Tag Archives: learning
Memory, cognition, and learning are of high interest here at QS Labs. Ever since Gary Wolf published his seminal piece on SuperMemo, and it’s founder Piotr Wozniak, in 2008, we’ve been delighted to see how people are using space repetition software. Our friend and colleague, Steven Jonas, has been using SuperMemo since he read Gary’s article and slowly transition to daily use in 2010. Steven has been quite active in sharing how he’s used it to track his different memorization and learning projects with his local Portland QS meeup group. At the 2014 Quantified Self Europe Conference, Steven introduced a new project he’s working on, memorizing his daybook – a daily log he keeps of interesting things that happened during the day. Watch his fascinating talk below to hear him explain how he’s attempting to recall every day of this life. If you’re interested in learning more about spaced repetition we suggest this excellent primer by Gary.
You can also download the slides here.
What did you do?
I used a spaced repetition system to help me remember when an entry in my daybook occurred.
How did you do it?
Using Supermemo, I created a flashcard each morning. On the question side, I typed what I did the previous day. On the answer side, I typed down the date. SuperMemo would then schedule the review of these cards. I also played around with adding pictures and short videos from that day to the card, as well.
What did you learn?
First, that this seems to work. I’ve built up a mental map of my experiences, unlike anything I’ve ever experienced. I also learned that I hardly ever remember the actual date for a card. Instead, it’s a logic puzzle, where I can recall certain details such as, “It was on a Saturday, and it was in October, the week before Halloween. And Halloween was on a Thursday that year.” From there, I can deduce the most likely day that it occurred. I’m also learning which details are most helpful for placing a memory. Experiences involving other people and different places are very memorable. Noting that I started doing something, like “I started tracking my weight”, are not memorable.
Today’s post comes to us from June Lee and Jennifer Kotler. June and Jennifer are researchers at Sesame Workshop, where they are conducting work exploring children’s media use. Below you can read their description of a breakout session they led on the topic at the 2014 Quantified Self Europe Conference. If you have ideas about measuring media use or want to continue this conversation we invite you to join the discussion in the forum.
Measuring with Muppets
by June Lee & Jennifer Kotler
The goal of the session was to exchange ideas for ways to measure and track children’s media use across contexts (which include physical spaces such as work vs. school, and social contexts such as with whom they are using media). An ideal device would be a wearable device that’s a “Shazam meets LENA,” which would identify the media content being used, as well as capture the conversation taking place around media use.
Currently, different technologies approximate what we would like to do. For instance, iBeacon is used in shopping malls to track and deliver messages to shoppers; smartwatches could be good for capturing audio; Bluetooth recognition could identify devices that are nearby and partly capture the social context. Different apps, however, don’t use the same system and are difficult to integrate. The main takeaway from the session is that nothing exists yet that does what we would like to do. We would need different apps and systems.
The session generated other useful ideas, such as the asking what parents would like to track in terms of media and their child, and what parents currently track (if they do). Another suggestion was to look at the rare disease or health care community, which is ahead of the curve in terms of tracking and managing child health; Human-Computer Interaction departments or Interaction Design departments at universities could be another good resource. Many agreed that we could start with simple, low-tech approaches: observations and/or manual paper recording. Or do the research in stages, using technology that does exist. In short, we needed to narrow our research questions because the tool we’re looking for does not (yet) exist.
Editor’s note: While doing some research around measurement and children we stumbled upon this great Sesame Street video. Enjoy Elmo singing about the power of measuring!
Today’s post comes to us from Steven Jonas who led the Spaced Repetition breakout session at the 2014 Quantified Self Europe Conference. Spaced repetition is a common topic in the Quantified Self community and we’ve seen great examples from Jeopardy champion Roger Craig and Steven. In this breakout session, conference attendees discussed reasons for using spaced repetition, past experiences, and potential pitfalls. You’re invited to read the description of the session and then join the discussion on the QS Forum.
By Steven Jonas
The Spaced Repetition breakout was a knowledge sharing session around the use of spaced repetition tools, such as SuperMemo, Anki, and Memrise. There were two major themes during the discussion: what can spaced repetition be used for, and what is the value of it?
Many people use Spaced Repetition to memorize vocabulary while learning a foreign language. But it has other uses also. Novel uses of spaced repetition include: remembering the faces of authors of books and articles and memorizing entries from one’s own datebook to construct a mental timeline . We explored other possible uses of this powerful tool, such as remembering facts about people, or using it to keep in mind projects that one would like to do.
Why memorize information when most facts are just a web search away? We discussed a few reasons to commit facts to memory. One is that most breakthroughs come from connecting ideas together. So, by retaining what one has already learned, it makes it easier to make connections with new ideas as they are encountered.
Also, spaced repetition can be used to change your overall relationship with a subject of knowledge. One person told of how he tried to multiple times to learn Spanish with poor results. His conclusion was that he just wasn’t good at learning languages. After using spaced repetition to build his vocabulary, he changed his self-assessment. It wasn’t that he was bad at languages, he just needed a better process. Or consider the experience of memorizing poetry. Holding a poem in memory changes one’s relationship to it. Adding a poem to one’s repertoire creates a sense of ownership over the poem.
We acknowledged in our discussion that spaced repetition practice is fragile, because for it to be most effective it must be done every day. A neglected spaced repetition system leads to an overwhelming number of cards to be reviewed, which can lead to abandoning the practice altogether. This is a problem that, so far, does not seem to have a good solution.
If you’re interested in keeping this conversation going about what should happen to our data after we’re gone you’re invited to join the discussion on the QS Forum.
As both a researcher and participant in her local Portland QS Meetup group, Dawn Nafus has been engaging in a process of understanding how people learn about their lives through personal data. As part of this work Dawn and her colleagues at Intel Research have been working on creating Intel Data Sense. In this short talk, given at the 2014 Quantified Self Europe Conference, Dawn describes what she’s learned through the process of studying the QS community and building this new sense making platform.
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!
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.)
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.
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.
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.)
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.
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.)