About

Jeffrey A. Greene is the Associate Dean for Research and Faculty Development and holds the position of McMichael Professor of Learning Sciences and Psychological Studies (LSPS) at the UNC School of Education. His academic background includes a Ph.D. in Educational Psychology from the University of Maryland, obtained in 2007, along with a master's degree in Measurement, Statistics and Evaluation, and a master's in Education, Counseling and Personnel Services from the same institution. He earned his B.A. in Psychology from Carleton College in 1995. Jeff Greene's research focuses on how to help people use technology to learn and thrive in the modern world. His work explores questions related to online information seeking, belief formation, and distraction management, with a particular emphasis on self-regulated learning, epistemic cognition, and digital literacy. His research aims to make technology engaging again by helping individuals make technology work for them rather than against them. He has contributed to over 100 peer-reviewed publications and secured more than $8 million in research funding from organizations such as the National Science Foundation, the Institute of Education Sciences, and the Spencer Foundation. He is an elected Fellow of both Division 15 of the American Psychological Association and Division C of the American Educational Research Association. Greene's work demonstrates that teaching the science of learning enhances students' critical thinking skills and promotes greater academic and lifelong success. His research involves collaboration with students, colleagues from the School of Education, the UNC community, and international partners, all aimed at understanding and improving how learners self-regulate and critically engage with knowledge in digital environments.

Research topics

• Psychology
• Mathematics education
• Artificial Intelligence
• Computer Science
• Applied psychology
• Cognitive psychology

Selected publications

• Investigating motivational fluctuation and dropout among talented primary school children in a self-paced online mathematics course

  Open MIND · 2026-02-05

  articleOpen access
  OA PDFDOI

• Governing generative AI in higher education: a global Delphi study on policy and practice

  International Journal of Educational Technology in Higher Education · 2026-05-22

  articleOpen access

  Abstract As GenAI technologies become more pervasive in higher education (HE), scholars call for guidance on AI governance. To meet this need, a Delphi technique and collective writing was used in gathering expert perspectives from across 22 countries/locations and six continents. This resulted in the development of a HE GenAI policy/guidelines framework with eight core areas: (1) academic integrity, (2) ethical use and responsible use, (3) privacy and protection, (4) equitable access, (5) GenAI literacy, (6) integration strategy, (7) human oversight and accountability, and (8) institutional support and infrastructure. In addition, a six-part framework was developed to ensure that policies remain current and relevant: (1) creating a dedicated GenAI Committee, (2) conducting regularly scheduled policy reviews, (3) providing ongoing professional development and support, (4) communicating with all stakeholders, (5) evaluating the effectiveness and impact of GenAI, and 6) monitoring external developments. By providing a robust, eight-part framework for policy and guidelines, alongside a six-part mechanism for continued review, this study offers faculty, students, administrators, educational leaders, policymakers, and funders a responsible, adaptable, and consensus-driven blueprint for navigating the integration of GenAI in HE, ensuring that technological innovation serves pedagogical excellence.

  PublisherOA PDFDOI

• Ethical, Equitable, Informative, and Transformative Learning Analytics

  2026-04-22

  book-chapter1st authorCorresponding

  Digital tools can enhance learning and educational outcomes when they are designed and implemented via best practices and best intentions. These positive outcomes can be scaled up via the use of learning analytics, a field of study focused on understanding and enhancing learning by leveraging data produced when people use digital tools and learning environments. However, the positive potential of digital tools, the data they produce, and the learning analytics they afford come with a set of unique threats as well, including intentional and unintentional obfuscation of violations of ethics, principles, and justice. Thus, in this chapter, we discuss how to realize this great potential, and mitigate threats, via a commitment to ethical, equitable, informative, and transformative learning analytics.

  PublisherDOI

• Interpretable Predictive Analytics for Online Learning

  Journal of Learning Analytics · 2025-08-30 · 2 citations

  articleOpen access

  The increasing use of learning management systems (LMSs) generates vast amounts of clickstream data, opening new avenues for predicting learner performance. Traditionally, LMS predictive analytics have relied on either supervised machine learning or Markov models to classify learners based on predicted learning outcomes. Machine learning excels at pattern recognition but often overlooks temporal learning dynamics and obscures the reasoning behind predictions due to the black-box nature of many algorithms. Alternatively, Markov models provide an effective solution by capturing temporal learning dynamics for prediction, uncovering distinctive learning patterns between high and low performers. Despite these advantages, Markov model classification struggles with the heterogeneity of learning sequences, limiting its broad applicability. To address these limitations and bridge the gap between the two dominant approaches, we propose a hybrid framework: sequence-based Markov machine learning classification (seqMAC). Leveraging early-stage clickstream data, seqMAC provides an interpretable sequence classification method that captures critical behavioural transitions and identifies distinct learning patterns across performance groups. Tested on six LMS samples, seqMAC effectively identified at-risk students despite sequence heterogeneity, uncovering key predictive learning dynamics that differentiate performance groups. It also demonstrated promising generalizability, accurately identifying future at-risk students based on historical clickstream data.

  PublisherOA PDFDOI

• Leveraging complexity frameworks to refine theories of engagement: Advancing self‐regulated learning in the age of artificial intelligence

  UNC Libraries · 2025-03-21 · 1 citations

  articleOpen access

  Capturing evidence for dynamic changes in self‐regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected from n = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control, n = 48), (2) perform poorly and received intervention (treatment, n = 95) and (3) perform well (not flagged, n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments. Practitioner notes What is already known about this topic Self‐regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success. SRL is a dynamic, temporal process that leads to purposeful student engagement. Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed. What this paper adds A Markov process for measuring dynamic SRL processes using log data. Evidence that dynamic, interaction‐dominant aspects of SRL predict student achievement. Evidence that SRL processes can be meaningfully impacted through educational intervention. Implications for theory and practice Complexity approaches inform theory and measurement of dynamic SRL processes. Static representations of dynamic SRL processes are promising learning analytics metrics. Engineered features of LMS usage are valuable contributions to AI models.

  PublisherDOI

• Exploring Sequential Patterns in Academic Help-Seeking: Transitions and Performance in a College Science Course

  Proceedings. · 2025-06-10

  articleOpen accessSenior author

  Academic help-seeking is a critical self-regulated learning strategy that supports academic performance, particularly in complex learning environments like flipped classrooms.Previous researchers highlight the role of diverse help-seeking sources, yet little is known about how students transition between these sources over time.In this study, we examined academic help-seeking behaviors in an introductory science course throughout a semester.Undergraduates relied heavily on system-embedded help and generally remained within the same help category.Higher-performing students favored system-embedded and personalized support sources, whereas lower-performing students relied more on structured supplemental sessions and information search forums.These findings underscore the importance of understanding dynamic help-seeking patterns and their relationships to academic outcomes.This work advances the study of help-seeking behaviors in real-world educational settings.

  PublisherOA PDFDOI

• Using the fused graphical lasso to explore the motivational self-system after a multimedia self-regulated learning training: a brief research report

  UNC Libraries · 2025-04-02

  articleOpen access

  Introduction: The purpose of this study is to explore the effects of a randomized control trial designed to test the effect of a brief intervention used to improve self-regulated learning (SRL) in gateway biology courses using joint estimation of graphical models. Methods: Students (N = 265; n = 136) from three sections of a hybrid-format introductory biology course were randomly assigned to participate in the multimedia science of learning to learn or a multimedia control condition. All participants completed a self-report battery of motivational measures. Course performance data was also collected. Results: Network structures of motivation variables were estimated in two sub-groups (Treatment and Control). These networks showed a high level of correspondence in the relative magnitudes of the edge weights, however there were non-trivial differences in the edge weights between groups that may be attributed to the treatment and differences in predictability. While these findings suggest meaningful differences in motivational structures, the relatively small sample size may limit the stability of the estimated network models. The SRL strategy based interventions may have positioned the students motivationally to approach the challenging exam through activating the role of value and self-efficacy in their learning. Discussion: Many of the ways analyses of typical intervention studies are conducted ignore the underlying complexity of what motivates individuals. This study provides preliminary evidence how Gaussian Graphical Modeling may be valuable in preserving the integrity of complex systems and examining relevant shifts in variations between motivational systems between groups and individuals.

  PublisherDOI

• Using the fused graphical lasso to explore the motivational self-system after a multimedia self-regulated learning training: a brief research report

  Frontiers in Psychology · 2025-03-11

  articleOpen access

  Introduction: The purpose of this study is to explore the effects of a randomized control trial designed to test the effect of a brief intervention used to improve self-regulated learning (SRL) in gateway biology courses using joint estimation of graphical models. Methods: = 136) from three sections of a hybrid-format introductory biology course were randomly assigned to participate in the multimedia science of learning to learn or a multimedia control condition. All participants completed a self-report battery of motivational measures. Course performance data was also collected. Results: Network structures of motivation variables were estimated in two sub-groups (Treatment and Control). These networks showed a high level of correspondence in the relative magnitudes of the edge weights, however there were non-trivial differences in the edge weights between groups that may be attributed to the treatment and differences in predictability. While these findings suggest meaningful differences in motivational structures, the relatively small sample size may limit the stability of the estimated network models. The SRL strategy based interventions may have positioned the students motivationally to approach the challenging exam through activating the role of value and self-efficacy in their learning. Discussion: Many of the ways analyses of typical intervention studies are conducted ignore the underlying complexity of what motivates individuals. This study provides preliminary evidence how Gaussian Graphical Modeling may be valuable in preserving the integrity of complex systems and examining relevant shifts in variations between motivational systems between groups and individuals.

  PublisherOA PDFDOI

• Process mining measures students’ help-seeking transitions when completing assignments in an online learning and assessment platform

  Metacognition and Learning · 2025-10-29

  articleOpen access

  Abstract The shift towards active pedagogies in higher education that emphasize students’ engagement in their own learning in and outside of the classroom has increased the ubiquity of online learning and assessment platforms for engaging students in such learning. Online learning requires self-regulated learning, which is a cyclical and temporal process in which students plan, monitor, and control their cognition, motivation, behavior, and affect in pursuit of their learning goals. Help-seeking is a particularly important regulation strategy when learning online, but few researchers have examined the cyclical and temporal nature of help-seeking processes when students learn in an online learning and assessment platform. We conducted a micro-level analysis of the temporal help-seeking behaviors of 488 undergraduates in an online learning and assessment platform to explore how they sought help during learning and identify those who struggled in such a context. This exploratory study includes two levels of analysis, frequency analysis and process mining, to triangulate patterns of help-seeking transitions observed in an online learning and assessment platform and relate those to learning. Results indicated (1) less successful learners demonstrated an increase in incorrect submissions and transitions to and from incorrect submissions, and (2) lower performers used more maladaptive help-seeking strategies during independent learning before classes (e.g., via repetitive use of solutions). The findings demonstrate the benefits of applying multiple learning analytics methods to inform robust interpretations of micro-level self-regulated learning and suggest that such modeling can help prepare interventions that support undergraduate students’ effective use of help-seeking when learning online.

  PublisherOA PDFDOI

• College Undergraduates’ Use of Epistemic Criteria and Strategies While Designing Psychology Research Studies

  The Journal of Experimental Education · 2025-05-04 · 1 citations

  article
  PublisherDOI

Recent grants

• Collaborative Research: Improving Undergraduate Student Success in Introductory STEM Courses Via Campus Data Systems and Targeted Support for Self-Regulated Learning

  NSF · $1.1M · 2018–2023

Frequent coauthors

• Kristine Gibson

  Western Michigan University

  28 shared

• Matthew L. Bernacki

  Temple University

  28 shared

• Roger Azevedo

  23 shared

• P. Karen Murphy

  20 shared

• Kristi VanDerKolk

  Western Michigan University

  20 shared

• Robert D. Plumley

  18 shared

• Lisa Graves

  Stryker (United States)

  18 shared

• Kirsten A. Porter‐Stransky

  University of South Carolina

  16 shared

Education

• Ph.D. in Educational Psychology, Education

  University of Maryland

  2007

• MA in Measurement, Statistics, and Evaluation, Education

  University of Maryland

  2006

• M.Ed. in College Student Personnel, Education

  University of Maryland

  1998

• BA, Psychology

  Carleton College

  1995

Awards & honors

• Fellow of Division 15 of the American Psychological Associat…
• Fellow of Division C of the American Educational Research As…