About

Marcia C. Linn is the Evelyn Lois Corey Professor of Instructional Science at the University of California, Berkeley, within the Berkeley School of Education. Her specialization is in science and technology education. She is a member of the National Academy of Education and a Fellow of several prestigious organizations, including the American Association for the Advancement of Science (AAAS), the American Psychological Association, the Association for Psychological Science, and the International Society of the Learning Sciences (ISLS). Linn has served as President of the ISLS, Chair of the AAAS Education Section, and has been involved with various boards such as the Educational Testing Service Graduate Record Examination, the McDonnell Foundation Cognitive Studies in Education Practice, and the National Science Foundation Education and Human Resources Directorate. Her research interests encompass assessment and educational measurement, cognitive development, computer-mediated learning, curriculum design, diversity and educational equity, educational media, gender equity, information technology, participatory research, policy analysis, professional development for educators, research methods, school and non-school learning contexts, school-university collaboration, science education, simulation learning environments, teacher development, testing, urban schooling, cluster learning sciences, and social research methodologies. Linn earned her Ph.D. in Educational Psychology from Stanford University, working with Lee Cronbach, and has international research experience from her time working with Jean Piaget in Geneva, as a Fulbright Professor in Israel, and at University College in London. She has been a fellow at the Center for Advanced Study in Behavioral Sciences three times. Her authored books include titles such as 'Computers, Teachers, Peers,' 'Internet Environments for Science Education,' 'Designing Coherent Science Education,' 'WISE Science,' and 'Science Teaching and Learning: Taking Advantage of Technology to Promote Knowledge Integration.' Linn also chairs the Technology, Education—Connections series for Teachers College Press.

Research topics

• Computer Science
• Psychology
• Mathematics education
• Pedagogy
• Artificial Intelligence
• Medicine
• Human–computer interaction

Selected publications

• Investigating Students' Knowledge of Revision Strategies and Encouraging Use in an Online Inquiry Science Unit

  2026-01-01

  articleOpen access
  PublisherDOI

• Enhancing Thermodynamics Understanding: Promoting Revision of Science Explanations Through Peer Critique

  2025-10-02

  articleOpen accessSenior author

  Revising explanations allows learners to develop a coherent understanding of scientific phenomena by integrating different ideas, a key learning outcome according to the Knowledge Integration (KI) Framework. We explored the design of critique activities and analyzed how peer critique helps 1305 middle school students to revise science explanations. Students wrote a science explanation about thermodynamics in an online learning environment, critiqued the explanation of a fictitious peer and then, revised their own explanation. For the critique activity, they were randomly assigned to three conditions that differed in the characteristics of the fictitious peer’s explanation. Specifically, students either critiqued a peer’s explanation that describes intuitive ideas (intuitive), combines intuitive and scientifically normative ideas (partial), or connects several scientifically normative ideas (linked). We found that critique was valuable in each condition as students showed more integrated understanding in their revised responses to the explanation question compared to their initial response. Students in the linked condition received the highest KI scores. Students who started with a higher KI score had an advantage over those with a lower KI score in all conditions. This difference was strongest in the intuitive condition, indicating that the intuitive condition was the least beneficial for students with lower prior knowledge. Qualitative analysis of the types of revisions students made revealed that, in all conditions, students often adopted ideas from the answer they critiqued. When they adopted the intuitive idea, they were less likely to improve their explanation than when they adopted the link. These results illustrate the importance of critique for promoting integrated understanding. They reveal the need for more frequent and more powerful instruction to build skepticism when confronted with potentially unreliable information, e.g. by comparing two explanations with contrasting ideas.

  PublisherOA PDFDOI

• Centering Care in Justice-Centered Science Teaching and Learning

  The Science Teacher · 2025-03-03 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• From practice to theory: characterizing the gap in surgical simulation

  Global Surgical Education - Journal of the Association for Surgical Education · 2024-11-15 · 1 citations

  articleOpen access

  Abstract Purpose Simulation curricula continue to struggle with adequately preparing trainees for the operating room. One reason for this phenomenon may be the lack of application and enactment of learning and instructional theories into simulation curricular design and practice. Few educators have taken a reflective approach to understand how surgical simulation succeeds and fails to incorporate best practices for learning based on theory. As such, this study aims to examine simulation sessions to identify gaps in practice by nesting two key frameworks from general education into surgical simulation: learning integration and the cognitive apprenticeship model. Methods We conducted an observational qualitative study in which we recorded simulation sessions with fifteen trainees and surgeons and deductively applied components of the above frameworks to transcripts. Subsequently, we analyzed gaps in the transcripts with regard to the application of these frameworks as theoretical concepts informing the analysis and interpretation. Results We organized results around the four fundamental tenets of learning integration, with principles of the cognitive apprenticeship model explored to provide further units of analysis. In doing so, we identified that simulation instructors adequately modeled, coached, and scaffolded to enable early phases of learning integration. However, instructors less aptly enabled reflection and self-guided exploration, which are critical components of learning integration. Conclusions We found areas in which instruction diverged from ideal standards as informed by our theoretical frameworks, thus highlighting the importance of regular simulation review to ensure that well-designed and intentioned simulation curricula continue to reflect the best educational principles when enacted in practice.

  PublisherOA PDFDOI

• Combining Natural Language Processing with Epistemic Network Analysis to Investigate Student Knowledge Integration within an AI Dialog

  Journal of Science Education and Technology · 2024-11-04 · 2 citations

  articleOpen accessSenior author

  Abstract In this study, we used Epistemic Network Analysis (ENA) to represent data generated by Natural Language Processing (NLP) analytics during an activity based on the Knowledge Integration (KI) framework. The activity features a web-based adaptive dialog about energy transfer in photosynthesis and cellular respiration. Students write an initial explanation, respond to two adaptive prompts in the dialog, and write a revised explanation. The NLP models score the KI level of the initial and revised explanations. They also detect the ideas in the explanations and the dialog responses. The dialog uses the detected ideas to prompt students to elaborate and refine their explanations. Participants were 196 8th-grade students at a public school in the Western United States. We used ENA to represent the idea networks at each KI score level for the revised explanations. We also used ENA to analyze the idea trajectories for the initial explanation, the two dialog responses, and the final explanation. Higher KI levels were associated with more links and increased frequency of mechanistic ideas in ENA representations. Representation of the trajectories suggests that the NLP adaptive dialog helped students who started with descriptive and macroscopic ideas to add more microscopic ideas. The dialog also helped students who started with partially linked ideas to keep linking the microscopic ideas to mechanistic ideas. We discuss implications for STEM teachers and researchers who are interested in how students build on their ideas to integrate their ideas.

  PublisherOA PDFDOI

• A Comparison of Responsive and General Guidance to Promote Learning in an Online Science Dialog

  Education Sciences · 2024-12-17 · 3 citations

  articleOpen access

  Students benefit from dialogs about their explanations of complex scientific phenomena, and middle school science teachers cannot realistically provide all the guidance they need. We study ways to extend generative teacher–student dialogs to more students by using AI tools. We compare Responsive web-based dialogs to General web-based dialogs by evaluating the ideas students add and the quality of their revised explanations. We designed the General guidance to motivate and encourage students to revise their explanations, similar to how an experienced classroom teacher might instruct the class. We designed the Responsive guidance to emulate a student–teacher dialog, based on studies of experienced teachers guiding individual students. The analyses comparing the Responsive and the General condition are based on a randomized assignment of a total sample of 507 pre-college students. These students were taught by five different teachers in four schools. A significantly higher proportion of students added new accurate ideas in the Responsive condition compared to the General condition during the dialog. This research shows that by using NLP to identify ideas and assign guidance, students can broaden and refine their ideas. Responsive guidance, inspired by how experienced teachers guide individual students, is more valuable than General guidance.

  PublisherOA PDFDOI

• Beyond inquiry or direct instruction: Pressing issues for designing impactful science learning opportunities

  Educational Research Review · 2024-07-26 · 28 citations

  reviewOpen access

  We recently published a paper in this journal (de Jong et al., 2023) that presented an overview of the literature on learning in science domains through direct instruction and guided inquiry-based learning. This paper was, in part, a response to Zhang et al. (2022) who argued that the evidence firmly supported the superiority of direct instruction over inquiry learning. Sweller et al. (2024) recently replied by repeating this claim and also argued that we had ignored evidence against our position, questioned our analysis of the evidence, and claimed that direct instruction (unlike inquiry learning) is grounded in a strong theory. In this rebuttal we start by reemphasizing the conclusion from our previous paper: adequate instruction always involves different strategies, which should be thoughtfully selected based on contextual factors. Next, we demonstrate that inquiry-based learning is firmly rooted in both cognitive and socio-cultural theories of learning and conclude from recent literature that Sweller et al.‘s belief that direct instruction is overall more effective than inquiry learning is not supported by the data from empirical studies. • Inquiry-based instruction is firmly rooted in cognitive and sociocultural theories. • Well-designed instructional methods often combine direct instruction and student inquiry. • Teachers are key to successfully implementing methods in the classroom.

  PublisherDOI

• Impacts of Web-Based Inquiry Learning Environments Aligned with Knowledge Integration Pedagogy

  2024-11-29

  book-chapterSenior author

  Advances in learning science research and educational technologies combined in Authoring and Customizing Environments (ACE) have the potential to transform classroom science learning. Aligning inquiry instruction and assessment using learning science pedagogy such as Knowledge Integration ensures that designs incorporate advances in research and that impacts are validly measured. ACEs support partnerships of classroom teachers, discipline experts, learning scientists, and software designers to create inquiry curricula and to customize the instruction using evidence from classroom implementations. Meta-analyses and reviews of the impact of inquiry environments document the impact of ACEs on science learning. The chapter illustrates the features of ACEs including models, simulations, graphing technologies, collaborative tools, and automated guidance in an exemplar unit. It summarizes research on the impact of each feature and discusses how aligning combinations of these features with learning science pedagogies increases ACE impacts. Supporting partnerships to customize ACE-based instruction not only improves outcomes but also strengthens the pedagogy behind the ACE. The chapter closes with recommended next steps.

  PublisherDOI

• Teacher-informed Expansion of an Idea Detection Model for a Knowledge Integration Assessment

  2024-07-09 · 2 citations

  articleOpen accessSenior author

  Students come to science classrooms with ideas informed by their prior instruction and everyday observations. Following constructivist pedagogy, assessments that encourage students to elaborate their ideas, distinguish among them, and link the most promising ones can capture students' potential and help teachers plan their lessons. In this investigation, we study an assessment that engages students in a dialog to refine their response to a Knowledge Integration (KI) question. Our Research Practice Partnership (RPP) initially trained a Natural Language Processing (NLP) idea detection model on 1218 student responses from 5 schools and identified 13 student ideas. The original model had an overall micro-averaged F-score of 0.7634. After classroom testing, three RPP expert teachers with 10+ years of experience reviewed the classroom data and expanded the model, adding six additional ideas including two that they described as precursor ideas because they foreshadowed more sophisticated reasoning. We trained the idea detection model on these 19 ideas using a dataset from 13 teachers and 1206 students across 8 public schools. The updated model had a somewhat lower overall micro-averaged F-score of 0.7297. The two precursor ideas were among the top four detected ideas. The assessment, using the updated model, guided students to express significantly more ideas. A regression model showed that the updated model was associated with greater KI score gains. Expanding the model, thus, created an assessment that motivated students to express more ideas and to achieve higher KI scores. It also provides teachers with deeper insights into their students' understanding of science.

  PublisherOA PDFDOI

• Impact of an adaptive dialog that uses natural language processing to detect students’ ideas and guide knowledge integration.

  Journal of Educational Psychology · 2024-09-19 · 9 citations

  articleOpen accessSenior author
  PublisherDOI

Recent grants

• Helping districts respond to new science assessments--A partnership model for integrating technology, professional development and curriculum planning

  NSF · $1.7M · 2003–2007

• CLASS: Continuous Learning and Automated Scoring in Science

  NSF · $3.1M · 2011–2017

• Mentored and Online Development of Educational Leaders for Science (MODELS)

  NSF · $2.4M · 2005–2011

• Collaborative Research: Supporting Teachers in Responsive Instruction for Developing Expertise in Science

  NSF · $2.6M · 2018–2023

• Visualizing to Integrate Science Understanding for All Learners (VISUAL)

  NSF · $3.5M · 2009–2015

Frequent coauthors

• Libby Gerard

  University of California, Berkeley

  80 shared

• Marian Rice

  University of California, Berkeley

  29 shared

• Jonathan M. Vitale

  28 shared

• Camillia Matuk

  25 shared

• Michael Clancy

  21 shared

• Kevin W. McElhaney

  20 shared

• Allison Bradford

  University of California, Berkeley

  20 shared

• Nancy Butler Songer

  University of Utah

  18 shared

Education

• Ph, D. M.A. B,A,

  Stanford University

Awards & honors

• National Association for Research in Science Teaching Award…
• American Educational Research Association Willystine Goodsel…
• Council of Scientific Society Presidents first award for Exc…