What
is Learning Engineering?
Herbert
Simon, a Carnegie Institute of Technology professor, was credited as the first
to introduce the term “learning engineering” in his essay entitled “The
Job of College President” aimed at enhancing institutional management and
operations (Lee, 2023). The International Consortium for Innovation and Collaboration
in Learning Engineering (ICICLE) defined learning engineering is a process and
practice that applies the learning sciences, using human-centered engineering
design methodologies and data-informed decision-making to support learners and
learning (Goodell & Kolodner, 2023). This definition indicates that learning
engineering employs tools and methodologies that are a combination of learning
sciences research, engineering approaches, and data-driven decision-making,
with a focus on using these disciplines to transform education.
Learning
engineering as a transdisciplinary field
Learning
engineering is a transdisciplinary field that uses learning science principles
to create engaging, dynamic experiences, integrating psychology, neurology,
education, engineering, and design to address learners’ challenges. In other
words, learning engineering is a broad field that includes learning science,
design, data science, and technology. Learning engineering is
emerging as a professional discipline that combines software engineering,
development knowledge, learning science, design thinking, and pedagogy to
foster learner growth through human-centered design and data-driven
decision-making. Learning engineering includes software development,
human-computer interface design, artificial intelligence, intelligent tutoring
systems, and data science. It involves a collaborative effort among specialists
from various fields, including teaching, software engineering, instructional
design, learning science, and data science to develop data-driven learning
approach (Dede, Richards, & Saxberg, 2019).
Goals
of learning engineering
Learning
engineering is a rapidly evolving field that combines education, data science,
and engineering to create effective, scalable learning experiences. The
traditional era of education is being transformed into digitalized education as
a result of emerging technologies and data-driven new perspectives. The
evolution of learning engineering has been a major breakthrough in education. Learning
engineering uses big data to improve learning experiences by combining learning
analytics and educational data mining to understand student learning, optimal
instructional tactics, and valid evidence on learners’ mastery of goals (Dede,
Richards, & Saxberg, 2019).
Learning engineering is a practical methodology that aims to improve the
design, implementation, and assessment of learning systems and experiences
through the use of empirical data and rigorous analysis. It uses data-driven,
iterative techniques that aid in the continuous refinement and improvement of
structured and effective learning experiences based on recurrent data
collection and analysis results.
Learning
engineering is the art of optimizing learning and decision-making using data
analytics, computer-human interaction, modeling, measurement, instrumentation,
and continuous improvement (Wagner, 2021). Learning engineering focuses on
generating data-driven learning experiences that cater to learners and give
learning solutions. Learning engineering optimizes learning solutions by
understanding optimal conditions and learners, and developing robust, refined,
and scalable alternatives (Dede, Richards, & Saxberg, 2019). Learning
engineering comprises addressing challenges that extend beyond learning
experience design, with a focus on identifying the underlying causes of issues
influencing learners’ growth. Learning engineering can be regarded as a
data-driven continuous iterative process that involves designing, redesigning,
testing, redesigning, and improving learning conditions, starting with a
problem associated with the learner or learning, ranging from small to
large-scale projects, aiming to prepare students for challenging tasks, whereas
traditional instructional design is a linear process that involves design,
develop, and deliver, not initiated by evidence-based demand from learning or
learner.
Approaches
used in learning engineering
Learning engineering is an
innovative approach to education that emphasizes student-centered design and
multidisciplinary team decision-making. It employs cognitive task analysis
and item response theory to produce engaging, dynamic experiences that draw on psychology,
neurology, education, engineering, and technology. It involves a human-centered
approach, data collection, and analysis to observe performance and learner
behaviors. The approach focuses on data-generating learning design, enhancing
education through feedback systems and potential advancements like personalized
learning, augmented reality, virtual reality, artificial intelligence, and
machine learning. Learning engineering principles involve data-driven learning,
continuous activity development, human-centered design, goal achievement, and
appropriate technology use to optimize learning activities. It combines
education, data science, and engineering to create effective, scalable learning
experiences. It involves a human-centered approach, data collection, and
analysis to observe performance and learner behaviors. Learning is a
multifaceted issue, influenced by context and individual preferences.
Addressing this challenge requires putting students at the center of education
development, a crucial aspect of learning engineering.
Learning engineering improves
content and systems for diverse learners by addressing complex factors through
iterative, data-informed tactics, focusing on human-centered design and
multidisciplinary team decision-making within education. Learning engineering
is constantly evolving with new tools, design patterns, and AI components,
reducing the distinction between work and learning, with AI agents potentially
joining learning teams to collaborate (Craig et al., 2023). Learning
engineering is enhancing education through generating data-driven learning
experiences with feedback systems for students, teachers, designers, and the
learning sciences community. This data-driven design tracks performance and
growth, opening the path for potential advancements such as personalized
learning, augmented/virtual reality, artificial intelligence, and machine
learning (Craig et al., 2023).
Conclusion
Learning
engineering is a transdisciplinary field that combines learning sciences
research, engineering approaches, and data-driven decision-making to transform
education. It focuses on creating engaging, dynamic experiences using software
development, AI, and intelligent tutoring systems. The field uses cognitive
task analysis and item response theory to create data-driven learning
experiences, enhancing education through feedback systems and potential
advancements like personalized learning, augmented reality, and machine
learning.
References:
1. Craig, S. D., Goodell, J.,
Czerwinski, E., Lis, J., & Roscoe, R. D. (2023). Learning Engineering
Perspectives for Supporting Educational Systems. Proceedings of the
Human Factors and Ergonomics Society Annual Meeting, 67(1),
304-309. https://doi.org/10.1177/21695067231192886
2. Dede,
C., Richards, J., & Saxberg, B. (2019). Learning engineering for online education
theoretical contexts and design-based examples. Routledge.
3. Goodell,
J., & Kolodner, J. (2023). Learning engineering toolkit introduction: Evidence-based
practices from the learning sciences, instructional design, and beyond. Routledge.
4. Julianstodd (2023,
February 20). Learning Engineering: WorkingOutLoud on Learning Science.
Retrieved (October 9, 2024), from
5. Lee,
V.R. (2023). Learning sciences and learning engineering: A natural or
artificial distinction? Journal of the Learning Sciences, 32(2), pp. 288–304.
6. Wagner,
E. D. (2021). Becoming a Learning
Designer. Design for Learning: Principles, Processes, and Praxis.
