function has been integrated into our system to configure the frame depth map mode, which has improved recognition performance and can be used to create a multimodal database of video data of gesture words for the gesture recognition system.

Cultural Heritage (CH) refers to the representation of historical places and traditional customs of a specific city or country. Its principal aim is to transmit to future generations how their ancestors lived, and what their customs and buildings were like, etc. Nowadays, there are different technology systems and research investigations that are focused on CH education that use augmented reality (AR), virtual reality (VR), and mixed reality (MR). The aim of this document is to specifically identify if the use of AR improves students’ motivation to learn about topics related to CH. To this end, studies from different databases and specific journals, along with those concerning technology systems, were evaluated, and comparisons were made between them. Additionally, the aspects that should be considered in future research to improve student motivation and technology systems were identified.

In this decade, China has been pursuing an inclusive green growth strategy. Concurrently, the digital economy, which relies on the Internet of Things, big data, and artificial intelligence, has experienced explosive growth in China. The digital economy’s capacity to optimize resource allocation and reduce energy consumption potentially makes it a conducive channel towards sustainability. Using the panel data of 281 cities in China from 2011 to 2020, we theoretically and empirically explore the impact of the digital economy on inclusive green growth. Firstly, we theoretically analyze the potential impact of the digital economy on inclusive green growth using two hypotheses: accelerating green innovation and promoting the industrial upgrading effect. Subsequently, we measure the digital economy and inclusive green growth of Chinese cities using Entropy-TOPSIS and DEA approaches, respectively. Then, we apply traditional econometric estimation models and machine learning algorithms to our empirical analysis. The results show that China’s high-powered digital economy significantly promotes inclusive green growth. Moreover, we analyze the internal mechanisms behind this impact. We find that innovation and industrial upgrading are two plausible channels that explain this effect. Additionally, we document a nonlinear feature of diminishing marginal effects between the digital economy and inclusive green growth. The heterogeneity analysis shows that the contribution weight of the digital economy to inclusive green growth is more remarkable in eastern region cities, large and medium-sized cities, and cities with high marketization. Overall, these findings shed more light on the digital economy-inclusive green growth nexus and provide new insights into understanding the real effects of the digital economy on sustainable development.

This study explores how integrating simulations into lessons on electrical conductivity in aqueous solutions and electrolysis affects eighth-grade students’ academic achievement, motivation, and their perception of classroom climate. The study included 130 students (64 males, 66 females) from six classes in two Israeli middle schools, divided into an experimental group (68 students, simulation-integrated instruction) and a control group (62 students, traditional instruction). Participants completed pre- and post-achievement tests as well as motivation and classroom climate questionnaires. The results revealed significant improvements in achievement, especially for students with a lower initial performance. Additionally, when simulations were utilized, there was enhanced motivation to study chemistry. Simulations also improved students’ perception of classroom climate across all dimensions, with no significant gender differences observed. A strong positive correlation was found between achievements and motivation, as well as between classroom climate and motivation. The findings underscore the value of simulations and digital tools in education, emphasizing their role in creating more engaging learning experiences. These results also highlight the need for decision-makers to integrate such tools into science education to foster better outcomes in student learning experience.

This paper reports on teachers’ and students’ perceptions about the effectiveness of computer use to enhance the teaching and learning of chemistry in inclusive classrooms. This study aims to investigate how students with visual and those with hearing disabilities can easily access chemistry instructions. The study adopted a mixed-method approach where the qualitative and quantitative data were collected. A survey questionnaire was used to collect data from 15 chemistry teachers. Besides, a semi-structured interview was used to collect qualitative data from 10 students comprising 5 students with hearing disabilities and 5 students with visual disabilities from five inclusive schools selected purposely. The quantitative data were analyzed by descriptive statistics whereby percentages were computed. The qualitative data obtained through interviews were analyzed by discourse and interpretive approaches. The results revealed that the computer is effective for teaching chemistry since it supports students with hearing and visual disabilities to learn chemistry. Moreover, a computer helps teachers to teach chemistry concepts and share with students the required chemistry resources in their learning. This encourages learners to explore new concepts, brainstorm and search for relevant information for both teachers and students. The lack of enough computers adapted by students with hearing and visual disabilities and the limited teachers’ training in teaching students with disabilities were reported by some teachers as challenges. Therefore, the provision of computers and teachers’ training to deal with every student’s differences and needs were among the suggested potential solutions.