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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.

The overriding aim of this study was to conduct a side-by-side comparative evaluation of two online suicide prevention gatekeeper-training programs: Question, Persuade, Refer (QPR) and Making Educators Partners in Youth Suicide Prevention (MEP). Specific aims included identifying program components, instructional methods, and technology elements that are well received by school personnel and that increase knowledge and self-efficacy. QPR and MEP were directly assessed following levels one and two of Kirkpatrick's Model (i.e., reactions to training, program efficacy), and indirectly assessed at levels three and four (i.e., future gatekeeper behaviors, potential school community impact). QPR and MEP produced positive outcomes with respect to reactions, knowledge, and self-efficacy (i.e., Kirkpatrick levels one and two). MEP and QPR also produced partial support with respect to behavior and impact (i.e., Kirkpatrick levels three and four), given the limited objective data demonstrating consistent application of gatekeeper skills that reduce community suicidal behaviors. Taken together, future research should evaluate inclusion of innovative pedagogical approaches and strategic online classroom design, which may enhance learning motivation, attitudes, and self-efficacy. Research should also objectively evaluate intermediate and longer-term behavioral outcomes to identify population-level impact.

The current study aims to contribute to the literature on how organic chemistry students weigh various factors when predicting products of substitution and elimination reactions. This study focuses specifically on these mechanism types, as they are often the first instances where students must consider the "how" and the "why" of how reactions occur. Previous literature highlights that such reasoning can be challenging. To better support our students, it is essential to understand how they conceptualize these mechanisms. Here, we present results from an investigation into how students compare bimolecular and unimolecular substitution and elimination reactions (S[subscript N]1, S[subscript N]2, E1, E2). Students completed tasks involving case comparisons and "predict-the-product" exercises. Through the analysis of nine semi-structured interviews using coordination class theory, we found that (1) students placed a greater emphasis on the importance of the starting substrate in the outcome of a reaction, and (2) focused less on the function of the nucleophile or base in each reaction. Using coordination class theory, we identified visual features and knowledge elements that students coordinated, allowing us to create "resource graphs" that represented students' conceptualizations. These graphs helped visualize the trajectories of students' predictions by illustrating how they balanced multiple factors. We discuss implications for supporting students in distinguishing among reaction mechanisms.

Objective: People with intellectual disabilities have historically often been excluded from cognitive based therapies, due to their cognitive deficits. However, adults with intellectual disabilities have been found to have the core cognitive abilities necessary to engage in Cognitive Behaviour Therapy. Despite this emerging evidence, the capacity for children with intellectual disabilities to engage with cognitive based therapies has not been fully explored. Method: Fourteen children, between the ages of 8 and 17 with intellectual disabilities, completed cognitive mediation tasks and a discrimination task. Five had a moderate intellectual disability, six had a mild intellectual disability and three had intellectual functioning in the borderline range. Inclusion criteria: These tasks completed assessed children's ability to identify, discriminate between, and link thoughts, emotions and behaviours. Results: Potential correlates of intelligence, verbal abilities and age were investigated. Participants' performance on the discrimination task was varied. High accuracy was seen in the cognitive mediation tasks. Conclusion: Results from this pilot study demonstrate that children with intellectual disabilities may have some of the skills required for Cognitive Behaviour Therapy, however children may require some training in cognitive mediation tasks before completing Cognitive Behaviour Therapy. As children have the foundational skills to engage in cognitive based therapies, this supports the need for future research trials investigating the use of adapted Cognitive Behaviour Therapy for children with intellectual disabilities.

Objective: Previous research demonstrates that sociocultural appearance pressures and internalization of appearance ideals lead to disordered eating (DE); however, only a subset of individuals exposed to these influences develop clinically significant DE. Identifying moderators of these associations may increase efficacy of targeted preventions for eating disorders. This study investigated whether the fear of negative evaluation (FNE) moderates these associations. Participants: 567 university students participated between November 2019 and 2020. Methods: Participants completed self-report questionnaires assessing appearance pressures, internalization of appearance ideals, FNE, and DE. Results: There was a significant interaction between appearance pressures and FNE in relation to DE. Individuals with high appearance pressures and high FNE had the highest levels of DE. The interaction between internalization of appearance ideals and FNE did not significantly contribute to DE. Conclusions: Eating disorder prevention programs that address FNE and appearance pressures may have beneficial effects, particularly for university students with heightened FNE.

The purpose of this exploratory qualitative study was to identify if and how a community college provided services to meet the needs of undocumented students seeking STEM degrees during the pandemic. The study is grounded in the framework of Institutional Undocu-Competence (IU-C) and draws from interviews with 16 students at an urban community college. The findings are critical for community colleges to develop Institutional Undocu-Competence for undocumented students in STEM during the COVID-19 pandemic and beyond.

Equity is increasingly seen as a core value for higher education systems around the world. (In)equity is often measured through construction of achievement gaps, quantifying the relative outcomes of two populations of students. Institution-level gaps are embedded in the policy landscape of HE, becoming performance metrics in their own right. These gap metrics increasingly inform the actions of governments, regulators, institutions and educators. This theoretical article scrutinises the technical and conceptual construction of achievement gaps through using the dominant UK conception of the institution level degree classification 'awarding gap'. Drawing on Adam's Equity Theory of Motivation, Rawls's Distributive Justice and the Capability Approach as theoretical perspectives, I highlight multiple structural weaknesses in the conception of the awarding gap. I illustrate the implications of this metric by analysing simulated awarding gap data for a fictional institution, and through the perspectives of five idealised stakeholders. I identify multiple technical and theoretical limitations of the institution level awarding gap metric, including examples where the threshold-based nature of the awarding gap fails to capture statistical differences between groups, thereby undermining its utility in identifying inequity. I call on the sector to develop metrics that more accurately capture (in)equity of outcomes and align better with theoretical frameworks, thereby creating more powerful explanatory metrics that can inform meaningful action.

By working in partnership with parents, school counsellors can assist in the early identification of mental health and wellbeing issues and facilitate timely access to professional support. However, very little is known about parents' understanding of the role of school counsellors and the barriers and enablers influencing the relationship between parents and school counsellors. Therefore, this qualitative study employed one-on-one interviews of 10 parents and employed a reflexive thematic analysis approach. Results revealed that parents expect school counsellors to provide a safe and supportive environment for students. Parents believe the school counsellor should be offering curriculum and behaviour support to teachers, individual and group work to students, and career counselling. In terms of the barriers and enablers influencing collaboration between school counsellors and parents, participants overwhelmingly felt that the role was hidden away and ill-defined. Advertising services more and getting the counsellor out into the school community at times suitable to working parents were seen as important considerations. Parents value the role of school counsellors. Future research on parental attitudes towards school counsellors could gain insights by diversifying participant profiles, including various genders, socio-economic backgrounds and occupations, to capture a wider range of perspectives.

Vertebrate species knowledge is one of the predictors of pupils' understanding of biodiversity. This study will describe vertebrate species identification skills of pupils from the Czech Republic. Altogether, the research tool included 30 vertebrate species, out of which five were fish, three were amphibians, three were reptiles, nine were birds, and 10 were mammals. The research tool consisted of 22 pictures, three footprints, two silhouettes, and three sounds. In addition, we evaluated the influence of variable factors on vertebrate species knowledge, which the research tool also contained. The paper will describe the percentage success rate of vertebrate species knowledge of 1537 respondents. On average pupils could identify nearly 15 species. The results showed that differences in species knowledge were statistically significant mostly by pupils' expectations (self-efficacy) or their results and educational level. In general, younger students identified animals worse than students of higher levels of education. Moreover, significant differences were confirmed between the five classes of vertebrates. Mammals were the best-identified class, followed by amphibians and fish; reptiles and birds were the least correctly identified. While educational level played a significant role in identification skills, the results revealed that the pupils' hometown did not play a significant role.

The overarching goal of this impact project is to make mycology accessible to more agriscience educators and students. Lesson plans were prepared to link core competencies and science standards to the Wild-Foraged Mushroom certification. Incorporating mycology into the classroom has many benefits, including discussions on food safety and regulation, the role of ecology in agroecosystems, and taxonomic identification skills. Fungi also play many different roles in the ecosystem, including decomposers, mutualists, and parasites. Lesson plans in three topic areas were produced: mushroom identification and fungal ecology, mushroom growth and food safety, and mushrooms as a renewable resource. Examples of hands-on learning and connections to the Wild-Foraged Mushroom certification are provided. This certification is available in the state of Michigan; however, lessons could be adapted for use in other regions of the United States. Looking at taxonomy, ecology, food science, and economics through the lens of mycology is an engaging way to motivate students while potentially helping them earn a certification.

Requiring students to create weed collections is a common technique for teaching weed identification. Data compiled over 18 years from students' weed collections in a college-level course included over 350 species of plants. Almost half of the specimens belonged to the Asteraceae or Poaceae. The 30 most frequently collected species accounted for almost two-thirds of the specimens but "Chenopodium album" L., the most frequently collected species, accounted for only 4.8% of the total. Overall, 73.1% of specimens were correctly identified to species. Five species ("Abutilon theophrasti" Medik., "Vicia cracca" L., "Portulaca oleracea" L., "Plantago major" L., and "Asclepias syriaca" L.) were correctly identified at least 97% of the time. Misidentification was highest with "Scorzoneroides autumnalis" (L.) Moench [synonym (syn.) "Leontodon autumnalis" L.], "Malva neglecta" Wallroth, "Erysiumum cheiranthoides" L., "Echinochloa crus-galli" (L.) Beauv., and "Erigeron canadensis" L. (syn. "Conyza canadensis") and within the genera "Sonchus" L., "Setaria" P. Beauv., and "Digitaria" Haller. Misidentification was the lowest in the Equisetaceae, Apocynaceae, Oxalidaceae, and Plantaginaceae and highest in the Lamiaceae, Poaceae, Brassicaceae, and Asteraceae. Variability in individual species' morphology may have contributed to misidentification.

Adults are increasingly self-identifying as autistic, and reporting problems being referred for an autism diagnostic assessment. This scoping review aims to ascertain: (1) what research has been conducted on the self-identification process of autism in adults, who do and do not have a formal diagnosis of autism, and (2) which aspects of the self-identification process could be used to improve the referral and the diagnostic process of an adult autism assessment. The main themes identified were: the diagnostic process from a client's perspective; the process of self-identifying as autistic from a lifespan perspective; an autistic identity; sexual identity and experiences, and the perception of autism as a difference or a disability. These themes could positively enhance the referral and diagnostic process.

Speech-language pathologists need to accurately identify structures/landmarks on swallow imaging. Foundational learning begins in graduate training. This study aimed to determine graduate student accuracy at identifying anatomical structures/landmarks during swallow evaluations and to determine if accuracy was predicted by type of imaging, anatomical structure, case type (i.e., normal/abnormal). Researchers recruited first-year graduate speech-language pathology students. Each participant reviewed five static images from lateral radiographic swallow studies and five static images from endoscopic swallow studies across 10 cases. Participants identified key anatomic structures and landmarks by clicking on the structure/landmark within a web-based platform. Two experienced speech-language pathologists reviewed and coded participant responses for accuracy. Sixteen graduate speech-language pathology students participated in a within-subjects design. Overall participant accuracy in identification of structure/landmarks was 69% (range 46%-88%). Binomial logistic regression was performed to study the effects of anatomical structure, case type (i.e., normal/abnormal), and image type on likelihood of participant accuracy in identifying anatomical structures (X[superscript 2](4) = 143.65, p < 0.001). Only anatomical structure was statistically significant (X[superscript 2](4) = 187.729, p < 0.001). The model explained 23.2% (Nagelkerke's R squared) of the variance in accuracy and correctly classified 78.4% of cases. Sensitivity was 92.1%, specificity was 47.3%, positive predictive value was 79.84%, and negative predictive value was 72.50%. The area under the ROC curve was 0.754, 95% CI [0.716, 0.791]. Graduate student's ability to correctly identify structures/landmarks overall was lower than desired and accuracy varied per structure. Results have implications for improving graduate student training for identification of structures/landmarks on swallow imaging.

Universal autism screening is recommended by the American Academy of Pediatrics at 18 and 24 months. However, many children are not identified until after the age of 4 years, and some not until adulthood, either due to mild or no indication of symptoms early in development, or to co-occurring conditions which may overshadow autism symptoms. This indicates a need for universal autism screening measures for school-age children. This project adapts the widely used toddler autism screening tool, the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up (M-CHAT-R/F), for use in school-age children, called M-CHAT-School (M-CHAT-S). The study follows the Patient-Reported Outcomes Measurement Information System guidelines for measure development to create parent- and teacher-report versions of the M-CHAT-S for 4- to 8-year-old children. Through expert consensus feedback via a Delphi pool and cognitive interviewing with stakeholders (i.e. parents and teachers), we developed two versions of the M-CHAT-S to be used for verbal and minimally verbal children. The M-CHAT-S poses several advantages to existing measures, including brevity, items updated based on current knowledge and conventions, and narrow age range to assure items are developmentally appropriate. Future steps include validation of the M-CHAT-S to determine its utility as an autism screener for young school-age children.

Early identification of social, emotional, and behavioral difficulties (SEBDs) in children is essential to provide support and reduce the risk of negative outcomes. Schools are considered ideal settings to identify SEBDs, and in many countries special educational needs coordinators (SENCOs) play a pivotal role in this respect. Although SENCOs may contribute to improving school-based identification of SEBDs by adopting a more systematic approach, they have a multitude of tasks and considerable professional discretion. As a result, there are differences between SENCOs in the quality of their identification practices in terms of the frequency of observations, the maintenance of a four-eyes principle, and the utilization of specialist knowledge, affecting whether and when SEBDs are identified. The aim of this study was to examine what factors can explain differences in these practices for early identification of SEBDs. Using a narrative qualitative approach, we interviewed 34 primary school professionals, studied school policy documents and observed team meetings. Thematic analysis revealed that an interplay of the factors: (1) (conformity to) school regulations, (2) team continuity, and (3) personal characteristics, explains why SENCOs decide differently on who conducts observations, and when. Generally, SENCOs are more likely to conform to frequent observations by at least two observers utilizing specialist knowledge, when schools have regulations that clearly define by whom and when observations should take place, with competent, committed, proactive school staff conforming to those regulations, within a stable team. Although each factor is important but not essential to ensure these practices, personal characteristics of the SENCO can compensate for a lack of clear school regulations or team discontinuity. Implications for school policy and practice are discussed.

This study investigates whether audiovisual phonetic training with hand gestures encoding visible or nonvisible articulation features has a differential impact on learning second language sounds. Ninety-nine Catalan-Spanish bilingual students were trained to differentiate English /ae/ and /[lambda]/, which differ in the visible lip aperture and nonvisible tongue position, with training involving no gestures, gestures representing the lip aperture, or gestures representing the tongue position. Before, immediately after, and 1 week after the training, participants' perception of the targets was assessed through a word-identification task, and their production was tested through paragraph-reading, picture-naming, and word-imitation tasks. Although all participants improved in perception and production, the lip hand gesture was more effective in adjusting lip aperture than the other two conditions in the paragraph-reading and picture-naming tasks. These results suggest that hand gestures encoding visible rather than nonvisible articulation features are more effective for improving second language pronunciation.

Although the accurate diagnosis and effective instruction for reading disorders through multi-tiered systems of support is one of the most foundational components of school psychology training and practice, there are significant opportunities for innovation, renewed excitement, and social justice. In this article, we identify reading assessments, interventions, and systems-level policies shown to be effective through rigorous, empirical research. These effective practices are not well known by school psychologists or commonly implemented in schools. We propose four areas to better align school psychology training and practice with the most cutting-edge reading research to improve student outcomes in the future: (a) building knowledge of reading development, (b) increasing the commitment to school-based careers, (c) implementing more instructionally-useful reading screening and special education assessment practices (including using the hybrid model of identification), and (d) promoting evidence-based reading instruction and intervention. Throughout each of these four areas, we highlight the need for multi-disciplinary collaboration.

Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish between biased and unbiased responses. This study proposes a new application of these classifiers to detect SDB by considering several person-fit indices as features or predictors in the machine learning methods. The results of a Monte Carlo simulation study showed that for a single feature, applying person-fit indices directly and logistic regression led to similar classification results. However, the random forest classifier improved the classification of biased and unbiased responses substantially. Classification was improved in both logistic regression and random forest by considering multiple features simultaneously. Moreover, cross-validation indicated stable area under the curves (AUCs) across machine learning classifiers. A didactical illustration of applying random forest to detect SDB is presented.

This research aimed to identify patterns, intellectual structure, contributions, social interactions, gaps, and future research directions in the field of academic integrity (AI). A bibliometric analysis was conducted with 1406 publications covering the period 1966-2023. The results indicate that there has been significant growth in AI literature over the last decade. The most influential publications focused on academic integrity violations such as cheating, plagiarism, and academic misconduct. The largest contribution to the field has come from journals that publish specifically on ethics and academic integrity. Studies in the historical origins of the field have focused on students' cheating behavior. The thematic structure of the field has focused on academic integrity and its violations, cheating, academic dishonesty, academic integrity in the context of online education, research ethics, and research on the detection of academic violations. The trending topics in the field are academic dishonesty, especially plagiarism and cheating, and online education. The UK, USA, Canada, and Australia have been the most collaborative and productive. More research is needed to address the AI field in the context of new developments.

Over the past 20+ years, researchers have worked toward identifying early behavioral predictors of autism spectrum disorder (ASD) and developing observation-based screeners to supplement existing parent-report methods. This study is a follow-up, 3 to 8 years later, with parents/caregivers of 57 children previously enrolled in a U.S. university-based study evaluating early ASD-risk. The original study evaluated infants' (ages 15-35 months) ASD-risk through both observation-based and parent-report screeners. At follow-up, caregivers completed a phone interview inquiring about their child's developmental progress and diagnostic outcomes. Results indicated screener at-risk status agreement in infancy predicted only one of the four parent-reported ASD diagnoses at follow-up. Single instrument at-risk status aligned with two additional ASD diagnoses (one per screener), and both screeners missed one ASD diagnosis at follow-up. Results did not indicate significant added utility for the observation-based screener over the commonly used parent-report screener, suggesting that ASD behavioral markers may be hard to observe at early ages.

Since autism diagnosis is directly linked to the availability of supportive services, identifying best practices for early diagnosis of autism has long been a concern of professionals and families. Meanwhile, studies show persistent racial disparities in autism diagnosis. Although numerous clinical diagnostic guidelines have been published, there is not enough discussion of diagnostic procedures through the lens of culturally diverse families. Purpose: This study focuses on the autism diagnostic experiences that Korean immigrant mothers had with their children. Methods: Eleven first-generation Korean-American mothers of children with autism were included in the study. The data was collected using semi-structured interviews in Korean. Results: The main five factors (i.e., cultural beliefs and values, language barriers, complex emotions, immigration and navigating systems, and facilitators and assets) that mainly influence the diagnosis process were identified through thematic analysis. Conclusion: Dynamics are interactive within and between the factors, influencing the entire diagnostic process by either delaying or facilitating the identification of a child's autism and the provision of treatment.

This study aimed to replicate and extend findings from the only known study that has conducted latent profile analysis of developmental profiles, measured by the Battelle Developmental Inventory, for children in Early Intervention (EI). Children (N = 57,966) who were enrolled in Massachusetts EI sites between 2011 and 2019 and completed a Battelle assessment at EI entry were included. Replicating previous findings, child Battelle profiles were best classified with four latent classes which were largely consistent with previously observed patterns (i.e., domain means within/between classes). Classes were labeled: "Marked communication delay, relative motor strength," "Communication delay, and average motor functioning," "Cognitive and motor delays, relative adaptive strength," and "Consistent mild delays." We described associations between class and sociodemographic factors and autism spectrum disorder (ASD) diagnosis. We found large effects of age and ASD. Two profiles that demonstrated communication delays were associated with older age. The "Marked communication delay" profile was associated with an elevated likelihood of ASD. Results suggest that Battelle developmental profiles may be an additional indicator to improve the identification of ASD in community settings and that profile membership could guide tailored interventions for specific developmental needs. Continued research will help determine whether class membership is stable and associated with differential response to EI.

Using a multistage sampling technique, the students completed the self-constructed Identification Battery on Comorbid Conditions (IBCC), which assesses 10 disorders, including dyslexia, dysgraphia, dyscalculia, dysphasia, dyspraxia, anxiety, attention deficit hyperactive disorder (ADHD), obsessive-compulsive disorder (OCD), autism, and tic disorder. The results revealed that 20.66% of the students exhibited comorbid conditions, with dyscalculia being the most prevalent, followed by dyslexia and others. Comorbid conditions varied, with some students having different combinations of disorders. The Jammu district had the highest prevalence. This study emphasizes the importance of early diagnosis and intervention, highlighting the need for increased stakeholder awareness to address these issues effectively.

This study explores the writing quality of two AI chatbots, OpenAI ChatGPT and Google Gemini. The research assesses the quality of the generated texts based on five essay models using the T.E.R.A. software, focusing on ease of understanding, readability, and reading levels using the Flesch-Kincaid formula. Thirty essays were generated, 15 from each chatbot, and evaluated for plagiarism using two free detection tools -- SmallSEOTools and Check-Plagiarism -- as well as one paid tool, Turnitin. The findings revealed that both ChatGPT and Gemini performed well in terms of word concreteness but demonstrated weaknesses in narrativity. ChatGPT showed stronger performance in referential and deep cohesion, while Gemini excelled in narrativity, syntactic simplicity and word concreteness. However, a significant concern was the degree of plagiarism detected in texts from both AI tools, with ChatGPT's essays exhibiting a higher likelihood of plagiarism compared to Gemini's. These findings highlight the potential limitations and risks associated with using AI-generated writing.

"Education is the key to success," one of the most heard motivational statements by all of us. People engage in education at different phases of our lives in various forms. Among them, university education plays a vital role in our academic and professional lives. During university education many undergraduates will face several challenges demanding from educational matters to socio-economic problems. In such situations, many undergraduates tend to abandon the degree programs halfway leaving them incomplete. Hence creating an Educational Early Warning Systems (EEWS) to predict and identify at-risk students in the early stages of the degree programs will improve the graduating ratio against the dropouts. Further, mentoring is another aspect in education where it can be used in undergraduate studies to address students individually. There exist many separate frameworks for EEWS and mentoring, but there exists a lacuna for an integrated framework for the two aspects. Having an integrated framework to identify at-risk undergraduates and matching the best matched mentor would be more impactful and effective for the universities to control dropouts. This study has proposed an integrated framework namely as "GRADGROOM" as a solution to the identified lacuna by extending EEWS framework with mentor matching which performs at-risk undergraduate prediction and mentor-mentee matching for them. Through two case studies at a local university, the study has concluded that a proper mentoring process conducted immediately after being identified as at-risk students will be highly beneficial to reshape their study patterns to align with the correct route of studying.

This research aims to investigate the use of an artificial intelligence-based mobile application with plants and plant identification techniques (AImPLANT) with prospective science teachers in outdoor activities. The goals of the study are: (i) to develop an AI-based mobile application from scratch based customized for education (ii) to enhance knowledge about common plants (iii) to increase awareness of plants, animals, and the natural environment, offering solutions to reduce plant blindness (iv) to support outdoor activities to observe, identify and taxonomically determine specific characteristics of plants (endemic, economic, health, aesthetic etc.) (v) guide students creating an individual virtual herbarium. The research will achieve the expected goals by coding an original "AI-Based Mobile Application" using ChatGPT and PlantNet APIs through technologies like Flutter, Firebase, Firestore, involving approximately 3200 lines of code. The mobile application includes components such as an "Plant collection (herbarium) project module", "AI-based chatbot", "AI-based plant identification module", "AIbased student assessment module", and a "Student assistance section". The application provides guidance in creating virtual plant collections (herbariums). Additionally, aids by the "help module" and "chatbot". Furthermore, an AI-based "self-assessment module" evaluates like a teacher based on the answers. The research question and sub problems were based on prospective science teachers' knowledge about plant identification and taxonomy, plant blindness, and opinions on studying with artificial intelligence outdoors with common plant species. The research, once again showed that artificial intelligence facilitates teaching biology, increases academic success, has positive contributions to eliminating plant blindness, and reduces teacher candidates' concerns about artificial intelligence and affects their opinions positively.

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.

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 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.

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.

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.

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.

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.

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.

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.