Quinnipiac Scholarship, Publishing, and Access

As of June 2024, in 2024 to 2025 Quinnipiac authors published

• 297 articles and conference proceedings. 
• 76 of those 297 articles open access.
• 13 books.
• 82 book chapters.

All data derived from Scopus.

Quinnipiac authored, most highly cited articles published in 2024-2025

Scopus
EXPORT DATE: 16 June 2025

Chataut R., Nankya M., Akl R.
AUTHOR FULL NAMES: Chataut, Robin (57202204154); Nankya, Mary (58697561900); Akl, Robert (8878315300)
57202204154; 58697561900; 8878315300
6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges
(2024) Sensors, 24 (6), art. no. 1888, Cited 69 times.
DOI: 10.3390/s24061888
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189027659&doi=10.3390%2fs24061888&partnerID=40&md5=398fdbe5faefe4317496a418db4021d7

ABSTRACT: In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning (ML), quantum communication, and reconfigurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. We also address critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study maps out the current landscape and lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI technologies, we aim to illuminate the path forward in this rapidly evolving field. © 2024 by the authors.
AUTHOR KEYWORDS: 5G; 6G; artificial intelligence; blockchain; Internet of Things; machine learning; millimeter waves; quantum communication; terahertz communication; ultra-massive MIMO
INDEX KEYWORDS: 5G mobile communication systems; Engineering education; Internet of things; Machine learning; Millimeter waves; Quantum communication; Queueing networks; Terahertz waves; 'current; 5g; 6g; Artificial intelligence technologies; Block-chain; Machine-learning; Technology application; Tera Hertz; Terahertz communication; Ultra-massive MIMO; artificial intelligence; blockchain; communication aid; human; internet of things; machine learning; review; wireless communication; Blockchain
DOCUMENT TYPE: Review
PUBLICATION STAGE: Final
SOURCE: Scopus

Chataut R., Gyawali P.K., Usman Y.
AUTHOR FULL NAMES: Chataut, Robin (57202204154); Gyawali, Prashnna Kumar (57194830715); Usman, Yusuf (58920624000)
57202204154; 57194830715; 58920624000
Can AI Keep You Safe? A Study of Large Language Models for Phishing Detection
(2024) 2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024, pp. 548 - 554, Cited 16 times.
DOI: 10.1109/CCWC60891.2024.10427626
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186746797&doi=10.1109%2fCCWC60891.2024.10427626&partnerID=40&md5=ee2f6a3cb36d46e2363cf0efaa233e11

ABSTRACT: Phishing attacks continue to be a pervasive challenge in cybersecurity, with threat actors constantly developing new strategies to penetrate email inboxes and compromise sensitive data. In this study, we investigate the effectiveness of Large Language Models (LLMs) in the crucial task of phishing email detection. With the growing sophistication of these attacks, we assess the performance of three distinct LLMs: GPT-3.5, GPT-4, and a customized ChatGPT, against a carefully curated dataset containing both phishing and legitimate emails. Our research reveals the proficiency of LLMs in identifying phishing emails, with each model showing varying levels of success. The paper outlines the strengths and limitations of GPT-3.5, GPT-4, and the custom ChatGPT, illuminating their respective suitability for practical applications in email security. These results underscore the potential of LLMs in effectively identifying phishing emails and their significant implications for enhancing cybersecurity measures and safeguarding users from the risks of online fraud. © 2024 IEEE.
AUTHOR KEYWORDS: Artificial Intelligence; ChatGPT; LLMs; phishing attacks; security
INDEX KEYWORDS: Computational linguistics; Computer crime; Cybersecurity; Risk assessment; Sensitive data; ChatGPT; Cyber security; Inboxes; Language model; Large language model; Phishing; Phishing attacks; Phishing detections; Security; Sensitive datas; Electronic mail
DOCUMENT TYPE: Conference paper
PUBLICATION STAGE: Final
SOURCE: Scopus

Przegalinska A., Triantoro T., Kovbasiuk A., Ciechanowski L., Freeman R.B., Sowa K.
AUTHOR FULL NAMES: Przegalinska, Aleksandra (57194697265); Triantoro, Tamilla (57200012339); Kovbasiuk, Anna (57831920700); Ciechanowski, Leon (57194106437); Freeman, Richard B. (55331486100); Sowa, Konrad (57218830701)
57194697265; 57200012339; 57831920700; 57194106437; 55331486100; 57218830701
Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives
(2025) International Journal of Information Management, 81, art. no. 102853, Cited 13 times.
DOI: 10.1016/j.ijinfomgt.2024.102853
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209082264&doi=10.1016%2fj.ijinfomgt.2024.102853&partnerID=40&md5=5091b3baa083940386f063d9471b8754

ABSTRACT: This research examines how artificial intelligence, human capabilities, and task types influence organizational outcomes. By leveraging the frameworks of the Resource-Based View and Task Technology Fit theories, we executed two distinct studies to assess the effectiveness of a generative AI tool in aiding task performance across a spectrum of task complexities and creative demands. The initial study tested the utility of generative AI across diverse tasks and the significance of AI-related skills enhancement. The subsequent study explored interactions between humans and AI, analyzing emotional tone, sentence structure, and word choice. Our results indicate that incorporating AI can significantly improve organizational task performance in areas such as automation, support, creative endeavors, and innovation processes. We also observed that generative AI generally presents more positive sentiment, utilizes simpler language, and has a narrower vocabulary than human counterparts. These insights contribute to a broader understanding of AI's strengths and weaknesses in organizational settings and guide the strategic implementation of AI systems. © 2024 The Authors
AUTHOR KEYWORDS: Generative AI; Human-AI collaboration; Resource-based view (RBV); Task technology fit (TTF); Work automation
INDEX KEYWORDS: Creatives; Generative AI; Human-AI collaboration; Organizational performance; Resource-based view; Task performance; Task technology fit; Work automation; Generative adversarial networks
DOCUMENT TYPE: Article
PUBLICATION STAGE: Final
OPEN ACCESS: All Open Access; Hybrid Gold Open Access
SOURCE: Scopus

Azok J.G., O'Donnell K.A., Long M.E., Wang H.C., Crook T., Pogemiller M.I., Momotaz H., Knight A.B., Burns R.A., Harrison V.A., Schmidt S.M., Van Opstal E.R., Weinstein A.R.
AUTHOR FULL NAMES: Azok, Jill G. (57218284446); O'Donnell, Katherine A. (57213349447); Long, Michele E. (56540711300); Wang, Helen C. (58533020600); Crook, Travis (57194213701); Pogemiller, Mark I. (57822320500); Momotaz, Hasina (57197806632); Knight, Abena B. (56393865800); Burns, Rebekah A. (55612013900); Harrison, Virginia A. (57838012400); Schmidt, Suzanne M. (57190011302); Van Opstal, Elizabeth R. (56705348200); Weinstein, Adam R. (14071826500)
57218284446; 57213349447; 56540711300; 58533020600; 57194213701; 57822320500; 57197806632; 56393865800; 55612013900; 57838012400; 57190011302; 56705348200; 14071826500
Factors Influencing Medical Students’ Career Choice to Pursue Pediatrics
(2024) Journal of Pediatrics, 265, art. no. 113525, Cited 10 times.
DOI: 10.1016/j.jpeds.2023.113525
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163815992&doi=10.1016%2fj.jpeds.2023.113525&partnerID=40&md5=4cffe8bc8b80450162fd3c9c94f03b5b

INDEX KEYWORDS: Career Choice; Child; Education, Medical; Humans; Motivation; Students, Medical; Surveys and Questionnaires; controlled study; decision making; demographics; Editorial; female; financial deficit; human; male; medical fee; medical student; pediatrics; personal experience; qualitative analysis; work-life balance; child; decision making; medical education; motivation; questionnaire
DOCUMENT TYPE: Editorial
PUBLICATION STAGE: Final
SOURCE: Scopus

Pikula A., Gulati M., Bonnet J.P., Ibrahim S., Chamoun S., Freeman A.M., Reddy K.
AUTHOR FULL NAMES: Pikula, Aleksandra (26431127400); Gulati, Mahima (57189660663); Bonnet, Jonathan P. (56419384200); Ibrahim, Sarah (35765464500); Chamoun, Svetlana (57226102205); Freeman, Andrew M. (36637272400); Reddy, Koushik (57202873137)
26431127400; 57189660663; 56419384200; 35765464500; 57226102205; 36637272400; 57202873137
Promise of Lifestyle Medicine for Heart Disease, Diabetes Mellitus, and Cerebrovascular Diseases
(2024) Mayo Clinic Proceedings: Innovations, Quality and Outcomes, 8 (2), pp. 151 - 165, Cited 9 times.
DOI: 10.1016/j.mayocpiqo.2023.11.005
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196716322&doi=10.1016%2fj.mayocpiqo.2023.11.005&partnerID=40&md5=791b9b084a7db1e9be0c6bb27bd8f8eb

ABSTRACT: The burden of noncommunicable chronic diseases has relevant and negative consequences to persons, health care systems, and economies worldwide. Chronic diseases are the leading cause of disability and mortality and are responsible for 90% of health care expenditure. The most common chronic diseases are diabetes mellitus (DM), cardiovascular disease, and cerebrovascular disease (stroke and vascular cognitive impairment). Modifiable risk factors (MRFs) for these conditions include hypertension, hyperlipidemia, smoking, poor diet, and low-physical activity; with hypertension being the most prevalent MRF. Most MRFs can be successfully targeted through lifestyle medicine (LSM), which is a medical specialty that addresses the root causes of chronic diseases through its primary, secondary, and tertiary preventative approaches. Lifestyle medicine comprises 6 pillars (nutrition, physical activity, sleep health, stress reduction, social connections, and substance use) which through various behavioral approaches, focus on regular physical activity, healthy eating, good quality and quantity sleep, and meaningful social connections coupled with the reduction of stress and substance use. This paper will briefly review the evidence and promise of individual LSM pillars in addressing the underlying MRFs of DM, cardiovascular and cerebrovascular disease (specifically stroke and vascular cognitive impairment). Lifestyle medicine holds a great promise for comprehensive and much improved population health. However, the adoption of LSM at the societal scale requires a multifaceted approach and widespread integration would galvanize a paradigm shift to prevent, treat or reverse chronic diseases from the root causes and achieve health equity. © 2024 The Authors
INDEX KEYWORDS: Article; cerebrovascular accident; cerebrovascular disease; chronic disease; cognitive defect; diabetes mellitus; diet; global disease burden; health equity; heart disease; human; lifestyle modification; nutrition; physical activity; physiological stress; population health; sleep; smoking; social connectedness; substance use
DOCUMENT TYPE: Article
PUBLICATION STAGE: Final
OPEN ACCESS: All Open Access; Gold Open Access; Green Open Access
SOURCE: Scopus

Yates E.F., Mulkey S.B.
AUTHOR FULL NAMES: Yates, Emma F. (58946293200); Mulkey, Sarah B. (36473614900)
58946293200; 36473614900
Viral infections in pregnancy and impact on offspring neurodevelopment: mechanisms and lessons learned
(2024) Pediatric Research, 96 (1), pp. 64 - 72, Cited 13 times.
DOI: 10.1038/s41390-024-03145-z
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188192580&doi=10.1038%2fs41390-024-03145-z&partnerID=40&md5=28bd160e4058ff5758019b9d0682dfa7

ABSTRACT: Abstract: Pregnant individuals with viral illness may experience significant morbidity and have higher rates of pregnancy and neonatal complications. With the growing number of viral infections and new viral pandemics, it is important to examine the effects of infection during pregnancy on both the gestational parent and the offspring. Febrile illness and inflammation during pregnancy are correlated with risk for autism, attention deficit/hyperactivity disorder, and developmental delay in the offspring in human and animal models. Historical viral epidemics had limited follow-up of the offspring of affected pregnancies. Infants exposed to seasonal influenza and the 2009 H1N1 influenza virus experienced increased risks of congenital malformations and neuropsychiatric conditions. Zika virus exposure in utero can lead to a spectrum of abnormalities, ranging from severe microcephaly to neurodevelopmental delays which may appear later in childhood and in the absence of Zika-related birth defects. Vertical infection with severe acute respiratory syndrome coronavirus-2 has occurred rarely, but there appears to be a risk for developmental delays in the infants with antenatal exposure. Determining how illness from infection during pregnancy and specific viral pathogens can affect pregnancy and neurodevelopmental outcomes of offspring can better prepare the community to care for these children as they grow. Impact: Viral infections have impacted pregnant people and their offspring throughout history. Antenatal exposure to maternal fever and inflammation may increase risk of developmental and neurobehavioral disorders in infants and children. The recent SARS-CoV-2 pandemic stresses the importance of longitudinal studies to follow pregnancies and offspring neurodevelopment. © The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc 2024.
INDEX KEYWORDS: Animals; Child Development; COVID-19; Developmental Disabilities; Female; Humans; Infant; Infant, Newborn; Influenza, Human; Neurodevelopmental Disorders; Pregnancy; Pregnancy Complications, Infectious; Prenatal Exposure Delayed Effects; SARS-CoV-2; Virus Diseases; Zika Virus Infection; animal model; brain malformation; coronavirus disease 2019; Coronavirus infection; epidemic; female; fetus brain; fever; human; inflammation; knowledge; nerve cell differentiation; newborn; nonhuman; pandemic; pregnancy; pregnancy outcome; progeny; Review; seasonal influenza; symptom; virus infection; animal; child development; complication; developmental disorder; epidemiology; etiology; infant; infectious pregnancy complication; influenza; mental disease; pregnancy; prenatal exposure delayed effect; Severe acute respiratory syndrome coronavirus 2; virology; Zika fever
DOCUMENT TYPE: Review
PUBLICATION STAGE: Final
SOURCE: Scopus

Hoang D., Errahmouni H., Chen H., Rachuri S., Mannan N., ElKharboutly R., Imani M., Chen R., Imani F.
AUTHOR FULL NAMES: Hoang, Danny (58696142800); Errahmouni, Hamza (57959926000); Chen, Hanning (57763637300); Rachuri, Sriniket (58928443200); Mannan, Nasir (57223119000); ElKharboutly, Ruby (55661987600); Imani, Mohsen (36468716900); Chen, Ruimin (57195732037); Imani, Farhad (57207536475)
58696142800; 57959926000; 57763637300; 58928443200; 57223119000; 55661987600; 36468716900; 57195732037; 57207536475
Hierarchical representation and interpretable learning for accelerated quality monitoring in machining process
(2024) CIRP Journal of Manufacturing Science and Technology, 50, pp. 198 - 212, Cited 8 times.
DOI: 10.1016/j.cirpj.2024.02.010
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187232968&doi=10.1016%2fj.cirpj.2024.02.010&partnerID=40&md5=fe08fc1850e2083d98e2f12d878a8358

ABSTRACT: While modern 5-axis computer numerical control (CNC) systems offer enhanced design flexibility and reduced production time, the dimensional accuracy of the workpiece is significantly compromised by geometric errors, thermal deformations, cutting forces, tool wear, and fixture-related factors. In-situ sensing, in conjunction with machine learning (ML), has recently been implemented on edge devices to synchronously acquire and agilely analyze high-frequency and multifaceted data for the prediction of workpiece quality. However, limited edge computational resources and lack of interpretability in ML models obscure the understanding of key quality-influencing signals. This research introduces InterpHD, a novel graph-based hyperdimensional computing framework that not only assesses workpiece quality in 5-axis CNC on edge, but also characterizes key signals vital for evaluating the quality from in-situ multichannel data. Specifically, a hierarchical graph structure is designed to represent the relationship between channels (e.g., spindle rotation, three linear axes movements, and the rotary A and C axes), parameters (e.g., torque, current, power, and tool speed), and the workpiece dimensional accuracy. Additionally, memory refinement, separability, and parameter significance are proposed to assess the interpretability of the framework. Experimental results on a hybrid 5-axis LASERTEC 65 DED CNC machine indicate that InterpHD not only achieves a 90.7% F1-Score in characterizing a 25.4 mm counterbore feature deviation but also surpasses other ML models with an F1-Score margin of up to 73.0%. The interpretability of the framework reveals that load and torque have 12 times greater impact than power and velocity feed forward for the characterization of geometrical dimensions. InterpHD offers the potential to facilitate causal discovery and provide insights into the relationships between process parameters and part quality in manufacturing. © 2024 CIRP
AUTHOR KEYWORDS: Computer-integrated manufacturing; Edge computing; Graph representation; Statistical quality control; Symbolic learning
INDEX KEYWORDS: Computer control systems; Cutting tools; Edge computing; Quality control; Computer-integrated manufacturing; Dimensional accuracy; Edge computing; F1 scores; Graph representation; Interpretability; Machine learning models; Statistical quality control; Symbolic learning; Workpiece; Graphic methods
DOCUMENT TYPE: Article
PUBLICATION STAGE: Final
SOURCE: Scopus

Pennekamp J., Matzutt R., Klinkmüller C., Bader L., Serror M., Wagner E., Malik S., Spiß M., Rahn J., Gürpinar T., Vlad E., Leemans S.J.J., Kanhere S.S., Stich V., Wehrle K.
AUTHOR FULL NAMES: Pennekamp, Jan (57195223332); Matzutt, Roman (56975507800); Klinkmüller, Christopher (42861636600); Bader, Lennart (57207997578); Serror, Martin (57189393659); Wagner, Eric (57207777569); Malik, Sidra (57209521235); Spiß, Maria (58654474300); Rahn, Jessica (58660076900); Gürpinar, Tan (57221617032); Vlad, Eduard (58450126500); Leemans, Sander J. J. (55565724400); Kanhere, Salil S. (8843325700); Stich, Volker (7004692468); Wehrle, Klaus (56253795600)
57195223332; 56975507800; 42861636600; 57207997578; 57189393659; 57207777569; 57209521235; 58654474300; 58660076900; 57221617032; 58450126500; 55565724400; 8843325700; 7004692468; 56253795600
An Interdisciplinary Survey on Information Flows in Supply Chains
(2024) ACM Computing Surveys, 56 (2), art. no. 32, Cited 9 times.
DOI: 10.1145/3606693
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174689861&doi=10.1145%2f3606693&partnerID=40&md5=7d911423227846ee29c47724e11242d4

ABSTRACT: Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010-2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points. © 2023 Copyright held by the owner/author(s).
AUTHOR KEYWORDS: data communication; data security; data sharing; Information flows; supply chain management; systematic literature review
INDEX KEYWORDS: Data communication systems; Information dissemination; Information management; Security of data; Computer scientists; Data Sharing; Data-communication; Entry point; Future research directions; Information flows; Security and privacy; Systematic literature review; Technical challenges; Supply chain management
DOCUMENT TYPE: Article
PUBLICATION STAGE: Final
OPEN ACCESS: All Open Access; Hybrid Gold Open Access
SOURCE: Scopus

Nankya M., Mugisa A., Usman Y., Upadhyay A., Chataut R.
AUTHOR FULL NAMES: Nankya, Mary (58697561900); Mugisa, Allan (59348388700); Usman, Yusuf (58920624000); Upadhyay, Aadesh (59316646300); Chataut, Robin (57202204154)
58697561900; 59348388700; 58920624000; 59316646300; 57202204154
Security and Privacy in E-Health Systems: A Review of AI and Machine Learning Techniques
(2024) IEEE Access, Cited 7 times.
DOI: 10.1109/ACCESS.2024.3469215
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205297766&doi=10.1109%2fACCESS.2024.3469215&partnerID=40&md5=08a0c7a2e42a03bafea92ce152a0c71a

ABSTRACT: The adoption of electronic health (e-health) systems has transformed healthcare delivery by harnessing digital technologies to enhance patient care, optimize operations, and improve health outcomes. This paper provides a comprehensive overview of the current state of e-health systems, tracing their evolution from traditional paper-based records to advanced Electronic Health Record Systems(EHRs) and examining the diverse components and applications that support healthcare providers and patients. A key focus is on the emerging trends in AI-driven cybersecurity for e-health, which are essential for protecting sensitive health data. AI's capabilities in continuous monitoring, advanced pattern recognition, real-time threat response, predictive analytics, and scalability fundamentally change the security landscape of e-health systems. The paper discusses how AI strengthens data security through techniques like anomaly detection, automated countermeasures, and adaptive learning algorithms, enhancing the efficiency and accuracy of threat detection and response. Furthermore, the paper delves into future directions and research opportunities in AI-driven cybersecurity for e-health. These include the development of advanced threat detection systems that adapt through continuous learning, quantum-resistant encryption to safeguard against future threats, and privacy-preserving AI techniques that protect patient confidentiality while ensuring data remains useful for analysis. The importance of automating regulatory compliance, securing data interoperability via blockchain, and prioritizing ethical AI development are also highlighted as critical research areas. By emphasizing innovative security solutions, collaborative efforts, ongoing research, and ethical practices, the e-health sector can build resilient and secure healthcare infrastructures, ultimately enhancing patient care and health outcomes. © 2013 IEEE.
AUTHOR KEYWORDS: Data Security; Electronic Health Records; Machine Learning; Predictive Analytics; Privacy; Quantum-Resistant Encryption; Telemedicine; Threat Detection
DOCUMENT TYPE: Article
PUBLICATION STAGE: Article in press
OPEN ACCESS: All Open Access; Gold Open Access
SOURCE: Scopus

Mattanah J., Holt L., Feinn R., Bowley O., Marszalek K., Albert E., Abduljalil M., Daramola D., Gim J., Visalli T., Boarman R., Katzenberg C.
AUTHOR FULL NAMES: Mattanah, Jonathan (6602286943); Holt, Laura (26867843100); Feinn, Richard (6602797347); Bowley, Olivia (59129165300); Marszalek, Katherine (59129541300); Albert, Elianna (59129920500); Abduljalil, Mohammed (59129165400); Daramola, Damilola (59129735400); Gim, Jasmine (59130104400); Visalli, Tom (9738724200); Boarman, Ryan (59129541400); Katzenberg, Courtney (59129355600)
6602286943; 26867843100; 6602797347; 59129165300; 59129541300; 59129920500; 59129165400; 59129735400; 59130104400; 9738724200; 59129541400; 59129355600
Faculty-student rapport, student engagement, and approaches to collegiate learning: exploring a mediational model
(2024) Current Psychology, 43 (28), pp. 23505 - 23516, Cited 6 times.
DOI: 10.1007/s12144-024-06096-0
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193272713&doi=10.1007%2fs12144-024-06096-0&partnerID=40&md5=fa195c2e9b6ca0753246754f77867756

ABSTRACT: Students value a close, supportive relationship with their professors, which has been shown to enhance their learning in higher education. However, more needs to be known about how quality faculty-student relationships shape students’ engagement and approaches to learning in higher education. In a diverse sample of 966 undergraduates from two different institutions of higher education, the current study explored the relationship between faculty-student rapport, student engagement, and deep and surface approaches to learning. Faculty-student rapport was positively correlated with student engagement (r =.50) and deep learning (r =.30), and negatively correlated with surface learning (r = -.21). Student engagement was positively correlated with deep learning (r =.70) and negatively with surface learning (r = -.32). Using multilevel modelling with students nested within classrooms, engagement was shown to mediate the effects of rapport on greater levels of deep learning (β =.31) and lower levels of surface learning (β = -.12). Although results held up across a range of demographic characteristics, some differences were noted for rapport-building among Asian American students and engagement across men versus women. These results have important implications for how faculty can engage students in the learning process by developing close, supportive relationships with their students and by extending their relationship with their students outside the classroom. © The Author(s) 2024.
AUTHOR KEYWORDS: Deep learning; Faculty-student rapport; Higher education educational processes; Student engagement in the classroom; Surface learning
DOCUMENT TYPE: Article
PUBLICATION STAGE: Final
OPEN ACCESS: All Open Access; Hybrid Gold Open Access
SOURCE: Scopus