YouTube Test Generation

Fascinating generation  self teaching and testing with AI quizzes ....

YouTube tests AI-generated quizzes on educational videos

In TechCrunch Lauren Forristal@laurenforristal / 1:08 PM EDT•July 7, 2023

YouTube is experimenting with AI-generated quizzes on its mobile app for iOS and Android devices, which are designed to help viewers learn more about a subject featured in an educational video. The feature will also help the video-sharing platform get a better understanding of how well each video covers a certain topic.

The AI-generated quizzes, which YouTube noted on its experiments page yesterday, are rolling out globally to a small percentage of users that watch “a few” educational videos, the company wrote. The quiz feature is only available for a select portion of English-language content, which will appear on the home feed as links under recently watched videos.

Not all of YouTube’s experiments make it to the platform, so it will be interesting to see if this one sticks around. We’re not sure how many people — especially if they’re no longer in school — want to take a quiz while they scroll through videos. ... '

Harvard's New Computer Science Teacher is a Chatbot

Once people get used to using particular chat framework, and comfortable with it use, they can be ready for any kind of training.   See my previous post on this where Bing makes the case. 

Harvard's New Computer Science Teacher is a Chatbot

By The Independent (U.K.),  June 23, 2023

Harvard University plans to use an AI chatbot similar to ChatGPT as an instructor on its flagship coding course.

Students enrolled on the Computer Science 50: Introduction to Computer Science (CS50) programme will be encouraged to use the artificial intelligence tool when classes begin in September.

The AI teacher will likely be based on OpenAI's GPT 3.5 or GPT 4 models, according to course instructors.

From The Independent (U.K.) 

Thinking Step by Step

Makes sense don't have the full article as yet ...

AIs become smarter if you tell them to think step by step

Artificial intelligence models can outperform humans at tasks AIs normally struggle with if they are told to think a certain way, but it doesn’t help them grasp sarcasm  ... ' 

TECHNOLOGY , 26 October 2022.  By Chris Stokel-Walker  in  NewScientist

Artificial intelligence can get better at tasks if told to think about things in steps

Telling artificial intelligence models to “think” step by step when carrying out a task can improve their performance so much that they can outperform humans at jobs AIs usually struggle with.

Using the phrase “let’s think step by step” to cajole AIs into taking more logical decisions was first suggested in a May study presented at a computational neuroscience conference. Such “chain-of-thought” prompting encourages these models, which include GPT-3, a text-generating AI developed by ... '   

FarmTubers Are Out there

Brought to my attention, I was introduced to XTubing maybe a year ago, and am astounded at the width and depth of most everything being delivered.   Teaching your customer efficiently?

What is a Farm Tuber?

I’m interested in everything. Not very deeply, but very broadly. One of the things that has caught my attention is the rise of FarmTubers in the YouTube ecosystem.

What is FarmTube?

Farmers have been taking to YouTube to help educate the public as to what goes into the day to day lives of farmers. The sheer breadth of farmers that are producing content is amazing: row croppers in the Midwest, legume and wheat farmers in the upper plains, cattle / dairy / pig farmers all over, sugar cane and peanuts and cotton in the south – there is a FarmTube channel for everyone. They do an amazing job at articulating what they do and the challenges they face.

So why am I interested in them?

There are two things that make me keep coming back to FarmTube. I come from a farming family. My mother’s family raised sheep and wheat in the mid-north of South Australia for years, and I spent summers at the farm “helping” out. Ask me how I know sheep bound out of the shearing shed once they’ve been liberated from their fleece, I’ll happily share that story with you.

That’s the personal reason. The research reason is that the guiding principle of the farmer is always the bottom line. Is this new practice, this new machinery, this new seed, is it going to contribute to my bottom line? Will it increase yield? Can it reduce costs? Am I making it easier to do my job?

And that’s where FarmTube comes into the picture. I see this over and over again as the farmers broadcasting (narrowcasting?) their days demonstrate the same principle, and particularly when it comes to automation. Farm automation is incremental: it starts with mechanization, and has slowly moved its way towards automation.

The advent of the tractor is about mechanization. Automation of tasks gave farmers section control in their sprayers, GPS-driven planting / spraying, and really the ability to take either tasks that were routine or they were difficult to get right, and turn them into an automated task. What’s been very exciting to see has been the introduction, albeit incrementally, of autonomy into the farming realm. While autonomous tractors that combine GPS and automation are the big splash, the introduction of autonomous vehicles such as sprayers and drones offer insight into the future of farming.

The thing about autonomy is that it allows the farmer dual benefits.

It improves the efficiency of the activity – the farmer doesn’t need to be there to be doing the task at hand, which means that they can do things that are more pressing or important for them (farmers have families too!)

It unlocks improvements to practices that weren’t possible before – if I can rely on an autonomous vehicle to spray crops, can I rely on it to remove weeds? To spray at the most optimal time for the crop, rather than the convenient time for the human? Can I improve the quality or yield of the crop by being able to use non-disruptive application / management techniques?

To be sure, it introduces yet more technology dependence from a traditional human based activity, and that’s not without its costs. But watch a FarmTuber thinking past the potential problems to the opportunities and you’ll see what I’m talking about.     .... 

College in a MetaVersity?

Change is coming,  but the metaverse is also not ready.     Based on our own experiences,  there is much yet to be done to prepare content, delivery and users of training and interaction in this way.  Meta-reality is still hard.  

College in the Metaverse Is Here. Is Higher Ed Ready?

By Inside Higher Ed, August 8, 2022

This fall, students at 10 universities will attend metaversities, immersive virtual reality platforms where remote faculty and students don virtual reality headsets and meet synchronously as they would on a physical campus.

This fall, students at 10 U.S. universities will attend metaversities, virtual reality (VR) platforms where educators and students wear VR headsets and interact synchronously.

Advocates claim VR increases student engagement, achievement, and satisfaction, but some scholars worry about metaversity technology licensors putting revenues above academic freedom, exploiting students' data, or replicating biased narratives in VR.  Many such challenges can be addressed by aligning educational best practices, commercial incentives, and political resolution, while students may find metaversities better than engagement via remote, two-dimensional screens.

Last year, Morehouse College tested a proof-of-concept metaversity with courses in world history, biology, and chemistry, and participating world history students improved their grade point averages 10% compared to grades in the same class facilitated through Zoom and face-to-face. ... 

The University of Massachusetts' Nir Eisikovits believes on-campus education will ultimately supplement metaversities, not vice-versa. ... 

From Inside Higher Ed

View Full Article   

AI for Handwriting Skills

 Always looking for things that will look for patterns that need to be selectively taught.  Here a good example, teaching children to improve handwriting.  Other examples?  Coding?  AI Management?

Innovative application helps students learn to write

EPFL startup School Rebound has developed a revolutionary application that uses artificial intelligence to help students improve their handwriting in a fun and personalized way. Nearly 15,000 students in Switzerland, France, and Italy are already putting it to work.

Handwriting problems affect nearly 25% of children aged 5 to 12. These problems, if not managed early on, can negatively impact them throughout their school years. EPFL startup School Rebound has provided a concrete solution to this problem by developing an application that uses tablets and artificial intelligence to better detect potential handwriting problems and support children as they learn. The subscription app is called Dynamilis, and can be used by all children learning to write – with difficulties or without – at home or at school. Dynamilis has already been downloaded more than 10,000 times and will be released soon in England and the United States.

While Dynamilis was free during its testing phase, it switched to a subscription model this past March. After a free trial week, parents, therapists, and schools are offered a monthly or annual subscription. Costs depend on the number of children using the application.

L. Boatto, A. Peguet, T. Asselborn, S. Viquerat, P. Dillenbourg © 2022 Dynamilis / Sven Viquerat

Strengths and weaknesses at a glance

School Rebound was founded in 2021 based on the research done by CEO Thibault Asselborn for his PhD thesis at EPFL’s Computer-Human Interaction in Learning and Instruction Laboratory (CHILI). “Some children have handwriting problems that may stay hidden for months. Because the problems are not obvious, parents and teachers may hesitate to consult specialists,” says Asselborn. “During this time, the students can accumulate learning difficulties as these handwriting problems monopolize their concentration and prevent them from developing other skills. This could cause students to lose confidence and develop significant educational blocks.”

As part of his PhD research, Asselborn helped develop an algorithm that can rapidly analyze a child’s handwriting. The child only has to write for 30 seconds on an iPad with an Apple Pencil for the application to establish their handwriting profile. “The tests we ran in the lab lasted about 20 minutes, but they didn’t take certain factors into account, which may reduce the accuracy of the analyses,” says Asselborn. Dynamilis evaluates dynamic aspects of handwriting that the human eye can’t see such as stability, pressure, speed, and angle. “Our app gives detailed analyses about the motor aspects of handwriting. This can give parents an initial indication before determining whether their child has difficulties with handwriting and, if so, to what degree. If the difficulties surpass a certain level, they’re recommended to go see a specialist.”

Learning dressed up like a game

Based on a child’s handwriting profile, the app recommends personalized activities to practice the fundamental aspects of handwriting – all while playing games. “The in-app tests don’t look like medical tests in the strictest sense,” says Asselborn. “It’s important to have an air of fun to avoid making children feel like they’re taking an exam, which can put them on edge.” The playful aspect during the testing phase is crucial for the School Rebound team and their Chairman, Pierre Dillenbourg, also head of CHILI. “While we were developing Dynamilis, our aim was to help children,” says Dillenbourg. “To do that, we knew we had to go beyond a simple handwriting-analysis program and give them activities to support learning and, for the more severe cases, correction. Games are an effective solution for children who are having problems in school because of their issues with handwriting.”  ... ' 

Proposed Easier Way to teach Robot Skills

Work out of MIT,  of particular efficiency and relearning interest

ACM TECHNEWS

An Easier Way to Teach Robots New Skills, By MIT News, April 28, 2022

Massachusetts Institute of Technology (MIT) researchers have developed a machine learning method that enables robots to acquire new skills using a handful of human examples.  The technique allows a robot to pick up and place objects in never-before-seen random poses, within about 15 minutes.

It involves a Neural Descriptor Field neural network designed to reconstruct the three-dimensional geometry of objects, whose knowledge the system taps to grasp new objects similar to those seen in demonstrations.  The researchers used simulations and a robotic arm to show that the system can manipulate never-before-seen mugs, bowls, and bottles arranged randomly, using just 10 examples.

"Our major contribution is the general ability to much more efficiently provide new skills to robots that need to operate in more unstructured environments where there could be a lot of variability," said MIT's Anthony Simeonov. .... 

The new system could allow a human to reprogram a robot to grasp never-before-seen objects, presented in random poses, in about 15 minutes. ... ' 

MIT news  

Teaching in Person and Online

 Been reading the book below, so far looks very good and to the point in these times.  Have not finished, but good so far.  The technical specifics may get out of date, but the rest should hold up. 

The Hybrid Teacher, Using Technology to teach in Person and OnLine,  by Emma Pass

A practical educational technology resource for educators teaching remotely or in the classroom

The most effective hybrid teachers are those that have a vast knowledge of instructional strategies, technologies, tools, and resources, and can masterfully build meaningful relationships with students in person and through a screen. The Hybrid Teacher: Using Technology to Teach in Person and Online will teach educators to leverage the technology they have access to both in their traditional brick-and-mortar classrooms and in remote learning environments, including established online and hybrid schools; emergency response models for pandemics, natural disasters; rural education; and connecting with students who can't make it to school.    ( Do note that Emma is a middle school teacher, so some of her examples are from that realm,  but still I think useful.)

Many of us had to adapt to online teaching during the COVID-19 pandemic, but we still need resources for optimizing our instruction and becoming the best teachers we can be. This book is a practical guide for teachers who want to prepare for current and future remote instruction or leverage the best practices of remote instruction and EdTech tools to bring back to their brick-and-mortar classrooms. You'll learn about the impact of social and economic differences on classroom technology, and you'll find strategies and advice for maximizing success in each situation.  From Amazon

Can or Will AI Destroy Education?

Not till it can become very adaptive to many contexts.  Replacing if it does better than teachers perhaps.

Many contexts are at play. Selective humans are still the most adaptive teachers. Great Question.

Will AI Destroy Education?, By Moshe Y. Vardi

Communications of the ACM, January 2022, Vol. 65 No. 1, Page 7  10.1145/3501359

Artificial intelligence is everywhere these days. The National AI Initiative Act became law in the U.S. on Jan. 1, 2021, aiming "to accelerate AI research and application for the Nation's economic prosperity and national security." The U.S. National Science Foundation launched in 2020 several AI Research Institutes to push forward the frontiers of artificial intelligence. One of the themes of this research initiative is "AI-Augmented Learning."

This quest to improve education via technology reminds me of "Profession;" a 1957 science-fiction story by Isaac Asimov. The story takes place in the 66th century, where children are educated via direct computer-brain interface, a process known as "taping." At the end of the story, the protagonist realizes that, unlike taping, reading books produces "men and women with capacity for original thought." This 1957 warning—perhaps in response to a U.S. push for educational technology following the Sputnik shock—against a techno-solutionist approach to education is probably more relevant today than it was then. After all, 15 years ago Facebook had the beautiful sounding goal to make "the world more open and connected." In 2021, a massive leakage of internal documents revealed the company knew of the serious societal harm caused by its technology but ignored it in the pursuit of profits.

The previous techno-solutionist wave aimed at education surged in the fall of 2011 when approximately 450,000 students signed up for three computer-science courses offered by Stanford University, launching the MOOC ("massive open online course") tsunami, with the lofty goal of "reaching the quality of individual tutoring." In 2012, I authored a Communications column, "Will MOOCs Destroy Academia?"a I argued the enormous buzz about MOOCs is not due to the technology's intrinsic educational value, but due to the seductive possibilities of lower costs. As we now know, MOOCs did not destroy academia, probably because of their low educational value. But more than decade after the 2008-2009 recession, state spending on public higher education remains well below historical levels in the U.S. Yet MOOCs have become a fixture in U.S. higher education; my own institution run dozens of them. While the availability of free or almost-free academic courses is, of course, beneficial to students, such MOOC-based programs make nominal profits only by ignoring the true cost of faculty labor involved in producing and running MOOCs.

AI-augmented learning also seems to be a technology in search of a problem. The drive comes from the tech industry, for whom AI is a new "shiny hammer in search of nails." The goal of NSF-funded AI Institutes in this area is "AI-driven innovations to radically improve human learning and education." But we do not know what needs to be improved, so how we will know that we have succeeded? I see many big questions and few answers: What problems are we trying to solve? How do we measure improvements? Are we trying to improve teaching or replace teachers? What are the drivers? Societal need? Technology? Money? Finally, since AI Ethics is a hot topic these days, is it ethical to deploy AI in education without a clear understanding of its benefits? Using AI in education is inevitable, I suspect, and it can be used for good, I hope, but these questions must be addressed.

How Effective are top Education Apps?

 Good feedback as to what works in the new contexts.

Top Educational Apps for Children Might Not Be as Beneficial as Promised

Penn State News, Katie Bohn, May 11, 2021

An analysis of the most frequently downloaded educational apps for kids by a team of researchers led by the Pennsylvania State University Brandywine found such apps may not provide high-quality educational experiences. The researchers used previous research on the pillars of learning to develop criteria for the assessment of the top 100 children's educational apps from the Google Play and Apple apps stores, among others. After the apps were scored from 0 (low) to 3 (high) for each pillar of learning, the researchers found a score of 1 was most common for each app with regard to all four pillars. The University of Michigan’s Marisa Meyer said, “If app designers intend to engender and advertise educational gains through use of their apps, we recommend collaborating with child development experts in order to develop apps rooted in the ways children learn most effectively. .... '

Advances for Reinforcement Learning

Very interesting,   The very first para below does a good job of  'why' this could change RL methods,  the rest of the article then carries on more technically.  Supporting images are at the link . Is this a big deal?  Humans determine they have reached a solution by comparing it to something they perceive is 'correct'.  Like an image of correctness.   Considering how this would be most useful.    Could we teach a system to learn to learn patterns of correctness? 

Recursive Classification: Replacing Rewards with Examples in RL

Wednesday, March 24, 2021    Posted by Benjamin Eysenbach, Student Researcher, Google Research

A general goal of robotics research is to design systems that can assist in a variety of tasks that can potentially improve daily life. Most reinforcement learning algorithms for teaching agents to perform new tasks require a reward function, which provides positive feedback to the agent for taking actions that lead to good outcomes. However, actually specifying these reward functions can be quite tedious and can be very difficult to define for situations without a clear objective, such as whether a room is clean or if a door is sufficiently shut. Even for tasks that are easy to describe, actually measuring whether the task has been solved can be difficult and may require adding many sensors to a robot's environment.

Alternatively, training a model using examples, called example-based control, has the potential to overcome the limitations of approaches that rely on traditional reward functions. This new problem statement is most similar to prior methods based on "success detectors", and efficient algorithms for example-based control could enable non-expert users to teach robots to perform new tasks, without the need for coding expertise, knowledge of reward function design, or the installation of environmental sensors.

In "Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification," we propose a machine learning algorithm for teaching agents how to solve new tasks by providing examples of success (e.g., if “success” examples show a nail embedded into a wall, the agent will learn to pick up a hammer and knock nails into the wall). This algorithm, recursive classification of examples (RCE), does not rely on hand-crafted reward functions, distance functions, or features, but rather learns to solve tasks directly from data, requiring the agent to learn how to solve the entire task by itself, without requiring examples of any intermediate states. Using a version of temporal difference learning — similar to Q-learning, but replacing the typical reward function term using only examples of success — RCE outperforms prior approaches based on imitation learning on simulated robotics tasks. Coupled with theoretical guarantees similar to those for reward-based learning, the proposed method offers a user-friendly alternative for teaching robots new tasks.  ... " 

Technology Tailoring in Education

Always an interesting question.  The nature of instruction.   In theory it could be precisely tailored to every student and by testing it could be altered in real time to get better results.  Personalized to get the best possible results for each student.  How practical is this,  how will it alter the business of education?   How much is the human touch needed?  

Using Technology to Tailor Lessons to Each Student,  The New York Times,  Janet Morrisey

Computer algorithms and machine learning are helping to personalize instruction to individual students, a trend experts say is long overdue. Some think the Covid-19 pandemic is accelerating U.S. schools' migration to personalized learning programs; American Federation of Teachers president Randi Weingarten said, "Innovations like this can help educators meet students where they are and address their individual needs." Companies like New Classrooms are striving to advance personalized learning; the nonprofit's Teach to One 360 algorithm gives each student access to multigrade curriculums and skills, in order to better address learning gaps in those who are several grades behind. Other companies working aggressively on personalized learning solutions include Eureka Math, iReady, and Illustrative Mathematics.

Tailoring Intelligent Tutoring

Quite interesting approach, will like to see some measures about how it achieves teaching goals.

AI Enables Teachers to Rapidly Develop Intelligent Tutoring Systems
Carnegie Mellon University Human-Computer Interaction Institute
April 30, 2020

Carnegie Mellon University (CMU) researchers have created an artificial intelligence-based technique to enable educators to rapidly develop intelligent computerized tutoring systems. The teachers can teach the computer by demonstrating several ways to solve problems in a topic, and correcting the system if it responds erroneously. The system can not only solve the problems as it was trained to do, but also can generalize to solve all other problems in the topic in ways that the teacher did not demonstrate. The method employs a machine learning algorithm that models how students learn, and a user-friendly teaching interface that uses a "show-and-correct" process that is significantly easier than programming. CMU's Ken Koedinger said the technique may enable teachers to produce a 30-minute lesson in about half an hour, which he described as "a grand vision" among intelligent tutor developers. ... "

Games for Education

Will games work for this purpose?  Look forward to see a report on how well.  A very quick look at this makes me wonder, but look forward to seeing more details.  Gamification can provide a means of engagement, but must also have the best possible goals.    Thats why its very useful to emphasize recognized fundamentals in education.   Will this address these fundamentals?

 With Coronavirus Closing Schools, Here’s How Video Games are Helping Teachers
The Washington Post
By Elise Favis

The quarantine caused by the COVID-19 pandemic has prompted teachers to utilize popular video games like Assassin's Creed, Minecraft, and Roblox to conduct lessons on a range of topics. Game publishers are facilitating this trend by making their platforms as accessible as possible to educators during the crisis. In 2018, Ubisoft added a new mode to Assassin's Creed: Origins, which is set in ancient Egypt, called Discovery Tour. This mode lets players embark on guided tours through famous historical sites and cities. This mode was recently adapted for Assassin's Creed: Odyssey—set in ancient Greece—complete with additional content like quizzes. Minecraft also comes with an education mode, which Microsoft has made free for educators and students through June 2020 due to the pandemic. Roblox is a platform that lets players create their own video games from scratch. The company has partnered with more than 170 educators from 35 countries to discuss applications for the game. ... "

Assistant Telling Stories in the Classroom

Other examples of this out there?  I would rather have kids reading for themselves.   The basics still mean something.

Alexa in the classroom? Amazon’s voice assistant leads kids’ story time   By  Matt Day

The group of kids, aged 7 to 12, sat around a table, trying to follow along with the reading assignment. It was after lunch. Energy was high, attention spans short.

Nobody held books, though. And a teacher wasn’t reading.

Amazon.com Inc.’s Alexa was conducting story time, queuing up professional narrators to read the story aloud, quizzing the kids in its robot voice and offering hints when someone flubbed an answer to questions about “Davy Duck’s Grumpy Day.”

Voice software has colonized smartphones, car dashboards and the living room. If the technology follows the trail blazed by tablet and cloud computing, the next frontier may be the classroom. ... "

Considering Google's Teachable Machine

Was reminded of this as a means of demonstrating and playing with the idea.   Writing a short piece on its limitations now a few years later.  Thoughts?

Google created a fun way to learn about simple AI
"Teachable Machine" lives up to its name.
By Devindra Hardawar, @devindra in Engadget

You've probably heard the term "machine learning" quite a bit -- basically, it refers to training computers to learn without directly programming them. It's a particularly hot topic these days when it comes to AI, since machine learning is the best way to create artificial neural networks, which function similar to the human brain. To help us wrap our minds around these ideas, Google created Teachable Machine, a web tool that lets you create simple programs using your webcam. ..... "

https://teachablemachine.withgoogle.com/

How do Machines Learn?

Completely non technical description, a simple place to start from.   Though here I add its not only how do we teach machines, but also how do they apply that teaching.   And how much do we trust how they apply that teaching?  So we trust an electronic calculator or computer to add many  numbers to get the correct results.  But if we ask something like:  Predict future demand for this product.   Despite the amount of training we may have provided,  there is often complex context to be considered, and except in the simplest examples, no exact answer.    Still an imprecise answer can be useful.  (from a conversation yesterday)

Artificial Intelligence: Here’s What You Need To Know To Understand How Machines Learn in 7WData 

Artificial Intelligence: Here’s What You Need To Know To Understand How Machines Learn
From Jeopardy winners and Go masters to infamous advertising-related racial profiling, it would seem we have entered an era in which artificial intelligence developments are rapidly accelerating. But a fully sentient being whose electronic “brain” can fully engage in complex cognitive tasks using fair moral judgement remains, for now, beyond our capabilities.

Unfortunately, current developments are generating a general fear of what artificial intelligence could become in the future. Its representation in recent pop culture shows how cautious – and pessimistic – we are about the technology. The problem with fear is that it can be crippling and, at times, promote ignorance.

Learning the inner workings of artificial intelligence is an antidote to these worries. And this knowledge can facilitate both responsible and carefree engagement.

The core foundation of artificial intelligence is rooted in machine learning, which is an elegant and widely accessible tool. But to understand what machine learning means, we first need to examine how the pros of its potential absolutely outweigh its cons.

Simply put, machine learning refers to teaching computers how to analyse data for solving particular tasks through algorithms. For handwriting recognition, for example, classification algorithms are used to differentiate letters based on someone’s handwriting. Housing data sets, on the other hand, use regression algorithms to estimate in a quantifiable way the selling price of a given property. ... " 

Google Assignments and More

Here is something I was not expecting.  Google sets up a system called 'Assignments' which works in combination with Learning Management Systems (LMS).    Was unaware they were doing this.   Its workflow management for teachers.    I can not longer use this directly, but there were some times where it would have been very useful.  Can do the regular grading stuff ... and even some originality checking.   Templates for feedback.   Might it even be used for business information crowd sourcing or crowd checking?  Thinking that.

Google Assignments, your new grading companion
Instructors lose valuable time doing cumbersome tasks: writing the same comment on multiple essays, returning piles of paper assignments, and battling copy machine jams. These frustrations are most often felt by instructors with the highest teaching workloads and the least time. For the last five years, we’ve been building tools—like Classroom and Quizzes in Google Forms—to address these challenges. Now you can take advantage of these tools if you use a traditional Learning Management System (LMS). 

Assignments brings together the capabilities of Google Docs, Drive and Search into a new tool for collecting and grading student work. It helps you save time with streamlined assignment workflows, ensure student work is authentic with originality reports, and give constructive feedback with comment banks. You can use Assignments as a standalone tool and a companion to your LMS (no setup required!) or your school admin can integrate it with your LMS. Sign up today to try Assignments.

If you're one of the 40 million people using Classroom: you've got the best of Assignments already baked in, including our new originality reports. For everyone else, Assignments gives you access to these features as a compliment to your school’s LMS.   ... "

CSI Talk: Teaching Data Science

From last week's CSIG Talk, 

Speaker:  Dr. Mine Cetinkaya-Rundel, Duke University:
Suppose our goal is to educate the new generation of data scientists working on machine learning and artificial intelligence problems, and especially those who are not intimidated by learning new computing technologies. Where do we start their education at the college level? Which topics do we cover in their first course, and which topics do we postpone till later? In this talk, we propose an introductory data science course that places a heavy emphasis on exploratory data analysis and modeling as well as collaboration, effective communication of findings, and ethical considerations as a welcoming and horizon broadening introduction to the discipline at large.

Mine Çetinkaya-Rundel is the Director of Undergraduate Studies and Associate Professor of the Practice in the Department of Statistical Science at Duke University as well as Data Scientist and Professional Educator at RStudio. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera as well as numerous courses on DataCamp. ... 

Data Science in a Box: http://datasciencebox.org     

Good inclusion of data visualization, exploratory analysis, decision making, cautions to bias ...

Slides from talk.
Recording of talk.

Teaching Languages with AI Assistance

This week's CSIG talk featured Lewis Johnson of Alelo, a company that has an online AI assisted language teaching system.  Natural language voice language processing is used.  The assistance includes tracking progress, and analyzing how the teaching should be adapted.  The things that good teaching should do.  This essentially uses a chatbot to provide the teaching.  To 'Empower Learning and Teaching'.    When I taught at Columbia I had an excellent (human) teaching assistant,  here we can see how the best of machine assistance might look.   Would like to see how such a systems works in practice, and how it can add behavioral assistance to help learning.   Could there be a gaming of competition included?

The slides are here.     Recording of the talk here.