جست‌وجوی استاد و پژوهشگر

• Engineering Education Assessment
• Educational Psychology
• Data-Driven Educational Practices
• +۳ مورد دیگر

Dr. Kerrie A. Douglas is an Associate Professor of Engineering Education and Co-Director of the SCALE initiative at Purdue University's College of Engineering. She holds a Ph.D. in Educational Psychology (2012) with a focus on evaluation and assessment. Her research emphasizes improving assessment practices in engineering education, particularly how multi-source data integration enhances learner evaluation and supports pedagogical improvement. She leads initiatives on culturally responsive assessments, microelectronics education, and professional skill development for engineering students. Dr. Douglas has co-authored over 50 peer-reviewed articles since 2018, focusing on topics like social capital in education, sketching skill assessment, and pandemic-era learning dynamics. Her work frequently bridges theory and practice, with projects funded by NSF and industry partnerships. She also develops educational technologies like the Sketchtivity intelligent tutoring system. Her current projects include NSF-funded studies on LGBTQI+ student inclusion, MOOC-based professional development for engineers, and bias mitigation in AI-driven assessment tools. She collaborates with institutions worldwide to advance equitable assessment practices and has advised over 20 graduate and undergraduate research projects.

LaTasha R. Holdenمشاهده پروفایل

استادیار

• Cognitive Psychology
• Developmental Psychology
• Social-Personality Psychology
• +۹ مورد دیگر

LaTasha R. Holden serves as Assistant Professor in the Department of Psychology within the College of Liberal Arts & Sciences at the University of Illinois Urbana-Champaign, with additional affiliation at the Beckman Institute for Advanced Science and Technology. Her research bridges cognitive, developmental, and social-personality psychology to investigate memory systems, learning processes, and equity in educational contexts. Education: Ph.D., Princeton University Research Focus: Dr. Holden's work examines working memory capacity and long-term memory consolidation through the lens of cultural identity and individual differences . She pioneers culturally responsive methodologies to address stereotype threat in standardized testing while developing achievement interventions grounded in open science principles . Her LAMP Lab actively promotes equitable pedagogy through secondary data analysis and community-engaged research. Publication Trends: Analysis of her 12 publications (2018-2025) reveals consistent focus on cognitive mechanisms within educational equity frameworks. Key trajectories include stereotype threat mitigation (2023-2025), pandemic-era learning disruptions (2024), and intelligence assessment reform (2021-2023), demonstrating interdisciplinary integration of cognitive science with social justice imperatives. Scientific Awards: No awards documented in provided materials. Mentorship & Funding: Dr. Holden directs the LAMP Lab, currently recruiting Fall 2025 research assistants for achievement motivation studies. While specific grants aren't itemized, her sustained publication output and lab operations indicate active external funding supporting her equity-focused cognitive science program. Laboratory: The Learning and Achievement Motivation Processes (LAMP) Lab operates at the intersection of cognitive neuroscience and educational practice, utilizing both experimental and secondary data approaches to develop culturally sustaining interventions for historically marginalized learners.

• Health Psychology
• Mental Health
• Adolescent Psychology
• +۵ مورد دیگر

Dr. Mira Tschorn is a researcher at the Department of Education and Psychology, Division of Health Psychology, at Freie Universität Berlin. Her work focuses on health psychology, mental health, and public health, particularly in adolescent populations, refugees, and environmental psychology. She contributes to peer-reviewed research and is affiliated with the junior research group AMBER (Active mobility for maintained benefits of health and environment). Research Interests Dr. Tschorn investigates the interplay of social, psychological, and neurobiological factors in mental health outcomes. Key areas include alcohol misuse in adolescents , eco-anxiety , depressive symptoms in university students , and mental health interventions for refugees . Her studies often employ cross-sectional, longitudinal, and systematic review methodologies. Recent Articles Her recent publications span public health , neuroscience , and environmental psychology , with a focus on pandemic impacts, climate-related stress, and neurobiological correlates of mental health. Notable journals include BMC Public Health , JAMA Network Open , and Frontiers in Psychology . Labs & Collaborations She is part of the junior research group AMBER and collaborates with consortia such as IMAGEN, STRATIFY, and GIGA. Her work intersects with precision medicine, socioemotional development, and environmental health studies.

Eunsook Kimمشاهده پروفایل

استاد

• Psychometrics
• Structural Equation Modeling
• Multilevel Modeling
• +۴ مورد دیگر

Dr. Eunsook Kim is a Professor in the College of Education at the University of South Florida (USF Tampa campus). Her research specializes in advanced quantitative methodologies for educational and psychological measurement. Research Focus: Dr. Kim's work centers on psychometric innovations, including multilevel modeling, structural equation modeling, factor mixture approaches, and rigorous testing of measurement invariance in complex datasets. She frequently employs Monte Carlo simulations to evaluate methodological robustness in cross-cultural, longitudinal, and hierarchical research designs. Publication Trends: Her recent articles (2011-2017) demonstrate consistent focus on improving statistical methods for detecting measurement bias, validating instruments across populations, and developing reporting standards for multilevel analyses. Collaborative work features prominently, often addressing methodological challenges in large-scale educational and psychological assessment.

• Psychometrics
• Cognitive Psychology
• Educational Psychology
• +۱۲ مورد دیگر

Martin E. Arendasy is currently affiliated with the University of Graz, Austria, and is an active researcher in psychometrics, cognitive psychology, and educational testing. His work focuses on test design, measurement invariance, and cognitive processes underlying intelligence and creativity. Research interests include working memory training, neural efficiency, transcranial stimulation, and gender differences in cognitive tasks. He employs advanced psychometric models like Item Response Theory (IRT) and structural equation modeling in his studies. The 15 most recent articles highlight his contributions to understanding retest effects, planning ability, and the interplay between cognitive control and creativity. Many studies involve collaborations with co-authors like M. Sommer and S. Kober. Key methodologies: automatic item generation, latent profile analysis, fMRI, and transcranial alternating current stimulation (tACS). Findings address test fairness, neural correlates of intelligence, and strategies to minimize practice effects in cognitive assessment.

• Psychology
• Mental Health
• Machine Learning
• +۵ مورد دیگر

Joshua Curtiss is an Assistant Professor in the Department of Applied Psychology at Bouve College of Health Sciences, with additional affiliations in Community Health and Behavioral Sciences. His research focuses on leveraging machine learning and network approaches to advance precision mental healthcare for emotional disorders. Affiliations: Bouve College of Health Sciences, Applied Psychology Department Research Themes: Digital mental health, mindfulness-based interventions, emotion regulation, and computational psychiatry Key Collaborations: Focused on anxiety disorders, depression, and behavioral neuroscience His work analyzes how anxiety sensitivity modulates mindfulness experiences (BMC Psychiatry, 2025) and evaluates machine learning's capacity to predict treatment outcomes for depression (Clinical Psychology Review, 2025). Current projects examine coaching dynamics in digital CBT-BDD interventions (Internet Interventions, 2024) and develop novel emotion regulation assessment tools (Emotion, 2024). Scientific contributions include: 2024 Excellence in Research Writing Award National Institutes of Health grant for forecasting emotional disorder symptoms (2025) Methodological innovations span cross-cultural validation of psychological scales, dynamic systems modeling in mental health research, and anti-localizationist approaches to human neuroscience.

Frederik Baucksمشاهده پروفایل

پژوهشگر ارشد

• Educational Data Mining
• Learning Analytics
• Curriculum Analytics
• +۳ مورد دیگر

Dr. Frederik Baucks serves as a Research Fellow at the Institute of Neuroinformatics (INI), part of the Faculty of Computer Science at Ruhr University Bochum, Germany. His work bridges neural computation principles with practical applications in educational technology, focusing on data-driven solutions for higher education challenges. He completed his doctoral dissertation Curriculum analytics: Data-driven insights for educational decision-making at Ruhr University Bochum in 2025, establishing foundational work in educational data mining. His academic journey reflects specialization in quantitative methods for educational systems. Dr. Baucks' research centers on curriculum analytics and learning analytics , with particular expertise in Item Response Theory applications for course difficulty analysis. His work systematically addresses bias detection in grading systems, equity in educational outcomes, and development of decision-support tools for academic administrators. Current projects extend into Generalized Linear Models for educational datasets, emphasizing practical implementation in university settings. His publication trajectory (2022-2025) reveals consistent focus on translating complex statistical methods into actionable educational insights, with increasing emphasis on dashboard development and real-world implementation through projects like KI:edu.nrw. Key contributions include novel frameworks for differential course functioning analysis and prerequisite-free curriculum simulation. Scientific recognition includes: Best Paper Nominee at LAK24: 14th International Learning Analytics and Knowledge Conference (2024) Best Paper Nominee at DELFI 2023: 21st Conference on Educational Technology Dr. Baucks teaches lab courses in Python for data analysis (Summer Terms 2023-2024) and actively collaborates on grant-funded initiatives, though specific grant details aren't documented in the source material. His advisory role focuses on technical implementation rather than student supervision. As core member of the INI's KI:edu.nrw project team, he develops the academic advising dashboard in collaboration with Prof. Laurenz Wiskott's research group. This cross-disciplinary team integrates neuroinformatics principles with educational technology, operating within the Faculty of Computer Science's infrastructure at Universitätsstr. 150, Bochum.

Brandon Vaughnمشاهده پروفایل

دانشیار

• Statistics Education
• Educational Psychology
• Measurement and Assessment
• +۴ مورد دیگر

Dr. Brandon Vaughn is an academic researcher specializing in statistics education, educational psychology, and measurement theory. His scholarly work spans over two decades, with recent publications focusing on innovative approaches to data analytics education and statistical pedagogy. As evidenced by dissertation acknowledgments, he serves on doctoral committees, guiding graduate students through their research process. Dr. Vaughn's research interests center on improving statistical education through balanced amalgamation of teaching strategies, incorporating active learning with traditional methods. His work explores multilevel modeling applications in educational assessment, differential item functioning analysis, and the use of technology for enhancing statistical visualization. He has made significant contributions to understanding how contextual factors influence measurement quality and how collaborative learning can be optimized through heedful interrelating. His publication record shows consistent productivity across multiple domains, with recent work examining narrative approaches to data analytics education (2022) and foundational contributions to psychometric modeling. The trajectory of his research demonstrates evolving expertise from traditional statistical classification methods to contemporary applications in educational technology and data science pedagogy. Among his notable contributions are investigations into: The role of dialect in intelligence perception Contextual effects on assessment quality Mobile applications for statistical visualization Integration of social contexts in classification analysis Dr. Vaughn has advised graduate students including Sarah R. Daniel, whose dissertation acknowledged his "full support and unfailing enthusiasm" throughout her research process. His collaborative work spans multiple disciplines, connecting statistical methodology with practical educational applications.

• Mechanical Engineering
• Engine Efficiency
• Emissions Control
• +۳ مورد دیگر

Eric Holloway is a Professor of Engineering Practice in the School of Mechanical Engineering at Purdue University, with a courtesy appointment in the Engineering Education faculty. He is based in West Lafayette, Indiana, and his research bridges mechanical engineering systems and engineering education. His work focuses on engine efficiency, emissions control, and professional skills development for engineering students. He holds an office in HLAB 2014 and can be reached at eholloway@purdue.edu. Research interests span two core areas: (1) mechanical systems optimization, including diesel and natural gas engine performance, cylinder deactivation strategies, and EGR systems; and (2) engineering education innovation, particularly in professional skills certification, validation frameworks for learning outcomes, and assessment tools for work-integrated programs. He explores how makerspaces and industry partnerships enhance student career readiness. Recent publications highlight advancements in engine thermal management, off-road engine electrification, and frameworks for evaluating engineering students' professional development. He has contributed to studies on survey fairness, social capital in engineering education, and pandemic-era skill development challenges. His work emphasizes practical applications, from patent innovations in engine control systems to curricular improvements in professional skill measurement. No scientific awards are explicitly listed in the provided materials. His advising and grant activities remain unspecified, though his extensive publication record suggests involvement in collaborative research initiatives. Holloway is associated with Purdue's Mechanical Engineering infrastructure but no specific labs or teams are detailed in the text.

Zachary A. Pardosمشاهده پروفایل

دانشیار

• Adaptive Learning
• Artificial Intelligence (AI) in Education
• Knowledge Representation
• +۳ مورد دیگر

Zachary A. Pardos is an Associate Professor of Education at the University of California, Berkeley , where he directs the Computational Approaches to Human Learning (CAHL) Research Lab . He is also affiliated with the Cognitive Science program and serves as the Head Graduate Advisor there. PhD in Computer Science from Worcester Polytechnic Institute Bachelor's in Computer Science from Worcester Polytechnic Institute His research focuses on adaptive learning systems , AI in education , and knowledge representation using behavioral and semantic data to enhance postsecondary student mobility. Key projects include the OATutor open-source adaptive tutoring system and the ATAIN infrastructure network. Recent publications analyze large language models in education, Bayesian knowledge tracing optimizations, and psychometric validation of AI-generated content. Awards include Best Short Paper at EDM 2024 and Best Paper at L@S 2019 . Active grants from Foundation for California Community Colleges , College Futures Foundation , and Accendium Past funding from NSF , Google , and Bill & Melinda Gates Foundation He teaches DATA 144 (Data Mining and Analytics), EDUC C260F (Machine Learning in Education), and EDUC W161 (Digital Learning Environments). The CAHL research group explores equity, diversity, and AI's role in K-16 education.

Kristen Murphyمشاهده پروفایل

استاد

• Chemical Education Research
• Scale Literacy
• Assessment Design
• +۳ مورد دیگر

Kristen Murphy is Professor and Chair of Chemistry & Biochemistry at the University of Wisconsin-Milwaukee, with research centered on chemical education. Her work investigates scale literacy development, assessment equity through differential item functioning analysis, and factors influencing STEM persistence, leveraging methodologies from cognitive science and psychometrics. Education includes a PhD in Chemistry from the University of Wisconsin-Milwaukee. Research integrates three primary streams: 1) Designing scale-themed curricula to enhance nanoscale conceptualization in general chemistry; 2) Validating assessment fairness using eye-tracking and psychometric analyses to address subgroup performance gaps; 3) Developing instruments measuring self-efficacy and outcome expectations to predict STEM retention. Recent publications reflect innovations in concept mapping (ACCM framework) and flipped classroom efficacy. Publications demonstrate consistent advancement in culturally responsive assessment design and quantitative analysis of learning progression. As Director of ACS Exams Institute, she oversees national test development and validity studies.

Michael R. Peabodyمشاهده پروفایل

استادیار مدعو

• Quantitative and Psychometric Methods
• Assessment
• Evaluation
• +۲ مورد دیگر

Michael R. Peabody is an Adjunct Assistant Professor in the Department of Educational Policy Studies and Evaluation at the University of Kentucky. He concurrently serves as Lead Psychometrician at IXL Learning, overseeing operational psychometrics for formative and interim benchmark assessments. His academic role includes teaching courses on measurement, statistics, and advanced psychometric methods. He is an active member of the National Council for Measurement in Education (NCME), previously chairing the Publications Committee and currently serving on the editorial board for the Journal of Educational Measurement. His research focuses on differential item functioning, equating, standard setting, and practice analysis in educational measurement. No specific grants, awards, or student advisees are listed in the provided information.

Yan Liuمشاهده پروفایل

استادیار

• Psychometrics
• Statistical Methods
• Cognitive Development
• +۶ مورد دیگر

Yan Liu is an Assistant Professor in the Department of Psychology at Carleton University, within the Faculty of Arts and Social Sciences. He holds a Ph.D. from the University of British Columbia. His research focuses on developing innovative psychometric and statistical methods to address issues in well-being and cognitive development, incorporating causal models, machine learning, and natural language processing. He also explores quantitative methods for analyzing response processes, longitudinal data, and complex distributions. Liu has contributed to studies on exercise interventions for older adults, psychological mediators of behavior change, and technology-enhanced educational tools. His work spans interdisciplinary collaborations in health psychology, education, and statistical methodology. Education: Ph.D. (The University of British Columbia). Research Interests: Psychometrics, causal modeling (e.g., propensity score matching), machine learning applications in large-scale assessments, and eye-tracking studies on cognitive processes. He emphasizes translating statistical innovations into practical solutions for mental health and educational challenges. Awards: None explicitly listed. Advising/Grants: Supervised multiple student researchers (e.g., Dunlop, Ruissen, Chen) across health, education, and quantitative methodology projects. His grants likely support interdisciplinary research in aging populations and statistical innovation. Labs/Teams: Engaged with Carleton's Psychology department research groups focusing on quantitative methods and applied health psychology.

Eric Lokenمشاهده پروفایل

دانشیار

• Latent Variable Models
• Bayesian Inference
• Reproducible Science

Eric Loken is an Associate Professor in the Department of Educational Psychology at the University of Connecticut's Neag School of Education. He earned his PhD from Harvard University, specializing in advanced statistical modeling with applications to educational testing. His research focuses on latent variable models, Bayesian inference, and reproducibility in science. Recent publications explore missing data in computerized adaptive testing (2025), person-specific heterogeneity in IRT models (2024), and methodological frameworks for multi-analyst studies (2024). Loken's work addresses critical methodological challenges including measurement error impacts on replication, model specification in mixture models, and patterns of omitted responses in language proficiency testing. He maintains active research in parenting intervention effects on child development outcomes. Methodological focus: Bayesian statistical modeling Psychometric theory Reproducibility science Large-scale educational assessment

Pei-Ying Linمشاهده پروفایل

دانشیار

• Educational Measurement
• Psychometrics
• Special Education
• +۵ مورد دیگر

Dr. Pei-Ying Lin is an Associate Professor in the Department of Educational Psychology and Special Education at the University of Saskatchewan. She focuses on assessment practices for students with disabilities and English language learners, combining quantitative, qualitative, and experimental methodologies. Her work addresses inclusive education challenges, teacher training, and psychometric innovations. PhD from Ontario Institute for Studies in Education (OISE/UT), Canada MEd from National Changhua University of Education, Taiwan BEd from National University of Tainan, Taiwan Research interests include: Educational measurement and psychometrics Inclusive assessment design Accommodation effects for marginalized students Teacher education in inclusive practices Educational neuroscience applications Recent publications (2014-2020) emphasize cognitive processes in reading, accommodation equity, and cross-cultural assessment challenges. Her work often uses advanced statistical methods like IRT and latent class analysis. Professional service includes: Executive board member, Canadian Education Researchers’ Association (CERA) Board member, Saskatchewan Preventive Institute Advisory work includes supervising 1 MEd graduate student (Baiochi, 2017). Active in securing grants including a 2020 SSHRC award for social sciences research. Key collaborations involve cross-disciplinary teams investigating bilingual language processing and neurocognitive aspects of learning disabilities.