Tag Archives: learning

European Conference Preview: Breakout Sessions

ConferencePreview

At its core, Quantified Self is a community-driven effort to extract personal meaning from personal data. Our conferences reflect that by providing opportunities to learn what others are doing in their Quantified Self practice. Through our Show & Tell presentations you get to see first-hand accounts of how data is being collected and put to use in order to understand and investigate personal phenomena, but that’s not all our conference have to offer. In the spirit of collaborative learning we also schedule “Breakout Sessions” alongside our wonderful Show & Tell talks. These sessions, like all our conference programming, are developed and and facilitated by our wonderful attendees. Here’s a preview of just a few of the many fantastic Breakouts we have scheduled.

Title: The Self in Data
Breakout Leader: Sara Watson
Description: In my research on the QS community, I’ve found that we talk a lot about our technical requirements of data, and about how we want to use data. What we don’t often talk about is what it means to know ourselves through data. This breakout is an opportunity to discuss what data tells us about ourselves and how we relate to our data.

Title: On Sleep Tracking
Breakout Leader: Christel De Maeyer
Description: Does self-monitoring with devices like myZeo, Body Media create enough awareness and persuasion to change behavior and to maintain new habits? We would like to use this session to learn and share our experiences.

Title: Tracking breathing as a Unifying Experience
Breakout Leader: Danielle Roberts
Description: During this session we can exchange experiences on the tracking of respiration and tracking and visualising of life group data in general. You’ll have the opportunity to take part in a demo using custom breath tracking wearables and real time visualisation of breath data.

Title: Activity trackers
Breakout Leader: Michael Kazarnowicz
Description: We’ll take a look at the most common activity trackers on the market today. We will look at the trackers (maybe even play around with them hands-on) and compare the functions and the data you can get from them.

Title: QS as a Catalyst for Learning?
Breakout Leader: Hans de Zwart
Description: In this session we will explore whether quantifying yourself can act as a catalyst for learning. Can it speed up the learning process? Can it help us in achieving the holy grail of learning, a personalized tutor? What perverse effects might it have in the context of learning?

The Quantified Self European Conference will be held in Amsterdam on May 11th & 12th. Registration is now open. As with all our conferences our speakers are members of the community. We hope to see you there!

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Neil Bachelor on Quantifying Lifelong Learning

Neil Bachelor has been tracking his daily learning for the past two and a half years, with 3,200 discrete learning events. One of his motivations for this is to create a data-based CV that reflects his real work and learning habits. Neil uses Faviki to bookmark things he’s learned. In the video below, he describes his process, shows different visualizations of his learning, and explains the challenges he faces in managing so much data. (Filmed by the London QS Show&Tell meetup group.)

Lifelong Learning from Ken Snyder on Vimeo.

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QS Primer: Spaced Repetition and Learning

Many people think the Quantified Self mostly involves physical metrics: heart rate, sleep, diet, etc. but what about what goes on in our brains? Can we quantify that? There have been several inspiring Quantified Self talks about tracking learning and memory. This post will collect all them into one place, along with good resources for further exploration.

Memorization is only a small part of learning, but it in many circumstances it is unavoidable. There is an ideal moment to practice what you want to memorize. Practice too soon and you waste your time. Practice too late and you’ve forgotten the material and have to relearn it. The right time to practice is just at the moment you’re about to forget. If you are using a computer to practice, a spaced repetition program can predict when you are likely to forget an item, and schedule it on the right day.

In this graph, you can see how successive reminders change the shape of the forgetting curve, a pattern in our mental life that was first discovered by one of the great modern self-trackers, Hermann Ebbinghaus. With each well-timed practice, you extend the time before your next practice. Spaced repetition software tracks your practice history, and schedules each review at the right time.

Graph Illustrated Effect of Spaced Practice

Convenient tools to take advantage of fast memorization techniques have been around since Piotr Wozniak began developing his Supermemo software in the early 1980s. (I wrote a profile of Wozniak for Wired in 2008, which is cited in some of these talks.) Many of us in the Quantified Self use spaced repetition. We’ve put together this page to list resources, share experiences, and invite comments and questions. We hope you find it useful. If you do, please contribute some knowledge or questions to the comments.

Continue reading

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Jeremy Howard on Language Acquisition Performance

Jeremy Howard has been studying Chinese for the last two years. The method he uses is called spaced repetitive learning, found in SuperMemo and Anki, in which you prompt yourself to remember something just before you’re about to forget it. Jeremy wrote his own software to track his learning, including variables such as time of day, what he ate, when he slept, what activity he was doing, etc, and correlated it with his learning. In the video below, he shows some of his data and talks about what surprised him along the way. (Filmed by the Bay Area QS Show&Tell meetup group.)

Jeremy Howard – Language Acquisition Performance from Gary Wolf on Vimeo.

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Personal Informatics for Self-Regulated Learning

Ryan Muller is a PhD student at the Human-Computer Interaction Institute at Carnegie Mellon University. He researches principles for designing technology that stimulates our intrinsic drive for mastery-based learning.


Although the internet has fundamentally changed the speed and the scale of accessing information, that change has not seen such an impact in traditional forms of education. With popular new efforts like the video and exercise resource Khan Academy and online courses from Stanford (now spinning off into sites like Udemy and Coursera), people are talking about a revolution of personalized education – learners will be able to use computer-delivered content to learn at their own pace, whether supplementing schoolwork, developing job skills, or pursuing a hobby.

How personal informatics can help learning

There’s a problem here: learning on one’s own is not easy. Researchers have repeatedly found that people hold misconceptions about how to study well. For instance, rereading a passage gives the illusion of effective learning, but in reality quizzing oneself on the same material is far more effective for retention. Even then, people can misjudge the which items they will or will not be able to remember later.

The process of self-regulated learning works best when people accurately self-assess their learning and use that information to determine learning strategies and choose among resources. This reflective process fits well into the framework of personal informatics used already for applications like keeping up with one’s finances or making personal healthcare decisions.

For most people, their only experience quantifying learning is through grades on assignments and tests. While these can allow some level of reflection, the feedback loop is usually not tight enough. We are unable to fix our mistakes, making grades feel less like a opportunity for improvement and more like a final judgement.

How personal learning data can be collected

With computer-based practice, there is a great opportunity for timely personalized feedback. Several decades of research in the learning sciences have developed learner models for estimating a person’s knowledge of a topic based on their actions in a computer-based practice environment, often called an intelligent tutoring system. For example, a learner model for a physics tutor may predict the error rate of responses in defining the potential energy as a step in a physics problem — we see that the error rate decreases over the number of opportunities to use that skill, indicating learning (see below; from the PSLC DataShop). Such systems can not only track progress and give feedback but also make suggestions for effective learning strategies.

Our proposal envisions a web API that collects data from web-based learning resources into a personal central repository. Learner models analyze the data to provide quantified indicators of learning progress. The advantage of a central location is to compare and combine information across heterogeneous resources, as well as to enable self-experimentation with different types of learning interventions or strategies. Accumulation of enough data would allow findings to be shared among the community and give researchers access to data that could be used to improve learning. Finally, the API could also push back recommendations to the learning resources, taking advantage of the combined data and saving resource developers the difficulty of implementing learner model algorithms.

With personal informatics in learning, we see an opportunity not only for improving self-paced learning of more-or-less traditional content, but a grand vision of personalized learning: setting a vision of your future self, using the wealth of resources on the web to achieve your learning goals, and tracking your steps along the way.

 

This article is a summary of a position paper by Ryan Muller and his colleagues that will be discussed at the Personal Informatics in Practice workshop at CHI 2012 in Austin, TX on May 6, 2012. The workshop will be a gathering of researchers, designers, and practitioners exploring how to better support personal informatics in people’s everyday lives.

 

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Roger Craig Wins Jeopardy Championship with Knowledge Tracking

Roger Craig had a dream of being on Jeopardy, and he took a QS approach to making it happen. He downloaded all historical Jeopardy questions and answers into a database, clustering the questions by topic and keyword, and built a web tool around it to quiz himself. He visualized his answers to see where his knowledge gaps were and help him optimize his learning. Roger actually got to test his system by playing some actual rounds of Jeopardy, with surprising results! UPDATE: This week, Roger won the Jeopardy! Tournament of Champions and became the show’s 4th highest winner in history. Congratulations, Roger! If you haven’t seen his video below, check it out. (Filmed at the NY Quantified Self Show&Tell #13 at NYU ITP.)

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Announcing: the Quantified Self Forum

By popular request, we have just launched a global QS forum at: http://forum.quantifiedself.com/

Gary, Dan Dascalescu, and I took some exciting topics from the conference and turned them into forum discussions, with expert moderators to help explore ideas and answer questions. You’ll find discussions on:

- Apps & Tools, moderated by Dan Dascalescu

Data Ownership & Privacy, moderated by Ryan Calo

Design, moderated by Steve Dean

Habit Change, moderated by Ernesto Ramirez and Gary Isaac Wolf

Fitness, moderated by Alexandra Carmichael and Dan Dascalescu

Learning & Cognition, moderated by Nick Winter

Medical tests, moderated by Dave Asprey

Mood, moderated by Margie Morris

Nutrition, moderated by Alexandra Carmichael and Dan Dascalescu

Sleep, moderated by Alexandra Carmichael and Gary Isaac Wolf

Self Experimentation, moderated by Seth Roberts

QS Startups, moderated by Eri Gentry and Ernesto Ramirez

QS Research & Media, moderated by Rajiv Mehta

QS Open Forum for any Quantified Self topic not covered above, moderated by Alexandra Carmichael and Gary Isaac Wolf

Please join in the conversations, ask questions, share what you’ve learned, and come play with us!

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