How do we align the deeply personal journey of learning with the methods, models, measures, instrumentation and standardization required to build valid, reliable engineering solutions that scale?
One of the first things to do is to determine similarities and differences between and among points of view contributing to the conversation. Not surprisingly, it turns out that engineers think differently about how to approach opportunities for leveraging scientifically valid research in practice than learning scientists do.
The Learning Agency has managed a Google group for Learning Engineering since June 20, 2019. As of May 20, 2024, the Group has 2,976 members. The Group has created 2,293 conversations that address a significant array of topics of interest. Using a number of sorting procedures, one can read all or some of the things the group has been talking about for the past 5 years. In particular, Piotr Mitros has been an active contributor to these conversations, underscoring the importance of engineering practices and sensibilities within learning engineering.
Here are six examples that demonstrate how engineers think about learning engineering differently than learning scientists do:
1. Engineers want proven science to develop plans, practices and platforms for learning solutions that scale
Engineers build reliable platforms using established science. They are less interested in exploring the frontier where science is being discovered. There is a wealth of existing knowledge with significant effect sizes that has not been fully utilized in educational practice. By focusing on established principles, learning engineers can create more reliable and impactful educational systems and solutions.
2. Engineers integrate predictive modeling and simulation into their work to anticipate conditions and minimize trial and error
In mature engineering disciplines, predictive models are fundamental tools. Predictive modeling is still relatively new in educational practice setting although predictive models and methods are now a part of student success platforms and practices to better anticipate the performance of educational interventions Predictive modeling and analysis should be to create designs and robust models that can predict educational outcomes with high reliability. This capability would allow for more efficient and effective design processes, minimizing the need for extensive trial and error.
3. Engineering applies a broad array of empirical investigative methods to build reliable solutions at scale, extending the scientific foundations upon which social science research is based.
While data-driven approaches, A/B testing, and educational data mining are valuable, they represent just one subset of the engineering toolkit. Learning engineers should draw from a wide range of engineering disciplines, including mechanical, civil, and systems engineering, to apply diverse methodologies, such as modeling, measurement, instrumentation and continuous formative improvement, to the design and development of learning systems. This broad perspective allows for more robust and adaptable solution development.
4. Engineers Adapt Mature Engineering Practices for Use in Emergent Settings
Learning engineering can benefit from the practices of more mature engineering disciplines. For instance, while software engineering often relies on rapid iteration and testing, fields like civil and mechanical engineering emphasize getting designs right the first time due to high stakes and costs. Learning engineers can adopt a balanced approach, incorporating rapid prototyping and iteration where appropriate but also ensuring rigorous design and validation processes to avoid costly mistakes.
5. Engineers Consider Long-Term Development and Impact Requirements
Educational systems aim to develop complex skills over extended periods, often spanning many years. Learning engineers must account for the long-term nature of educational outcomes, which cannot be measured or optimized through short-term experiments alone.
6. Engineers Ensure Learning System Safety and Reliability
Learning systems, like any engineered systems, must prioritize safety and reliability. This means designing educational interventions that minimize harm to students and ensure consistent performance across diverse conditions. Learning engineers can adopt an ethos from fields like aerospace and medical device engineering, where safety and reliability are paramount. By incorporating these principles, learning engineers can create systems that are robust and trustworthy.
Conclusion
Learning engineering will require a diverse and comprehensive set of skills and aptitudes to realize its full potential. By incorporating principles from various engineering disciplines, learning engineers can create robust, scalable, and effective learning systems. This approach not only addresses immediate educational challenges but also lays the groundwork for a mature and impactful discipline that can transform education for the better.
