Learning Engineering is an emerging field at the intersection of learning science, educational technology, data analytics, and design thinking. It treats the design of educational environments as an engineering problem, iterative, evidence-based, and systematically optimized to produce measurable learning outcomes.
At TED University Faculty of Education, Learning Engineering is not simply a research interest, it is a foundational philosophy that shapes how we design curricula, prepare teachers, and evaluate impact across all our programs.
'''Learning Engineering is a process and practice that applies the learning sciences using human-centered engineering design methodology and data-informed decision-making to support learners and their development.''
— IEEE Learning Engineering Working Group
Fakültemiz bu öğrenme mühendisi tanımı benimseyerek, onu Türkiye’deki yükseköğretim bağlamına uyarlamaktadır. TEDU Eğitim Fakültesi olarak, insanların nasıl öğrendiğini anlamanın ve öğrenmeyi daha etkili, eşitlikçi ve kalıcı hale getiren ortamlar tasarlamanın modern eğitimin en önemli zorluğu olduğuna inanıyoruz.
By embracing this definition of the learning engineer, our faculty adapts it to the context of higher education in Türkiye. As the TEDU Faculty of Education, we believe that understanding how people learn and designing environments that make learning more effective, equitable, and enduring constitute one of the most significant challenges of modern education.
"Educational institutions must evolve from being merely places where knowledge is transmitted into personalized, technology-supported, experiential learning environments."
— Prof. Dr. Kürşat Çağıltay, Dean, Faculty of Education
Learning Engineering brings this vision to life. Our Dean’s emphasis on data-informed decision-making, ethical responsibility, and the use of technology as a means to support learner-centered education is directly reflected in the Learning Engineering framework.
The “next-generation educator” described in the Dean’s message is, at its core, a learning engineer: someone who designs learning environments, responds to evidence, applies digital tools purposefully, and leads with both pedagogical expertise and analytical rigor.
Learning Engineering follows an iterative cycle grounded in evidence-based practice and human-centered design. This cycle is integrated into both our research activities and our approach to teacher education.
1. Defining the Learning Challenge
The learning problem is identified through stakeholder engagement, analysis of learner needs, and examination of the current context. The problem is framed as an engineering challenge with clearly defined and measurable learning outcomes.
2. Applying the Learning Sciences
The development of design hypotheses is informed by principles from the learning sciences, including cognitive load theory, retrieval practice, spaced repetition, motivation research, and theories of social learning.
3. Design and Prototyping
Educational solutions encompassing curricula, assessment tools, digital technologies, and physical learning environments are designed and prototyped to address the identified problem. These prototypes are tested with real learners and refined through an iterative process of evaluation and improvement.
4. Data Collection and Analysis
Learning analytics data, assessment results, engagement indicators, and qualitative feedback are systematically collected and analyzed. Analytical methods enable the identification of patterns and insights that may not be readily observable through direct observation alone.
5. Improvement and Scaling
Designs are continuously refined based on the evidence gathered. Successful interventions are documented, shared, and scaled for application across broader contexts, contributing both to institutional knowledge and to the advancement of the field as a whole.
Learning Engineering at TEDU Faculty of Education is built on four interlocking pillars, each embedded in our programs and research activities.
🧠 Learning Science
Evidence from cognitive psychology, neuroscience, and educational research informs every design decision. We ask: what does the science say about how memory, motivation, and metacognition work?
💻 Educational Technology
AI, adaptive systems, virtual/augmented reality, and digital pedagogies are deployed not as ends, but as tools in service of deeper learning goals and individual student needs.
📊 Data Analytics
Learning analytics and data-driven feedback loops allow educators to detect early warning signals, personalize instruction, and continuously improve educational environments.
🔄 Design Thinking
Human-centered design principles guide iterative cycles of problem framing, ideation, prototyping, and testing — placing learner needs at the heart of every educational solution.
The Faculty's commitment to student-centered learning, as articulated on our FAQs page is, in essence, the pedagogical expression of Learning Engineering principles:
- Active learning environments created through inquiry and critical thinking
- Project-based learning ensuring authentic, participatory engagement
- Students as responsible co-constructors of knowledge, not passive recipients
- Faculty as learning environment designers, not merely content transmitters
These are not pedagogical preference, they are LE-informed conclusions drawn from decades of learning science evidence.
The following resources provide valuable theoretical foundations, practical examples, and current research related to the field of Learning Engineering.
Professional and Institutional Resources
- IEEE ICICLE Learning Engineering Initiative
- Learning Engineering Virtual Institute (LEVI)
- Learning Engineering Community Portal
- Learning Engineering Body of Knowledge (LEBOK)
Universities and Research Centers
- Carnegie Mellon University – Open Learning Initiative
- Carnegie Mellon LearnLab Certificate Program
- Johns Hopkins University – Learning Engineering Career Path
- Arizona State University Learning Engineering Institute
Academic Publications
- Introduction to Learning Engineering
- Learning Engineering (Ellen Wagner)
- Learning Engineering in Educational Technology Research
- Journal of Educational Technology & Online Learning Article
- American Psychological Association (APA) Publication
- SCITEPRESS Conference Paper (2025)
Books
Webinars and Presentations