Miller

Large language models (LLMs) are increasingly used in dental education, but research has focused mainly on answer accuracy. Whether LLMs can identify the cognitive demand of examination items remains unclear. This study evaluated LLM classification fidelity for endodontic multiple-choice questions (MCQs) across three educational taxonomies and compared four publicly accessible models. Two hundred endodontic MCQs from the Turkish Dentistry Specialty Examination (DUS, 2012–2025) were classified by ChatGPT, Gemini, Kimi, and DeepSeek according to Revised Bloom’s Taxonomy, the SOLO Taxonomy, and Miller’s Pyramid. A three-layer expert consensus procedure comprising calibration training, procedural audit, and structured joint deliberation established the expert consensus reference standard. Agreement was assessed using weighted Cohen’s kappa (κw) with bootstrap 95% confidence intervals; classification performance was evaluated using accuracy and macro-averaged F1. Inter-model differences in item-level accuracy were tested using Cochran’s Q with Kendall’s W, followed by Holm–Bonferroni-corrected pairwise McNemar tests. For Revised Bloom’s Taxonomy, Gemini (κw=0.676) and DeepSeek (κw=0.667) achieved substantial agreement, while Kimi (κw=0.428) and ChatGPT (κw=0.365) performed lower. For SOLO, Gemini achieved substantial agreement (κw=0.622), while the remaining models showed moderate agreement. For Miller’s Pyramid, agreement did not exceed fair, with ChatGPT highest (κw=0.372). Inter-model differences were significant for all three taxonomies (all p<0.001). ChatGPT ranked lowest for Revised Bloom and SOLO but highest for Miller. Revised Bloom levels of expert consensus reference standard increased significantly across examination years (Spearman ρ=0.301, p<0.001). LLMs showed meaningful classification fidelity for Revised Bloom and SOLO taxonomies, particularly Gemini and DeepSeek, but performance for Miller’s Pyramid remained weak. Bloom, SOLO, and Miller should not be treated as interchangeable frameworks, and LLM selection should be tailored to the taxonomy and assessment purpose. Answer accuracy and taxonomy classification fidelity represent distinct capabilities that warrant independent evaluation.

This action research study examined the relationship between social–emotional learning (SEL) and early mathematical achievement among transitional kindergarten and kindergarten students in a large urban Title I elementary school. National organizations emphasize that high-quality mathematics instruction for young children must consider cognitive, linguistic, physical, and social–emotional development. Guided by this framework, the study investigated whether systematic SEL instruction using the district adopted Second Step curriculum would increase students’ SEL competencies and support growth in foundational math skills measured by the Eureka Math Module 4 assessment. Using a pre- and post-test design, data were collected from eight students who received structured SEL and mathematics instruction over one semester. Results indicated consistent improvement in students’ SEL competencies, including executive functioning skills, alongside significant gains in mathematical performance. These findings align with prior research suggesting that SEL supports school readiness, adaptive learning behaviors, and academic achievement.

Introduction Deception elicits measurable cognitive and linguistic changes, yet accurately identifying deceptive language in high-stakes environments remains a persistent challenge. Although natural language processing (NLP) and machine learning (ML) techniques have shown promise in detecting misinformation and low-stakes deception, their performance on spontaneous, stress-induced speech remains poorly understood. This study investigates linguistic markers of deception and evaluates the performance of supervised ML models and a knowledge-grounded language model, FranklinAsk™, across both synthetic and real-world datasets. Methods Linguistic analyses were conducted on responses from a National Center for Credibility Assessment (NCCA) military training exercise involving truthful and deceptive participants. A synthetic dataset was generated using MOSTLYAI to provide a balanced corpus for ML training. Linguistic features were extracted using pretrained NLP models, and supervised classifiers, including logistic regression, Random Forest, Naïve Bayes, and Support Vector Machine (SVM), were trained using LIWC-22 features with 10-fold cross-validation. The FranklinAsk™ RAG system was evaluated using the same participant responses to compare knowledge-grounded reasoning with statistical learning. Results Two linguistic features, cardinal numbers (nominal p = 0.041) and particles (nominal p = 0.0486), reached the unadjusted significance threshold, each with a moderate effect size ( d = 0.55); neither remained statistically significant after Benjamini-Hochberg FDR correction (adjusted p = 0.668 for both). No significant sentiment differences were observed. On the synthetic dataset, logistic regression performed best (F1 = 0.930; ROC AUC = 0.978), but all supervised models showed substantial performance declines when applied to the real-world dataset. In contrast, FranklinAsk™ achieved the highest F1-score (0.519), highest accuracy (0.487), and second-highest recall (0.700) on the military exercise data. Discussion These findings highlight the limited generalizability of synthetic-trained deception models to real-world, high-stakes communication and motivate further investigation of knowledge-grounded language models as an alternative approach when labeled real-world training data are limited. Future research should prioritize expanding ecologically valid datasets and exploring hybrid approaches that combine statistical modeling with knowledge-grounded reasoning.

Over the past decade, there has been a surge in mental health challenges among adolescents. While social and emotional learning (SEL) programs are positioned to bolster young people's social, emotional, and academic success and mental health, too few SEL programs center adolescent development in the present context, largely ignoring the realities and needs of Black, Indigenous, and People of Color (BIPOC), LGBTQ2SIA+, and students with disabilities. To fill this gap and need for transformative SEL, we hosted a series of focus groups with fifteen BIPOC middle schoolers to ask what they need to belong and thrive at school, given our belief that the youth most marginalized carry the genius to transform schools for every student. In this paper, we share their insights on how educators can create learning environments that ensure their safety, honor their humanity, cultivate joy, and foster their social, emotional, and academic well-being through transformative SEL and liberatory teaching practices. Youth shared easy-to-implement examples of how educators can nurture relationships with students, create safe and caring classroom environments, humanize education, and advance social justice and civic engagement. Based on what we learned from youth, we urge SEL program developers and educators to start by discovering who young people are and what they need to flourish socially, emotionally, and academically, then to use what students share to inform instructional practice, create and select academic content, guide interactions, and build communities where students feel a sense of trust, belonging, and care.

در دهه گذشته، چالش‌های سلامت روان در میان نوجوانان افزایش چشمگیری داشته است. در حالی که برنامه‌های یادگیری اجتماعی و عاطفی (SEL) برای تقویت موفقیت اجتماعی، عاطفی و تحصیلی و سلامت روان جوانان طراحی شده‌اند، تعداد کمی از این برنامه‌ها بر رشد نوجوانان در بستر کنونی تمرکز دارند و عمدتاً واقعیت‌ها و نیازهای نوجوانان سیاه‌پوست، بومی و رنگین‌پوست (BIPOC)، افراد LGBTQ2SIA+ و دانش‌آموزان دارای معلولیت را نادیده می‌گیرند. برای پر کردن این خلأ و پاسخ به نیاز به SEL تحول‌آفرین، مجموعه‌ای از گروه‌های متمرکز با پانزده دانش‌آموز راهنمایی BIPOC برگزار کردیم تا بپرسیم برای تعلق داشتن و شکوفایی در مدرسه به چه چیزهایی نیاز دارند، با این باور که جوانان بیش از همه به حاشیه رانده‌شده، توانایی تحول مدارس برای همه دانش‌آموزان را دارند. در این مقاله، دیدگاه‌های آنان را درباره چگونگی ایجاد محیط‌های یادگیری توسط معلمان که امنیت، انسانیت، شادی و بهزیستی اجتماعی، عاطفی و تحصیلی آنان را از طریق SEL تحول‌آفرین و شیوه‌های تدریس رهایی‌بخش تضمین می‌کند، به اشتراک می‌گذاریم. نوجوانان نمونه‌هایی ساده و قابل اجرا از چگونگی پرورش روابط توسط معلمان با دانش‌آموزان، ایجاد محیط‌های کلاسی ایمن و مراقبت‌آمیز، انسانی‌سازی آموزش و پیشبرد عدالت اجتماعی و مشارکت مدنی ارائه کردند. بر اساس آنچه از نوجوانان آموختیم، از توسعه‌دهندگان برنامه‌های SEL و معلمان می‌خواهیم که ابتدا با کشف این‌که جوانان چه کسانی هستند و برای شکوفایی اجتماعی، عاطفی و تحصیلی به چه نیاز دارند آغاز کنند و سپس آنچه دانش‌آموزان به اشتراک می‌گذارند را برای اطلاع‌رسانی به شیوه تدریس، ایجاد و انتخاب محتوای آموزشی، هدایت تعاملات و ساخت جوامعی که در آن دانش‌آموزان احساس اعتماد، تعلق و مراقبت می‌کنند، به کار گیرند.

Abstract Previous research finds that vocal emotions are recognized above chance, with higher accuracy typically observed for one’s own cultural group – an “in-group” advantage. However, this may not apply uniformly, especially for bilinguals who are highly proficient in a second language (L2) but not members of the L2 cultural in-group. This study investigates Dutch-English bilinguals’ recognition of anger, fear, happiness, sadness and neutral emotion in Dutch (first language; L1), English (second language; L2) and two unfamiliar languages, Arabic and Hindi, using pseudo-utterances. Listeners were highly accurate in recognizing emotions in both Dutch and English. Recognition in Arabic matched their known languages, while Hindi was lower. Additionally, accuracy in English emotion recognition was positively correlated with English proficiency. These results suggest that the in-group advantage can extend to a highly proficient L2, indicating that cultural membership is not strictly necessary for highly accurate vocal emotion recognition.

پژوهش‌های پیشین نشان داده‌اند که هیجان‌های صوتی بالاتر از سطح تصادف شناسایی می‌شوند و معمولاً دقت بیشتری برای گروه فرهنگی خود فرد ــ مزیت «درون‌گروهی» ــ مشاهده می‌شود. با این حال، این امر ممکن است به طور یکنواخت صدق نکند، به ویژه برای دوزبانه‌هایی که در زبان دوم (L2) بسیار ماهر هستند اما عضو گروه فرهنگی زبان دوم نیستند. این مطالعه به بررسی شناسایی خشم، ترس، شادی، غم و هیجان خنثی در زبان هلندی (زبان اول؛ L1)، انگلیسی (زبان دوم؛ L2) و دو زبان ناآشنای عربی و هندی توسط دوزبانه‌های هلندی-انگلیسی با استفاده از شبه‌گفتارها می‌پردازد. شنوندگان در شناسایی هیجان‌ها در هر دو زبان هلندی و انگلیسی بسیار دقیق بودند. شناسایی هیجان در زبان عربی با زبان‌های آشنا برابری می‌کرد، در حالی که در زبان هندی پایین‌تر بود. همچنین، دقت در شناسایی هیجان‌های انگلیسی با مهارت زبان انگلیسی همبستگی مثبت داشت. این نتایج نشان می‌دهد که مزیت درون‌گروهی می‌تواند به زبان دوم با مهارت بالا نیز گسترش یابد و عضویت فرهنگی برای شناسایی بسیار دقیق هیجان صوتی کاملاً ضروری نیست.

ABSTRACT As colleges and universities respond to students’ basic needs, structures and resources such as food pantries, emergency scholarships, housing access, transportation accommodations, mental and physical health efforts, and more become mechanisms for support. One additional structure to respond to and support students’ basic needs is a campus student government. Student governments often act as the voice of the student body to administrators and community members (Goodman 2021; Goodman and Licata‐Hoang 2023; Miller et al. 2006; Patrick 2023; Templeton et al. 2018), and addresses various issues associated with students’ needs (Goodman et al. 2021; Houze 2021). From campaigning to governing, student government leaders across the United States focus intently on how their advocacy efforts can support student access to fundamental necessities (Goodman et al. 2023). This article illuminates student governments’ role in addressing students’ basic needs. Institutions are called on to listen to and support student government leaders through legislation in the form of decision‐making, and leadership in the form of student leaders’ work and advocacy efforts.

چکیده: همان‌طور که دانشگاه‌ها و مؤسسات آموزش عالی به نیازهای اساسی دانشجویان پاسخ می‌دهند، ساختارها و منابعی همچون بانک‌های غذا، بورسیه‌های اضطراری، دسترسی به مسکن، تسهیلات حمل‌ونقل، تلاش‌های مرتبط با سلامت روانی و جسمانی و موارد دیگر به عنوان سازوکارهایی برای حمایت مطرح می‌شوند. یکی دیگر از ساختارهایی که می‌تواند به نیازهای اساسی دانشجویان پاسخ دهد و از آن‌ها حمایت کند، دولت دانشجویی در دانشگاه است. دولت‌های دانشجویی اغلب به عنوان صدای بدنه دانشجویی نزد مدیران و اعضای جامعه عمل می‌کنند (Goodman 2021؛ Goodman و Licata‐Hoang 2023؛ Miller و همکاران 2006؛ Patrick 2023؛ Templeton و همکاران 2018) و به مسائل مختلف مرتبط با نیازهای دانشجویان می‌پردازند (Goodman و همکاران 2021؛ Houze 2021). از زمان تبلیغات انتخاباتی تا دوران مدیریت، رهبران دولت دانشجویی در سراسر ایالات متحده با دقت بر این موضوع تمرکز دارند که چگونه تلاش‌های حمایتی آن‌ها می‌تواند دسترسی دانشجویان به نیازهای اساسی را تقویت کند (Goodman و همکاران 2023). این مقاله نقش دولت‌های دانشجویی در پاسخ به نیازهای اساسی دانشجویان را روشن می‌سازد. از مؤسسات خواسته می‌شود که از طریق قانون‌گذاری در قالب تصمیم‌گیری و رهبری در قالب فعالیت‌ها و تلاش‌های حمایتی رهبران دانشجویی، به سخنان و حمایت از رهبران دولت دانشجویی توجه کنند.