Search

Purpose: Many students perceive plant anatomy as difficult due to the complexity of the material. Additionally, conventional teaching techniques often neglect the importance of information retention in the learning process. Therefore, this study examines the effects of Retention-Based Learning on students' learning outcomes compared to conventional learning without Retention-Based Learning. Methodology: A multiple-group time series research design was used to measure the effectiveness of Retention-Based Learning on students' learning outcomes including information retention, cognitive load, and learning achievement. Retention interventions in the Retention-Based Learning class included watching videos, identifying images and answering questions. The participants in this study were seventy-eight 10th-grade public high school students in Bandung, West Java, Indonesia, divided into two research groups. Findings: This study found that students in the experimental group had better information retention in each lesson and a significantly higher ability to process information with less mental effort and lower cognitive load than the control group. Additionally, the experimental group showed significantly higher learning achievement than the control group. These findings demonstrate the importance of maintaining information retention to maximize learning outcomes in plant anatomy lessons. Significance: This study indicates that maintaining retention can be a simple and powerful learning approach to help high school teachers teach complex material. The study highlights the significance of maintaining student retention to improve learning performance.

AI-generated paraphrasing tools, especially QuillBot and Paraphrasing Tool, play a crucial role in preventing plagiarism in academic writing. However, their effectiveness and proficiency have been questioned, particularly regarding the adequacy of their strategies. This qualitative study analyzed the paraphrasing strategies and proficiency levels of QuillBot and Paraphrase Tool. Using a purposive sampling technique, all 30 abstracts from one issue of the "Journal of Second Language Writing" were paraphrased using the two paraphrasing tools in their standard modes, and the results were analyzed using the frameworks of Keck (2014) and Nabhan et al. (2021). The results of the study indicated that both tools primarily used synonym substitution, with QuillBot favoring word-level changes and Paraphrase Tool emphasizing sentence restructuring. QuillBot tended to show minimal revision, followed by moderate revision, while Paraphrase Tool exhibited more moderate revision, followed by minimal and substantial revision. Paraphrase Tool exhibited broader paraphrasing capability than QuillBot, but both tools show some paraphrasing limitations. Overall, while these tools may enhance some writing, writers should thoroughly review the core concepts of the original texts and grammatical structures in specific contexts. For novice writers, paraphrasing practice in classrooms should be conducted under teachers' guidance. AIgenerated tools should be secondary.

Identifying gifted students in today's complex context requires precision in both the definition of the concept and its constituent characteristics. However, there are discrepancies worldwide in the instruments established to diagnose gifted students. This study undertook a systematic literature review (SLR) to identify the most representative characteristics of this profile. A database of 676 articles from Scopus and WoS was analyzed, of which 37 were screened for further study using a content analysis approach. The study aimed to answer two research questions: (1) What are the prevailing characteristics of research on giftedness? (2) What are the challenges and barriers to current research and practice in gifted research? These inquiries were addressed through a series of disaggregated questions divided into four categories: publication metrics, study methodology, identification and support of giftedeness, and technology used. The main findings were a) the lack of standardized measures for identifying gifted individuals; b) the important role of teacher training in identifying and supporting gifted profiles; and c) the inherent limitations of research on giftedness, which constrain the generalizability of its findings. It was concluded that emotional intelligence plays a critical role in giftedness. Key challenges identified include discrepancies in giftedness identification instruments and the impact of cultural and socio-economic factors on gifted education.

This study examined the integration of artificial intelligence-powered speech recognition technology within early reading assessments in Morocco's Teaching at the Right Level (TaRL) program. The purpose was to evaluate the effectiveness of an automated speech recognition tool compared to traditional paper-based assessments in improving reading skills among 100 Moroccan first to third-graders. The mixed-method approach combined pre-post standardized reading tests with qualitative feedback. Results showed students receiving the AI-enabled speech recognition assessments demonstrated significant gains in reading achievement compared to peers assessed via traditional methods. Qualitative findings revealed benefits of instant feedback and enhanced engagement provided by the speech recognition tool. This study contributes timely empirical evidence on adopting learning technologies, specifically AI-driven automated speech assessment instruments, to enhance foundational literacy development within under-resourced education systems implementing student-centered pedagogical techniques like TaRL. It provides valuable insights and guidance for integrating innovative speech analysis tools within localized teaching and learning frameworks to strengthen early reading instruction and monitoring.

Two graduate students and a faculty member of the Department of Entomology at the University of Kentucky developed and implemented a two-year extension program aimed to train Amish farmers on identification and management of their major agricultural problems. Students conducted periodical visits to the community, inquired about farmers' needs, and identified relevant issues, which were outlined to plan two field days offered in their properties. Amish farmers gained knowledge on diverse topics and were eager to receive handouts and publications. Amish were open to apply new technologies and implement them without main changes in their traditional methods of agriculture. The graduate students acquired skills in developing and implementing outreach extension program. Communication and relationship continued with this community after this program was completed.

Artificial intelligence (AI) first made its entry into higher education in the form of paraphrasing tools. These tools were used to take passages that were copied from sources, and through various methods, disguised the original text to avoid academic integrity violations. At first, these tools were not very good and produced nearly incomprehensible output. However, through the use of generative artificial intelligence and natural language processing, the current engines supporting these tools have become better and more efficient at producing quality output. Recently, the artificial intelligence research company, OpenAI, developed a groundbreaking artificial intelligence engine to drive a conversational chat frontend application called ChatGPT. Backed by an expansive knowledge base that could rival any university library in volume, this AI-driven conversational application can produce well-written, seemingly academic, responses to questions. This paper examines text-based artificial intelligence tools that can be used for both ethical and unethical purposes and what simple methods can be used to recognize AI-generated output whose use may indicate plagiarism.

Primary school educators in South Africa often experience difficulties in applying recommended adaptive assessment methods in large multilingual classes, with these challenges being exacerbated when teaching and assessing English as First Additional Language (EFAL). In this article, we report on a study that explored Grade 6 educators' knowledge and use of adaptive assessment methods when teaching EFAL. The national Policy on Screening, Identification, Assessment and Support ([SIAS] Department of Basic Education [DBE], Republic of South Africa [RSA], 2014) underpins our study as a theoretical framework. We followed a qualitative research approach to examine the knowledge as well as the classroom practices of 6 purposively selected educators who taught EFAL. The study was grounded in the interpretivist paradigm and investigated educators' lived experiences integrating adaptive assessment procedures into the teaching of EFAL in Grade 6 inclusive classrooms. We relied on semi-structured interviews, observation, and document analysis to collect data and performed thematic analysis to identify, analyse and report repeated patterns. The findings from our study indicate that the participating educators perceived their training in inclusive education and their use of adaptive assessment methods as inadequate, especially in the context of large, under-resourced classrooms. As a result, we recommend that educators should receive the necessary support from the school-based support teams and the district-based support teams to use adaptive assessment methods when teaching EFAL.

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.

This essay critiques the emphasis on detecting artificial intelligence (AI) usage in student submissions and advocates for a shift towards the meaningful integration of AI in education. Citing data from Turnitin, it highlights the significant yet understated prevalence of AI in academic work. The discussion underscores the ideological, detection, and moral challenges associated with AI in education, arguing for a reconceptualization of assessment and pedagogy to accommodate AI tools ethically and effectively. It calls for collaborative efforts to redesign curricula and assessments, ensuring educators and students are equipped to navigate the evolving educational landscape. The essay concludes by emphasizing the necessity of preparing graduates for a future in which AI plays a central role in learning and professional practice.

Field-Based Practicum (FBP) about Pterydophyta diversity material in Low plant botany learning is very important, even though the facts in the field show that FBP is still minimally carried out. This research aims to utilize the potential diversity of Pterydophyta in the Tuban-Lamongan Pantura area as a support for FBP about low plant botany learning. The type of research is descriptive exploratory with a transect method, which divides the research area into five plots. Data collection techniques involve observing abiotic parameters and fern morphology in each plot and counting the number of individuals of each species. Data analysis used the Shannon & Wiener index for diversity and qualitative descriptive analysis for morphology. The research results showed 19 species of ferns with 210 individuals divided into two classes, namely Polypodiopsida and Psyotopsida. The fern diversity index is in the medium category (H´=2.10). The diversity of ferns obtained shows that the Tuban-Lamongan Pantura area has a high potential to support FBP about low plant botany learning to provide insight and direct experience to students about the diversity of ferns in the surrounding environment.

The quality of the data and the amount of correct information available is key to informed decision-making. Higher education institutions (HEIs) often employ various decision support systems (DSSs) to make better choices. However, there is a lack of systems to assist with decision-making to promote innovation in teaching and learning. In this study, we evaluate an analytic tool called PROF-XXI that supports strategic decision-making of teaching and learning centres (TLCs) by identifying their competencies in teaching and learning innovation. Through a multiple case study conducted with three Latin American universities and supported by quantitative and qualitative data, we observed how this tool is used and how it facilitates strategic decision-making. Our findings indicate that the tool is accessible, user-friendly, and effective in 1) initiating identification and systematic reflection of institutional competency levels in teaching and learning innovation, 2) enhancing understanding of strengths and weaknesses as well as identifying opportunities for innovation, 3) supporting TLCs with short- and long-term decision-making, and 4) continuously evaluating their strategies, programs, and initiatives. This research can benefit policymakers in higher education who are involved in measuring institutional competencies to improve teaching quality or in making strategic decisions related to teaching and learning innovation.

This project report describes the process of a team of undergraduate researchers (Chemistry and Nursing majors), who analyzed 20th-century medical kits housed at The Columbus Museum (Columbus, GA, USA). Curators and museum personnel were unfamiliar with the contents and needed assistance in identifying the various chemical contents. Items were identified by the Taylor Lab, which was followed by fully elucidating the chemical information in a chemical report and student-curated exhibit. The intent of this project was to help the museum be aware of how to properly curate and store the medical collections for an extended period. Laboratory analyses were executed to determine the composition of the aged items in the collections. The historical context of these kits and their contents provided knowledge of medicine to the community of Columbus, Georgia, in addition to explaining the use of medically related items in the 20th century.

It is characteristic that besides other duties teacher competence management and development is becoming a part of school responsibilities. Still, school leaders lack the experience and instruments to accomplish these duties. From a teacher's perspective competence management and development can be seen as the process of identification and implementation of professional development (PD). An effective competence management approach that is widely used in business environments is the identification of required and actual competence profiles to judge which development is needed. Such a competence management approach isn't characteristic of school environments, still a promising perspective on how to solve challenges regarding teacher PD (TPD). TPD interventions are often criticized as being too general ("one size fits all" dominates); therefore, the search for practices on how to "tailor" PD initiatives to individual teacher needs is topical for TPD research. An effective way to personalize TPD could be the determination of TPD profiles and the design of the PD around these profiles. Person-centered approaches dominate in the identification of science teacher profiles, as surveys and tests are commonly used. Examples, of how the identification of science teacher profiles can be done using lesson observation data are missing. Science teaching is a complex process; to limit the complexity of this study, the authors focus on teaching that promotes student conceptual understanding (CU). A mixed method study was designed and conducted in a sample of 26 science teachers, who represented urban municipalities' science teacher population. The study included science teacher lesson observation and analysis, and determination of science teacher performance regarded teaching that promotes student CU. Science teacher performance data were used to identify teachers with similar performance across the selected criteria and to create science teacher profiles. A methodology for how lesson observation data can be used to identify teacher profiles in small teacher samples is described. Six various science teacher profiles in teaching that promote student understanding were identified, characterizing the variety of science TPD needs.

We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most influential in dropout prediction. To evaluate its effectiveness, we compared the predictive performance of our hybrid model with two other survival analysis models: the piecewise exponential model and a hybrid model combining a fully-connected neural network with a piecewise exponential model. Additionally, we compared it to five other cross-sectional models: Ridge Logistic Regression, Lasso Logistic Regression, CART decision tree, Random Forest, and XGBoost decision tree. Our findings demonstrate that the hybrid model outperforms or performs comparably to the other models when predicting dropout among students at the University of Delaware in Spring 2020, Spring 2021, and Spring 2022. Moreover, by categorizing predictors into three distinct groups--academic, economic, and social-demographic--we discovered that academic predictors play a prominent role in distinguishing between dropout and retained students, while other predictors may indirectly influence predictions by impacting academic variables. Consequently, we recommend implementing an intervention program aimed at identifying at-risk students based on their academic performance and activities, with additional consideration for economic and social-demographic factors in customized intervention plans.

Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a classification problem (successful or unsuccessful), predict students' academic success, and identify those at risk. The primary objective was to predict the academic success status with 26,708 students enrolled in Istanbul University open and distance education programs between 2011 and 2017. Predictions were based demographic data and success grades in Turkish, Atatürk's Principles and History of Revolution, English, and Disaster Culture courses. The study utilized classification models from supervised learning algorithms and was conducted using the SPSS Modeler 18 program. Initially, the data was divided into 70% training and 30% test data. Then, models were constructed by using Random Forest, Tree-AS, C&RT, C5.0, CHAID, QUEST, Naive Bayes, Logistic Regression, NeuralNet, and SVM algorithms. Model performances were compared according to accuracy, sensitivity, specificity, F1 score, positive predictive value, negative predictive value, and Matthews Correlation Coefficient criteria. The C&RT model demonstrated the best performance, achieving the highest specificity value of 0.915.

This study investigates the validity and reliability of the Intrinsic Motivation Inventory (IMI) in the context of blended learning. In the digital age, the fusion of online components with traditional classroom instruction has become integral to modern pedagogy, giving rise to blended learning--a flexible approach accommodating diverse learning needs. In such multifaceted environments, intrinsic motivation emerges as pivotal for sustaining student engagement, bridging the gap between in-person and online learning components. Utilizing a quasi-experimental quantitative research design, the research focuses on first-year university students enrolled in a blended learning economics course. Thorough statistical analysis affirms the IMI's effectiveness in accurately measuring intrinsic motivation levels within the diverse framework of blended learning. The findings not only validate the IMI as a tool for assessing intrinsic motivation but also underscore its importance for educators. Understanding students' intrinsic motivation is essential for tailoring instructional strategies, identifying at-risk students, and proactively preventing potential dropouts in blended learning environments. The validation of the IMI enhances the accuracy of assessments, interventions, and program evaluations, ultimately contributing to the improvement of education quality for students engaged in the integrated format of traditional and online instruction.

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.

An organization's infrastructure rests upon the premise that cybersecurity professionals have specific knowledge in administrating and protecting it against outside threats. Without this expertise, sensitive information could be leaked to malicious actors and cause damage to critical systems. In order to facilitate this process, the presented work addresses the use of Infrastructure as Code (IaC) and DevOps to automate the deployment of cyber ranges. An approach closely related to virtualization and containerization as the code's underlying infrastructure helps lay down this burden. Notably, placing emphasis on using IaC tools like Ansible eases the process of configuration management and provisioning of a network. Lastly, several up-to-date vulnerabilities that are constantly messing with the lives of individuals and organizations are explored, most related to Privilege Escalation, Remote Code Execution attacks, and Incident Forensics, allowing the improvement of skills concerning Red team and Blue team scenarios. In short, one of the key takeaways of this work is contributing to better prepare specialists in ensuring that the principles of the National Institute of Standards and Technology (NIST) Cybersecurity Framework hold, namely: prevent, detect, mitigate, and recover.

In the realm of AI-driven education, it is pivotal to evaluate the viability of ChatGPT as a substitute for human teachers in English classrooms. This study aims to explore learners' behaviors, perceptions, and attitudes to ChatGPT usage in English language learning. Participants were 120 I.T. students in Vietnam -- the Korea University of Information and Communication Technology and the University of Da Nang- who were learning English as a non-specialized subject. Data collection was conducted with multiple choices, a 4-point Likert scale questionnaire, and in-depth interviews. The findings highlight students' need for teacher's instruction and physical classroom despite recognizing ChatGPT's efficacy for ESP vocabulary acquisition, translation, grammar checking, and paraphrasing. Students predominantly exploited ChatGPT to find instant solutions to English learning difficulties. The research underscores the importance of guiding learners to appropriately utilize ChatGPT, emphasizing the need for further investigation into plagiarism-detecting tools to mitigate potential misuse of the technology.

Identifying mathematically gifted students is an important objective in mathematics education. To describe skills typical of these students, researchers pose problems in several mathematical domains whose solutions require using different mathematical capacities, such as visualization, generalization, proof, creativity, etc. This paper presents an analysis of the solutions to two problems by 75 students (aged 11-14), as part of the selection test for a workshop to stimulate mathematical talent. These problems require the use of the capacity for mathematical generalization of solution methods. We define a set of descriptors of such capacity, use them to analyze students' solutions, and evaluate how well students with high capacity for generalization can be distinguished from average students. The results indicate that the two problems are suitable for identifying potential mathematically gifted students and several descriptors have high discriminatory power to identify students with high or low capacity for generalization.

Autism is increasingly viewed as an expression of neurodiversity deserving accommodation, rather than merely as a disorder in need of remediation or even prevention. This reconceptualization has inspired calls to broaden the ethical debate on early autism care beyond matters of efficient screenings and effective interventions. We conducted 14 in-depth interviews with 26 parents of infants at an increased likelihood for autism (siblings, preterms and children with persistent feeding difficulties) to understand which benefits and risks these parents see for the implementation of a systematic, early autism detection program in our region. With this study, we aim to contribute empirically to the ethical debate on good and just early autism care in the age of neurodiversity. Data were analyzed according to the QUAGOL-methodology. Three main themes emerged from our analysis. In their evaluation of early autism detection, parents discussed how a diagnosis helps gain a different perspective fostering understanding and recognition for both child and parent. Second, a diagnosis supports parents in adjusting their parenting practices, to justify this deviation from "normal" parenting and to strive for such adjusted environments beyond the nuclear family. Third, an autism diagnosis induces ambiguities parents need to navigate, involving questions on whether and when to mobilize the diagnostic label and which language to use to talk about autism. We discuss the complex position of parents of a (potentially) autistic child in terms of moving back and forth across the ab/normal binary and describe implications for the ethical debate on early autism detection.

Background: This study explored the impact of different cut-off points used to identify children with reading difficulties on the distribution of these children across the three poor reader groups predicted by the simple view of reading (dyslexia, specific comprehension difficulty and mixed difficulty). Additionally, the study investigated whether the cognitive profiles of these poor reader groups remained consistent across varying levels of reading impairment. Methods: This study included 209 primary school children from New Zealand, in Years 4-6 and aged 8-10 years, who experienced reading comprehension difficulties. Using a two-step cluster analysis, participants were assigned to one of three poor reader groups: dyslexia, specific comprehension difficulty (SCD) and mixed difficulty. We examined the distribution of children across these groups at eight levels of reading comprehension difficulty and conducted strengths and weaknesses profiles at four levels of reading ability across 14 tests that assessed a range of reading-related skills. Additionally, we compared the performance of children in these groups who performed above and below the 10th percentile on a reading comprehension assessment across these tests. Results: The results showed that the proportion of children assigned to each group varied across the reading levels, with children at the lower end of the reading comprehension continuum more likely to exhibit the mixed difficulty profile than the SCD and dyslexia profiles. Overall, the research found that dyslexia, SCD and mixed difficulty profiles could be identified at all levels of reading comprehension difficulty, though weaknesses in their reading-related skills increased as reading comprehension difficulties also increased. Conclusions: Our findings suggest that struggling readers demonstrate distinct cognitive profiles across all levels of reading ability. These results have important implications for identifying and supporting struggling readers, as well as enhancing our understanding of the underlying mechanisms of reading difficulties. The possible relationship between the results obtained in this study and tiered models of reading support is discussed.

Dropout refers to the phenomenon of students leaving school before completing their degree or program of study. Dropout is a major concern for educational institutions, as it affects not only the students themselves but also the institutions' reputation and funding. Dropout can occur for a variety of reasons, including academic, financial, personal, and social factors. Therefore, understanding the factors that contribute to dropout and developing effective strategies to prevent it is a critical challenge for educational institutions. In this study, we propose a hybrid deep learning model based on Long Short-Term Memory and Deep Neural Network algorithms for school dropout prediction. The proposed model was compared with previous works and several other machine learning algorithms, including Deep Neural Network (DNN), K-Nearest Neighbors (KNN), Naive Bayes (NB), Multi-Layer Perceptron (MLP), Decision Trees (DT), Support Vector Machine (SVM), and Random Forest (RF). The results showed that the proposed DNN-LSTM model outperforms the other models in terms of accuracy and efficiency.

In this study, an early warning system predicting first-year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first-year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental analyses includes 11,698 freshman students' data. The problem is constructed as classification models predicting whether a student will be successful or unsuccessful at the end of the first year. A total of 69 input variables are utilized in the models. Naive Bayes, decision tree and random forest algorithms are compared over model prediction performances. Random forest models outperformed others and reached 90.2% accuracy. Findings show that the models including the fall semester CGPA variable performed dramatically better. Moreover, the student's programme name and university placement exam score are identified as the other most significant variables. A critical discussion based on the findings is provided. The developed model may be used as an early warning system, such that necessary actions can be taken after the second week of the spring semester for students predicted to be unsuccessful to increase their success and prevent attrition.

Detection of metal ions in solution has been performed without employing H[subscript 2]S or any other sulfide materials. The new method is free from the interference from anions. Identification of Na[superscript +], K[superscript +], and NH[subscript 4][superscript +] has been made possible directly from an aqueous extract of the sample mixture. A technique has been developed to identify Mg[superscript 2+] directly without group separation. Metal ions have been classified into four groups by sequential treatment with 5 N HCl, Na[subscript 2]SO[subscript 4], and 5 N NaOH as group reagents. In comparison to the existing method, it is much faster and, at the same time, reliable enough.

Autistic students report feeling lonely, rejected by peers which is associated with poorer well-being and lower academic results. This study aims to evaluate the effect of autism phenotype and diagnosis disclosure on students' desire for social distance from autistic students and factors related to social distancing. 303 students (72.3% women; mean age = 22.37 years) participated in this study. Each participant was assigned to one of the six vignettes depicting students of different gender and diagnostic status. Respondents' willingness to engage in different social situations, type and quality of prior contact with autism, autism knowledge and social desirability bias were measured. Results showed that students desired greater social distance from autistic males compared to autistic females, as well as from peers with non-disclosed diagnosis compared to those with disclosed diagnosis. Greater autism knowledge was related to less desire for social distance in non-disclosure, disclosure and in male autism phenotype conditions. Higher quality of contact was associated with less desire for social distance from autistic students with disclosed diagnosis. Type of contact was not related to social distance. Findings suggest that autistic male students and individuals with non-disclosed diagnosis might be more vulnerable to social exclusion.

Early diagnosis of children with reading disorders is essential for intervention and academic success. Many children with reading difficulties have a deficit in phonemic awareness. A web-based, group-administered screening vehicle, the Risk Factor Screen for Reading (RiFS-Reading), was developed to quickly identify students who are "At Risk" for reading difficulties by assessing knowledge of phonemic awareness. The goal was to establish the validity of the RiFS-Reading measure. Results supported construct validity through factor analysis and criterion validity by comparisons with performance on well-established measures of phonemic awareness and reading. Phonemic awareness on the RiFS-Reading was independent of other language skills.

Indirect indices for faking detection in questionnaires make use of a respondent's deviant or unlikely response pattern over the course of the questionnaire to identify them as a faker. Compared with established direct faking indices (i.e., lying and social desirability scales), indirect indices have at least two advantages: First, they cannot be detected by the test taker. Second, their usage does not require changes to the questionnaire. In the last decades, several such indirect indices have been proposed. However, at present, the researcher's choice between different indirect faking detection indices is guided by relatively little information, especially if conceptually different indices are to be used together. Thus, we examined and compared how well indices of a representative selection of 12 conceptionally different indirect indices perform and how well they perform individually and jointly compared with an established direct faking measure or validity scale. We found that, first, the score on the agreement factor of the Likert-type item response process tree model, the proportion of desirable scale endpoint responses, and the covariance index were the best-performing indirect indices. Second, using indirect indices in combination resulted in comparable and in some cases even better detection rates than when using direct faking measures. Third, some effective indirect indices were only minimally correlated with substantive scales and could therefore be used to partial faking variance from response sets without losing substance. We, therefore, encourage researchers to use indirect indices instead of direct faking measures when they aim to detect faking in their data.

Exceptionally talented refugee students are often underrepresented in allocating to gifted programs because of inadequate identification methods in Arab countries. This study investigates the Arabic version of the Having Opportunities Promotes Excellence (HOPE) Scale for identifying gifted refugee students. Students (n = 13,598) from refugee camp schools in Jordan were surveyed in the 2020/2021 academic year. Elementary, middle, and high school teachers (n = 423) completed the translated Arabic version of the HOPE Scale, measuring 11 items on the academic and social construct of giftedness. Confirmatory factor analysis indicated an excellent fit; however, multigroup confirmatory factor analysis results revealed that the scale needed more invariance across groups. The results suggest that although the HOPE Scale--Arabic version has sufficient validation evidence, careful consideration is required when applying it to different subgroups of refugee students. Constructs of cultural relevance may need to be added to better assess the item's validity.

This study aimed to develop a parent rating scale (PRSG) for screening children for further identification process in terms of giftedness. The participants of the study were 255 parents of gifted and non-gifted students. The PRSG, consisting of 30 items, was created by consulting parents and reviewing instruments existent in the literature. As part of the validity testing, the content, construct, and criterion-related validities were examined. Expert opinion was sought for content validity. Construct validity was achieved as the findings of the confirmatory factor analysis confirmed the three-factor model in the 27-item instrument. The parents rated their own children after the researchers showed them how to rate their children. One hundred and sixty parents had a gifted child. Finally, the scores given by the parents of gifted children and those of the non-gifted were compared, which yielded a statistically significant difference between the mean scores in favor of the scores given by the parents of the gifted. The Cronbach alpha value was found to be .95 for the whole instrument.

The use of generative artificial intelligence (GAI) large language models has increased in both professional and classroom technical writing settings. One common response to student use of GAI is to increase surveillance, incorporating plagiarism detection services or banning certain composing activities from the classroom. This paper argues such measures are harmful and instead proposes a "CARE" framework: critical, authorial, rhetorical, and educational--a nuanced approach emphasizing ethical and contextual AI use in technical writing classrooms. This framework aligns with plagiarism best practices, initially devised from when rhetoric and composition scholars considered the pedagogical implications of the Internet.

Teaching Institutions could benefit from Early Warning Systems to identify at-risk students before learning difficulties affect the quality of their acquired knowledge. An Early Warning System can help preemptively identify learners at risk of dropping out by monitoring them and analyzing their traces to promptly react to them so they can continue their learning in the best conditions. This paper presents a novel method for predicting at-risk learners based on their performance-based behavior in e-learning environments. The proposed approach can identify and predict learners with difficulties and intervene autonomously to assist them in overcoming them. A novel algorithm is developed to forecast learners who are prone to struggle or drop out. We experimented in a learning environment at a higher education institution that used the proposed strategy to examine its effectiveness, and the findings supported the proposed approach's efficacy.

Artificial Intelligence (AI) can foster education but can also be misused to breach academic integrity. Large language models like ChatGPT are able to generate solutions for individual assessments that are expected to be completed independently. There are a number of automated detectors for AI assisted work. However, most of them are not dedicated to programming and/or they rely on existing student submissions (i.e., the learning approach). This paper presents a straightforward detector for AI assisted code, relying on code anomaly. No existing student submissions are needed. The detector employs 34 features covering constants, data structures, branches, loops, functions, and others. According to our evaluation on three data sets, the detector and its normalized variation are effective with 89% top-"K" precision. However, allowing discussion among colleagues and access to the internet might reduce the effectiveness by 25%. The effectiveness is further reduced by about the same amount when AI assistance is only used on some tasks, not the whole submissions. Although our detectors should be used with caution due to the limitations, it sufficiently shows that code anomaly can be distinctive for identifying AI assisted work. Instructors can start looking for the code anomaly among the submissions for such identification.

This phenomenological study explored how five participants used their Texas state-approved definition of dyslexia to identify children with dyslexia as different from those with other global reading difficulties. Drawing on theories from disability studies in education and dis/ability critical race theory, I interviewed participants who identified dyslexia in terms of sociocultural differences, beliefs about intelligence, and reading ability. They frequently left unexamined concerns of race and how explanations of dyslexia privileged white, middle-class children over children of color and children in low-income schools. This study contributes to research that critically examines how these definitions of dyslexia segregate students with reading difficulties based on beliefs about intelligence, sociocultural factors, and the lack of conversations about race. Implications suggest that a growing focus on dyslexia may exacerbate inequalities already present in school systems. Stakeholders should critically examine their identification practices of reading difficulties to ensure that all children receive reading support.

Typically developing children learn to name things without explicit teaching (Fiorile & Greer, 2007), but this is not always possible when teaching a child with a disability such as autism (Olaff et al., 2017). Labels of nonvisual and internal stimuli are generally harder to teach than visual ones due to the absence of physical reference and the transient nature of nonvisual stimuli. This article outlines seven steps of teaching labels of nonvisual and internal stimuli to learners with disabilities.

This study analyzed the mediating role that implicit theories of intelligence and academic self-efficacy may play in the relationship between psychopathology and mathematical performance. The sample consisted of 838 students from first and second year of high school. A numerical calculation test was applied, followed by psychopathology self-report scales, implicit theories of intelligence, and academic self-efficacy. Serial multiple mediation models were tested. The results showed a complete mediation of the effect of psychopathology on mathematical performance through implicit theories of intelligence and academic self-efficacy. The effect of entity theories of intelligence on mathematical performance was both direct and through academic self-efficacy, whereas the effect of incremental theories of intelligence was only through academic self-efficacy. Identifying the variables that mediate the relationship in question is crucial to develop targeted interventions to prevent the development of a vicious circle between psychopathology and poor school performance.

Developmental monitoring/screening predict early identified autism spectrum disorders (ASD), but studies have not yet robustly controlled for a key health care service impacting early identification: medical home. National Surveys of Children's Health (NSCH; 2016-2020) were used to determine the relationship between medical home, developmental monitoring/screening, and identified ASD. NSCH overall medical home variable had a minimal relationship with ASD (under 5 years of age, under 5 identified in last year, under 5 identified over a year prior). Usual source of care was positively, and care coordination negatively, associated with ASD identified in last year, suggesting the overall medical home variable may mask variance from subscales. Research is needed to determine how medical home relates to identification in applied settings.

The current study evaluated a brief, informant-based autism interview: the Developmental, Dimensional and Diagnostic Interview -- Adult Version (3Di-Adult). Feasibility, reliability and validity of the Dutch 3Di-Adult was tested amongst autistic participants (n = 62) and a non-autistic comparison group (n = 30) in the Netherlands. The 3Di-Adult consists of two scales based on DSM-5 criteria: A scale 'Social communication and social interaction' and B scale 'Restricted, repetitive patterns of behavior, interests or activities'. ROC curves were used to determine cut-off scores for the A and the B scale, using an ASD diagnosis made by an independent clinician as the criterion. Mean administration time was 42 min. Internal consistency of the A scale ([alpha] = 0.92) and the B scale ([alpha] = 0.85) were good. Inter-rater reliability (ICCs = 0.99) and inter-rater agreement (ICCs [greater than or equal to] 0.90) were promising. The 3Di-Adult showed good sensitivity (80.6%) and specificity (93.3%). Positive and negative predictive value were 96.2% and 70.0% respectively. Comparisons with the Autism-Spectrum Quotient-Short to investigate the convergent validity showed moderate, significant correlations with the 3Di-Adult in the total sample. Males, as compared to females, displayed significantly more autistic features on the 3Di-Adult. No relationship was found of the 3Di-Adult with education level, intelligence and age of the participants or informants. The feasibility and psychometric properties of the Dutch 3Di-Adult are promising, indicating that it can be a time-efficient, valid and reliable tool to use in diagnosing autism in adults according to DSM-5 criteria.

Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for defining, identifying, and estimating causal effects. In this paper, we review the potential outcomes framework with a focus on potential outcomes notation to define individual and average causal effects. We then show how three canonical assumptions, Unconfoundedness, Positivity, and Consistency, may be used to identify average causal effects. The identification results motivate methods for estimating causal effects in practice, which include model-based estimators, such as regression, inverse probability weighting, and doubly robust estimation, and procedures that target covariate balance, such as matching and stratification. Examples and discussion are grounded in the context of a running example of a study aimed at assessing the causal effect of receipt of special education services on 5th grade mathematics achievement in school-aged children. Practical considerations for education research are discussed.

The specific learning disabilities (SLD) identification literature is replete with competing narratives concerning the advantages and disadvantages of various techniques and methods. Until a widely accepted and empirically proven SLD identification methodology is universally supported, evaluators should seek to improve the existing alternatives. This article describes the value of using norm-referenced testing of intellectual development to comprehensively identify specific learning disabilities (SLD) as advocated by the Core-Selective Evaluation Process (C-SEP). To this end, we will define intellectual development and describe practices such as integrated data analysis and task demand analysis.

Reading comprehension is one of the most valuable academic skills taught in school. Selecting the appropriate assessment instrument to ensure early identification and intervention is important as there is an amalgam of cognitive abilities and academic skills involved in reading comprehension. The GORT-5 is the most recent edition of a test that measures reading rate, accuracy, fluency, and comprehension among school-age children and young adults. The current study evaluated the concurrent validity of the GORT-5 with the WJ-IV ACH for a group of referred students. Findings indicate that Pearson correlation coefficients for all GORT-5 and WJ-IV ACH score comparisons were very large (0.70-0.89). Generally, GORT-5 scores were 2-3 points higher than WJ-IV ACH cluster scores. The diagnostic accuracy of the GORT-5 is excellent, with very few students' scores yielding a false negative.