Educational Technology: Crash Course Computer Science #39

Hi, I’m Carrie Anne, and welcome to Crash
Course Computer Science! One of the most dramatic changes enabled by
computing technology has been the creation and widespread availability of information. There are currently 1.3 billion websites on
the internet. Wikipedia alone has five million English language
articles, spanning everything from the Dancing Plague of 1518 to proper toilet paper roll
orientation. Every day, Google serves up four billion searches
to access this information. And every minute, 3.5 million videos are viewed
on Youtube, and 400 hours of NEW video get uploaded by users. Lots of these views are people watching Gangnam
Style and Despacito. But another large percentage could be considered educational, like what you’re doing right now. This amazing treasure trove of information
can be accessed with just a few taps on your smartphone. Anywhere, anytime. But, having information available isn’t
the same as learning from it. To be clear, we here at Crash Course we are
big fans of interactive in-class learning, directed conversations, and hands-on experiences
as powerful tools for learning. But we also believe in the additive power
of educational technology both inside and outside the classroom. So today we’re going to go a little meta,
and talk specifically about how computer science can support learning with educational technology. Intro Technology, from paper and pencil to recent
machine-learning-based intelligent systems, has been supporting education for millennia
– even as early as humans drawing cave paintings to record hunting scenes for posterity. Teaching people at a distance has long been
a driver of educational technology. For example, around 50 CE, St. Paul was sending
epistles that offered lessons on religious teachings for new churches being set up in
Asia. Since then, several major waves of technological
advances have each promised to revolutionize education, from radio and television, to DVDs
and laserdiscs. In fact, as far back as 1913, Thomas Edison
predicted, “Books will soon be obsolete in the schools… It is possible to teach every branch of human
knowledge with the motion picture. Our school system will be completely changed
in the next ten years.” Of course, you know that didn’t happen. But distributing educational materials in
formats like video has become more and more popular. Before we discuss what educational technology
research can do for you, there are some simple things research has shown you can do, while
watching an educational video like this one, to significantly increase what you learn and
retain. First, video is naturally adjustable, so make
sure the pacing is right for you, by using the video speed controls. On YouTube, you can do that in the right hand
corner of the screen. You should be able to understand the video
and have enough time to reflect on the content. Second, pause! You learn more if you stop the video at the
difficult parts. When you do, ask yourself questions about
what you’ve watched, and see if you can answer. Or ask yourself questions about what might
be coming up next, and then play the video to see if you’re right. Third, try any examples or exercises that
are presented in the video on your own. Even if you aren’t a programmer, write pseudocode
on paper, and maybe even give coding a try. Active learning techniques like these have
been shown to increase learning by a factor of ten. And if you want more information like this
– we’ve got a whole course on it here. The idea of video as a way to spread quality
education has appealed to a lot of people over the last century. What’s just the latest incarnation of this
idea came in the form of Massive Open Online Courses, or MOOCs. In fact, the New York Times declared 2012
the Year of the MOOC! A lot of the early forms were just videos
of lectures from famous professors. But for a while, some people thought this
might mean the end of universities as we know them. Whether you were worried about this idea or
excited by it, that future also hasn’t really come to pass and most of the hype has dissipated. This is probably mostly because when you try
to scale up learning using technology to include millions of students simultaneously with small
numbers of instructional staff – or even none – you run into a lot of problems. Fortunately, these problems have intrigued
computer scientists and more specifically, educational technologists, who are finding
ways to solve them. For example, effective learning involves getting
timely and relevant feedback – but how do you give good feedback when you have millions
of learners and only one teacher? For that matter, how does a teacher grade
a million assignments? Solving many of these problems means creating
hybrid, human-technology systems. A useful, but controversial insight, was that
students could be a great resource to give each other feedback. Unfortunately, they’re often pretty bad
at doing so – they’re neither experts in the subject matter, nor teachers. However, we can support their efforts with
technology. Like, by using algorithms, we can match perfect
learning partners together, out of potentially millions of groupings. Also, parts of the grading can be done with
automated systems while humans do the rest. For instance, computer algorithms that grade
the writing portions of the SATs have been found to be just as accurate as humans hired
to grade them by hand. Other algorithms are being developed that
provide personalized learning experiences, much like Netflix’s personalized movie recommendations or Google’s personalized search results. To achieve this, the software needs to understand
what a learner knows and doesn’t know. With that understanding, the software can
present the right material, at the right time, to give each particular learner practice on
the things that are hardest for them, rather than what they’re already good at. Such systems – most often powered by Artificial
Intelligence – are broadly called Intelligent Tutoring Systems. Let’s break down a hypothetical system that
follows common conventions. So, imagine a student is working on this algebra
problem in our hypothetical tutoring software. The correct next step to solve it, is to subtract
both sides by 7. The knowledge required to do this step can
be represented by something called a production rule. These describe procedures as IF-THEN statements. The pseudo code of a production rule for this
step would say if there is a constant on the same side as the variable, then subtract that
constant from both sides. The cool thing about production rules is that
they can also be used to represent common mistakes a student might make. These production rules are called “buggy
rules”. For example, instead of subtracting the constant,
the student might mistakenly try to subtract the coefficient. No can do! It’s totally possible that multiple competing
production rules are triggered after a student completes a step – it may not be entirely
clear what misconception has led to a student’s answer. So, production rules are combined with an
algorithm that selects the most likely one. That way, the student can be given a helpful
piece of feedback. These production rules, and the selection
algorithm, combine to form what’s called a Domain Model, which is a formal representation
of the knowledge, procedures and skills of a particular discipline – like algebra. Domain models can be used to assist learners
on any individual problem, but they’re insufficient for helping learners move through a whole
curriculum because they don’t track any progress over time. For that, intelligent tutoring systems build
and maintain a student model – one that tracks, among other things, what production
rules a student has mastered, and where they still need practice. This is exactly what we need to properly personalize
the tutor. That doesn’t sound so hard, but it’s actually
a big challenge to figure out what a student knows and doesn’t know based only on their
answers to problems. A common technique for figuring this out is
Bayesian knowledge tracing. The algorithm treats student knowledge as
a set of latent variables, which are variables whose true value is hidden from an outside
observer, like our software. This is also true in the physical world, where
a teacher would not know for certain whether a student knows something completely. Instead, they might probe that knowledge using a test to see if the student gets the right answer. Similarly, Bayesian knowledge tracing updates
its estimate of the students’ knowledge by observing the correctness of each interaction
using that skill. To do this, the software maintains four probabilities.. First is the probability that a student has
learned how to do a particular skill. For example, the skill of subtracting constants
from both sides of an algebraic equation. Let’s say our student correctly subtracts
both sides by 7. Because she got the problem correct, we might
assume she knows how to do this step. But there’s also the possibility that the
student got it correct by accident, and doesn’t actually understand how to solve the problem. This is the probability of guess. Similarly, if the student gets it wrong, you
might assume that she doesn’t know how to do the step. But, there’s also the possibility that she
knows it, but made a careless error or other slip-up. This is called the probability of slip. The last probability that Bayesian knowledge
tracing calculates is the probability that the student started off the problem not knowing
how to do the step, but learned how to do it as a result of working through the problem. This is called the probability of transit. These four probabilities are used in a set
of equations that update the student model, keeping a running assessment for each skill
the student is supposed to know. The first equation asks: what’s the probability
that the student has learned a particular skill which takes into account the probability
that it was already learned previously and the probability of transit. Like a teacher, our estimate of this probability that it was already learned previously depends on whether we observe a student getting a question correct or incorrect, and so we have these two equations to pick from. After we compute the right value, we plug
it into our first equation, updating the probability that a student has learned a particular skill,
which then gets stored in their student model. Although there are other approaches, intelligent
tutoring systems often use Bayesian knowledge tracing to support what’s called mastery
learning, where students practice skills, until they’re deeply understood. To do this most efficiently, the software
selects the best problems to present to the student to achieve mastery, what’s called
adaptive sequencing, which is one form of personalization. But, our example is still just dealing with
data from one student. Internet-connected educational apps or sites now allow teachers and researchers the ability to collect data from millions of learners. From that data, we can discover things like
common pitfalls and where students get frustrated. Beyond student responses to questions, this
can be done by looking at how long they pause before entering an answer, where they speed
up a video, and how they interact with other students on discussion forums. This field is called Educational Data Mining,
and it has the ability to use all those facepalms and “ah ha” moments to help improve personalized
learning in the future. Speaking of the future, educational technologists
have often drawn inspiration for their innovations from science fiction. In particular, many researchers were inspired
by the future envisioned in the book “The Diamond Age” by Neal Stephenson. It describes a young girl who learns from
a book that has a set of virtual agents who interact with her in natural language acting
as coaches, teachers, and mentors who grow and change with her as she grows up. They can detect what she knows and how’s
she’s feeling, and give just the right feedback and support to help her learn. Today, there are non-science-fiction researchers,
such as Justine Cassell, crafting pedagogical virtual agents that can “exhibit the verbal
and bodily behaviors found in conversation among humans, and in doing so, build trust, rapport and even friendship with their human students.” Maybe Crash Course in 2040 will have a little John Green A.I. that lives on your iPhone 30. Educational technology and devices are now
moving off of laptop and desktop computers, and onto huge tabletop surfaces, where students
can collaborate in groups, and also tiny mobile devices, where students can learn on the go. Virtual reality and augmented reality are
also getting people excited and enabling new educational experiences for learners – diving
deep under the oceans, exploring outer space, traveling through the human body, or interacting
with cultures they might never encounter in their real lives. If we look far into the future, educational
interfaces might disappear entirely, and instead happen through direct brain learning, where
people can be uploaded with new skills, directly into their brains. This might seem really far fetched, but scientists
are making inroads already – such as detecting whether someone knows something just from
their brain signals. That leads to an interesting question: if
we can download things INTO our brains, could we also upload the contents of our brains? We’ll explore that in our series finale
next week about the far future of computing. I’ll see you then.


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