Validating the Critical Teaching Behaviors Midterm Feedback Instrument for enhanced teaching effectiveness and student engagement by Carmen Van Ommen, Chad Rohrbacher, Claudia Cornejo Happel, Jenna Korentsides, Joseph R. Keebler, Kimberly N. Williams, Lauren Barbeau, Teha Cooks

Subjects: “…midterm student feedback…”
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The impact of personal values on the learning process and student accomplishments by Elena V. Bakshaeva, Elena A. Ganaeva, Larisa I. Tararina, Maria M. Sharafutdinova, Alla A. Rybolovleva, Oleg A. Skutelnik, Aleksandr A. Novikov, Elena V. Novikova

Subjects: “…Student youth…”
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Law Student Wellbeing: Benefits of Promoting Psychological Literacy and Self-Awarness Using Mindfulness, Strengths Theory and Emotional Intelligence by Colin James

“…The higher education experience is a significant financial, socio-cultural and emotional challenge for an ever more diverse cohort of students. Nowhere is this more pronounced than in the law school where a growing number of studies reveal a disproportionate level of psychological distress and depression amongst students. …”
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Student employment forecasting model based on random forest and multi-features fusion by Zhenguo Xing, Xiao Wu, Jiangjiang Li

Subjects: “…student employment forecasting…”
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ANALYSIS OF STUDENT RESPONSE TO THE IMPLEMENTATION OF THE ETHNO-FLIPPED CLASSROOM MODEL ASSISTED BY COLLABORATIVE CLOUD CLASSROOM by Rahmi Ramadhani, E. Elvis Napitupulu, Pardomuan Sitompul

Subjects: “…student response…”
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How generative artificial intelligence transforms teaching and influences student wellbeing in future education by Marcin Jukiewicz

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Student Responses to The Application of The Number Head Together Learning Model in Physics Subjects by J Jufrida, A Astalini, D Darmaji, T Tanti, D A Kurniawan, E Erika, R Hayi, W Sukarni

“…This model can make students more communicative during the physics learning process. …”
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Leading AI-Driven Student Engagement: The Role of Digital Leadership in Higher Education by Melita Kovacevic, Tamara Dagen, Miroslav Rajter

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Student Comprehension of Primary Literature is Aided by Companion Assignments Emphasizing Pattern Recognition and Information Literacy. by Sarah Shannon, Brian Winterman

“…While citing a primary literature article was required of all students, more students using the modified curriculum cited primary articles in their laboratory reports (95% vs. 65% average in control sections). …”
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Profile of Planned Happenstance of Student Skills Based on Gender, Age, Regional, and Ethnicity by Akhmad Harum

“…Furthermore, on the aspect of regional origin, it shows that there is no difference in students' planned happenstance career seen from the regional origin variable as well as in the ethnic aspect, there is no difference in students' planned happenstance career. …”
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Student support and motivation in online learning: evidence from pilot online course implementation by Rasa Tamulienė, Alina Liepinaitienė, Edvinas Ignatavičius

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Student challenges in English Medium Instruction (EMI) courses: Insights from an International Student Mobility Program by Nurmala Elmin Simbolon, Nizamuddin Sadiq, Samantha Curle

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Empowering Collaboration: Practical Approaches to Student Equity Program Evaluation by Nadine Zacharias, Elisa McGowan, Catie Gressier, Riett Kriel, Katie Douglas

Subjects: “…student equity…”
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Factors influence student satisfaction in pharm education program: a case study in private universities in Vietnam by Nguyen Thanh Nghia, Harwindar Singh

“…This study investigates the driving factors of student satisfaction in Pharmacy education at private universities in Vietnam. …”
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Predicting New York Heart Association (NYHA) heart failure classification from medical student notes following simulated patient encounters by Ishan R. Perera, Taylor Daniels, Janella Looney, Kimberly Gittings, Frederic A. Rawlins

“…This study aims to determine the prediction accuracy of a random forest model to derive NYHA Classification from medical students’ free-text history of present illness (HPI). …”
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Interactive Mitigation of Biases in Machine Learning Models for Undergraduate Student Admissions by Kelly Van Busum, Shiaofen Fang

“…Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. …”
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Development of a Student Depression Prediction Model Based on Machine Learning with Algorithm Performance Evaluation by Penni Wintasari Simarmata, Putri Taqwa Prasetyaningrum

“…This research explores the implementation of machine learning to predict depression among university students using a dataset of 2.028 responses containing PHQ-9 scores and academic-demographic attributes. …”
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Critical thinking development in a Quality Matters-based online learning: student insights from a South African higher education context by Nusirat Ojuolape Gold, Nusirat Ojuolape Gold, Husain Coovadia, Tasneem Mahmood

“…This study investigates students' perception of the impact of online learning designed using QM on critical thinking (CT) development among accounting students at a South African university. …”
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The role of residence advisors in student academic advice and success: insights and experiences from a university of technology by Isheachida Manatsa, Mzwandile Khumalo

Subjects: “…Student housing…”
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Instructional Design and Student Learning in Professional Legal Education by Julie Macfarlane, Pat Boyle

“…To this end, the researchers sought from student respondents as wide a range as possible of personal responses and reactions to their learning experiences and environment. …”
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