About

Matt Bernacki, Ph.D., is an Associate Professor in the Learning Sciences and Psychological Studies area at the UNC School of Education. He serves as the Program Coordinator for Learning Sciences and Psychological Studies and is the Area Chair for Learning, Development, and Psychological Sciences. Dr. Bernacki's educational background includes a Ph.D. in Educational Psychology from Temple University, obtained in 2010, along with a Master of Social Work from Temple University and degrees in Experimental Psychology and Psychology from Saint Joseph’s University. Prior to his current position at UNC, he held post-doctoral appointments at the Learning Research & Development Center at the University of Pittsburgh and the Human Computer Interaction Institute at Carnegie Mellon University through the jointly-housed LearnLab. He also held a faculty appointment in Educational Psychology and Higher Education at the University of Nevada, Las Vegas. His research focuses on understanding the roles that motivation and metacognitive processes play when learners engage with technologies such as hypertext, intelligent tutoring systems, and learning management systems. He develops interventions and software aimed at promoting effective learning strategies and motivation, as well as creating personalized learning environments tailored to students' interests. Outside of his academic pursuits, Dr. Bernacki is a dad, cook, gardener, hiker, and a nature-food-and-baseball-tourist. He is involved in research related to learning analytics, metacognition, motivation, and educational technology, contributing to the advancement of personalized learning and educational innovation.

Research topics

• Computer Science
• Mathematics education
• Psychology
• Artificial Intelligence
• Multimedia
• Engineering
• Data science
• Management science
• Sociology
• Cognitive psychology
• Knowledge management
• Engineering ethics
• Applied psychology
• Developmental psychology

Selected publications

• Investigating bifactor modeling of biology undergraduates’ task values and achievement goals across semesters.

  Journal of Educational Psychology · 2023 · 9 citations

  • Psychology
  • Mathematics education
  • Cognitive psychology
  PublisherDOI

• How do students’ achievement goals relate to learning from well-designed instructional videos and subsequent exam performance?

  Contemporary Educational Psychology · 2023 · 21 citations

  • Computer Science
  • Psychology
  • Artificial Intelligence

  Well-designed instructional videos are powerful tools for helping students learn and prompting students to use generative strategies while learning from videos further bolsters their effectiveness. However, little is known about how individual differences in motivational factors, such as achievement goals, relate to how students learn within multimedia environments that include instructional videos and generative strategies. Therefore, in this study, we explored how achievement goals predicted undergraduate students’ behaviors when learning with instructional videos that required students to answer practice questions between videos, as well as how those activities predicted subsequent unit exam performance one week later. Additionally, we tested the best measurement models for modeling achievement goals between traditional confirmatory factor analysis and bifactor confirmatory factor analysis. The bifactor model fit our data best and was used for all subsequent analyses. Results indicated that stronger mastery goal endorsement predicted performance on the practice questions in the multimedia learning environment, which in turn positively predicted unit exam performance. In addition, students’ time spent watching videos positively predicted practice question performance. Taken together, this research emphasizes the availing role of adaptive motivations, like mastery goals, in learning from instructional videos that prompt the use of generative learning strategies.

  PublisherDOI

• A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?

  Educational Psychology Review · 2021 · 394 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science
  DOI

• Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions

  Journal of Research on Technology in Education · 2020 · 268 citations

  Senior authorCorresponding
  • Computer Science
  • Sociology
  • Computer Science

  This article introduces a special issue comprising research on efforts to personalize learning in different academic subjects. We first consider the emergence of personalized learning (PL) and the myriad of definitions that describe its essential features. Thereafter, we introduce the articles in the special issue by examining their alignment to extant theories of learning, the instructional design features that personalize the learning experience based on a learner characteristic, and the relationships between PL design and outcomes achieved in an educational context. Based on observations of contemporary PL research, we identify key issues to be addressed by the field and recommendations for future researchers to undertake to advance a PL theory. Chief among issues with PL are the role of technology, the agency of the learner, and the absence of a consistent theoretical grounding to motivate PL design choices. Future directions that would advance PL include the adoption of a theory of change in PL design, a design-based research approach to refine PL initiatives, more intensive and iterative research in authentic classroom contexts, and a greater focus on student input into and ownership of the PL experience.

  DOI

• A latent profile analysis of undergraduates’ achievement motivations and metacognitive behaviors, and their relations to achievement in science.

  Journal of Educational Psychology · 2020 · 98 citations

  • Psychology
  • Mathematics education
  • Developmental psychology

  Achievement motivation theories propose that student motivation is composed of multiple factors. Models of self-regulated learning adopt this assumption and further articulate that multiple metacognitive processes—planning, monitoring learning, and self-evaluation—are essential to guide progress toward a learning goal. Learners’ motivations are theorized to influence these metacognitive processes, which in turn influence learning outcomes. Latent profile analyses (LPA) enable a person-centered approach and capture multiple dimensions of motivation as they co-occur when learners engage in a task. This study documents the emergent motivation profiles of 1326 undergraduate biology students comprising efficacy beliefs, achievement goals, and perceptions of the value and costs of an anatomy and physiology course. Traces obtained from the learning management system provide data on students’ use of tools designed to support metacognitive processes including planning, monitoring learning, and self-evaluation. Latent profiles document the emergence of motivation, and metacognition profiles and a 3-step process reveals how student demographics predict motivation profile membership, and how the motivation profiles are related to metacognition profiles. Four motivation profiles (High Cost, Moderately Motivated, High Goals and Values, Mastery-Driven) and 3 metacognitive learning profiles (Infrequent Metacognitive Processing; Planning and Self-Evaluation; Monitoring via Self-Assessment) emerged. Demographic information was found to predict motivation profile membership. Members of Mastery-Driven and High Cost groups were less likely to use tools that support metacognitive processing. Learners in High Goals and Values and Mastery-Driven groups outperformed those in other groups, and learners in Planning and Self-evaluation and Monitoring performance outperformed those with little metacognitive activity. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

  DOI

Recent grants

• Learning Theory and Analytics as Guides to Improve Undergraduate STEM Education

  NSF · $500k · 2014–2019

Frequent coauthors

• Jeffrey A. Greene

  University of North Carolina at Chapel Hill

  28 shared

• Wonjoon Hong

  21 shared

• Robert D. Plumley

  18 shared

• Candace Walkington

  17 shared

• Donald Hayes

  Centers for Disease Control and Prevention

  16 shared

• Nicholas Voorhees

  Human Computer Interaction (Switzerland)

  16 shared

• Shelbi Kuhlmann

  University of Memphis

  12 shared

• Kelly A. Hogan

  12 shared

Education

• PhD, Educational Psychology

  Temple University

  2010

• MSW, Social Administration (Management & Planning)

  Temple University

  2006

• MS, Experimental Psychology

  Saint Joseph's University

  2003

• BS, Psychology

  Saint Joseph's University

  2002

Similar researchers at University of North Carolina at Chapel Hill

• Barry M. Popkinh-170
• Cynthia Bulikh-134
• Doug Perkinsh-115
• Paul Watkinsh-107
• Linda Adairh-106