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This research aimed to investigate the impact of word decoding speed and accuracy on reading comprehension in Turkish, which is characterized by a highly transparent and morphologically complex orthographic system. The study involved 160 students, half of whom were identified as poor readers, while the other half were classified as good readers. These participants were selected from the second and fourth grades of public elementary schools. The assessment process involved evaluating participants' isolated word decoding skills using a task that measured their ability to decode both real words and pseudowords. Additionally, reading fluency and comprehension were measured using grade-level appropriate reading texts. The analysis included a series of MANOVAs as well as a mediation model (Model 7) employing Haye's PROCESS macro for SPSS. The findings indicated that the poor readers' limited reading comprehension abilities were primarily attributed to their overall deficiency in word decoding fluency, with a particular emphasis on their inadequate phonological decoding skills. These results are significant as they shed light on the challenges that children with reading comprehension difficulties may face in transparent orthographic systems. The attainment of proficient reading comprehension in such languages appears to be influenced by the specific aspects of word decoding fluency.

This study delves into the opportunities and challenges associated with the deployment of AI tools in the education sector. It systematically explores the potential benefits and risks inherent in utilizing these tools while specifically addressing the complexities of identifying and preventing academic dishonesty. Recognizing the ethical dimensions, the paper further outlines strategies that educational institutions can adopt to ensure the ethical and responsible use of AI tools. Emphasizing a proactive stance, the paper suggests that by implementing these strategies, schools can harness the benefits of AI tools while mitigating the risks associated with potential misuse. As the adoption of AI tools in education continues to expand, all stakeholders must stay abreast of the latest developments in the field. This knowledge equips educators to navigate the opportunities and challenges posed by AI tools, fostering a learning environment that is both secure and conducive to empowering students to realize their full potential.

In higher education, generative chatbots have infiltrated teaching and learning. Concerns about how and if to utilize chatbots in the classroom are at the forefront of scholarly discussion. This quick-hit article presents a plan to teach learners about generative AI writing tools and their ethical use for writing purposes. As generative AI tools continue to emerge, this guide can support instructors from all disciplines to engage learners in getting the most accurate information.

This study investigated the effects of a reading assistant software on the fluency and accuracy of speech production among EFL university students, employing a one-group pretest-posttest design. The participants included 50 Thai undergraduate English majors in their first and second years. Data were analyzed using paired samples t-tests to measure L2 oral fluency, specifically pruned speech rate (PSR) and mean length of run (MLR). Accuracy rate was assessed by quantifying the proportion of error-free clauses within the participants' speeches from the pretest and posttest and calculating the average number of error-free clauses per T-unit. Results indicated significant improvements in speech fluency, evidenced by longer uninterrupted speech segments, fewer disfluencies like filled pauses and repairs, and increased overall fluency. Additionally, the post-test showed a higher percentage of error-free clauses compared to the pretest, reflecting a substantial enhancement in grammatical accuracy. This suggests that the intervention had a positive impact on the participants' ability to produce grammatically accurate and error-free clauses. These outcomes suggest that the reading assistant software effectively enhances both the fluency and grammatical accuracy of speech production in EFL learners, underscoring its potential benefits in language education contexts.

Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners' cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners' cognitive levels during the online learning process. However, most of the currently used techniques for evaluating cognitive levels rely on labour-intensive and time-consuming manual coding. In this study, we explore the machine learning (ML) algorithms and taxonomy of Bloom's cognitive levels to explore features that affect learner's cognitive level in online assessments and the ability to automatically predict learner's cognitive level and thus, come up with a recommendation or pedagogical intervention to improve learner's acquisition. The analysis of 15,182 learners' assessments of a specific learning concept affirms the effectiveness of our approach. We attain an accuracy of 82.21% using ML algorithms. These results are very encouraging and have implications for how automated cognitive-level analysis tools for online learning will be developed in the future.

Recent advancements in generative artificial intelligence (GAI) have sparked debates and research on its transformative potential in education. This study explored how a specific group of educators partner with GAI tools, particularly ChatGPT, to complement and enhance their teaching. Within an interpretative paradigm, the study used an exploratory case study design to investigate how 13 educators enrolled as students in a structured master's programme at an open distance learning university in South Africa use GAI tools in their teaching. Their posts on a discussion forum were used to collect data. Findings reveal that they actively employ GAI to streamline their teaching practices, specifically to personalized tutors, simplifying English texts and translations, assessments, lesson planning, and critical thinking tasks. The study also highlights the need to understand the limitations and boundaries of GAI, including concerns about accuracy, biases and reliability. Importantly, the absence of institutional guidelines on GAI raises questions about the necessity for clear guidelines and policies to ensure responsible and ethical integration in educational contexts. The paper concludes by emphasizing the transformative potential of GAI and the fact that it will increasingly act as a partner in teaching and influence modern teaching practices. Ultimately, GAI's foothold in education is undeniable, challenging educators and policymakers to navigate its implications while maximizing its potential to support effective teaching.

This study examines the role of recast frequency and its effectiveness in the acquisition of English articles in a computer-mediated context. Sixty-one preintermediate university language learners in Turkey were randomly divided into four main groups: high frequency recast (HF), low frequency recast (LF), test control, and task control groups. The learners in the HF and LF recast groups completed five and two tasks, respectively, in a video-conferencing environment and received oral recasts on their incorrect use of English articles. Learners in the test control group only took the pre and posttests, and learners in the task control group completed five tasks without receiving feedback on the target structure. The outcome was measured through online picture description and error correction tasks. Findings showed that in the picture description task, learners in the HF group performed significantly better than those in the LF recast group and the control groups. In the error correction task, the results revealed a short-term advantage for learners in the HF group, which faded away in the delayed posttest. Significant correlations were also found between the recast frequency and learners' score improvement in the immediate and delayed picture description tasks but not in the error correction tasks. These results suggest that recast quantity may play an important role in improving learners' accuracy of their oral production.

Since its release, ChatGPT has raised concerns in many teaching contexts given its threat to reliably evaluating learners' knowledge and skills. Within task-based pedagogy, however, this technology opens new avenues for second language (L2) teaching when adopting the technology as a writing buddy. Our study explores how ChatGPT as a model impacts the revision process of advanced L2 writers of German. Twenty-two university students participated in a two-week classroom-based intervention, producing two summaries of popular-scientific texts in L2 German. After writing a first draft, they compared their summaries with texts produced by ChatGPT (3.5) and revised, where necessary, their own text. In this paper, we analyze all students' rubrics-based ratings of the ChatGPT models and present data of six focus students' screen-recorded revision processes that we coded for revision focus, source, and success. Results reveal students' growing awareness of characteristics of ChatGPT-output, such as linguistic accuracy and fluency, as well as its flaws in content provision. Revision data demonstrate that students skillfully made use of the models to improve their own texts. Our study provides evidence that using ChatGPT as models in writing and revision processes can stimulate higher-order thinking in the revision process of advanced L2 students.

This research paper delved into the critical issue of applying English as a Lingua Franca (ELF) assessment principles in local English language tests used for non-native English speakers in Iranian language institutes. A qualitative content analysis was made on 60 local tests, dissecting them into domains, dimensions, and rating rubrics to scrutinize their alignment with ELF assessment principles. The study unveiled that despite some alignment with ELF assessment principles, key aspects like local communicative context, intercultural competence, and linguistic diversity are often overlooked. In particular, writing and reading tests failed to fully reflect these principles, and listening and speaking assessments showed biases towards native English varieties. The study provides crucial insights for test developers to foster a more nuanced and accurate assessment of non-native English speakers' abilities. Moreover, it highlights the need to embed ELF principles into test construction, argues for broader assessment scopes and a focus on locally relevant tasks, and contributes to more equitable and contextually relevant English language proficiency tests by emphasizing linguistic diversity in assessment frameworks.

This study examines the effects of corrective feedback (CF) on language learners' writing anxiety, writing complexity, fluency, and accuracy, and compares the effectiveness of feedback from human teachers with an AI-driven application called Poe. The study included three intact classes, each with 25 language learners. Using a quasi-experimental design with pretest and posttest measures, one class received feedback from the teacher, one from the Poe application, and the third received no response to their writing. Data were generated though tests and a writing anxiety scale developed for the study. Data analysis, conducted using one-way ANOVA tests, revealed significant effects of teacher and AI-generated feedback on learners' writing anxiety, accuracy, and fluency. Interestingly, the group that received AI-generated feedback performed better than the group that received teacher feedback or no AI support. Additionally, learners in the AIgenerated feedback group experienced a more significant reduction in writing anxiety than their peers. These results highlight the remarkable impact of AI-generated CF on improving writing outcomes and alleviating anxiety in undergraduate language learners at East China University of Political Science and Law . The study demonstrates the benefits of integrating AI applications into language learning contexts, particularly by promoting a supportive environment for students to develop writing skills. Educators, researchers, and developers can use these findings to inform pedagogical practices and technological interventions to optimize the language learning experience in primary school settings. This research highlights the effectiveness of AI-driven applications in language teaching. It highlights the importance of considering learners' psychological well-being, particularly anxiety levels, when developing effective language learning interventions.

Though there is an urgent need to improve Thai graduates' ability to speak English fluently, very few studies have focused on students' fluency in language teaching in Thailand. The study investigated 86 third-year university students' beliefs regarding English-speaking fluency, including the characteristics and importance of fluency and the factors promoting and hindering students' English-speaking fluency. Through a mixed- methods design, i.e., a questionnaire, semi-structured interviews, and reflective journal entries, the results revealed that majority of the participants perceived fluency as referring to the flow of speech with few reasonable pauses. Most of them believed that fluency was important for speaking skills, that it enabled effective communication, that fluency should be prioritized over accuracy, and that those who could speak English fluently looked impressive. However, the findings showed different views among the student participants on whether accuracy can lead to fluency. Moreover, a large proportion of them still believed that speaking accurately was also important for effective communication. Interestingly, the findings also showed that some of the participants believed that fluency constituted a high rate of speech which is not supported by the literature. They believed that exposure to English, living in a good English environment, delayed corrective feedback from the teachers, using media, and participating in speaking activities could improve their English-speaking fluency. Contrastingly, individual affective factors including anxiety, lack of motivation and confidence, lack of linguistic knowledge, and immediate corrective feedback hindered their English-speaking fluency. The results also raise an awareness about Thai EFL university students' beliefs regarding English-speaking fluency for teachers, educators, students, curriculum designers, and material creators.

As part of a study investigating research productivity of sociology PhD students in Australia, an application to the federal government's Department of Education, Skills and Employment (DESE) aimed to establish a baseline count of completed sociology doctorates by university. It was anticipated that university totals might be different from PhD completions within the main sociology teaching units. The government data furnished by this inquiry have proved to be problematic in several ways. The gaps in the data may be explained by the fact that universities report sociology PhDs to DESE in more generic rather than discipline-specific Field of Education (FoE) codes or as PhDs in other disciplines. DESE has either not required or been able to enforce consistent reporting. This raises questions about the time and cost value of these annual compliance efforts. It also limits the value of these data to researchers in charting current and potential future trends.

The proliferation of digital information technologies has underscored the heightened necessity for online information search. This study is directed towards the analysis of the strategies adopted by prospective teachers in the selection of digital content employed for accessing information, their preferences concerning the use of digital and online tools, and the rationale that underpins these preferences. To achieve this aim, the case study method was used. Employing the purposive sampling method, the study involved a participant pool of 72 teacher candidates in the 3rd and 4th grades within the faculties of education. To collect data, a personal information form, an online interview form, a focus group interview form, and a word association test were employed. The data were analyzed using the content and descriptive analysis methods. At the end of the study, the prospective teachers expressed a common tendency that when it comes to scientific research, they often prefer to search for articles whereas for more general inquiries, they have a propensity for utilizing internet searches. Most of the prospective teachers follow a step-by-step research process when accessing information. In addition, they employ both goal-oriented and general-content resources while in pursuit of scientific information. They pay attention to data reliability, accurate information, speed, and access to the Internet in the process of obtaining information. They consider the tools they use adequate and reliable. Teacher candidates establish complex conceptual networks among the array of tools they employ to access information. A comprehensive discussion concerning the outcomes of the research is also presented.

In the present study, the Teacher Formative Assessment Literacy Scale (TFALS), developed by Yan and Pastore (2022a), was translated into Turkish to examine the factor structure and psychometric characteristics of the scale in a Turkish sample. Data was collected from 318 teachers, of whom 168 were males and 150 were females, working in different state schools in a city in the Central Anatolia Region of Türkiye. As the first step, the scale items were translated and back-translated by experts in both English and Turkish. Afterward, experts were presented with the scale to check for consistency and accuracy based on the feedback received. A pilot study was carried out to establish the linguistic equivalence of the scale, followed by an examination of its structural validity in a Turkish sample. To demonstrate evidence of the confirmed factor structure of TFALS with 22 items, the confirmatory factor analysis (CFA) was conducted. The CFA results demonstrated that the three-factor model of the TFALS-Turkishversion had adequate fit indices. The Cronbach's alpha coefficient of the total scale was found to be 0.93. The Cronbach's alpha for each of the dimensions of TFALS was 0.90 for the conceptual dimension (7 items), 0.86 for the practical dimension (8 items), and 0.88 for the socio-emotional dimension (7 items). Composite reliability coefficients of the dimensions ranged from 0.78 to 0.84. The results of this study provide important evidence for the validity and reliability of the Turkish version of the TFALS, confirming that it has good psychometric properties in a Turkish sample.

This study seeks to clarify whether teachers are able to distinguish between essays written by English L2 students or generated by ChatGPT. 47 instructors who hold experience teaching English to native speakers of Japanese in universities or other higher education institutions were tested on whether they could identify between human written essays and ChatGPT generated essays. The ICNALE written corpus (Ishikawa, 2013) was used to find and randomly select the essays of four Japanese university students' written work who studied English at roughly CEFR A2 level. The AI chatbot, ChatGPT, was used to generate four essays utilizing prompts which directed the chatbot to mimic grammar mistakes common to nonnative speakers of English. Teachers were requested to identify which of the eight essays they believed to be human written or ChatGPT generated. On average, the teachers were able to identify 54.25% of items accurately. This result is slightly better than random chance, and implies that most teachers cannot make an accurate assessment on a ChatGPT generated essay when ChatGPT is prompted to make grammar mistakes.

Recently, ChatGPT, a cutting-edge large language model, has emerged as a powerful Generative Artificial Intelligence (GenAI) tool with the capacity to influence education. ChatGPT provides ample opportunities for learners, researchers, educators, and practitioners to achieve the intended learning outcomes in various disciplines. This special issue examines the diverse applications and implications of GenAI tools including ChatGPT in education, highlighting their potential to enhance teaching and learning across various contexts. Key findings from seventeen studies collected in this special issue demonstrate that GenAI tools can significantly improve educational outcomes by providing personalized feedback, facilitating language learning, and supporting both qualitative and quantitative research methodologies. The findings emphasize GenAI's capacity to increase learner engagement and motivation, yet also underscore the need for robust ethical guidelines and human oversight due to potential issues with privacy, bias, and accuracy. This special issue also highlights the challenges GenAI faces, such as limitations in contextual understanding and its impact on critical thinking skills. In addition, it provides a foundational framework for exploring effective and responsible GenAI integration, aiming to enrich educational experiences. We conclude that future research should focus on the longitudinal effects of GenAI tools on learning outcomes, developing ethical frameworks for their use, and ensuring their adaptability to diverse learner populations to promote inclusive educational practices.

Previous research has shown that L1-L2 congruency is a facilitative factor in collocation processing. The present study explores the congruency effect between learners' L2 and L3. Thirty-three proficient Norwegian learners with Lithuanian as their L1 and English as their L2 completed a phase acceptability task consisting of three groups of Norwegian collocations: congruent with L2, congruent with both L1 and L2, and incongruent with both. The results revealed that collocation L2-L3 congruency facilitates collocation processing.

Purpose: ChatGPT is a generative artificial intelligence (AI) technology that can solve multiple complex tasks. ChatGPT-4 can facilitate educational empowerment in China through technology to understand and generate Chinese text. Although ChatGPT's benefits have been widely discussed, its educational capabilities have not been systematically assessed. This study provides evidence of and insights into the educational applications of AI tools in China. Design/Approach/Methods: This study uses various tests to systematically assess the latest iteration of the AI chatbot ChatGPT-4, including the Watson-Glaser Critical Thinking Appraisal (WGCTA), Five Core Competencies Questionnaire, and written test of China's 2022 National Teacher Certificate Examination (NTCE). Findings: The WGCTA results suggest that ChatGPT requires strong critical thinking. Compared with the other four competencies, the tool showed a lower aptitude for creativity. Regarding its educational applications, ChatGPT performed well on the 2022 NTCE written test. As technology enhances, ChatGPT and similar AI tools have potential applications in China for lesson planning, student self-learning, classroom interaction, and checking assignments. Originality/Value: This study systematically tested ChatGPT at a logical level and assessed its core competencies and educational applications. The study innovatively used 2022 NTCE data to test ChatGPT, with results providing support for the application of generative AI in future curricula and instruction in China.

This brief report presents the results of a re-analysis of data by Yi, Man, and Maie (2023), who investigated L1 and L2 intuitive knowledge of phrasal frequency and collocation strength in multiword sequences. We utilized an individual-differences approach and examined which participant variables (age of onset, length of residence, language use, and L2 proficiency) predicted the participants' accuracy in judging the phrasal frequency and association strength of multiword sequences in English. We found that the demographic variables were only related to the accuracy in judging association strength, but those variables differentially predicted the accuracy depending on whether the collocations were of high or medium association strength.

This study aims to develop a scale to measure the design thinking process and to evaluate the reliability and validity of this scale. It fills this gap by introducing a 36-item scale specifically designed to measure design thinking abilities across the five key stages of the design thinking process: empathize, define, ideate, prototype, and test, to provide an accurate assessment of individual abilities across these stages. Aligned with the well-established design thinking process, the scale comprises five sub-dimensions, each focused on a specific stage. It employs a 5-point Likert-type response format, ensuring nuanced feedback. Rigorous psychometric evaluations confirmed the scale's trustworthiness. Internal consistency within each sub-dimension was exceptional, with Cronbach's alpha coefficients exceeding 0.80 across all stages. Remarkably, the overall scale achieved an outstanding 0.95 Cronbach's alpha, signifying unparalleled cohesiveness and reliability. Validity was further established through rigorous statistical analyses. Exploratory component analysis (EFA) accounted for 60% of the variance, confirming the structure and demonstrating strong item-to-factor loadings (ranging from 0.31 to 0.74). Confirmatory factor analysis (CFA) provided additional validation, with all fit indices exceeding established benchmarks, solidifying the scale's accuracy and applicability. Beyond these impressive psychometric qualities, the study offers several key advantages. The concise 36-item format ensures efficient administration and analysis, while the five distinct sub-dimensions allow for targeted evaluation of specific strengths and weaknesses within each stage. This detailed feedback holds immense value for researchers, educators, and professionals seeking to cultivate design thinking capabilities in diverse settings. By providing a validated and reliable tool for design thinking assessment, this study empowers a comprehensive understanding of individual strengths and weaknesses across the five critical stages. This, in turn, enables targeted interventions and fosters the development of effective design-thinking skills in various contexts.

In recent years, there has been growing interest in the use of data-driven learning (DDL) in L2 writing instruction. This paper examined whether and to what extent DDL activities could enhance the writing complexity, accuracy, and fluency (CAF) of 30 Thai EFL learners. The presentation of DDL in this study was hands-on concordancing with the assistance of the Corpus of Contemporary American English (COCA). The study was held in the context of an English writing workshop focusing on opinion paragraphs, divided into two phases: four weeks for the pattern-hunting activities and two weeks for the pattern-refining activities. Applying the CAF Profile of Larsen-Freeman (2006), the pretest and posttest writings were analyzed. The results showed that DDL can enhance learners' writing lexical complexity, accuracy, and fluency, whereas no statistically significant evidence was found in terms of grammatical complexity. Furthermore, the results from the questionnaire and the interview indicated that the learners had positive attitudes towards DDL as a useful reference resource to enhance their vocabulary acquisition, refine their writing, revise their compositions, generate fresh ideas for writing, and boost their writing confidence. These findings underscore the potential of DDL as a pedagogical tool in L2 writing instruction.

Morphological awareness is the metalinguistic realization that words consist of meaningful roots and affixes that can be isolated and manipulated. Learners at different proficiency levels use various forms of background knowledge such as cultural knowledge, technical knowledge, religious knowledge, vocabulary knowledge, and contextual visuals. The purpose of this research was to see whether morphological awareness and background knowledge affected the fluency and accuracy of Turkish EFL learners. The participants consisted of 80 Turkish EFL students. At first, participants were homogenized to select samples and eliminate outliers, then a pretest was used to assess participants' writing fluency and accuracy prior to treatment. During the treatment phase, materials were presented to participants, and at the end, a posttest was used to assess the effect of treatment on participants' writing fluency and accuracy. According to the result of the data analysis, there was a significant difference in morphological awareness of EFL learners' writing performance in terms of accuracy from the pre- to posttests. Furthermore, the findings revealed a similar amount of progress in participants' writing fluency.

Although there are claims that ChatGPT, an AI-based language model, is capable of assessing the writing of L2 learners accurately and consistently in the classroom, a number of recent studies have shown discrepancies between AI and human raters. Furthermore, there is a lack of studies investigating the intrareliability of ChatGPT scores. Accordingly, this study aimed to examine the accuracy and consistency of ChatGPT compared to teachers, as well as with itself, after being trained on a rubric. To accomplish this goal, the study adopted a quantitative correlational non-experimental design. A dataset of 100 writing assignments, submitted by a cohort of B1-level students at an international branch university in Egypt, was analyzed quantitatively. These assignments were initially evaluated and moderated by trained teachers (n=11), and subsequently, the same assignments were also assessed twice by ChatGPT. The findings indicated that teachers' scores exhibited a higher level of accuracy compared to those generated by ChatGPT. The results also revealed that ChatGPT exhibits a moderate, yet questioned, level of intra-rater reliability. The weak-to-moderate correlations between ChatGPT and teacher scores raise concerns about the accuracy and consistency of ChatGPT's scoring of writing assignments. The implications of the findings highlight the potential applications and limitations of ChatGPT in L2 writing assessment. This study contributes to the ongoing discourse on the use of AI technologies in language education and provides insights into the accuracy and reliability of ChatGPT as an evaluation tool for L2 writing.

Despite the recent anti-CRT (Critical Race Theory) movement within U.S. education, teachings of Native histories and perspectives have never been accurately taught, or even taught. From their perspectives as teacher educators in predominantly white institutions (PWI), the authors share counterstories from their existing IRB-approved research projects to explore the impacts of CRT bans on teacher education and how the bans continue to perpetuate systemic erasure of Native perspectives. They review how legislators in the Western U.S. passed anti-CRT laws as well as its impact on teacher education. Using the TribalCrit framework with an emphasis on the first tenet, "colonization [being] endemic to society" (Brayboy, 2005, p. 430), the authors discuss how Native invisibilization and erasure are perpetuated in predominantly white classrooms by silencing Native perspectives in policy making and curriculum implementation, banning Natives in public education, and explicit refusal of white teachers to learn culturally sustaining pedagogies (Paris & Alim, 2017). While erasure and colonization may no longer be explicit U.S. federal policy aims in the education of Native youth, the subjugation of Native rights, cultures, knowledges, and histories remains a contemporary feature of state-sanctioned public education. Telling these stories of structural violence toward Native peoples reflected in the ignorance enforced by mainstream teachers and educational policymakers makes salient the overwhelming need to teach Native history and content at all levels of public education.

The proliferation of artificial intelligence (AI) has impacted scholarly publishing. This study explores the use of AI tools by Arab researchers who publish in English. A survey was conducted to gather information on AI tool adoption in research conception, writing, editing, and publishing. It collected responses from 84 participants, who provided insights into their usage patterns, challenges encountered, and ethical considerations when using AI. The findings reveal diverse adoption rates of AI tools, with high usage for language correctness and content enhancement such as grammar and spelling checking. The ethical implications of AI tool usage, including the need to ensure data quality, prevent plagiarism, and maintain ethical standards, were emphasized by respondents. Tools such as Grammarly, Endnote, and QuillBot were recognized for their utility in addressing common challenges. However, certain research-related tasks, notably research design, exhibited lower AI tool integration. The study underscores the gap between awareness and adoption of AI tools, indicating potential barriers to their utilization. Challenges such as cost, integration issues, and concerns about the accuracy and appropriateness of AI-generated content were identified. These findings offer insights for researchers, institutions, and publishers to enhance editorial processes, promote ethical AI tool usage, and bridge the gap between awareness and adoption.

The 2023 Tribal Leaders qualitative study is an emergent perspective from twelve Tribal leaders on education, Tribal sovereignty, leadership, and change presented as a poster session at the 2023 NRMERA conference in Omaha, Nebraska. This conceptual paper presents a review of literature acknowledging a lack of research inclusive of the voice of Tribal leaders as well as research, particularly in the areas of Tribal-specific/Native-centered leadership models and change frameworks. The research findings are presented across the four focus areas and of note: In education, Tribal leaders indicated the need for Tribal voices to be heard, accurate American History to be taught, adequate funding to be provided, and more culturally relevant instruction provided inclusive of language and cultural teaching were among the more significant findings. In the area of Tribal sovereignty, Tribal leaders indicated that Tribal sovereignty was something they wanted. They were not convinced that Tribal sovereignty was something in reality that could be achieved and, once achieved, could be held onto. In terms of leadership, Tribal leaders indicated a preference for Servant Leadership and did not specify any specific Tribal leadership definitions, models, or frameworks. With regard to change, Tribal leaders discussed change with no specific Tribal leadership models or frameworks. The findings are discussed and interpreted through a TribalCrit lens (Brayboy, 2022), with a conclusion and next steps presented.

This study investigated the efficacy of two distinct virtual training platforms, TeachLivE™ and Zoom with actors, in preparing preservice practitioners for behavior management in real-world settings. This exploratory study aimed to investigate individual participant skill acquisition and assess which platform better equipped participants with the necessary skills and strategies. This study employed Behavior Skills Training as the foundational pedagogical framework, utilizing structured training and feedback to foster skill acquisition and retention. The two groups, one using TeachLivE™ and the other Zoom with actors, received identical training modules and practice scenarios. After the training, participants were assessed on their ability to apply behavior management strategies in simulated scenarios closely mirroring authentic classroom settings. Results from the study revealed an observable difference in the percent correct performance between the two groups. The TeachLivE™ group exhibited a higher level of success in applying behavior management strategies compared to the Zoom with actors group. This outcome suggests that the immersive nature of TeachLivE™, with its lifelike avatars and interactive virtual classrooms, provided a more effective training environment for preservice practitioners in behavior management.

The Poya Songbook, an integral part of Zhuang traditional culture, embodies rich music and folklore passed down through generations. This study aims to examine the historical development and literacy transmission of the Poya Songbook for education and literacy studies in Funing County, Wenshan Prefecture, Yunnan Province, China. Drawing upon a combination of ethnographic methods and semiotic analysis, the research delves into the intricate journey of the Poya Songbook, contrasting its state before and after the pivotal year 2006, when it received official recognition as an intangible cultural heritage. Three key informants, deeply rooted in the local cultural milieu, provide valuable insights into the songbook's transformation, its role in education, and the challenges of preserving its authenticity amidst modern influences. The data analysis discerns shifts in transmission methods, content, and performance, shedding light on the evolving relationship between tradition and cultural identity. The findings underscore the importance of balancing preservation with adaptation to ensure the continued vitality of this cherished cultural heritage. Suggestions for the sustained safeguarding of the Poya Songbook include innovative educational initiatives that integrate its teachings into contemporary curricula, fostering a deeper understanding and appreciation of Zhuang cultural identity.

The objective of the research is to explore the consistency of specialized nursing competency models of nursing students. The participants in this research are 165 Year 4 nursing students at Boromarajonani College of Nursing, Chakriraj, Thailand. The data was collected using a questionnaire consisting of 45 questions, answered with the use of a 5-level scale. The questionnaire was evaluated by three experts, and the questionnaire's accuracy ratings ranged from 0.70 to 1.00. The internal reliability of the questionnaire was measured using Cronbach's alpha coefficient, resulting in values of 0.763 for educational excellence, 0.757 for socio-economic factors, 0.806 for lifestyle, and 0.814 for personal factors. The study's findings showed that competency. 1) Education throughout nursing student training. 2) The socio-economic context. 3) Habits pertinent to everyday existence. 4) Attitude towards the nursing profession. 5) GPAx represents the grade point average. 6) OSCE refers to the objective structured clinical examination. The confirmatory factor analysis of specialized nursing aptitude is consistent with the empirical data, Chi-Square = 14.226; df = 8; Relative Chi-Square = 1.778; p-value = 0.076; GFI = 0.974; NFI = 0.943; TLI = 0.951; CFI = 0.974; RMSEA = 0.069; RMR = 0.113. Each element has a standard element weight value of between 0.010 and 0.817, with a standardized weight value ranging from 0.010 to 0.817.

English speaking accuracy is one of the most important aspects of Business English. While most studies are conducted to improve English speaking accuracy, they neglect phrasal semantics. This present study involves 30 participants from Phu Em Company. The study employs a mixed-methods design in which the quantitative approach helps quantify the speaking accuracy scores made by learners while the qualitative approach explores how learners perceive the use of interrogative constructions. The instruments are a questionnaire and an interview. Data analysis procedures include a theme analysis of the interview data, a statistical analysis of the test and survey data, and integrated findings. The findings from this research provide evidence that using interrogative constructions does not improve grammatical or phonological accuracy but improves expressing accuracy. The study also reveals that 58% of EG learners provide positive feedback on the use of interrogative constructions in learning English speaking skills. Based on the findings, several suggestions are offered to teachers and learners.

With the fast development of artificial intelligence and emerging technologies, automatic recognition of students' facial expressions has received increased attention. Facial expressions are a kind of external manifestation of emotional states. It is important for teachers to assess students' emotional states and adjust teaching activities accordingly. However, existing methods for automatic facial expression recognition have the limitations of low accuracy of recognition and poor feature extraction. To address the problem, this study proposed a novel deep learning model called DenseNetX-CBAM to improve facial expression recognition by utilizing a variant of densely connected convolutional networks (DenseNet) to reduce unnecessary parameters and strengthen the reuse of expression features between networks; moreover, convolutional block attention module (CBAM) was integrated to allow the networks to focus on important special regions and important channels when representing features. The proposed model was tested using 217 video clips of 33 students in an online course. The results demonstrated promising effects of the method in improving the accuracy of facial expression recognition, which can help teachers to accurately recognize students' emotions and provide real-time adjustment in online learning environments.

This study sought to identify the challenges and needs of TOEFL iBT candidates in achieving C1 level scores in the speaking and writing sections of the exam. To this end, the researcher employed a mixed-method approach to collect data from a population of 46 students, both male and female, between the ages of 22 and 30. The participants were enrolled in two TOEFL preparation classes at Universidad Nacional de Educación. The data were collected through a survey, class observations, and TOEFL speaking and writing mock tests, and were subsequently analyzed both qualitatively and quantitatively. The findings indicated that the participants encountered challenges in discussing unfamiliar topics fluently, struggled with producing language at the C1 level while employing a variety of grammar and vocabulary, and demonstrated reduced accuracy due to influences from their first language (L1). To address these challenges, it is recommended to offer a bridging course prior to exam preparation courses wherein the primary focus is devoted to enhancing candidates' accuracy and communicative competence in discussing unfamiliar topics through corrective feedback. The findings of this study could offer valuable insights to TOEFL iBT test preparation instructors, enabling them to better understand the challenges students face and plan strategies to address these challenges effectively within preparation courses.

Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th, 7th, and 8th grades attending a school in Central Anatolian City in Turkey, participated in this study in the 2022-2023 school year. Data was collected with a. A deep learning method called deep neural network, one of the ways of machine learning, was used in data analysis. The average number of books read per year had the highest importance among the variables affecting academic performance in science courses. In addition, deep learning predicted students' final science scores with 90% accuracy. According to the results of this study, the percentage of the academic achievement prediction might be raised by reproducing the required data set for the data analysis method with deep learning. A forecast of the student's academic achievement with artificial intelligence and detecting the importance of variables' percentage might be researched for other courses in addition to the Science course.

This paper opens with a family anecdote in which my future mother in-law, when asked what wise advice she would offer undergraduate university students, replied, "I would tell them it's not going to be okay." Can we learn to keep engaging with the world despite its inevitable disappointments? I propose that Stoic philosophy, by "orienting" our "attention" and "courage," can help us navigate the troubled post-COVID world we share. To help make this more concrete, I describe a critical moment I observed in which a maskless shopper insulted fellow patrons in a grocery store for wearing a mask. I then develop the Stoic themes of acknowledgement (a commitment to the facts) and affinity (reaching out to others to build community). In the conclusion I return to the "maskless shopper" incident to consider how my two Stoic themes might help open a dialogue with this person. After discussing the limitations to such an undertaking, given the surge in populism over the last decade, I conclude with the appropriately tough-minded Stoic proposition that despite the obstacles, we must keep trying.

Movement training is thought to be very important in terms of learning both basic motor skills and new techniques in sports branches. Rhythmic gymnastics apparatus technique applications, which is a widely used method in movement training; it aims to display predetermined choreography in harmony with various tools. In this context, the aim of this experimental study is to examine the effect of rhythmic gymnastics equipment technique, which is predicted to be a good tool to improve reaction performance for movement educators and athletes, on various hand and foot reaction parameters. A total of 42 female athletes, including 22 experimental group members who had not previously practiced rhythmic gymnastics and 20 control group members, were included in the study on voluntary basis. The reaction performances of the athletes were determined using the ÇAGIN Hand and Foot Reaction Test. Data were analyzed using the SPSS 26.0 package program using Repeated Measures-ANOVA and descriptive statistics. In the pretest-posttest results of the reaction time of the individuals, it was determined that the hand simple and discriminative reaction time and foot discriminative reaction time of the experimental group decreased significantly (p<0.05). In the pretest-posttest results regarding the individuals' correct reaction rates, it was determined that only the foot selective reaction correct rate of the experimental group increased significantly (p<0.05). As a result of the study, it was observed that the application of the apparatus technique generally positively affected the athletes' hand and foot reaction times and the accuracy of the reactions.

This study aims to compare item and student parameters of dichotomously scored multidimensional constructs estimated based on unidimensional and multidimensional Item Response Theory (IRT) under different conditions of sample size, interdimensional correlation and number of dimensions. This research, conducted with simulations, is of a basic nature. The standard errors of the item and student parameters estimated according to both models were evaluated by the mean of the error squares. The results of the study indicate that there is no significant difference between the errors of the item parameters obtained from the unidimensional and multidimensional IRT in the cases of two-dimensional data structures and high interdimensional correlations. However, when the data structure are three and five dimensional, the item parameter errors resulting from the unidimensional IRT increase. Standard errors for item parameters decreased as the sample size increased. When the standard errors arising from ability parameters were analysIRTed, it was observed that multidimensional IRT estimated with lower errors for all conditions. As a result of the study, it is established that multidimensional IRT provides more accurate results in the analysis of multidimensional constructs, especially in the estimation of student parameters and in making decisions about students. Therefore, it is recommended that multidimensional models should be used for student ability estimation in national and international large-scale examinations.

The article is in the second phase of research is about "the big data architecture for pre-teacher preparation supply chain with prescriptive analytics of higher education in Thailand". The objectives of the study were (1) to study the pre-teacher preparation supply chain in Thailand, (2) to develop a model the big data system for the pre-teacher preparation supply chain management with prescriptive analytics of higher education in Thailand, (3) to design the big data architecture for the pre-teacher preparation supply chain management with prescriptive analytics of higher education in Thailand, (4) to develop the big data system for the pre-teacher preparation supply chain prescriptive of higher education in Thailand, (5) to assess accuracy of the predictive analytics in the pre-teachers needs of higher education in Thailand, and (6) to assess accuracy of the prescriptive analytics in the pre-teacher preparation of higher education in Thailand. In the study, the research procedures were divided into 6 steps according to the objectives, and all steps were assessed on the system suitability by 25 experts throughout the procedures.

Educational data mining has allowed for large improvements in educational outcomes and understanding of educational processes. However, there remains a constant tension between educational data mining advances and protecting student privacy while using educational datasets. Publicly available datasets have facilitated numerous research projects while striving to preserve student privacy via strict anonymization protocols (e.g., k-anonymity); however, little is known about the relationship between anonymization and utility of educational datasets for downstream educational data mining tasks, nor how anonymization processes might be improved for such tasks. We provide a framework for strictly anonymizing educational datasets with a focus on improving downstream performance in common tasks such as student outcome prediction. We evaluate our anonymization framework on five diverse educational datasets with machine learning-based downstream task examples to demonstrate both the effect of anonymization and our means to improve it. Our method improves downstream machine learning accuracy versus baseline data anonymization by 30.59%, on average, by guiding the anonymization process toward strategies that anonymize the least important information while leaving the most valuable information intact.

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performance comparisons between groups, without considering the behavior of the models or the severity of the biases in the outcomes. To address this gap, we proposed a novel metric in a previous work (Verger et al., 2023) named "Model Absolute Density Distance" (MADD), measuring algorithmic unfairness as the difference of the probability distributions of the model's outcomes. In this paper, we extended our previous work with two major additions. Firstly, we provided theoretical and practical considerations on a hyperparameter of MADD, named "bandwidth," useful for optimal measurement of fairness with this metric. Secondly, we demonstrated how MADD can be used not only to measure unfairness but also to mitigate it through postprocessing of the model's outcomes while preserving its accuracy. We experimented with our approach on the same task of predicting student success in online courses as our previous work, and obtained successful results. To facilitate replication and future usages of MADD in different contexts, we developed an open-source Python package called maddlib (https://pypi.org/project/maddlib/). Altogether, our work contributes to advancing the research on fair student models in education.

The practice of Corrective Feedback (CF), which is situated within a Second Language Acquisition (SLA) Paradigm, is currently positioned towards an accuracy-orientated delivery based on native speaker norms. This is despite the recognition in different areas of linguistic research that there is considerable variation in the way that English is spoken around the world. This paper argues that the epistemological assumptions and methodological approaches to investigate CF within an SLA paradigm have various underlying weaknesses that undermine research findings. These findings purport to provide support for an accuracy-orientated CF in the English classroom. However, it is suggested in this paper that a meaning-orientated CF would be more reflective and beneficial for students given the transformative changes that have occurred to English over the past 30 years. This perspective is discussed in relation to one teacher's approach to CF who participated in a larger project which examined CF conducted in a British-Sino University.

Research indicates that deceitful videos tend to spread rapidly online and influence people's opinions and ideas. Because of this, video misinformation via deepfake video manipulation poses a significant online threat. This study aims to discover what factors can influence viewers' capability to distinguish deepfake videos from genuine video footage. This work focuses on exploring deepfake videos' potential use for deception and misinformation by exploring people's ability to determine whether videos are deepfakes in a survey consisting of deepfake videos and original unedited videos. The participants viewed a set of four videos and were asked to judge whether the videos shown were deepfakes or originals. The survey varied the familiarity that the viewers had with the subjects of the videos. Also, the number of videos shown at one time was manipulated. This survey showed that familiarity with subjects has a statistically significant impact on how well people can determine a deepfake. Notably, however, almost two-thirds of study participants (102 out of 154, or 66.23%) were unable to correctly identify a sequence of just four videos as either genuine or deepfake. This study provides insights into possible considerations for countering disinformation and deception resulting from the misuse of deepfakes.

This study aimed to study the association between diglossic reading skills and reading comprehension in first grade students with and without learning disabilities. In this study population, students presented diglossia in literary and colloquial Arabic: 30 first grade students with learning disabilities and 30 first grade students without learning disabilities were assessed using a reading test (accuracy and fluency in both languages: literary and colloquial) and a reading comprehension test. This study's findings show significantly better measures of reading skills and reading comprehension in the students without learning disabilities, and significant interaction between reading and reading comprehension in the entire study population. In view of the marked gaps between the two populations of students, it is important to pay special attention to the population with learning disabilities and strive to help these students improve on the measures tested in this study (reading skills and reading comprehension). This could have a positive impact on a variety of other pedagogical-related phenomena, such as academic achievements in school subjects.

The use of large language models like ChatGPT is widely discussed for educational purposes. Using this technology requires teachers to have appropriate competences that incorporate knowledge of how to make use of this technology. In this study, we investigate pre-service teachers' knowledge through the lens of the KTMT model ("Knowledge for Teaching Mathematics with Technology" model), a domain-specific variant of the TPACK-model. One component is represented in mathematical fidelity as knowledge of the mathematical accuracy of the technology, which in case of large language models is of special interest, as it may produce erroneous but plausible-sounding information. Furthermore, prompting techniques are of interest as technological knowledge, which influence mathematical fidelity. For this study, eleven pre-service teachers were asked to solve four different mathematical tasks with the help of ChatGPT. The chatlogs and information provided in an interview after working on the tasks are analyzed using qualitative content analysis. Results show that both correct and incorrect answers were produced for all tasks. The rate of pre-service teachers providing an incorrect answer is high when having been presented with an incorrect answer generated by the large language model. Despite having access to ChatGPT as a tool, many of the participants were not able to provide correct answers to all tasks. Furthermore, the mathematical fidelity was often over- and, in some cases, underrated. The mathematical knowledge seems to have changed while working with ChatGPT. Based on the applied prompting techniques, the pre-service teachers showed a deficiency in technological knowledge.

Knowledge exploration refers to actively seeking information, ideas, and experiences, often beyond immediate task requirements. Previous research on exploratory behaviour has predominantly focused on visual and perceptual forms of exploration, overlooking the academic aspect, where the aim is to bridge knowledge gaps. To test the effect of task and individual factors on knowledge exploration, an experimental study was conducted on 100 high school students in two sessions. In the first session, participants answered 15 multiple-choice questions without receiving immediate feedback but were given the opportunity to explore the answers. In the second session, immediate feedback was provided after each response. The results indicated that knowledge exploration was more likely to occur when students answered the questions wrongly, and the feedback on the accuracy of answers significantly enhanced knowledge exploration for incorrect answers. Session 2 also investigated the impact of factors such as Feeling of Confidence (FOC), confidence error, and intrinsic and extrinsic academic motivation on knowledge exploration. A multilevel ordinal logit model was employed to analyse the within-person and between-person relationships among these variables. The findings revealed that FOC, confidence error, and intrinsic motivation positively predicted knowledge exploration, whereas extrinsic motivation had a negative effect. These findings have significant implications for educational practice and policy. Educators can structure their course materials to cultivate students' metacognitive awareness, thereby promoting deep engagement and enhancing their learning experience.

The relationship between confidence and accuracy has long been an important and controversial topic within the field of human memory. In a recent review article, Schwartz (2024). "Inferential theories of retrospective confidence." Metacognition & Learning.) competently summarized some of the key empirical findings on this issue and clearly articulated two different extant theoretical approaches to understanding this relationship. The "direct access" view states that one's confidence in a memory is tied directly to the strength of the encoded memory trace, predicting a strong and near ubiquitous positive relationship between confidence and accuracy. In contrast, the "inferential view" holds that confidence is inferred from the heuristic use of available cues, and that any positive relationship between confidence and accuracy stems from the use of cues that correlate positively with accuracy. Here, I propose an alternative view that blends aspects of both accounts. Termed the "indirect access" account, I argue that memory signals and their experiential correlates form the basis of confidence judgments. This approach anticipates reported dissociations between confidence and accuracy, and accommodates a broad range of empirical findings. By this view, rare instances of weak confidence-accuracy relationships stem from strong misleading memory signals that experientially mimic strong accurate memory signals. Because strong memory signals are largely accurate, this view predicts a pervasive and robust positive relationship between confidence and accuracy.