Even simpler to read and understand

The first question was “Do you think climate change is a global emergency?” (Yes/No).

And Italy is in first position together with UK: 81% of respondents from Italy think climate change is a global emergency.

The second question was “If yes, what should the world do about it?” (a. Do everything necessary, urgently / b. Act slowly while we learn more about what to do / c. The world is already doing enough / d. Do nothing)

And Italy is the first one here: 78% of respondents from Italy who answered Yes to the first question say we should do Everything Necessary, Urgently in response.

I did not have at all the perception that in Italy there is a high (and higher than all other countries) awareness about climate change as a global emergency but let me appreciate it anyway: now it is time to move from belief and awareness to action.

The survey asked people which of 18 climate policies they would like their country to pursue to address climate change. Overall, the most popular among participating countries were

– Conserve forests and land (54%)
– Use solar, wind and renewable power (53%)
– Climate friendly farming techniques (52%)
– Invest more money in green business and jobs (50%)

In reality, I’m in the list that appears in a paper titled “Updated science-wide author databases of standardized citation indicators” published on Plos Biology on October 16, 2020 by three authors and the first author is affiliated with Stanford University.

Anyway it is a good news and I guess there is not better place to brag about this than in my not-so-active-anymore blog ;)

If you want to have a look at the complete list of scientists, check this copy of the (very long) excel file.

Authors compare a tangible interface to robotics programming and a non-tangible interface. The tangible interface is KIBO, a robotic kit programmed with wooden blocks, basically making Scratch physical. (I totally love KIBO, so I’m biased ;)

The non-tangible interface is ScratchJr, i.e. robots are programmed via the screen of a tablet.

They compare it with children in the age range 4-7 (average age of 5.86 years old!). Both groups had 14 children. The curriculum for both groups explored the same computational thinking concepts: sequencing, repeat loops, and conditionals.

Results:

1) COMPUTATIONAL THINKING: The evaluation compared the two groups with respect to four computational thinking categories: Sequencing, Repeat Loops, Conditional Statements, and Debugging.
(kibo > scratchjr) Students in the tangible KIBO group scored higher across all four computational thinking categories in comparison to the graphical ScratchJr group. Differences for sequencing and debugging tasks were statistically significant.

2) POSITIVE TECHNOLOGICAL DEVELOPMENT. The evaluation compared the two groups with respect to 6 positive behaviors (6 C): content creation, creativity, communication, collaboration, community building and choices of conduct.

• content creation and creativity:
  (kibo) children in the tangible group focused on the goal at hand first and once, that was complete, they moved onto exploring other ideas. They also explored the different functions within KIBO’s programming language as their main creative outlet instead of focusing on the art materials around them.
  (scratchjr) Those in the graphical group often got distracted by the multitude of options within the application, but eventually completed the challenge given to them. They spent time using the paint editor features that ScratchJr offers to edit and create characters and backgrounds as their way of making their projects stand out from the rest.
• communication and collaboration:
  (kibo) In the tangible group, students were able to easily look around the room and see other student’s robots. This allowed them to explore what everyone was doing and to prompt them to ask one another questions and receive peer help and input on their own work. It also allowed counselors and research assistants to easily see who was on or off task, and to see who needed help.
  (scrtachjr) In contrast, in the graphical group, it was much more difficult for counselors to see what children were working on and whether or not they were off task. It was difficult to see what was on each child’s iPad screen at any given time, meaning that both children and counselors needed to go out of their way to find out what everyone was working on and to ask questions. Finally, it put more responsibility on the child to ask for help, since the adults could not always tell if there were any problems with the kids’ programs.
• choise of conduct and community building (i.e. general ambiance of the room)
  (kibo) In the tangible group, children were often moving around with their robots or going over to other groups to explore their projects. The children were also generally on the louder side, especially when sharing their projects in the tech circle. They seemed very engaged and eager to share their learning.
  (scratchjr) With the graphical group, children were generally very quiet and respectful in the traditional classroom sense. They were often either hyper-focused on their own work, or on the people close to them. Students only occasionally walked around to explore other people’s projects. It was clear from these observations that both groups demonstrated positive conduct and community building, but it different ways.

From my personal subjective preferences, I would say that manipulating things with hands (tangible) is better than manipulating abstract things on a tablet screen. Montessori docet! ;)

Reference: Sullivan, A., Bers, M., & Pugnali, A. (2017). The impact of user interface on young children’s computational thinking. Journal of Information Technology Education: Innovations in Practice, 16(1), 171-193.

You can read the paper and download the pdf at the paper page. Enjoy!

You might also want to check the very interesting articles from Google’s People + AI Research team. 

Below two screenshots from the beginning of the video when Josh makes the case for toaster and automatic doors being examples of AI.

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View the code on Gist.

I love XKCD graphs, for example the following one, and you can create them with Python!!!

XKCD graph

We are currently recruiting and now there are 3 open positions as research fellow in the Design Research Lab. The duration of the contract is for 12 months. The gross amount is 19.668 euros.
All the details are in the call.
The broad goals of the Design Research Lab (and the tasks of the research fellows) are to effectively promote in public and private organizations the culture of services and their design as levers of product creation and central factors of local development.

The deadline for applying is 12 April 2017 (hurry up!)
Feel free to ask me any question. We might end up working together ;)

• First insight: they were 3 founders in California with a stagnating company (Airbnb), they could have kept staying in their office trying to improve the site, write more software code and instead what did they do? Realising that apartments in New York all had horrible photos, they took a flight (from California to New York!), rented a camera, knock on doors of Airbnb users in New York, took better photos of their apartments and replace them on the site. As Joe says in the interview, “for the first year, we sat behind our computer screens trying to code our way through problems”, instead going to meet their users is one of the pillars of design thinking, its very human-centred focus. Just after this intervention, revenues which were stagnating at 200 dollars per week went up to 400 dollars per week. Near the end, Joe says “if you ever want to understand your product, go stay in the home of your customer” (well, this applies only to Airbnb … and maybe also to Couchsurfing ;)
• Actually the previous suggestion was given to Airbnb founder by Paul Graham (of Ycombinator, I loved his “hackers and painters” essay!) which suggested it’s okay to do things that don’t scale. What is the meaning? I think it’s again about being very human-centred, going out of the building, develop empathy with specific persons and really understand him/her. So that you can make improvements that really satisfy real needs (of at least one real person!). Scaling to millions of persons will come later, if needed.
• Another suggestion by Paul Graham was go meet the people which again is the very human-centred side of design thinking. The interviewer asks “what if your company is not for < go out and meet people?> and Joe replies “well, be pirate”, a sort of “do it anyway” but then I asked my self how do you get it accepted? This reminded me of the pragmatic book Undercover User Experience Design.
• And what can the employee bring back from this “go out and meet people” to the company? Joe replies “visible, tactical, tangible insight that came from somebody is consuming your product or your service”
• Joe suggests to become the patient (of your service/product). For example, every new employee at Airbnb, during the first week, makes a trip (using Airbnb of course), document it and share insights with his/her new department. Wow!
• Joe also cites the stars vs heart icons story: when you start as new employee at Airbnb, you ship (a new small feature, something) on day one, so that new employees can experience shipping on day one. A newly hired designer was given the task of looking at the star functionality (an icon you click in order to save a listing you find interesting). After few hours he or she comes back with something like “I think the stars are the kinds of things you see in utility-driven experiences. Instead Airbnb is so aspirational. Why don’t we tap into that? I’m going to change that to a heart.” And Joe “Wow, okay. It’s interesting” and they just shipped the new feature, to the entire userbase (not a/b testing or just shipping it to 10% of the users)! They also added some code in it in order to track it and see how behavior change. And the next day they checked the data and the engagement with the icon increased by over 30%, that simple change from a star to a heart increased engagement by over 30%! In short, let people be pirates, ship stuff and try new things.

In 2010 Norman labelled design thinking as a powerful but false myth with questions such as “Why should we perpetuate such nonsensical, erroneous thinking?” and statements such as “what is being labeled as “design thinking” is what creative people in all disciplines have always done”. Norman argues that designers are not “mystically endowed with greater creativity (…) but they have one virtue that helps them: they are outsiders. People within a group find it difficult to break out of the traditional paradigms, for usually these seem like givens, not to be questioned. Many of these beliefs have been around for so long that they are like air and gravity: taken for granted and never thought about. Outsiders bring a fresh perspective, particularly if they are willing to question everything, especially that which seems obvious to everyone else.”

But in 2013, Norman writes a new post titled “Rethinking Design Thinking” in which he changes a bit his position. The part I like the most is the conclusion where he posits “That is design thinking. Ask the stupid question.” basically arguing that:

What is a stupid question? It is one which questions the obvious. “Duh,” thinks the audience, “this person is clueless.” Well, guess what, the obvious is often not so obvious. Usually it refers to some common belief or practice that has been around for so long that it has not been questioned. Once questioned, people stammer to explain: sometimes they fail. It is by questioning the obvious that we make great progress. This is where breakthroughs come from. We need to question the obvious, to reformulate our beliefs, and to redefine existing solutions, approaches, and beliefs. That is design thinking. Ask the stupid question.

Well, I think I can wonderfully get along with this suggestion, so if you happen to pass by while I’m asking a stupid question (or you are the one I asked the stupid question to), don’t be judgemental, I’m just doing my (new) job ;)

P.S.: I hope you can forgive me for putting so close Don Norman and myself in the title ;)

The pdf of “Gender Gap In Wikipedia Editing: A Cross-Language Comparison” is available for you to read.
But I suggest you to try to answer to the following questions beforereading the answers (which are in the paper) so that you might play a bit with your stereotypes and prejudices about culture and women around in the world ;)

1) Which language edition of Wikipedia has the largest percentage of registered users setting their gender as female? What is this percentage? It is more or less than 50%?
And 2) what is the language of the Wikipedia with the smallest percentage of women? How close to 0% might this be …?
3) Try to order the following language editions of Wikipedia from the largest percentage of female registered users to the smallest: Arabic, Bulgarian, Catalan, Chinese, French, German, Hindi, Japanese, Korean, Persian, Swedish, Thai. Where does the largest Wikipedia (the English one) is placed?
4) Moreover, considering that setting the gender on Wikipedia is optional and actually few users do it (see details in the paper). Which percentage of users set their gender on English Wikipedia? What is the Wikipedia in which most users set their gender? What is this percentage?

Note that, as written in the paper, of course languages do not map directly to countries. For example, Spanish Wikipedia is heavily edited from Spain but also Latin America and a similar point can be made from Arabic Wikipedia. India has many official languages Hindi, Bengali, Malayalam, Tamil, Marathi but also English. On the other hand, Italian Wikipedia or Catalan Wikipedia are much more “localized”.
Note also that in the paper we arbitrarily decided to consider only editions with at least 20.000 registered users since we computed percentages on registered users (a Wikipedia with 2 users setting their gender would have had percentages of 0%, 50% or 100% clearly not informative) and this filtering step reduced our sample to 76 Wikipedias with a large number of registered users (at least 20.000).
Note also that data refers to March 16, 2013 but we released the Python script as open source so you can re-run it if you are curious about the current situation. You can get the script on Github.

Ok, now you can go to read the paper “Gender Gap In Wikipedia Editing: A Cross-Language Comparison” to get the answers to the previous questions and hopefully be amazed! Enjoy! ;)

Manypedia is online at http://www.manypedia.com.

The paper is at http://www.gnuband.org/papers/manypedia-comparing-language-points-of-view-of-wikipedia-communities/. If you like Manypedia and the paper, please cite it. Thanks!

In fact, today I discovered via Gizmodo that Santiago Ortiz has used our Wikipedia Gender API for creating a fantastic visualization of Wikipedia pages based on how many female and male contributors each of the articles has.
Using the cool visualization you can for example “discover” that currently, out of more than 4 million pages in the English Wikipedia, JUST ONE article is edited more by females than males!!! That article, with 7 male editors and 9 male editors, is Cloth menstrual pad.
Note that the API we released is based on data from Wikipedia and that only users who specified their gender in Wikipedia are counted in (these users are a minority, around 10%). Note also that in our Wikitrip visualization we show a plot with number of edits from gendered users while Santiago show the number of different users. For example in Wikitrip you see that the page Cloth menstrual pad has 62 edits from females and 15 edits from males but the different users who edited the page are 7 male and 9 female.

Now I give you some more info about the APIs released with WikiTrip so that you can use them as well in your mashups if you wish so.

1. api.php

   Get various stats about a page

   Options:

   • article: page title
   • lang: desidered language (default: en)
   • family: wiki family (default: wikipedia)
   • year_count: show edit count per month (default: false)
   • editors: show unique editors for the page (default: false)
   • max_editors: maximum number of editors displayed (only if “editors” option is set)
   • anons: show anonymous unique editors (default: false)
   • top_ten: show top 10% of editors (default: false)
   • top_fifty: top 50 editors (default: false)

   Example

2. api_gender.php

   Get timestamp and gender for any edit by a registered user that specified his gender on a specific page (might be quite slow)

   Options:

   • article: page title
   • lang: desidered language (default: en)
   • family: wiki family (default: wikipedia)

   Example

3. api_geojson.php

   Get a GeoJSON for anonymous edits on a specific page

   Options:

   • article: page title
   • lang: desidered language (default: en)
   • family: wiki family (default: wikipedia)

   Example

P.s.: the coder of all Wikitrip awesomeness is the amazing Federico “fox” Scrinzi!

The mid-1990s, Microsoft started an encyclopedia called Encarta. They had deployed all the right incentives, all the right incentives. They paid professionals to write and edit thousands of articles. Well-compensated managers oversaw the whole thing to make sure it came in on budget and on time. A few years later another encyclopedia got started. Different model, right? Do it for fun. No one gets paid a cent, or a Euro or a Yen. Do it because you like to do it.

Now if you had, just 10 years ago, if you had gone to an economist, anywhere, and said, “Hey, I’ve got these two different models for creating an encyclopedia. If they went head to head, who would win?” 10 years ago you could not have found a single sober economist anywhere on planet Earth who would have predicted the Wikipedia model.