[life] Nice game of Small World with Titouan

Final  Score: 94 (Titouan) to 119 (Jérémy) with many successful enumerations, additions and subtractions!

Many thanks to Titouan for a very nice game!

From Alice to App Inventor, from the PC to your pocket.

Something I wrote to introduce a cycle of talks, that I thought (some of) you might be interested in: In the same way that =Personal Computers= took computing out of its traditional industry bounds, =Smartphones=, =Tablets= and =SmartTelevisions= pushed computing further into daily life, organizing everything from our communications, time scheduling, to our social relations or even health and diet. In parallel, pedagogy brought tools such as "Scratch" (http://scratch.mit.edu/), "Alice" (http://www.alice.org), and "App Inventor" (http://appinventor.googlelabs.com/) which simplify the task of describing what one desires from a computing device to the point where anyone, from preschool students to their grand-parents, can finally communicate his desiderata to the computer, and see them realized.

BootStrapping Databases: Repositorium And TripDroid

1. Introduction
   1. Motivations

      Whereas the development of the digital media has enpowered people with the ability to create and distribute documents of very high quality, still many of those documents are not shared, even when there is demand for it (This is known as the pareto law of web 2.0: 80% of the documents accessed are produced by a minority of 20% of the people).

      We assert that this lack of use of sharing tools is due in large part to the lack incentives for sharing, and we focus on the cases where such documents are already produced, when privacy issues are inexistant, and where the effort to share is minimal. A natural solution, taken by existing communities (e.g. repository of pedagogical problems in theoretical computer science of the Institute of Science and Technology of Japan), is the creation of collective repositories, which content accessed is conditioned to the participation to the community, yet this leaves open the problem of assessing this participation in a way which scales with its participation. A centralized evaluation, by a finite group of experts, will necessarily result in a limit in the growth rate of the repository.

      We propose an extension of this natural solution, where users of a collective repository participate by both contributing new documents and by evaluating new submissions, the quality of their evaluation being monitored by mixing a small amount of "challenges" among their evaluation tasks, in a way inspired by "ReCaptcha" word recognition challenges.

   2. Illustrating examples
      1. Structured Touristic information produced by tourists

         A tourist finding an error in a Tourism guide possess information which could be usefull to other tourists. Ideally, he/she would send an errata to the editors of the tourism guide (paper or digital) in order to help future tourists, yet there is no strong incentive to do so, apart maybe having your name in a future edition.

         Conditioning the access to information provided and checked by previous users to the participation to the guide, either by providing new information or validating information entered by others, creates such an incentive.

      2. Pedagogical Material produced by instructors

         The average lecturer at an average university produces lecture notes, slides, assignments, examens, marking schemes, solutions to the problems, which are highly reusable by his colleagues in other universities. Some do publish the best fragments of their production in books, yet the competition and large effort involved prevent most to do so, and results in the hardly desirable situation of expensive printed textbooks, and very few databases of digital pedagogical material.

      3. Sharing of Digital data and code in Research

         The Scientific Method requires by definition the replication of scientific experiments by distinct research teams in order to independantly confirm the results. While in theory peer-reviewed publications in journal requires a description of the experimental setup sufficient to allow the replication of the experiment, in practice essential elements must be shared by other means: laboratory of biological research use courriers to send samples across the world.

         In the cases where the essential elements of the experience are digital (such as software in computer science, but also survey data in social sciences, detailed experimental data in hard sciences, etc…), collaborative repositories should insure their diffusion, yet have failed so far [Olivier Temam, head of architecture group at LRI, University Paris XI, France, personal communication], the only strategy showing some results being competitions where participants have to share their data in order to register (centralized quality control).

      4. Other examples
         • Release of Anonymized Medical Data?
         • Source Code intensively commented and documented
         • sharing and validation of scientific publications
2. Prototypes
   1. Prototype Repositorium
      • Target:
        • Sharing and evaluation of digital documents, and in particular pedagogical material produced by instructors for their own usage.
      • Example of Usage:
        1. Arturo is professor of physics and has invested some time to write a set of documents (e.g. multichoice conceptual questions) for his course Physics101. He is interested in getting feedback and additions on it.
           • He initiates http://physics.repositorium.cl with
             • his documents A,B,C,D,E for sharing,
             • the simple quality criteria "Is this question correct and adequate for teaching Physics101?"
             • some examples x,y of documents which do not satisfy this criteria.
           • He points out his new repository to some colleages at other universities, among which Brigit and Carlos.
        2. Brigit connects to the repository as user and
           • evaluates the quality criteria on {A,x,y,C}, and wins some credit as she answered correctly according to Arturo's evaluation.
           • The repository records that her evaluation agrees with the official evaluation of the documents.
           • Brigit then spends some of her credit
             • to search and download documents from the repository, and
             • to upload her own documents F,G,z,w in the hope to receive credit for them in the future.
        3. Carlos connects to the repository as user and
           • evaluates the quality criteria on {y,D,F,z}.
           • Carlos wins some credit as he answered correctly according to Arturo's evaluation of y and D, and
           • the repository records that
             • his evaluation of y and D agrees with their official evaluation, and that
             • he accepted F and rejected z: those statistics will be added to the evaluations by other users.
           • Carlos then spends some of his credit to search and download documents from the repository.
        4. Arturo connects to the repository as expert and
           • sees that 4 unvalidated documents have been added to the 5 validated documents initially in the repository, among which 2 have been (partially) evaluated. Without waiting for more evaluations, he
           • checks and confirm Carlos' evaluations of documents F and z: those are added to A,B,C,D,E and x,y among the validated documents used to assess user's expertise.
           • Arturo does not consider documents G and w: he will wait for more users to first evaluate it.
           • Arturo happily constats that user evaluations agree with the one in the repository, otherwise he might have to inquire for a misclasification.
           • Based on the documents which were previously rejected, Arturo adds a new quality criteria, "Is this document free of English mistakes?". He validates a positive (A) and a negative (x) example for this criteria. All other documents in the database are now unvalidated for this new criteria, to be evaluated by the users before being validated by an expert.
      • Known (Most General) Issues:
        1. Textual Documents: The current version is limited to text documents. Future version will manage the inclusion of images, the display of latex and HTML code, and the management of pdf documents.
        2. Dynamic Repositories: The current version manages a single repository with a single set of quality criteria. The next version will allow any user to create a new repository with its own quality criterias and content.
        3. Detection of Duplicates: The current version does not detect the submission of documents already present in the repository, whether in the exact same form (which could be automatically detected) or in an altered form (which requires a human to assess the identity). A future version will add automatic detection and human identity challenges (triggered by identity of tagging, and serving to refine the tags).
      • Open Issues:
        1. Tags versus Clusters: A document might be of interest to more than one community: having it assigned to a single repository limits its usage, having it assigned to several repositories and evaluating it separately wastes resources. On the other hand, having one single repository for all documents supposes
           • one single set of quality criteria for all documents
           • some additional system to decide which user is able to evaluate which criteria on which document.
        2. The expertise model is flat: each user is expected to correctly evaluate documents among each quality criteria, and each expert is expected to validate any document among any quality criteria. If this is more or less ok in the case of instructors teaching a similar course, expected to be experts on all parts of the course; such a feature unfortunately limits the use of a collective repository for sharing between users of distinct levels, such as:
           • users experts in distinct fields learning from each other when a user A is expert in field 1 (e.g. Spanish) and learner in field 2 (e.g. Chinese), while a second user B is expert in field 2 while being learner in field 1;
           • users at distinct levels of expertise when users experts on a topic 1 (e.g. basic data-structures) and learners on a topic 2 (e.g. advanced data structures) exchange access to level 2 material by evaluating submissions by users learning level 1 material.
   2. Prototype TripDroid
      • Targets:
        • Foreigners (or long term tourists) who wish to share and update touristic information about the city visited on their smartphone.
        • Inhabitants of a city who wish to share and update the location of available resources (ATMs, Restaurants, Gaz Stations) which change over time.
      • Example of Usage:
        1. Arturo launches the application "Trip Droid" on his smartphone while visiting the "Place of Arms" of Santiago.
           • He sees the tags "place", "statue", "artists" associated to the location, and
           • submits the tag "water display", to be added to the database as unvalidated information.
        2. A bit later, Beatriz also visits the "Place of Arms" of Santiago and launches the application "Trip Droid".
           • she is new to the application and does not have any credit: she sees only basic information, and a suggestion to answer some evaluation query.
           • She solicits a random evaluation query about the location, and receives "water display", Arturo's submission, with the option to validate, invalidate, or pass:
           • she can clearly see the sources, so she validates the tag "water display" and add a tag "Post Office".
           • she won some credit and can see more information now.
        3. At the end of the month Charlie, working for the tourism office, connects to the application as administrator.
           • He quickly validates the tags submitted which have been evaluated by tourists such as Arturo and Beatriz in the same way more than 20 times, and
           • he checks the few tags on which contradictory validations have been received.
           • He downloads the information to the server of the ministry of tourism, to be distributed to tourism agency and offices across the city.
      • Issues:
        • Probabilistic Quality Control: Asking only one validation query for each interaction (preferable given the context of usage of a smartphone), one cannot mix validation queries with challenges in each interaction, and must resort to randomness, the probability of which depending on the expertise of the user. How the rules of update of this probability affect the truthfulness of the process is not clear.
        • Semi-hidden Expertise: Having only one validation query per interaction, the credit update after answering it potentially reveals information about which is a challenge of a validation query to the user. The current solution is to partially hide the result, showing only the rounded log of the user score.
3. Our work

   Our work so far has consisted in putting this concept in practice in the context of two applications:

   • TripDroid for georeferenced information, aimed at the collective production and checking of touristic information; and
   • Repositorium for digital documents, aimed at the collective production and checking of any type of pedagogical documents (limited to textual in the alpha version).

   Both projects are still in a cycle of development and user implementation. They have been developped so far by students under academic supervision, their source code being made publically available under the philosophy that was is paid by public money should be kept freely available to the public.

   Our future work will consist in

   • testing more extensively the alpha-versions of those projects with users (only TripDroid so far has been user-tested);
   • extend the functionalities of these prototypes (e.g. extension from textual to digital for both)
   • explore the mathematical concepts underlying the collective quality control system

   Collaborations, concerning both the development or the assessment of boot-strapping tools, are more than welcome.

4. Acknowledgement
   1. Development of the prototypes:
      • TripDroid
        • Jorge Romo,
        • Vanessa Peña and
        • Christian Palomares
      • Repositorium
        • Hernan Fierro,
        • Pablo Estefo,
        • Felipe Banados,
        • Nicolas Perez,
        • Mauricio Quezada, and
        • David Contreras.
   2. Comments on the general design:
      • Sergio Ochoa (University of Chile)
      • Barbara Poblete (University of Chile)
      • Jo Piquer (University of Chile)
      • Eric Mazur (Harvard University)
      • Therese Bield (University of Waterloo)
      • Hans De Sterck (University of Waterloo)
      • Julien Sebot (Intel)
      • Balint Miklos (Google)
      • Claudio Gutierrez (University of Chile)
      • Christo Dichev (Winston Salem State University, North Carolina)

People Teach People: what could be with Boot Strapping Databases

Year 2020, Kevin hurries back home from his soccer game at school to play his new networked music game: he hates to play it on his /companion/ (even though projecting the game on the wall is pretty neat), and prefers by far to play it at home with the 3d projector, and a sound amplifier with effects that could simulate three philarmonic orchestra in full formation. "Maestro, me voilà!"

Once in his bedroom he plugs his digital theremin in and logs to the /PTP/ ("People Teach People") server. There he is asked to evaluate three unes input, potentially from less experimented players or automatically generated to test the serious of his evaluation.

Kevins sighs a bit because he was hoping to play directly. He read somewhere that they kept the random factor to enhance an addiction feeling, he shrugs it off. He feels only midly annoyed: he knows that it won't be long, and it is really not too much work to provide, in exchange for all the data he gets in return, almost for free. He listens to the three midi inputs, validate two of them with a part quickly annotated in the second one, and refuse the third one: spam or computer generated, it is anyway a pretty clear cut.

This took Kevin less than five minutes, and now he can play. He checks the new tracks available at his current level, and choose one with a jazzy beat to start to play. The color of the light gives him feedback about how far he is from the tune, converging to an appeasing blue green with some flickr of orange on the most difficult passages, quickly reaching the all star mode: he is on his way to a high score!

In the next room, Kevin's sister tries to concetrate: she si learning Corean on the /CIME/ ("Computer In the Middle Education") server. She finished the previous level last weeck, where she was learning vocabulary and word pronunciation using indexed images, in exchange of indexing other images in English, recording her pronunciation in English and validating the similar work from other native speakers and from learners in a shuffle.

The level that Sarah is now playing is more complex: she must correct the English grammar of some foreign learner through a precise interface, so that her corrections can be compared to the past and future corrections of others, and hence evaluated. If her corrections are validated, she will gain access to this service in Korean, along with some time online with some certified Korean user who can correct her pronunciation. Sarah is eager to finish this level: with her friend Kathy they are planning to visit Korea next summer, and Kathy posted yesterday on her profile that she had reached the next level, where she plays video games with native Korean speakers: she has to catch up!

Reading again Kim Stanley Robinson

In a tentative to move back to a mode where I do not work (so much) on week-ends, I started reading again the books by Kim Stanley Robinson, which I like a lot. After re-reading my all-time favorite Mars Trilogy, I am currently reading again "Forty Signs of Rain", hoping to move later to the other tomes of the "Science in the Capital" serie, which I did not read before (I read "Forty Signs of Rain" when it came out, and did not have the time to follow up after). As always with Robinson's work, I strongly identify with the scientist character (I loved "Saxifrage" in Red Mars), but feels very close to the anthropologist views of the "Frank" characters in both books ("Frank Chalmers" in the Mars trilogy, and "Frank Vanderwall" in the "Science in the Capital" serie). On the second reading I am getting quite a distinct perception of the book: this is in part because I am now much more familiar with global warming news and science (in great part thanks to the first reading of the book), but also because I got much more bitter about the academic environment, and feels more touched by pessimistic stances such as the following one:

People plugging away in the same old ways, trying to do things according to the plan, even a flawed plan: normal science, in Kuhnian terms, as well as in the more ordinary sense. All so normal, so trusting that the system worked, when obviously the system was both rigged and broken. How could they persevere? How could they be so blinkered, so determined, so dense? Kim Stanley Robinson, voicing Frank Vanderwall in "Forty Signs of Rain"

I was so stricken by how Robinson put in words my exact feelings, that I had to stop reading and copy this quote in my notes, where it joined another one from another book of Robinson:

How be optimistic, when there was so much wrong, with so much? In a world coming apart it had become a kind of stupidity. Kim Stanley Robinson, voicing an "active optimist", in Antartica

Don't be misguided by the negativity in those two statements: Robinson is a very positive writer. In all his novels, some of the characters are very cynical and critics of the system in which they operate, yet they always work hard to change it (little by little, through sweat and constant effort, not using magical, punctual solutions) and better it. I feel deeply stirred by such stories: it put words on my actions in a way that only a writer (which I am not) could have.

Lumosity: Fiction or... Fiction?

Some time ago, I created a free Lumosity account. As a teacher and wannabe pedagogue, I was curious about their claim to "make people smart". The games offered are so simple that CS students in their second year could program them, but hey, maybe it is the design by neuro-scientists and the like which is expensive, I don't know. I played a bit, got tired of it, registered to the mailing list for scientific articles on the topic and went back to develop my own techniques to "make people smart".

In the few years since I registered, I received quite a lot of spam from the mailing list, but no scientific article. Today, I got an email which lists four of my main doubts about their technique, and promise some final answers. Here are some comments about it, and a recommandation for a better use of your time.

• FICTION OR FICTION? Cognitive exercise only makes you better at cognitive exercise. FICTION! Controlled clinical trials show that the right kinds of cognitive exercise — like those on Lumosity — can fundamentally enhance the brain’s ability to process the information needed for daily and professional tasks. Studies with Lumosity — including research performed at Stanford and Berkeley — have demonstrated 10-20% improvements in memory, attention, and speed of processing. Users report being more confident in their ability to remember names and faces, study for exams, and concentrate at work.
  • Is the "FICTION OR FICTION?" a typo or a kind of joke that I don't get in the first question?
  • BEING MORE CONFIDENT is quite distinct from REMEMBERING BETTER. It seems quite predictable that after investing time in pedagogical games and improving one's performance in those, users feel more confident. Experiences in measuring memory performance in multiple dimensiosn (and of related tasks) has been around since the 60's.
• Cognitive training can help anyone at any age. FACT! Everyone can improve with Lumosity! Over 13 million users of all ages from over 190 countries are using Lumosity to enhance their cognition. In fact, 97% of all users who train for at least 10 hours see improvements in performance. Who couldn’t benefit from enhanced memory and attention? They should be more precise, whether they talk about game performance or memory performance. Given that there is no real test of memory in the games, I assume that they are talking about Game Performance. Then this statement means only that the learning curve of the game insures that 97% of users improve after 10 hours of play. The same can be told about almost any game!
• Lumosity can make you smarter. FACT! Clinical studies have shown that cognitive training with the exercises on Lumosity can enhance memory, attention, and fluid reasoning abilities. These abilities are a big part of what makes someone “smart.”
• People don’t need brain training if they do crosswords or similar activities. FICTION! We recommend doing a variety of things to keep your brain in top form, including proper diet, physical exercise, adequate sleep, and staying socially active (check out Brain Grade to get specific recommendations). However, none of these lifestyle factors specifically target the core brain regions that underlie cognitive functions such as attention, memory and speed of processing. Because cognitive training does exactly this, Lumosity represents a key part of a brain-healthy lifestyle.

Designed by our neuroscientists and tested by researchers at Stanford, Berkeley, and other prestigious institutions, Lumosity draws on cutting-edge science to improve cognitive performance. "research performed at Stanford and Berkeley", "tested by researchers at Stanford, Berkeley, and other prestigious institutions" a link to the relevant publications in peer-reviewed conferences or journals would be appreciated here. Also, it seems odd to include neuropsychologue and no pedagogue in your references: is the educational system so rotten that no relation is left between it and techniques to "make people smart"? Feel free to try their site, but before committing too much time to their games, be sure to consider rather reading the books "Brain Rules" and "Brain Rules for Babies" by John Medina. In my (humble) opinion, this will be a better use of your time.

Boot Strapping Database of Concept Questions: First Prototype

The first prototype of Boot Strapping Database is live!

So far the graphic interface is in Spanish, there are only concept questions in Spanish about one course in Computer Science, a single criteria of evaluation, a single category of documents, the interface is still pretty crude, and only me and my teaching assistant as both users and experts: it is not ready for you to try it yet, but I wanted to celebrate the first step by a post, as one celebrate the birth of a child with whom you can't talk nor play, but whom you are happy about anyway :)

The graph above summarizes its use, from the point of view of the user and the expert (the third type of user, the administrator, serves just to study how well people behave and to correct glitches). Sorry of the labels are in Spanish, I wrote it for my students, but here goes the translation with a few explanations, describing the roles of the logged user ("Usuario (con login)", at the top) and of the expert ("Experto", at the bottom):

• (Search and) Download ("Bajar", on the top-right) concept-questions among validated documents (green, on the righ), which have been personally checked by an expert, helped by the previous evaluations of other users, in exchange of a few "credits".
• Evaluate ("Evaluar", top center) some documents, receiving a small bunch and returning their evaluation. Among those documents some have been already validated by an expert, and some have been just uploaded by users. If the evaluation of validated documents agrees with the expert's, the user receives some credits, otherwise he is asked to evaluate another, bigger, set of documents. Such credit is used in the two other interactions.
• Upload ("Subir", top left) a new document, paying first a few credits in the hope to receive much more after 1) several users were asked to evaluate this document and 2) an expert validated their evaluations and moved this document from the "submitted" documents (in purple, at the left) to the "validated" documents, on the right.

Of course the expert can delete and correct any document, he can add documents without being subject to the point system, actions summary symbolized by the spiral in the center of the graph, and the arrow pointing to a green bin on the bottom right.Users who want to access the content will have to contribute to it both in volume (uploading new documents) and in quality (evaluating other user's uploads), so that the more people use it, the faster the database grows and the better its content becomes.The capacity of each user to evaluate submissions is measured by mixing to genuine new documents some "challenge" documents, which have been validated by the expert (as clearly "good" or "bad" documents): the user's evaluation on new documents is considered only if the user's evaluation on validated documents agrees with the expert's.

The hope is that this design, along with a few security measures, will allow the expert to quickly validate large bunch of documents by browsing a compilation of user's evaluation, so that documents massively accepted or refused by users do not take up its time, and only problems where users can't agree on an evaluation require some attention: we will see how it goes further down the line. By the end of June, for the last month of the term, I hope to open it up to students, with the first incentive for them to discover questions (and their answer) which might fall at the exam, and the second incentive to maybe have one of the problem they propose chosen for use in the exam. The features I hope to see developed by then or shortly after, are

• more quality criteria (each new one generated by the expert, with a "seed" of positive and negative examples, generally chosen from the validated documents already in the database);
• more domains, each independent of each other with its own experts, so that people can experiment with the interface with other contents, like concept questions on physics (for Eric Mazur to try it), or even LaTeX solved problems on algorithms (for me and Therese Bield to upload our solved problems);and
• a better interface, with better filters to facilitate the expert's work, (safe) interpretation and visualization of HTML and LaTeX code in the documents.

And of course I expect and hope to find and receive many criticisms and comments, so that the second prototype will be even better and we will help people all over the world to teach each others: comments are welcome!!!!

On Sleep Debt

I am extremely careful to sleep enough. Lack of sleep can make you sick! (Likewise, if I don't exercise at least once every two week I get sick.)

sleep deprivation often triggers erratic behavior in people with certain psychological conditions, such as bipolar disorder (formerly called manic depression),

(http://www.sciam.com/article.cfm?id=lunacy-and-the-full-moon&page=2) Friends have been horrified to hear that I schedule 10 hours for sleeping every day, when they routinely sleep for 6 hours. It bears for an explanation: I schedule so much time available for sleep only so that if I want to think in the evening, while in bed, I can do so and still sleep enough. Likewise, if I wake up early in the morning and feel well, I do not force myself to sleep more and start getting things done.

The (1h) video above seems to give an explanation or justification of why it is a good idea: in an experience they forced young military people to stay in bed for 14 hours every night for a week. The first night they did almost sleep 14 hours, and the sleeping time lowered to an average sligthly above 9 hours per night: the interpretation is that as the subjects recover from their sleep debt, they converge to their ideal time of sleep. The most interesting part of this video was when the speaker showed how sport performance improved as sleep debt was reduced to zero: I would like to see a similar study on the performance of students, or even scientists!

I noticed along the years how the scientists that I admire the most (e.g. Rosie Redfield, David Kirkpatrick, Ian Munro,...) are all careful to exercise regularly, but never heard any mention of special care of sleeping pattern or rates, beside the usual complain of jet lags and the like. How much time does a scientist sleeps on average? Is there a difference between "theoretical" and "applied" scientists?

Teach them to ask questions!

One of my worst academic memory, once after the chairman asked "any questions?" at the end of a distinguished lecture with some 200 attendants, I raised my hand. The Chairman said: "Jeremy, of course. Why am I not surprised? Anybody else has a question?". This was repeated a few months ago, in a different location, with a different chairman. Sometime I will start to wonder if I may be asking too many questions ;)

From Jérémy "The Thingker"

On the door of my office, there is a poster of A. Einstein with the note "Even Einstein asked questions". I added a post-it above "Even" and made it read "In particular". In the words of Glencora Borradaile, aka "Silent Glen":

“Listening to a talk with the goal of asking one or two questions results in increased attention and a deeper understanding of the material.” (http://www.glencora.org/silent-glen-speaks/a-flat-6-pack-of-soda/)

In high-school, older schoolmates beat me up for asking too many questions in biology class, "annoying the professor" and "making the class last overtime". As an undergraduate, the head of the department took a dislike in me because I was asking too many questions in his compilation class. Things got better once the TA (thanks Martine!) explained to him that I was not all bad. Still an undergraduate, I was hushed from asking a question at the Ph.D. defense of Philippe Andary about genetic algorithms (he was my TA and I had played with implementing GA myself), not knowing that "this is not done!": he did forgive me :)

As a doctorate student, I saw my advisor always asking questions at seminars, often not waiting for the end of the talk, flexing her mind around new ideas and clarifying concepts early so that not to waste the following. When we organized the "interdisciplinary" seminar with Christophe Genolini and Antoine Ducoulombier, we made sure that people were encouraged to ask question during the talks. I always have questions, sometime too many, some times too far fetched, (hopefully) few times expressing my own error of understanding (again, hopefully early enough in the talk that it improves my understanding of the rest). If I don't ask a question at the end of a talk, it probably means that I did not pay attention to it. Sorry :(.

Contrarily to the saying, there is a thing as "bad" questions, too personal, too out of topic, too time-consuming. There is no room for interruptions in a conference talk packing several years of scientific work into 20mns. But learning to ask good questions is one of the main attribute of scientific research, and more generally of a sane way of life (children, do learn to question advertisements! Adults, do learn to question the condition of your health coverage!). We should jump on any opportunity to teach the art of asking questions! And yet, from high-school to academia, questions in class or seminars (conferences are another theme, since the time is so short) are met with mixed reaction, from fun and mocking to great annoyance. In (some) Mathematic seminars, nobody asks questions at the end of the talk, let alone during the talk. I was told that in Germany this was not welcome either, even in Computer Science.

In my teaching I teach to my students to ask questions (or at least to answer mine). During the first weeks of class in Orsay and Waterloo, I was eating apples while waiting for students to ask or answer questions: any fruit finished in class meant a "harder" problem at the exam. Those were exceptional students and they learned fast :). Recently, I learned from Eric Mazur about Peer Instruction, who put me to think about how to implement it in Computer Science, and Theoretical Computer Science in particular (could not find a trace of someone already working on it?). Hopefully the next generation of academics will ask more questions :)

MyArt

Some ideas of logos for a (bootstrapping) database of knowledge (e.g. solved problems and concept questions), some contributed by Daniela.