PapersFlow Research Brief
Experimental Learning in Engineering
Research Guide
What is Experimental Learning in Engineering?
Experimental Learning in Engineering is an instructional approach in engineering education that builds students’ conceptual and practical competence through structured, hands-on activities—such as peer instruction, active learning, and flipped-classroom work—paired with feedback and reflection.
Experimental Learning in Engineering is commonly operationalized through classroom designs such as peer instruction and other active-learning formats that replace part of traditional lecture time with guided practice and formative assessment. "Active learning increases student performance in science, engineering, and mathematics" (2014) synthesized evidence that active learning improves student performance across STEM, including engineering. The provided topic dataset contains 106,216 works on Experimental Learning in Engineering, and the 5-year growth rate is reported as N/A.
Research Sub-Topics
Active Learning in Engineering Classrooms
This sub-topic evaluates peer instruction and think-pair-share methods in engineering courses. Researchers measure gains in conceptual understanding via pre/post testing.
Flipped Classroom Models in Engineering
This sub-topic implements video lectures for pre-class preparation and in-class problem solving. Researchers assess impacts on higher-order learning and student engagement.
Problem-Based Learning in Engineering Design
This sub-topic applies PBL to authentic engineering challenges like heat transfer projects. Researchers study team dynamics and skill transfer to capstone design.
Hands-On Laboratory Experiences in Engineering
This sub-topic designs virtual and physical labs for systems like digital control. Researchers evaluate learning outcomes through concept inventories and failure analysis.
Just-in-Time Teaching in Engineering Courses
This sub-topic uses student pre-class responses to adapt lectures in real-time. Researchers apply it to electromagnetics and signal processing for misconception correction.
Why It Matters
Experimental learning matters in engineering because it targets the kinds of performance engineers are assessed on—conceptual reasoning, quantitative problem solving, and the ability to apply models to new situations—rather than only passive recall. Crouch and Mazur’s "Peer Instruction: Ten years of experience and results" (2001) reported increased student mastery of both conceptual reasoning and quantitative problem solving after implementing Peer Instruction in introductory physics, a foundational gateway subject for many engineering programs. Freeman et al. (2014) in "Active learning increases student performance in science, engineering, and mathematics" reported improved student performance when active learning replaces purely lecture-based instruction, aligning with engineering education’s need to raise success rates in high-enrollment STEM courses. Sams and Bergmann’s "Flip Your Classroom: Reach Every Student in Every Class Every Day" (2012) provides a concrete implementation model: shifting content delivery outside class to free in-class time for problem solving and instructor-supported troubleshooting, which is directly applicable to engineering problem sets, design studios, and laboratory preparation.
Reading Guide
Where to Start
Start with Freeman et al.’s "Active learning increases student performance in science, engineering, and mathematics" (2014) because it summarizes evidence across STEM and provides an empirical justification for adopting experimental (active) learning approaches in engineering contexts.
Key Papers Explained
Freeman et al. (2014), "Active learning increases student performance in science, engineering, and mathematics," provides the broad evidence base that active learning improves performance across STEM. Crouch and Mazur (2001), "Peer Instruction: Ten years of experience and results," then offers a concrete, well-documented classroom method and reports gains in conceptual reasoning and quantitative problem solving. Sams and Bergmann (2012), "Flip Your Classroom: Reach Every Student in Every Class Every Day," complements these by specifying an implementation pattern that reallocates class time toward coached practice—an enabling structure for active learning and peer instruction. The engineering-domain texts—"Fundamentals of Heat and Mass Transfer" (1985), "Digital control of dynamic systems" (1980), "Linear System Theory and Design" (1995), and "The scientist and engineer's guide to digital signal processing" (1997)—can be treated as content backbones for designing high-quality activities, question banks, and problem sequences that fit core engineering curricula. Bentler’s "EQS : structural equations program manual" (1989) is relevant when researchers evaluate interventions using structural equation modeling for survey or construct-based outcomes.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
A current frontier is scaling experimental learning approaches beyond single-course implementations into program-level workforce preparation initiatives that emphasize hands-on training in STEM priority areas. The news items "Experiential Learning for Emerging and Novel Technologies (ExLENT)" (2025-02-24) and "U.S. National Science Foundation and Micron Foundation invest nearly $38M to provide American workers with opportunities to develop skills in AI, biotechnology and other STEM priority areas" (2025-12-19) indicate active investment in experiential training tied to emerging technologies and workforce development.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | EQS : structural equations program manual | 1989 | Medical Entomology and... | 9.8K | ✕ |
| 2 | Active learning increases student performance in science, engi... | 2014 | Proceedings of the Nat... | 8.7K | ✓ |
| 3 | Advanced engineering electromagnetics | 1989 | — | 7.0K | ✕ |
| 4 | Flip Your Classroom: Reach Every Student in Every Class Every Day | 2012 | Medical Entomology and... | 3.9K | ✕ |
| 5 | Fundamentals of Heat and Mass Transfer | 1985 | — | 3.7K | ✓ |
| 6 | Digital control of dynamic systems | 1980 | — | 3.2K | ✕ |
| 7 | Digital communications : fundamentals and applications | 2017 | — | 3.1K | ✓ |
| 8 | Linear System Theory and Design | 1995 | — | 3.1K | ✕ |
| 9 | The scientist and engineer's guide to digital signal processing | 1997 | — | 2.8K | ✕ |
| 10 | Peer Instruction: Ten years of experience and results | 2001 | American Journal of Ph... | 2.6K | ✕ |
In the News
U.S. National Science Foundation and Micron ...
# U.S. National Science Foundation and Micron Foundation invest nearly $38M to provide American workers with opportunities to develop skills in AI, biotechnology and other STEM priority areas Decem...
Experiential Learning Through the NSF ExLENT Program ...
# NSF ExLENT Program Expands Experiential Learning to Strengthen the US STEM Workforce ## New NSF ExLENT awards expand hands-on STEM training in AI, biotechnology, quantum science, and advanced man...
Experiential Learning for Emerging and Novel Technologies (ExLENT)
Through this initiative, the Directorate for STEM Education (EDU) and the Directorate for Technology, Innovation and Partnerships (TIP), in partnership with Micron Technology, Inc. (Micron) throug...
Giving students the space to tinker and build workplace- ...
By 2015, the initiative’s enthusiasm and impact had attracted industry interest from Ansys, Quanser, D2L, Xinyi Glass, Skyjack and Rockwell. These foundational partners, combined with Faculty’s fin...
Brown Teaching Grants encourage premier ... - Rice News
The grants enhance undergraduate student learning through teaching innovation. Last year, the university doubled the total funding available each year. In 2024 and 2025, 13 proposals were funded in...
Code & Tools
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This project is intended to simplify and standardize running ILP experiments. It aims at evaluating and comparing different ILP systems on usual be...
|Library|Description|Link| unsloth|Fine-tune LLMs faster with less memory.| Link | PEFT|State-of-the-art Parameter-Efficient Fine-Tuning library.| ...
This Python package facilitates the fast prototyping of machine learning model with great scalability and flexibility. Characteristics of this pack...
A curated, list of 100+ libraries and frameworks for AI engineers building with Large Language Models. This toolkit includes battle-tested tools, f...
Recent Preprints
Applying Kolb's experiential learning cycle for deep learning
A systematic review was conducted on studies on experiential learning in higher education. Out of 30 articles, 2 fully adhered to the guidelines of Kolb's cycle. Weaknesses found in the remaining a...
Exploring an experiential learning project through Kolb's ...
Exploring a Community Service Experiential Learning Project through Kolb’s Learning Theory using Qualitative Research Method Experiential learning pedagogy is taking a lead in the development of ...
Experiential Learning to Support Digital and Artificial ...
This mixed-methods study examined how experiential learning theory (ELT) can support the development of digital and artificial intelligence literacies in postsecondary education through the integra...
An Innovative and Universal Teaching Model in ...
In light of Industry 5.0’s emphasis on human-centric approaches, future engineering education should prioritise the development of soft skills to complement students’ digital and technological comp...
Integrating experiential learning theory with innovation and ...
To address the identified gap, the present study adopts Experiential Learning Theory (ELT) as an analytical framework to examine medical students’ engagement with IEE. Originally developed by David...
Latest Developments
Recent developments in experimental learning in engineering research include the increasing integration of digital transformation, problem-based learning, and technology-enhanced approaches, with a notable rise in publications since 2020 driven by the COVID-19 pandemic and digital tools, as well as ongoing efforts to map its perceptions and transformations (International Journal of Teaching, Learning and Education, 2025; MDPI, 2023; Lumivero, 2026).
Sources
Frequently Asked Questions
What is Experimental Learning in Engineering?
Experimental Learning in Engineering is an approach to engineering education that emphasizes learning by doing through structured activities such as active learning, peer instruction, and flipped-classroom practice. Freeman et al. (2014) in "Active learning increases student performance in science, engineering, and mathematics" synthesized evidence that these approaches improve student performance in STEM, including engineering.
How does active learning differ from traditional lecture in engineering courses?
Active learning replaces some lecture time with activities that require students to retrieve, apply, and discuss concepts during class. Freeman et al. (2014) in "Active learning increases student performance in science, engineering, and mathematics" reported improved student performance under active learning across STEM, supporting its use in engineering classrooms.
How is Peer Instruction used as an experimental learning method in engineering-adjacent STEM?
Peer Instruction uses conceptual questions and structured peer discussion to surface misconceptions and strengthen reasoning during class. Crouch and Mazur (2001) in "Peer Instruction: Ten years of experience and results" reported increased student mastery of both conceptual reasoning and quantitative problem solving after implementing Peer Instruction in introductory physics.
Which flipped-classroom practices are most directly applicable to engineering problem-solving courses?
A flipped classroom moves initial content exposure outside class so class time can be used for guided problem solving and instructor feedback. Sams and Bergmann (2012) in "Flip Your Classroom: Reach Every Student in Every Class Every Day" describe this rationale explicitly: students need teachers present when they get stuck on assignments more than when they listen to lectures.
Which highly cited sources can support experimental learning design in quantitatively intensive engineering subjects?
Several highly cited engineering texts can anchor the technical content around which experimental learning activities are built, including "Fundamentals of Heat and Mass Transfer" (1985), "Digital control of dynamic systems" (1980), "Linear System Theory and Design" (1995), and "The scientist and engineer's guide to digital signal processing" (1997). These sources are commonly used to define canonical problem types and concepts that can be converted into in-class active-learning tasks or peer-instruction questions.
Which tools are used to analyze learning data in experimental learning research designs?
When experimental learning studies use latent-variable or survey-based models, a structural equation modeling tool may be used to analyze relationships among constructs. Bentler’s "EQS : structural equations program manual" (1989) is a highly cited reference associated with structural equations workflows, which can be relevant for analyzing educational data when such models are used.
Open Research Questions
- ? Which specific active-learning task designs (e.g., concept questions vs. multi-step problems) best improve both conceptual reasoning and quantitative problem solving in engineering gateway courses, as emphasized in "Peer Instruction: Ten years of experience and results" (2001)?
- ? How should flipped-classroom time be allocated between instructor coaching, peer discussion, and individual work to maximize the performance gains reported in "Active learning increases student performance in science, engineering, and mathematics" (2014)?
- ? Which assessment instruments and modeling choices are most appropriate for evaluating experimental learning outcomes with latent constructs (e.g., self-efficacy or engagement) when using structural equation approaches referenced by "EQS : structural equations program manual" (1989)?
- ? How can experimental learning interventions be integrated into mathematically dense engineering curricula (e.g., signals, control, heat transfer) while preserving coverage of core topics represented by widely used texts such as "Digital control of dynamic systems" (1980) and "Fundamentals of Heat and Mass Transfer" (1985)?
Recent Trends
The provided dataset reports 106,216 works on Experimental Learning in Engineering, indicating a large literature base, while the 5-year growth rate is listed as N/A. In the highly cited core, evidence syntheses and classroom methods remain central: Freeman et al.’s "Active learning increases student performance in science, engineering, and mathematics" and Crouch and Mazur’s "Peer Instruction: Ten years of experience and results" (2001) continue to define performance-oriented rationales and mechanisms for in-class experimentation with learning.
2014Recent emphasis in public-facing initiatives has shifted toward experiential learning as workforce preparation for emerging technology areas; for example, "U.S. National Science Foundation and Micron Foundation invest nearly $38M to provide American workers with opportunities to develop skills in AI, biotechnology and other STEM priority areas" highlights large-scale funding tied to hands-on skill development.
2025-12-19Research Experimental Learning in Engineering with AI
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