This study develops and validates a multicriteria decision-making framework for assessing the structure and development of academic soft skills in higher education. A total of 241 undergraduate students (104 first-year and 137 final-year) participated in the study. We operationalized soft skills—communication, creativity, and critical thinking—through 15 subcriteria derived from validated instruments, applied objective weighting methods to determine the relative importance of subcriteria, and used K-means clustering to identify structural groupings. Results indicated consistent dominance of communication-related criteria across weighting methods and increased structural differentiation of soft skills in final-year students. Clustering analysis revealed a transition from generalized skill groupings in early academic stages to more specialized and integrated clusters in later stages. The proposed framework enables systematic comparison of soft skill structures and provides a reproducible approach for analyzing their development in higher education.

This study extends research on the connection between specialized programming and curricula and retention for high-achieving students, using data from the 2019–2022 administrations of National Survey of Student Engagement (NSSE) to explore honors program participation and intention to return in a sample of over 13,800 first-year students at 22 colleges and universities. A binary logistic regression model suggests that even after controlling for demographic and institutional characteristics, honors program participation is a positive predictor of intention to return to an institution for the following school year. This finding supports the important role of honors programs within colleges and universities, providing evidence for the positive outcome of retention for first-year students. Implications for educators and other stakeholders are also discussed.

The persistent underrepresentation of girls among top performers in STEM has long been a concern in talent development research. Recent studies, however, suggest that this gender gap may be narrowing. This study investigates whether gender and academic achievement shape how students perceive STEM classroom situations, using the DIAMONDS framework, a taxonomy of psychological meaningful situational characteristics (e.g., Duty , Intellect ). Data were analyzed from 1,024 German eighth-grade students. In contrast to historical trends, our sample showed equal representation of boys and girls among the top 10% of STEM achievers. While no interaction effects were found between gender and achievement, consistent main effects emerged: girls reported higher levels of Duty and Intellect , but also greater Negativity and Deception ; boys perceived STEM lessons more positively overall. High-achieving students, regardless of gender, experienced STEM situations more positively than their lower-achieving peers. These findings in our context suggest that gender disparities in top STEM performance may diminish, but that gender differences in classroom perception persist.

School attachment encompasses students’ interrelated emotional, behavioral, and cognitive dimensions. Self-regulation involves setting goals and activating self-monitoring and self-reinforcement processes. Social skills refer to specific verbal and nonverbal behaviors essential for effective interpersonal interactions. In this study, we examined the mediating effects of social skills and perceived self-regulation on the school attachment levels of gifted students. We also investigated whether school attachment levels differed by gender and grade level. The sample consisted of 347 gifted middle school students (180 girls). We conducted a mediation analysis in Jamovi using 5,000 bootstrap samples. We used independent-samples t -tests and one-way analysis of variance to analyze gender and grade-level differences in school attachment, respectively. We applied the Gabriel post hoc test to identify the source of significant differences between grade levels. We found no significant differences in school attachment levels based on gender. However, grade level significantly influenced school attachment, with 7th graders exhibiting higher attachment levels than 6th graders; other grade comparisons were not statistically significant. Furthermore, self-regulation skills mediated the relationship between social skills and school attachment among gifted students. This study contributes to the gifted education literature by highlighting the role of personal competencies specifically, social skills and self-regulation in shaping students’ connection to school. Our findings suggest that strengthening these individual skills may foster a greater sense of school belonging among gifted learners, offering practical implications for educators and program designers.

Twice-exceptional (2E) learners’ readiness levels and learning profiles provide unique challenges in providing meaningful, rigorous learning experiences, making it difficult for general education teachers to consistently meet the needs of neurodiverse gifted learners. Differentiation, an instructional strategy which benefits all learners, is frequently used by many teachers to support below-level learners. But teachers do not readily or always effectively use differentiation to support advanced learners. Educators who dually differentiate for depth and complexity for the 2E learner improve both practice and student learning outcomes. Education preparation programs (EPPs) should provide learning opportunities for teacher candidates to explore what, how, and why differentiation works for 2E learners. This praxis paper in the special issue on poster session proceedings shares how an EPP implemented a scenario to develop teacher candidates’ pedagogy related to differentiating for 2E learners, and applies this to inservice professional learning to enhance practicing teachers’ effective use of differentiation.

Rigorous academic preparation in high school is a strong predictor of postsecondary success. The College Board's Advanced Placement (AP) program allows students to earn college credit while in high school; however, disparities in equitable participation remain a persistent concern. This qualitative multiple-case study explored equity-focused AP program implementation in two urban school districts in the Midwest. From interviews with 29 school leaders, the study examined participants’ beliefs, leadership practices, and reflections on equitable AP access. Findings highlight examples of community-engaged, social justice leadership that promote inclusive participation in AP coursework.

It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the question “Is there any way to automate this process by using computers and AI methodology as tools?” In this study, we answer this question by showing various machine learning models used to analyze students’ academic data to identify talented students. For this, various academic data for Computer Science undergraduate students at Troy University is collected and used to train several machine learning-based classification models to identify/predict talented students. Moreover, 90% of the data was used to train the model, while the remaining 10% was used to verify our hypothesis; the model could identify talented Computer Science students based on various aspects of their past academic histories. This division ensures that the model is trained on a substantial portion of the data while retaining a separate set for unbiased evaluation of its performance. By training several machine learning-based classification models and analyzing the results, we confirmed that the models could be used to identify/predict talented students based on their academic performance in the past by carefully selecting features for the models. In addition, the models can also detect false positives. This means it can filter out students who were initially identified as talented but ultimately proven to be “not talented” during their college studies.

In this study, we applied elastic-net logistic regression to explore critical predictors of students’ mathematics and science levels using data from eighth-grade students in the 2019 Trends in International Mathematics and Science Study (TIMSS). The elastic-net approach, a machine learning based approach combining Ridge and Least Absolute Shrinkage and Selection Operator (LASSO) regression, addresses multicollinearity while enabling variable selection, offering a robust methodology for analyzing large-scale educational datasets. Key findings highlight the critical role of home educational resources and student confidence in predicting outcomes in both subjects. Additionally, reduced disruptive behaviors during math lessons emerged as significant contributors to mathematics proficiency level. The models achieved high accuracy (up to 77.91% for mathematics and up to 70.74% in science) and balanced sensitivity and specificity, underscoring their practical relevance in educational contexts. This research demonstrates the potential of elastic-net logistic regression to provide insights into selection of predictors for student science and mathematics proficiency levels.

Mathematically gifted students need differentiated tasks in mixed-ability classrooms, and studies evaluating the use of these tasks in classrooms are of crucial importance. Thus, the present study examines how the use of differentiated tasks nurtures mathematically gifted students from the perspectives of mathematics teachers and their gifted students. Nineteen mathematically gifted students and five mathematics teachers from public and private schools in Turkey constituted the study sample. The data obtained through qualitative methods point out three categories: contributions of differentiated tasks to mathematically gifted students' cognitive, emotional, and social needs. Study findings reflected the importance of differentiated tasks in classrooms from the perspective of the mathematically gifted students, and their teachers.