Marsha Lovett
Teaching Professor and Vice Provost Carnegie Mellon University
- Pittsburgh PA
Lovett and team help instructors apply learning science and educational technology to create effective courses (in-person + online).
Biography
Areas of Expertise
Media Appearances
Learnvia: Research-Driven Learning
Carnegie Mellon University News online
2026-01-26
“This is a rare opportunity to scale a CMU approach that’s both rigorous and practical,” said Marsha Lovett, vice provost for teaching and learning innovation at Carnegie Mellon University and a member of Learnvia’s board.
CMU Examines How AI Tools Are Reshaping Learning for Both Teachers and Students
Carnegie Mellon University News online
2025-04-03
“Before we can productively govern AI tools in education, we need to understand their impacts,” said Marsha Lovett, CMU’s vice provost for teaching and learning innovation.
Advancing Student Learning at CMU Through Generative AI
Carnegie Mellon University News online
2023-10-23
“We are taking a very scientific approach to this — a learning science approach,” said Marsha Lovett, vice provost for teaching and learning innovation and co-coordinator of The Simon Initiative, CMU’s learning-engineering ecosystem that works to improve student learning outcomes.
CMU Launches New Online Graduate Certificate Programs
Carnegie Mellon University News online
2023-08-24
Marsha Lovett, vice provost for Teaching and Learning Innovation, said the programs are designed for busy, working professionals who want to deepen or expand their knowledge to include modern methods and apply that new knowledge at work.
Signature Courses Lead the Way
Carnegie Mellon University News online
2020-08-05
"We are taking this opportunity to move the needle on the number of faculty who are using CMU's learning engineering approach and adopting our learning science research," Lovett said. "We have put in a lot of work to make sure all of our fall classes are fantastic. With the signature courses, we're making sure that work, and the learning science research we have been doing for years, is set to improve the university in the long-term, a model for the way forward at CMU."
Media
Social
Industry Expertise
Education
Carnegie Mellon University
Ph.D.
Cognitive Psychology
1994
Carnegie Mellon University
M.S.
Cognitive Psychology
Princeton University
B.A.
Cognitive Science
Articles
Supporting Technical Professionals’ Metacognitive Development in Technical Communication through Contrasting Rhetorical Problem Solving
Technical Communication Quarterly2016
This article presents an experimental pedagogical framework for providing technical professionals with practice on writing skills focusing on the development of their metacognitive rhetorical awareness. The paper outlines the theoretical foundation that led to the development of the framework, followed by a report of a pilot study involving IT professionals in a global setting using an online learning environment that was designed based on the framework.
Multimedia learning principles at scale predict quiz performance
Conference: the Fifth Annual ACM Conference2018
Empirically supported multimedia learning (MML) principles [1] suggest effective ways to design instruction, generally for elements on the order of a graphic or an activity. We examined whether the positive impact of MML could be detected in larger instructional units from a MOOC. We coded instructional design (ID) features corresponding to MML principles, mapped quiz items to these features and their use by MOOC participants, and attempted to predict quiz performance. We found that instructional features related to MML, namely practice problems with high-quality examples and text that is concisely written, were positively predictive. We argue it is possible to predict quiz item performance from features of the instructional materials and suggest ways to extend this method to additional aspects of the ID.
Improving Student-Driven Feedback and Engagement in the Classroom: Evaluating the Effectiveness of the Speed Dating Model
ACM SIGMIS Conference2018
Information Systems (IS) pedagogy research supports the use of collaborative learning strategies that are based on the belief that learning increases when students work together to solve problems and develop cooperative learning skills. The use of innovative active learning approaches instead of lecture-based approaches have helped to engage student learning and build a broader range of skills and experiences (e.g., [1, 2]). In this project, we present an empirical comparison of two active learning classroom approaches - the speed dating method and a traditional presentation format. The speed dating method supports low-cost rapid comparison of project ideas, design, application and progress in a structured and bounded series of serial engagements. In contrast, traditional student presentations allow individuals to provide content but offer somewhat limited interactions. We analyzed data from 174 student surveys and in-class researcher observations of student engagement in an undergraduate senior capstone course entitled, Innovation in Information Systems.
Distinguishing Two Types of Prior Knowledge That Support Novice Learners
CogSci2019
Prior knowledge has long been recognized as an important predictor of learning, yet the term prior knowledge is often applied to related but distinct constructs. We define a specific form of prior knowledge, ancillary knowledge, as knowledge of concepts and skills that enable learners to gain the most from a target lesson. Ancillary knowledge is not prior knowledge of the lesson's target concepts and skills, and may even fall outside the domain of the lesson. Nevertheless, ancillary knowledge affects learning of the lesson, e.g., lower ancillary knowledge can hinder performance on lesson-related tasks. We measured ancillary knowledge, prior knowledge of the domain, and controlled for general ability, and found that (a) stronger ancillary knowledge and general ability predicted better performance on transfer tasks, but (b) prior knowledge of the domain did not. This research suggests that enhancing instruction by remediating gaps in ancillary knowledge may improve learning in introductory-level courses.
Nightly sleep duration predicts grade point average in the first year of college
Proceedings of the National Academy of Sciences2023
Academic achievement in the first year of college is critical for setting students on a pathway toward long-term academic and life success, yet little is known about the factors that shape early college academic achievement. Given the important role sleep plays in learning and memory, here we extend this work to evaluate whether nightly sleep duration predicts change in end-of-semester grade point average (GPA). First-year college students from three independent universities provided sleep actigraphy for a month early in their winter/spring academic term across five studies. Findings showed that greater early-term total nightly sleep duration predicted higher end-of-term GPA, an effect that persisted even after controlling for previous-term GPA and daytime sleep. Specifically, every additional hour of average nightly sleep duration early in the semester was associated with an 0.07 increase in end-of-term GPA. Sensitivity analyses using sleep thresholds also indicated that sleeping less than 6 h each night was a period where sleep shifted from helpful to harmful for end-of-term GPA, relative to previous-term GPA.
