In the field of education, learning engineering is new — and potentially revolutionary.
To help shape the field and create a network of learning engineering, we have created Learning
Engineering Google Group. Email ulrich@the-learning-agency.com if you want to join the
conversation.
Inspired by work in other fields like bioinformatics, academics are leading the
push for learning engineering, and many universities like the University of California, Irvine and Carnegie
Mellon are either already offering learning engineering programs or working on creating one.
Inspired by work in other fields like bioinformatics, academics are leading the
push for learning engineering, and many universities like the University of California, Irvine and Carnegie
Mellon are either already offering learning engineering programs or working on creating one.
At the same time, a number of foundations like the Chan Zuckerberg Initiative (CZI) and Schmidt Futures are funding new areas of work in learning engineering. Indeed, Bror Saxberg, Vice President of Learning Science at CZI, has long been one of the leading figures in the field of learning engineering.
Even the federal government is getting interested, and the head of the Institute of Education
Sciences at the Department of Education, Mark Schneider, posted a blog item recently
touching on, among other things, the need for more support for learning engineering.
“As researchers consider
applying for the systematic replication grants,” Schneider writes, “we are particularly interested in
technologies that can test many more students more quickly and more cheaply. New platforms are emerging that can
do this, perhaps leading to changes in our ‘standard’ model of RCTs [randomized controlled trials].”
In a way,
learning engineering seeks to solve a very simple problem: We don’t know enough about how people learn — and
technology can help solve this issue by leveraging big data.
So far, ed tech platforms have not invested much in
a learning engineering approach. As a computer scientist Neil Heffernan argues in a recent op-ed, “educational technology platforms don’t share
enough of their data.”
“It doesn’t need to be this way,” Heffernan continues. “Educational technology
companies and researchers can open up the back end of their platforms so researchers can access data and
run experiments.”
This back end is at the heart of learning engineering. A bit of history will help us
understand why.
A study concluded that language can indeed reveal noncognitive traits and help gauge a student’s learning potential. To be more exact, a student’s college essay predicted their success in college.”
This back end is at the heart of learning engineering. A bit of history will help us understand why.
Learning Engineering: History And Promise
It seems that AI expert Herbert A. Simon coined the term “learning engineering” about 50 years
ago.
Simon was one of the first to apply technical competencies to learning, and he also played a
central role in the development of computer science more broadly.
Simon was one of the first to apply technical competencies to learning, and he also played a
central role in the development of computer science more broadly.
What’s new are two things. For one, there are
large-scale ed tech platforms that are beginning to generate a huge amount of data.
What’s more, there is the
new and powerful tools of computer science — rich computation, nascent AI, natural language processing. These
tools are having a big impact on fields like biology, physics, and astronomy, and they
promise the same benefits for education research and development.
Take, for example, a new study titled “Language as Thought: Using Natural Language Processing to Model
Noncognitive Traits that Predict College Success.” Released this year, the researchers, including grit expert
Angela Duckworth, studied how language can help to predict a student’s future by studying noncognitive traits.
The researchers built off of years of studies that suggest that thought and language are inextricably coupled.
As part of the work, the team used natural language processing, machine learning, and manual coding to analyze
college application essays and infer writers’ noncognitive traits, such as goal orientation and perseverance.
The study concluded that language can indeed reveal noncognitive traits and help gauge a student’s learning
potential. To be more exact, a student’s college essay predicted their success in college.
This information can
be helpful in learning how factors other than GPA and academic record can impact a student’s career as well as
in counseling students and building careers.
By leveraging data analytics and technology, learning engineering can generate genuine insights.”
What Do Learning Engineers Do? What is Learning Engineering Exactly?
“Learning engineering” is a relatively new term, to be sure, and there are a number of
competing definitions. Indeed, some believe that learning engineering is the same as saying “data-driven
instructional design.” Others believe that is deserves particularized attention.
From my perspective, learning
engineering takes place at the intersection of computer science and the science of learning. Much as
computational biology did not replace biology as a discipline, learning engineering will not replace the study
of the science of learning.
Learning engineers must, then, have a broad
knowledge of learning science, data science, and computer science. They must be able to understand different
aspects of engineering processes, including everything from pedagogy to artificial intelligence.
The main aim is
to use data to improve learning and teaching. Learning engineers use technologies, standards, and science to
propose, test, and implement solutions.
One of the most useful things learning engineering can do is to take
some of the guesswork out of education. In the past, educators have largely been in the dark about how well
different instructional tools and resources are working.
For example, it can be very difficult for teachers to
know how much to credit their students’ ratings of different educational resources. Educators may worry that
students find a tool useful that is not in fact helping them learn, or, vice versa, they might dislike one that
is nonetheless teaching them successfully.
Learning engineering can change this dynamic. For instance, a study from Jacob Whitehill and
colleagues suggests that teachers should take students’ responses seriously. Students, it seems, know what’s
working for them.
In that study, the researchers compared learner’s subjective ratings for open educational
resources with the learning gains students realized from those resources. They found a strong correlation.
Subjective ratings were highly predictive of students’ level of improvement on tests taken before and after they
used the resources.
We have created Learning Engineering Google Group. Email ulrich@the-learning-agency.com if you want to join the conversation.”
Learning Engineering And the Promise of Continuous Improvement
Although there has been an explosion in the number of digital learning platforms, not enough
attention is paid to learning science in their design and implementation. These platforms should be built so
that they can both provide data and experimental environments for learning scientists, and accommodate
research-based insights in future updates to the platform.
In other words, too little has been done to take
advantage of the data created by learning platforms. By implementing learning engineering principles, these
systems can create feedback loops that promote our knowledge of learning while improving the platforms
themselves.
Learning engineering can also help fill serious gaps in education research, offering researchers a
built-in experimental infrastructure, with large sample sizes, low set-up costs, and customizable conditions.
Consider for instance this recent learning engineering study that looks at the impact of formative feedback and elaborative questioning, with
more than 5,000 subjects.
Powerful New Insights and Outcomes From Learning Engineering
In the last few months alone, there have been a number of other impressive studies showing
the power of learning engineering. A reminder system based on information learned via learning analytics boosted student grades by 0.4 standard deviations.
Another research team used learning analytics to provide tailored feedback to students about their progress,
significantly increasing student satisfaction with feedback and improving student achievement.
Digital tutoring
software has shown a lot of promise as well. Meta-analyses suggest that
intelligent tutoring systems can lead to better outcomes than conventional classrooms. Moreover, they can be
integrated by teachers into a variety of pedagogical environments and “perform as effective classroom
assistants.”
Another study suggested tutoring software might be useful in addressing achievement gaps. The researchers compared math scores from groups of students
in two different after-school math programs: one which was teacher-led and the other which used ALEKS, learning
software that personalizes instruction through adaptive questioning. They found evidence suggesting the gaps in
performance between white and African-American students were lessened in the group using ALEKS.
The main aim is to use data to improve learning and teaching. Learning engineers use technologies, standards, and science to propose, test, and implement solutions. ”
Learning Engineering And The Power of Context
Although these successes show the promise of learning engineering, there is still a long way
to go. A big reason is that what works for one subject area or one student population will likely not be
generalizable. Strategies that improve learning in high school algebra class may not work for college-level data
science or middle school civics.
To find sustainable solutions to a variety of different educational problems in
a variety of different educational contexts, we can’t rely on quick fixes. What’s required is, rather, an
approach that acknowledges the need for fine-grained analysis and long-term experimentation. By leveraging data
analytics and technology, learning engineering can generate genuine insights.
Although we are not close to the
finish line, we are not at square one either. Learning science has already generated
a number of instructional design principles that can be applied to different kinds of courses. These
principles include, for example, the importance of spacing out learning, integrating different elements in a
course, and varying examples.
But it will take time, patience, talent, ingenuity, and, perhaps most importantly,
cooperation to implement these and similar insights in new technology. We will need to find out what works in
different contexts, and iterate for continuous piecemeal improvement. It’s time, then, to get learning
engineering to work.
Follow Along and Join the Google
Group
At the Learning Agency, we have been fostering a Learning
Engineering Google Group.
Email ulrich@the-learning-agency.com if you want to join. We’d
love to have you!
6 thoughts on “Learning Engineering: It’s A Game Changer”
Recently hired as a Learning Engineer at Carnegie Mellon’s Open Learning Initiative.
Congrats. Tell us how it all shakes out–see you in the group too, I hope!
This is very true. I think learning engineering is a passion, you need to give your sweat and blood for this career because you will building lives, future and convenience to the people who trust you. Engineering also is one of the reasons to modernization. <a href=”https://glotechps.com.au/”>environmental engineering</a>, electrical engineering, mechanical, etc. Its all about convenience and modernization. and for the future.
I am a communicator & teacher with a sound interest in learning & technology. I am obsessed with the pros&cons of technology in learning.Looking always for answers about what it works when it comes to good learning. I am deeply concerned with the big gaps in education within minorities ( I evidence that on a daily basis in the classroom ) and quite worried about WHAT I consider a current “Huge crisis in the Education system “!!! It is good to know I am not the ONLY one .. and I really believe and have BIG HOPE this could be partly solved with the GOOD USAGE of Technology. I am interested in being part of that change .. and considering becoming a “learning engineer “ to contribute to the change .
Thanks, Charles. You should join the Learning Engineering Google Group! And agree clear pros and cons of technology in learning.
Very Informative Article