Educational Technology: Crash Course Computer Science #39

Summary

In this Crash Course Computer Science episode, Carrie Anne explores the impact of computing technology on education, highlighting the vast amount of information available online. She discusses the importance of active learning strategies when consuming educational content and the challenges of scaling education with technology. The video delves into Intelligent Tutoring Systems, production rules, and Bayesian knowledge tracing, illustrating how they personalize learning. It also touches on the future of educational technology, including virtual reality, augmented reality, and the potential for direct brain learning.

Takeaways

• 🌐 The internet has dramatically increased the availability of information, with 1.3 billion websites and platforms like Wikipedia and YouTube facilitating access to vast amounts of content.
• 🔍 Google handles billions of searches daily, reflecting how people rely on technology to find information.
• 📱 Information is increasingly accessible through smartphones, allowing for learning anytime and anywhere.
• 🎓 Crash Course emphasizes the importance of interactive learning, including in-class discussions and hands-on experiences, alongside educational technology.
• 📚 Historically, technology has been used to support education, from early cave paintings to modern machine learning systems.
• 📈 The concept of distance learning has evolved with technology, with St. Paul's epistles being an early example of educational technology.
• 📊 Thomas Edison once predicted that motion pictures would replace books in education, highlighting the long-standing belief in the potential of technology to transform learning.
• 📈 Active learning techniques, such as pausing videos to reflect and asking questions, can significantly enhance learning outcomes.
• 🏫 MOOCs were once seen as a revolutionary form of education, but the hype has since diminished due to scaling challenges.
• 🤖 Intelligent Tutoring Systems use AI to provide personalized learning experiences, adapting to individual learners' needs.
• 🧠 Educational Data Mining analyzes data from millions of learners to identify common challenges and improve educational strategies.
• 🚀 The future of educational technology may include more immersive experiences like virtual reality and direct brain learning.

Q & A

How many websites are currently on the internet according to the script?

There are currently 1.3 billion websites on the internet.

What does the script say about the educational value of videos on platforms like YouTube?

The script suggests that a large percentage of video views on platforms like YouTube could be considered educational.

What is the significance of interactive in-class learning and hands-on experiences according to the script?

The script emphasizes that interactive in-class learning, directed conversations, and hands-on experiences are powerful tools for learning.

What role does the script suggest that educational technology plays in supporting learning?

The script suggests that educational technology has an additive power in supporting learning both inside and outside the classroom.

What was Thomas Edison's prediction about the future of books in schools as mentioned in the script?

Thomas Edison predicted that 'Books will soon be obsolete in the schools... It is possible to teach every branch of human knowledge with the motion picture.'

How does the script describe the use of video speed controls while watching educational videos?

The script advises using video speed controls to adjust the pacing to a level that allows understanding the content and reflecting on it.

What is the role of Massive Open Online Courses (MOOCs) in the context of the script?

MOOCs are described as the latest incarnation of spreading quality education through video, with the New York Times declaring 2012 the Year of the MOOC.

How does the script address the challenges of providing feedback and grading in large-scale online learning?

The script discusses the creation of hybrid human-technology systems to address challenges such as giving feedback and grading assignments for millions of learners.

What is an Intelligent Tutoring System as described in the script?

An Intelligent Tutoring System is a system powered by Artificial Intelligence that uses production rules and algorithms to provide personalized learning experiences.

What is the purpose of a Domain Model in Intelligent Tutoring Systems according to the script?

A Domain Model is a formal representation of the knowledge, procedures, and skills of a particular discipline, used to assist learners on individual problems.

How does Bayesian knowledge tracing work in the context of Intelligent Tutoring Systems as explained in the script?

Bayesian knowledge tracing is used to update the estimate of a student's knowledge by observing the correctness of each interaction using that skill, considering probabilities of learning, guessing, slipping, and transit.

Outlines

00:00

🌐 The Power of Educational Technology

Carrie Anne introduces Crash Course Computer Science and emphasizes the transformative impact of computing technology on information access. With 1.3 billion websites and millions of educational resources on platforms like Wikipedia and YouTube, information is readily available. However, accessibility does not equate to learning. Crash Course advocates for a blend of in-class and educational technology to enhance the learning experience. The script delves into the historical progression of educational technology, from St. Paul's epistles to modern digital formats, and discusses the challenges and potential of Massive Open Online Courses (MOOCs). It also offers practical strategies for viewers to maximize learning from educational videos, such as adjusting playback speed and actively engaging with the content.

05:03

🤖 Intelligent Tutoring Systems and Personalized Learning

The script explains Intelligent Tutoring Systems (ITS) that use Artificial Intelligence to provide personalized learning experiences. It outlines a hypothetical algebra tutoring scenario where production rules, represented as IF-THEN statements, guide students and address common mistakes through 'buggy rules'. The combination of these rules and an algorithm forms a Domain Model, which represents the knowledge of a discipline. ITS also maintains a student model to track progress over time, using Bayesian knowledge tracing to estimate student knowledge and adapt the learning process. The paragraph discusses how ITS can present problems for mastery learning and how data from millions of learners can be analyzed through Educational Data Mining to improve future learning experiences.

10:08

🚀 The Future of Educational Technology

The final paragraph envisions the future of educational technology, moving beyond traditional computers to interactive tabletops, mobile devices, and immersive technologies like virtual and augmented reality. It speculates on the potential for direct brain learning and the ethical and practical implications of such advancements. The script also references science fiction as an inspiration for educational technologists and mentions the development of pedagogical virtual agents that can build relationships with students, enhancing the learning experience.

Keywords

💡Information

Information refers to the data, facts, or knowledge that is communicated or received concerning a particular subject or matter. In the context of the video, information is highlighted as a dramatically changed aspect of society due to computing technology. The video mentions the vast amount of information available on the internet, such as the number of websites and Wikipedia articles, emphasizing the role of technology in the creation and dissemination of information.

💡Educational Technology

Educational Technology encompasses the use of technology to enhance or facilitate learning and teaching processes. The video discusses how educational technology, from simple tools like paper and pencil to complex systems like intelligent tutoring systems, has been a driver of educational advancement. It also touches on the additive power of educational technology both inside and outside the classroom.

💡Active Learning

Active Learning is a teaching and learning strategy that involves students in doing things and reflecting on what they are doing. The video script suggests using active learning techniques, such as pausing videos to ask and answer questions or trying examples presented, to significantly increase learning and retention. This approach is contrasted with passive consumption of information.

💡MOOCs (Massive Open Online Courses)

MOOCs are online courses aimed at large-scale interactive participation and open access via the web. The video mentions MOOCs as a recent incarnation of educational technology, which gained significant attention in 2012. MOOCs are seen as a way to provide educational content to a massive number of students, often through video lectures from renowned professors.

💡Intelligent Tutoring Systems

Intelligent Tutoring Systems (ITS) are computer systems that provide personalized tutoring or instruction to individual students. The video explains how ITS use production rules and student models to adapt to a student's learning needs, providing feedback and personalized content. ITS are powered by AI and are designed to simulate the role of a human tutor.

💡Production Rules

Production Rules are a formal representation of knowledge or procedures, often used in expert systems and ITS. In the video, production rules are described as IF-THEN statements that guide the learning process. They can represent both correct steps and common mistakes, helping the tutoring system to provide appropriate feedback to students.

💡Student Model

A Student Model is a representation within an ITS that tracks a student's knowledge, skills, and learning progress over time. The video explains that student models are essential for personalizing the learning experience, as they allow the system to adapt to the individual learner's needs and track their progress through the curriculum.

💡Bayesian Knowledge Tracing

Bayesian Knowledge Tracing is a statistical method used to estimate a student's knowledge state based on their observed performance. The video describes how Bayesian knowledge tracing is used in ITS to update the student model by calculating probabilities of learning, guessing, slipping, and transit, which help in assessing a student's mastery of skills.

💡Adaptive Sequencing

Adaptive Sequencing is a method of selecting and presenting educational content in a sequence that is tailored to the individual learner's needs. The video mentions adaptive sequencing as a form of personalization in ITS, where the system selects the best problems for a student to achieve mastery of a skill.

💡Educational Data Mining

Educational Data Mining is the application of data mining techniques to analyze educational data and discover patterns that can improve educational practices. The video suggests that educational data mining can help in understanding common student pitfalls and areas of difficulty by analyzing data from millions of learners, such as response times and interactions.

💡Virtual Reality (VR) and Augmented Reality (AR)

Virtual Reality and Augmented Reality are technologies that create immersive experiences by simulating or enhancing real-world environments. The video points to VR and AR as exciting developments in educational technology that can provide new experiences for learners, such as exploring the human body or outer space, which might not be possible in traditional learning settings.

Highlights

The creation and widespread availability of information is one of the most dramatic changes enabled by computing technology.

There are currently 1.3 billion websites on the internet.

Wikipedia alone has five million English language articles.

Google serves up four billion searches every day to access information.

Every minute, 3.5 million videos are viewed on YouTube.

Educational technology can be accessed with just a few taps on a smartphone.

Crash Course believes in the additive power of educational technology both inside and outside the classroom.

Educational technology has been supporting education for millennia, from cave paintings to modern systems.

Thomas Edison predicted that books would become obsolete in schools due to motion pictures.

Massive Open Online Courses (MOOCs) were a significant development in educational technology.

Intelligent Tutoring Systems use production rules and algorithms to provide personalized feedback.

Production rules can represent both correct procedures and common student mistakes.

Bayesian knowledge tracing is a technique used to estimate student knowledge based on their interactions.

Educational Data Mining uses data from millions of learners to discover common pitfalls and improve learning experiences.

The future of educational technology may include pedagogical virtual agents that build trust and rapport with students.

Educational technology is moving from laptops to tabletop surfaces and mobile devices for collaborative and on-the-go learning.

Virtual and augmented reality are enabling new educational experiences, such as exploring the ocean or the human body.

The far future of computing might involve direct brain learning, where new skills can be uploaded directly into the brain.

Transcripts

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.