What is she best known for?
The fields of study she is best known for:
- Education
- Mathematics education
- Artificial intelligence
Her primary areas of investigation include Science education, Mathematics education, Knowledge integration, Pedagogy and Curriculum.
Her Science education research incorporates themes from Educational technology, Construct and Curriculum development.
Her Mathematics education research incorporates elements of Lifelong learning and Professional development.
The Knowledge integration study combines topics in areas such as Inquiry science, Rasch model, The Internet and Psychology of learning.
Marcia C. Linn interconnects Cognitive development, Learning environment, Software design pattern, Spatial intelligence and Undergraduate research in the investigation of issues within Curriculum.
Her Knowledge management study deals with Spatial ability intersecting with Developmental psychology.
Her most cited work include:
- Emergence and characterization of sex differences in spatial ability: a meta-analysis. (2232 citations)
- Gender differences in verbal ability: A meta-analysis. (1138 citations)
- Cross-National Patterns of Gender Differences in Mathematics: A Meta-Analysis. (826 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Mathematics education, Science education, Knowledge integration, Pedagogy and Curriculum.
Her Mathematics education study integrates concerns from other disciplines, such as Visualization and Cognition.
Her studies deal with areas such as Developmental psychology and Cognitive psychology as well as Cognition.
Her Science education research integrates issues from Engineering ethics, Educational research and Curriculum development.
Marcia C. Linn combines subjects such as Inquiry science, The Internet and Professional development with her study of Knowledge integration.
Her Pedagogy research is mostly focused on the topic Primary education.
She most often published in these fields:
- Mathematics education (42.98%)
- Science education (29.51%)
- Knowledge integration (23.21%)
What were the highlights of her more recent work (between 2011-2021)?
- Mathematics education (42.98%)
- Knowledge integration (23.21%)
- Science education (29.51%)
In recent papers she was focusing on the following fields of study:
Her scientific interests lie mostly in Mathematics education, Knowledge integration, Science education, Curriculum and Educational technology.
Her Mathematics education research is multidisciplinary, incorporating elements of Visualization and Chemistry.
Her Knowledge integration study combines topics in areas such as Learning environment, Inquiry science, Teaching method and Professional development.
Her Science education study introduces a deeper knowledge of Pedagogy.
Her Curriculum study incorporates themes from Engineering ethics and Personalization.
Her research integrates issues of Management science and Prior learning in her study of Educational technology.
Between 2011 and 2021, her most popular works were:
- Physical and Virtual Laboratories in Science and Engineering Education (362 citations)
- Undergraduate research experiences: Impacts and opportunities (260 citations)
- Women's Representation in Science Predicts National Gender-Science Stereotypes: Evidence from 66 Nations. (201 citations)
In her most recent research, the most cited papers focused on:
- Education
- Mathematics education
- Artificial intelligence
Her primary areas of study are Mathematics education, Knowledge integration, Science education, Visualization and Curriculum.
Her Educational technology, Active learning and Rubric study, which is part of a larger body of work in Mathematics education, is frequently linked to Unit and Automation, bridging the gap between disciplines.
Her Educational technology research includes themes of Science instruction and Formative assessment.
Her Knowledge integration research is multidisciplinary, incorporating elements of Learning environment, Teaching method and Science learning.
Marcia C. Linn has researched Science education in several fields, including Cooperative learning, Concept map, Educational research and Data science.
Her biological study spans a wide range of topics, including Human–computer interaction, Unobservable and Chemistry.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
