Theories & Strategies in STEM Education

Padlet - Key educational theorists   | Spreadsheet of theories

Theories Commonly Used in Science Teaching

references are from sections in The Sourcebook for Teaching Science

Active Learning: Learn by Doing

Active learning is a set of strategies that posits the responsibility for learning with the student.  Discovery learning, problem-based learning (22.3), experiential learning, and inquiry-based instruction (22.1) are examples of active learning. Discussion, debate (22.4), student questioning (5.1, 22.1, 23.1), think-pair-share (25.7), quick-writes (25.7), polling, role playing, cooperative learning (22.3, 22.5), group projects (13.1-8, 22.5), and student presentations (22.4) are a few of the many activities that are learner driven. It should be noted, however, that even lecture can be an active learning event if students processes and filter information as it is provided.  Cornell notes (3.1) and diagramming (16.2) are a couple of activities that can make lectures active learning events. 

Teaching to multiple learning modalities

We can learn through any of our five senses, but the three most valuable are vision, hearing, and touch. Theorists and practitioners claim that learners have a preference for one learning style over another.  Visual learners learn best by watching, while auditory learners learn best by verbal instruction, and kinesthetic learners learn best by manipulation.  Because of the demands of the profession, teachers often resort to the instructional style that requires the least time and preparation, namely lecture and discussion.  Although these may be valuable approaches to teaching and learning, they fail to take advantage of other learning modalities, and disenfranchise students whose primary modality is visual or kinesthetic.  Throughout this book we emphasize the use of all three modalities in teaching and learning.

Teaching to multiple intelligences

Intelligence is a property of the mind that includes many related abilities such as the capacities to reason, plan, solve problems, comprehend language and ideas, learn new concepts, and think abstractly. Historically, psychometricians have measured intelligence with a single score (intelligence quotient, IQ) on a standardized test, finding that such scores are predictive of later intellectual achievement.  Howard Gardner and others assert that there are multiple intelligences, and that no single score can accurately reflect a person’s intelligence.  More importantly, the theory of multiple intelligences implies that people learn better through certain modalities than others, and that the science teacher should design curriculum to address as many modalities as possible.  Gardner identifies seven intelligences, which are listed below.  The numbers in parentheses indicate sections in this book that address each intelligence. 

• Logical /Mathematical Intelligence is used when thinking conceptually (6.1-4, 7.1-7, 10.1-5, 13.9, 16.1-6, 18.1-3), computing (14.1-3, 15.1-7, 17.1-7, 20.1, 20.8), looking for patterns (1.1-4,16.4, 16.6, 17.5-7), and classifying (8.1-6, 19.1-5) 

• Linguistic/Language Intelligence is used when learning by listening (21.1), verbalizing (1.1-4, 3.1-4, 11.2-4, 22.6), reading (2.1-4), translating (14.1-3), and discussing (8.6, 22.4).

• Naturalist Intelligence is used to question (5.1, 22.1, 23.1), observe (5.2-3, 22.2), investigate (23.2), and experiment (5.1-10, 23.3-4).

• Visual / Spatial Intelligence is used when learning with models (12.1-5), photographs (16.4, 16.6), videos (16.5), diagrams (8.1-6, 16.1-3, 20.2-7), maps (21.1-7) and charts (20.2-7).

• Bodily kinesthetic intelligence is used to process knowledge through bodily sensations (12.2), movements (12.2), physical activity (labs in companion volumes, Hands-on Chemistry and Hands-on Physics), and manipulation (22.2).

• Interpersonal Intelligence is used when learning through cooperative learning experiences (22.3, 22,5), group games (13.1-8), group lab work (22.5), and dialog (8.6, 23.4).

• Intrapersonal Intelligence is used when learning through self-dialog (7.1-3,11.1), studying (11.2-4) and self-assessment (7.4-7). 

• Musical Intelligence is used when learning through rhythm, melody, and non-verbal sounds in the environment (24.8).

Metacognition: Teaching students to think about their thinking

John Flavel argues that learning is maximized when students learn to think about their thinking and consciously employ strategies to maximize their reasoning and problem solving capabilities.  A metacognitive thinker knows when and how he learns best, and employs strategies to overcome barriers to learning.  As students learn to regulate and monitor their thought processes and understanding, they learn to adapt to new learning challenges. Expert problem solvers first seek to develop an understanding of problems by thinking in terms of core concepts and major principles (6.1-4, 7.1-7, 11.1-4).  By contrast, novice problem solvers have not learned this metacognitive strategy, and are more likely to approach problems simply by trying to find the right formulas into which they can insert the right numbers. A major goal of education is to prepare students to be flexible for new problems and settings. The ability to transfer concepts from school to the work or home environment is a hallmark of a metacognitive thinker (6.4).

Developing higher order reasoning

Perhaps the most widely used classification of human thought is Bloom’s Taxonomy.  Benjamin Bloom and his team or researchers wrote extensively on the subject, particularly on the six basic levels of cognitive outcomes they identified – knowledge, comprehension, application, analysis, synthesis, and evaluation.  Bloom’s taxonomy (6.1) is hierarchical, with knowledge, comprehension and application as fundamental levels, and analysis, synthesis and evaluation as advanced (6.1-6.4). When educators refer to “higher level reasoning,” they are generally referring to analysis, synthesis and/or evaluation.  One of the major themes of this book is to develop higher order thinking skills through the teaching of science. 

Constructivism: Helping students build their understanding of science

Constructivism is a major learning theory, and is particularly applicable to the teaching and learning of science. Piaget suggested that through accommodation and assimilation, individuals construct new knowledge from their experiences.  Constructivism views learning as a process in which students actively construct or build new ideas and concepts based upon prior knowledge and new information.   The constructivist teacher is a facilitator who encourages students to discover principles and construct knowledge within a given framework or structure.  Throughout this book we emphasize the importance of helping students connect with prior knowledge and experiences as new information is presented, so they can dispense with their misconceptions (7.4-7) and build a correct understanding.  Seymour Papert, a student of Piaget, asserted that learning occurs particularly well when people are engaged in constructing a product.  Papert’s approach, known as constructionism, is facilitated by model building (12.5), robotics, video editing (16.5), and similar construction projects.  

Computer Supported Collaborative Science (CSCS)

Computer Supported Collaborative Science (CSCS) is a teaching methodology that uses collaborative web-based resources to engage all learners in the collection, analysis, and interpretation of individual data in the context of whole-class data. CSCS fosters scientific inquiry by using collaborative online resources to assess prior knowledge, collect and analyze student ideas, data, and comments, and provides instructors the opportunity to perform continuous formative assessments to inform and reform their own instruction. CSCS turns hands-on classroom activities into more authentic scientific experiences -- shifting the focus from cookbook data collection to thoughtful data analysis.  (Norman Herr)

Pedagogical content knowledge (PCK) in science

An expert scientist is not necessarily an effective teacher.  An expert science teacher, however, knows the difficulties students face and the misconceptions they develop, and knows how to tap prior knowledge while presenting new ideas so students can build new, correct understandings.  Schulman refers to such expertise as pedagogical content knowledge (PCK), and says that excellent teachers have both expert content knowledge, and expert PCK.  In How People Learn, Bransford, Brown and Cocking state: “Expert teachers have a firm understanding of their respective disciplines, knowledge of the conceptual barriers that students face in learning about the discipline, and knowledge of effective strategies for working with students. Teachers' knowledge of their disciplines provides a cognitive roadmap to guide their assignments to students, to gauge student progress, and to support the questions students ask.”  Expert teachers are aware of common misconceptions and help students resolve them. This book is dedicated to improving science teacher pedagogical content knowledge.

Bonwell, C. & Eison, J. (1991). Active Learning: Creating Excitement in the Classroom AEHE-ERIC Higher Education Report No.1. Washington, D.C.: Jossey-Bass.

Bruner, J. S. (1961). The act of discovery. Harvard Educational Review 31(1): 21–32. 

Gardner, H. (1993). Frames of Mind: The theory of multiple intelligences. New York: Basic Books.

Herr, N. and Cunningham, J. (1999). Hands-On Chemistry Activities with Real-Life Applications. San Francisco: Jossey Bass (John Wiley).

Cunningham, J. and Herr, N. (1994). Hands-On Physics Activities with Real-Life Applications. San Francisco: Jossey Bass (John Wiley).

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of 

 cognitive-developmental inquiry. American Psychologist, 34, 906-911. 

B. S. Bloom (Ed.) (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals. New York: David McKay Company, Inc. 

Bodner, G. (1986). Constructivism: A theory of knowledge. Journal of Chemical Education, 63(10), 873-877.

Piaget, Jean. (1950). The Psychology of Intelligence. New York: Routledge.

Papert, S. (1993). Mindstorms: Children, Computers, and Powerful Ideas. New York: Basic Books. 

Schulman, L.S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher 15(2), 4-14.

Bransford, D., Brown, E., and Cocking, R. (eds.) (1999). How People Learn: Brain, Mind, Experience, and School. Committee on Developments in the Science of Learning, National Research Council. Washington, D.C.: National Academy Press.

Lesson Planning

• Scaffolding

• 5E Lesson plan

• Claims - Evidence - Reasoning - CER

5E Instructional Model – A constructivist framework for science lessons organized around five phases: Engage, Explore, Explain, Elaborate, and Evaluate, promoting inquiry and conceptual understanding.

Active Learning – An approach where students participate in activities (discussion, experimentation, problem-solving) that require engagement beyond passive listening.

Advance Organizers – Introductory materials (concept maps, visuals, overviews) that connect prior knowledge to new learning and provide a conceptual framework.

Bloom’s Taxonomy – A hierarchy of cognitive skills (remember, understand, apply, analyze, evaluate, create) used to design instruction and assessment that target deeper levels of thinking.

Cognitive Dissonance – A learning process where encountering conflicting information creates discomfort that motivates conceptual change and deeper understanding.

Computer-Supported Collaborative Science (CSCS) – The use of digital tools and online platforms to enable shared data analysis, discussion, and inquiry among students and teachers.

Constructivism – The view that learners build knowledge actively by connecting new information to existing mental models through experience and reflection.

Continuous Formative Assessment – Ongoing checks for understanding that inform instruction and support student learning in real time.

Differentiation – Tailoring instruction to meet diverse learners’ needs, readiness levels, and interests while ensuring access to key science concepts.

Growth Mindset – The belief that intelligence and ability can be developed through effort and effective strategies; fosters resilience and persistence in science learning.

Guided Discovery – Teacher-supported exploration where learners investigate questions or problems with structured guidance to uncover scientific principles.

Inquiry-Based Learning – A student-centered approach where learners investigate questions, collect data, and construct explanations using scientific practices.

Learning Styles (VARK) – A model suggesting individuals have preferred ways of learning: Visual, Auditory, Reading/Writing, and Kinesthetic.

Metacognition – Awareness and regulation of one’s own thinking and learning processes; critical for self-directed learning in science.

Multiple Intelligences – Gardner’s theory proposing that intelligence is multifaceted (e.g., logical-mathematical, spatial, naturalistic), encouraging varied approaches to science learning.

Pedagogical Content Knowledge (PCK) – Specialized knowledge that combines subject matter understanding with effective teaching strategies for that content.

Peer Grading – A collaborative assessment method where students evaluate each other’s work, enhancing reflection and understanding of learning criteria.

Phenomenon-Based Learning – Learning centered around real-world phenomena, integrating multiple disciplines to develop conceptual understanding and inquiry skills.

Pooled Data Analysis – Collaborative collection and analysis of shared scientific data to enhance interpretation, reliability, and critical thinking.

Problem-Based Learning (PBL) – Learning through solving complex, real-world problems that drive inquiry and application of scientific concepts.

STS (Science, Technology, Society) – An approach linking science learning to social and technological contexts, emphasizing relevance, ethics, and civic engagement.

Three-Dimensional Learning (NGSS Framework) – Integrates Scientific and Engineering Practices, Crosscutting Concepts, and Disciplinary Core Ideas to support holistic science understanding.

Universal Design for Learning (UDL) – A framework for designing flexible, accessible instruction that accommodates learner variability in engagement, representation, and expression.

Zone of Proximal Development (ZPD) – Vygotsky’s concept describing the gap between what learners can do independently and what they can achieve with guidance.

Cognitive Rigor Matrix (CRM) – A tool combining Bloom’s Taxonomy and Webb’s Depth of Knowledge to align instruction and assessment with cognitive complexity.