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In this chapter, we focus on specially designed instruction (SDI) as a core value for the field of specific learning disabilities (SLD). SDI is at the heart of special education, and the field of LD has been built on the core value that effective instruction improves student outcomes. We describe a two-step test and an extended example of what is and is not SDI for Matt, a student with an SLD. Finally, we discuss some of the confusion surrounding SDI and the need for the field to return to its core value of individualized, intentional, targeted, evidence- or high leverage practice–based, and systematic instruction for students with SLD.

This paper aims to present a lesson that showcases how artificial intelligence (AI) tools may be chiefly used in L2 language classrooms to design culture-focussed telecollaboration tasks and aid their completion by students.

The paper begins by reviewing traditional approaches and guidance for developing telecollaboration tasks. It then models how tasks can be designed using the popular AI tool “Chat Generative Pre-training Transformer (ChatGPT)” and then simulates how tasks may be completed by learners using ChatGPT-generated information as a springboard for their own culturally appropriate outputs.

The simulated lesson illuminates the potential value of AI tools for teachers and students. However, it also highlights particular aspects of AI literacy that teachers and learners need to be aware of.

This paper has clear practical implications for teacher development by raising awareness of the importance of teachers upskilling in telecollaboration task design and in their understanding of how AI tools can collaborate with them in language classrooms.

The paper adds to the current body of literature on telecollaboration and more specifically adds weight to current discussions taking place around AI tools in language education. By the end of reading the paper, teachers will have a comprehensive grounding in how to use ChatGPT in their classrooms. In doing so, the author demystifies how teachers and students may start exploring these tools in ways that target developing intercultural communicative competence.

The sustainable development goals (SDGs) are introduced to guide achieving the sustainable goals and tackle the global problems. United Nations members may perform activities to achieve the predetermined goals and report on their SDG activities. The comprehension and commitment of several stakeholders are essential for the effective implementation of the SDGs. Countries encourage their stakeholders to perform and report their activities to meet the SDGs. The purpose of this study is to investigate the extent to which corporations’ annual reports address the SDGs to assess and comprehend their level of commitment to, priority of and integration of SDGs within their reporting structure. This research makes it easier to evaluate corporations’ sustainability performance and contributions to global sustainability goals by looking at the extent to which they address the SDGs.

In the study, it is revealed to what extent the reports meet the SDGs with the multilabel text classification approach. The SDG classification is carried out by examining the report with the help of a text analysis tool based on an enhanced version of gradient boosting. The implementation of a machine learning-based model allowed it to determine which SDGs are associated with the company’s operations without the requirement for the report’s authors to perform so. Therefore, instead of reading the texts to seek for “SDG” evidence as typically occurs in the literature, SDG proof was searched in relevant texts.

To show the feasibility of the study, the annual reports of the leading companies in Turkey are examined, and the results are interpreted. The study produced results including insights into the sustainable practices of businesses, priority SDG selection, benchmarking and business comparison, gaps and improvement opportunities identification and representation of the SDGs’ importance.

The findings of the analysis of annual reports indicate which SDGs they are concerned about. A gap in the literature can be noticed in the analysis of annual reports of companies that fall under a particular framework. In addition, it has sparked the idea of conducting research on a global scale and in a time series. With the aid of this research, decision-making procedures can be guided, and advancements toward the SDGs can be achieved.

This study aims to highlight the growth and development of Indo-German collaborative research over the past three decades. Moreover, this study encompasses an in-depth examination of funding acknowledgements to gain valuable insights into the financial support that underpins these collaborative endeavours. Together with this paper, the authors provide an openly accessible data set of Indo-German research papers for further and reproducible research activities (the “Indo-German Literature Dataset”).

The data were retrieved from the Web of Science (WoS) database from the year 1990 till the 30th of November 2022. A total of 36,999 records were retrieved against the used query. Acknowledged entities were extracted using a named entity recognition (NER) model specifically trained for this task. Interrelations between the extracted entities and scientific domains, lengths of acknowledgement texts, number of authors and affiliations, number of citations and gender of the first author, as well as collaboration patterns between Indian and German funders were examined.

The study reveals a consistent and increasing growth in the publication trend over the years. The study brings to light that Physics, Chemistry, Materials Science, Astronomy and Astrophysics and Engineering prominently dominate the Indo-German collaborative research. The USA, followed by England and France, are the most active collaborators in Indian and German research. Largely, research was funded by major German and Indian funding agencies, international corporations and German and American universities. Associations between the first author’s gender and acknowledged entity were observed. Additionally, relations between entity, entity type and scientific domain were discovered.

The study paves the way for enhanced collaboration, optimized resource utilization and societal advantages by offering a profound comprehension of the intricacies inherent in research partnerships between India and Germany. Implementation of the insights gleaned from this study holds the promise of cultivating a more resilient and influential collaborative research ecosystem between the two nations.

The study highlights a deeper understanding of the composition of the Indo-German collaborative research landscape of the past 30 years and its significance in advancing scientific knowledge and fostering international partnerships. Furthermore, the authors provide an open version of the original WoS data set. The Indo-German Literature Data set consists of 22,844 papers from OpenAlex and is available for related studies like literature studies and scientometrics.

This paper aims to trace how Asian American girls engaged with civic learning in a virtual out-of-school literacy community featuring a curriculum of diverse literary texts.

The researcher used practitioner inquiry to construct a virtual literacy education community dedicated to the civic learning of Asian American girls.

The paper explores how participants mobilized critical practices of textual consumption and production rooted in their intersectional identities and embodied experiences to make meaning of the civic constraints and affordances of marginalized identities and to read and (re)design author choices for civic purposes. These findings – examples of youths’ critical civic meaning-making – indicate how they claimed space for Asian American civic girlhoods in literacy education.

This paper foregrounds how Asian American girls mobilize critical processes of text consumption and production to assert civic identities in literacy education – a significantly under-examined topic in literacy studies. This work has implications for how literacy practitioners and scholars can prioritize Asian American civic girlhoods through pedagogy and research.

This study aims to explore the intelligibility of communication with stakeholders as a result of accrual accounting adoption. It focuses on changes in the use of visual forms and the readability of text that occurred immediately after the adoption of accrual accounting in performance reports of Italian public universities.

The authors collect the stakeholder section of performance reports published before and after accrual accounting adoption. Then, the authors use manual and computer-assisted textual analysis. Finally, the authors explore the data using principal component analysis and qualitative comparative analysis.

This study demonstrates that switching from cash to accrual accounting provokes immediate changes in communication patterns. It confirms the significant reduction of readability and increase in visual forms after accruals accounting adoption. The results indicate that smaller universities especially put effort into increasing intelligibility while implementing a more complex accounting system. This study also finds a relation between the change in readability and the change in visual forms that are complementary, with the exception of several very large universities.

The findings underline the possibility of neutralising the adverse effects of accounting reform associated with its complexity and difficulties in understanding by the use of visual forms and attention to the document’s readability.

This paper adds a new dimension to the study of public sector accounting from the external stakeholder perspective. It provides further insight into the link between accrual accounting adoption and readability, together with the use of visual forms by universities.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.

We utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.

Our findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.

Our study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.

We present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.

Intrinsic values to the field of special education include advocacy, inclusivity, individuality, and empiricism. From early days of providing custodial care in segregated settings, special education has evolved into a program that seeks to educate students with a wide range of learning needs in inclusive settings and identify a robust research base that informs its policies and practices. Important concepts such as inclusion and continuum of services have not only been valuable in conceptualizing and in providing intervention for students with disabilities but have also been valuable in advancing the field. Research in special education and students with disabilities has been instrumental in moving the field forward. In the future, special education will continue to be valuable in supporting students whose learning and survival needs deviate from the norm in meaningful ways by delivering responsive evidence-based instruction.

The article attempts to shed light on the social aspects of research that deal with Sustainable Development Goals (SDGs) and sustainable cities. The aim is to offer a global view of these facets' evolution and to provide information on people-centered smart cities.

The research is qualitative. A systematic bibliometric approach is a framework for the research. The unit of analysis is publications on SDGs and Smart Cities (SCs) indexed in Scopus. The authors used VOSviewer text mining functionality to construct co-occurrence networks of socially related critical terms extracted from textual data. The co-occurrence of keywords presents a valuable method and process for attaining in-depth analysis and fast comprehension of trends and linkages in articles from a holistic approach.

Social media, social sustainability and social capital are the three multifaceted social keywords that co-occur in SDGs and SCs. The paper provides a brief compendium of resources and frameworks to build a socially sustainable smart city.

The retrieval date was on 15 August 2019. The authors used the same search query for new papers released in 2019 and afterwards to update their findings. The authors collected 657 documents on SCs, compared to 2,975 documents about SDGs demonstrating that their findings are still trending in the same direction, emphasizing the importance of the research topic. SCs' social aspects are still chartered areas that require the attention to future research.

The authors’ decision to use two separate data sets for SCs and SDGs data files helps to provide a more comprehensive picture of the research landscape. It may identify areas where research is lacking or needs future research. The authors present an integrative agenda for a smart city to be socially sustainable. Innovative approaches to urban planning are required to empower the place and context and improve the users' satisfaction, where innovative solutions enable smart, sustainable and inclusive societies. Infrastructure governance is a critical keystone. It could guarantee that public investments contribute to sustainable urban development while enhancing city resilience, particularly in facing climate change and inclusive growth challenges.