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Alessandro Canossa is an Associate Professor at The Royal Danish Academy of Fine Arts, Schools of Architecture, Design and Conservation. He is the Czar of Player Experience at modl.ai and has been straddling between the game industry and academia for many years.
He has been an Assistant Professor at the IT University of Copenhagen, Associate Professor at Northeastern University in Boston and he’s now at the Royal Danish Academy of Fine Arts. In his research, he employs psychological theories of personality, perception, motivation and emotion to design games with the purpose of investigating individual differences in behavior among users of digital entertainment. His research focuses heavily on these topics: a) developing behavioral analysis methods that are able to account for granular spatial and temporal events, avoiding aggregation; b) designing and developing visual analytics tools that can enable any stakeholder to produce user-driven content leveraging advanced statistics and machine learning.
He was also Senior User Researcher and Data Scientist at Massive Entertainment a Ubisoft studio, where he enjoyed tremendously investigating occult behavioral patterns and novel player modeling approaches while identifying the best processes for transferring knowledge from academic research to industry practices. He’s now involved with Modl.AI, a company providing AI services to the game industry, where he’s exploring how to triangulate data-driven insights with surveys and lab observations to advance the field of predictive analytics.
Links to episode mentions:
- Proposed guests:
- Recommended book: Game Analytics: Maximizing the Value of Player Data
- Favorite games:
- Culyba’s Transformational Framework (free e-book)
- Triadic Game Design
There are many ways to get in touch with Professor Game:
Looking forward to reading or hearing from you,
Full episode transcription
Welcome to Professor Game Podcast, where we interview successful practitioners of games, gamification and game thinking who bring us the best of their experiences to get ideas, insights, and inspiration that help us in the process of getting the students to learn what we teach. And I’m Rob Alvarez.
I teach and work at IE Business School in Madrid, where we create interactive and engaging learning materials. Want to know more? Go to professorgame.com/subscribe, start on our email list and ask me anything! Engagers. Once again, we’re here on Professor Game podcast and we have Alessandro today, but before we get started, Alessandro, are you prepared to engage?
Of course, that’s my job.
Absolutely because he’s an associate professor at the Royal Danish Academy of fine arts school of architecture, design, and conservation. And he is also the czar of player experience a modl.ai and has been straddling between the game industry and academia for years clearly our type of profile. He has also been an assistant professor at it, university of Copenhagen associate professor at the Northeastern University in Boston. And he’s now as you know, in the Royal Danish Academy of fine arts, the research he employs psychological theories of personality, perception, motivation, and emotion to design games with the purpose of investigating individual differences in behavior among users of digital entertainment.
Rob (1m 37s):
He has very focused on topics like developing behavioral analysis methods that are able to account for granular spatial and temporal events, avoiding aggregation and as well, designing and developing visual analytics tools that can enable any stakeholder to produce user-driven content, leveraging advanced statistics and machine learning. So very, very, very interesting topics. He’s also been senior user research and data scientist at massive entertainment, a Ubisoft studio, and he enjoyed tremendously investigating the occult behavioral patterns and novel player modeling approaches while also identifying the best processes for transferring knowledge from academic research to industry practice.
Rob (2m 23s):
As you know, he’s involved now with modl.ai, which is providing AI services to the game industry, and he’s exploring how to triangulate the data-driven insights with surveys and lab observations to advance the field of predictive analytics. So a lot of things going on in your life Alessandro, is there anything that we’re missing?
Alessandro (2m 42s):
No, I guess you cover pretty much everything. In the past year, when I started working at the design school, I started working with the students actually creating novel interaction models. So we have a paper that was presented at the conference of games in August. I’m basically trying to use intimate controllers where players have to touch each other, very inappropriate in this period of history. We are actually looking at how touching each other makes a difference in how people perceive and process the experience of play.
Alessandro (3m 18s):
And we had some very interesting, you know, finding, but yeah, we can talk about it a bit later.
Rob (3m 22s):
So that’s very exciting. Like what were the, I mean, can you, without going into the, into the depths, but what was the, what did you find out?
Alessandro (3m 32s):
Well, we had a simple tower defense game, where two players have to cooperate to fend off hordes of enemies that are trying to eat your wedding cake basically. And then we made the game in two versions with normal controls where you press one button for the two entities that the player control to merge into one giant monster that can slay a lot more enemies. So we have a version where you use a button to do it. And another version where you basically have to hold hands, we’re using a MakeyMakey controller for the players to hold hands, basically what the brain so they can merge.
Alessandro (4m 7s):
And the games are virtually identical, but this little difference, just touching versus pressing a button, meant the players engage more with a story, which is basically inexistent. They’re more interesting, more involved, more engaged, but at the same time, they get weary and tired earlier. So it’s a, you know, some things that maybe we could have done, and all of this was done in combination with students from the design school. I need to mention Eric Anderson. He was actually the student responsible for making the game
Rob (4m 38s):
Very, very nice and very interesting results as well. What was it like when you got into this? Were you expecting something in particular or was this just what came up?
Alessandro (4m 48s):
Well, we had this hypothesis that they would be more engaged with the game because you know, you, you basically bring your body in the experience a lot more than just pressing a button. We didn’t really expect for them, for the players to get a bit wearier, a bit more tired earlier, but it makes a lot of sense when you think about the fact that they actually use more of their body. So the awareness probably comes from more engagement in the corporal sense.
Rob (5m 15s):
Hmm. Interesting, interesting, very interesting results as well. And Alessandro, we, we’ve now known some of your research. We also would like to get into other things as well. Some things like, what are you doing on a regular day? Like what does being at Alessandro on a day like today, look like what’s your, you know, I don’t know if daily routine or what are the things that you’re doing nowadays?
Alessandro (5m 38s):
Well, I mean, like, I guess I can describe today because it’s pretty representative. So I live with my son and we go take a little swim when we wake up and then we go to, I take him to school. Then I go to the office, answer emails I teach and I try to fit in a little bit of research if I can, I’m actually finishing a book with previous colleagues. It’s a sequel to our game analytics book.
Alessandro (6m 9s):
It’s actually a textbook for schools, you know, unravels the type, the methods, and the results that have been done in terms of game analytics. So yeah, I try to finish that. We are supposed to wrap it up next week. So there’s a lot of that,
Rob (6m 20s):
Lots of work going on there.
Alessandro (6m 21s):
Exactly. I pick up my son, cook a little bit. We play we build Lego. We started making board games together actually he is five but he is already a lot of fun. And then we have dinner. And then after dinner work, a bit more of the, in this particular period, finishing the book and then I have several funded projects that I’m attending to. So I have to like, yeah, deliver on that as well. The funding projects that actually one of them, it’s trying to validate a serious game that we developed together with the master students at the design school.
Alessandro (6m 57s):
And the game is based on an Ellen MacArthur Foundation report on a sustainable economy. And what’s it called circular production and disposal of food. So it’s like, a game, that tries to teach a lot to players, but is doing so not by exposing them to like the text to read, but just having players engaged with the systems. So we developed a questionnaire for the players to take before they play, then the play for half an hour.
Alessandro (7m 27s):
And then a couple of days after answering the questionnaire again, and we are noticing an increase of about 30% of accuracy of their answers from the questionnaire.
Rob (7m 35s):
Nice. So it seems they’re learning something or they’re changing their minds about something, right?
Alessandro (7m 39s):
Well, yeah, absolutely. Then, the way we’ve done it, we used Culyba’s Transformational Framework and also Casper Harteveld’s Triadic Game Design. Those are two philosophies for serious game design and they’ve been very helpful for transforming the report from the Mack MacArthur foundation into systems that players engage with. And yeah, I’ll give you an example. You have to constantly make decisions about what type of food products and you choose between vegetables, meat, or insects or alga.
Alessandro (8m 13s):
And some foods produce more calories for example, so that it can feed more population, but they have an impact on how much energy is used to produce them, how much money costs, how much money generates and what is the disposal and the waste generated as well. So while players make all these decisions, they are constantly informed about the consequences of their actions and that’s where the learning part seems to happen a lot.
Rob (8m 40s):
And it it’s, it’s actually, I mean, you, some people might think like, Oh no, but this, this is sort of thing is for simple things, you know, you can learn with using a game for some simple, But this seems to be like, you know, dense matter, this is something that, you know, it’s just, I don’t know, like some, that’s something simple that you can just figure out it’s actually dense. It’s interesting. And has it has depth is what I mean, it’s not a simplified topic, right?
Alessandro (9m 8s):
Exactly. Eh, as you said, it’s non-trivial information that reading just would only take you so far because it’s also sometimes a bit of an alien subject matter that will be removed. For example, thinking in terms of producing algae for food, of course, we all know that it’s a possibility, but we don’t do it daily. So engaging in the system as we design makes people understand exactly what are the implications of moving food production in that direction.
Rob (9m 39s):
Wow. Super, super interesting. And we’ve seen, we are already, we’re just like almost 10 minutes into the interview and we’ve already seen many interesting things that you’ve done, that you’re doing a lot of success as well, but we also like to sort of kickstart the interview as well and jumpstart it with something different. And, and you, you you’ve been involved in games in many times and you know, the games are not only about success but also about learning from failure. So we would like to know of a time when you had, you know, what you might call your favorite fail or first attempt in learning. And we want to, you know, be behind that story.
Rob (10m 9s):
We want to be there with you. Can you tell us that story?
Alessandro (10m 12s):
Okay. Well, my most cogent and memorable failure was actually when we were trying to assess personality and reveal attitudes using a game. So basically we used Fallout New Vegas. It’s a game that came out a long time ago, I think in 2011 or something like that. And basically we created a whole new world, a whole new mission, a whole new set of characters for that game. It comes with modern tools that basically allow us to generate a lot more content without having to code the mechanics again.
Alessandro (10m 48s):
And then we created the world, the mission and the characters thinking about what is the broadest possible spectrum of behaviors that the game mechanics allow. And we are trying to see if it’s possible to capture personality as coded by the big five theory, the big five or more scientifically the five-factor model. It’s a model of personality that looks at five big factors that define how people behave in certain situations. There’s an openness to new experience, extroversion, conscientiousness, neuroticism, or emotional stability, and agreeableness.
Alessandro (11m 25s):
For each one of these five factors. There are six facets. So all in all, we’re looking at 30 facets that are scored between zero and a hundred. That gives you a more or less personality fingerprint for, for every individual. And we had this idea that we could look at advanced statistics and machine learning, basically ways to connect the behavior in the game with the personality scores that all the players scored before starting to play.
Alessandro (11m 56s):
And our expectation was frustrated because if you look at it as a whole, the game is about maybe an hour, an hour and a half. So it’s like a kind of longer experience compared to the other one that I mentioned before about food production. And when you were looking at the five factors and the averages of behavior, in terms of one hour and a half of playtime, we found very weak correlations and we tried a lot. I’m not going into the details, but I will try that.
Alessandro (12m 27s):
It turns out actually that we just had to start looking at the, taking into consideration how the context shapes behavior. I’ll give you an example. When you start this game, you are in a house and you have like a few people to talk to. And we noticed two main differences. People that explore everywhere in these houses and people that just talk to the two, three people that they are supposed to, the non-playing characters, and then they get out of the house. As soon as they’re out of the house, the people that were exploring everywhere, they started being very much more Spartan with their movements.
Alessandro (13m 3s):
They were going from one NPC to another, from one area of interest to the other, without much exploration while the people that were really sparse in terms of exploration in the house, revert to the behavior and started roaming freely in this gigantic outdoor area that we designed. Now I, I can actually ask you to try to guess in terms of like these two groups of people, we noticed that the people that, so which one would you say was more open to new experiences? The people that explored the house very thoroughly and then were more sparse out outdoors or the people that were sparse indoors and just explore extensively the outdoors.
Rob (13m 44s):
Hmm. I would say that the ones that only spoke to the characters and went straight for the experience, that will be my guess.
Alessandro (13m 50s):
Yes, exactly. So it turns out that the same type of behavior is instantiated by different people in different contexts. So in the house, you are, is in a small space and you have, you have to speak to a few people. So people that are the players that were more or less open to new experiences were somewhat confident in exploring the small space of the hours. But as soon as they went outside, they were maybe somewhat intimidated by the openness. So they started being a lot more on rails while people that the players that reported a high level of openness to a new experience when they were in their house, they felt a little claustrophobic.
Alessandro (14m 31s):
They had to speak with the people they had to, the NPCs. And then as soon as they were out, they just felt like some kind of relief. And they started mining and drilling and exploring as much as they could. So being able to account for personality means being able to account also for the context where the actions happen. There’s a theory called the field theory that says that behavior is a function of personality and context. So before other researchers looked at the same thing, but discounted the power of context and we started actually looking into it so that the analysis we run after was not for the whole game, was for each individual area.
Alessandro (15m 16s):
Each individual area had his own different ways of relating to its own requirement in terms of things to do or things that they had to do. So once we started looking at this way, we started having really strong correlations between behavior and personality. Wow.
Rob (15m 33s):
Very, very interesting and very eyeopening. When, you know, thinking about these things, we like to talk about player types. We like to talk about, you know, who your audience is, understanding it as much as possible. And introducing the element of context is definitely super, super important. We were talking the other day. I know you’re familiar with Nick Yee from Quantic Foundry?
Alessandro (15m 53s):
Absolutely. Yes. Actually a lot of the work in this field, was done exactly by Nick.
Rob (15m 57s):
Exactly. So, so one of the things that he was talking about was, you know, sort of comparing player types to personality tests. So there are these sort of stable types and those things that, that sort of don’t change in somebody. But of course, depending on the context, some things might shift to a bit more to one side or to the other, but in the case that you’re mentioning here, it’s not that it’s changing is that if you’re an Explorer, you’re an open space person, you know, you’re, you’re, you’re in this house. So you’re trying to come out, you know, that the context is not letting you explore because you’re sort of closed inside this house.
Rob (16m 28s):
And that’s why you speak to those people and get out quickly. That’s what you want to do. You want to go out and explore. So it’s actually like, it reinforces the fact that you know, these, these personality types are, might be, you know, sort of stable, but depending on the context, the behavior is different. It doesn’t mean that they’re being other people or anything. So I don’t know, I find it very interesting and I love the way that you turned it around into like, you know, you’re not getting any correlation, which is super important when researching and you start looking around and seeing the other things that might be influencing this situation.
Alessandro (16m 60s):
Rob (16m 60s):
So Alessandro, again, we’ve talked about your successes as well. We’ve talked about this, this fail situation when you were approaching either your research on games or when you’re creating a game or researching about a game, do you have some sort of process that you’d go through? Like, how do you do it? Let’s say you, tomorrow, you start into a new project. What would be the things that you do would go, it comes first, second and third if you have some sort of structured way of doing these things.
Alessandro (17m 26s):
Well, I guess if this is the same for pretty much, every researcher is about mapping what’s been done before and figuring out what is the state of the art and then building or looking for partners with the expertise to be able to push that state of the art. So I have a couple of examples. One again was another research trying to map motivations with behavior. And in this case, it turns out that the correlations are just not enough linear correlation, looking at when two variables change at the same time.
Alessandro (18m 0s):
There’s actually a, is there’s a lot of evidence. The behaviors and personality don’t have necessarily a linear correlation. So we basically have to start looking at advanced statistics and machine learning methods to be able to, you know, make sense of the covariance of different departments. Nick Yee was actually pioneering the use of Monte-Carlo researches to be able to do these analyses. We actually started looking at support vector machines. Now, I’m not sure this is the right level of the audience, but stop me if you think it’s too much.
Alessandro (18m 36s):
So the machines actually seem to be a good, you know, tool for, for looking for these correlations or the not correlations anymore. Another example. So in this case, for example, I had to learn how to run an SVM out, to create SVMs, which was not in some other, SVM sorry, the support vector machine. Okay. Yeah. There are other situations where, like, I think you mentioned it earlier, the work in terms of predicting the motivation based on gameplay behavior.
Alessandro (19m 8s):
In that case, I actually partnered with researchers from the University of Malta, Georgios Yannakakis, David Melhart and Ahmad Azadvar, Ph.D. Students that I’m supervising in Ubisoft right now and together, we were able to solve that little problem. Yeah. Pulling a lot of different knowledge from a lot of different people. So in terms of process, I’d say looking at what’s been done before, figuring out what’s the bar and then pushing it by either learning something new that I can contribute or partnering with somebody that already is at the top of their game, in that field
Rob (19m 46s):
And validating and navigating all of that. And so that sounds, that sounds absolutely fantastic. Alessandro. Just a quick break before we continue. Are you enjoying this podcast? If you’re listening through a podcasting app, please subscribe and rate us on the app. This will be of great help to reach more engagers so we can change the world together through gamification. Again, within this field, within the field of game and player ºresearch. Is there something that you would say is sort of a best practice, something that, you know, if you’re, you’re doing that research for understanding your players, understanding their motivations, something that you should sort of taking into account, try to do almost every time and that would help you improve your results.
Alessandro (20m 26s):
Are you asking me if there is a fixed process to tackle
Rob (20m 30s):
A best practice? Like, Oh, if you, I mean, if you can, I’m just going to say something off the top of my head. If you always consider, you know, the players who are very explorative, that’s going to help you figure out the rest of the players. I don’t know. I, again, I’m making this up entirely
Alessandro (20m 46s):
Well. Yeah. I think it’s actually looking, looking at players, interacting with a game that is incredibly helpful because it can put you in the right direction. Yeah. I’ll give you an example. These, the results that I mentioned before, where we found out that context was too important to leave out, we kind of discovered it by looking at people playing. And you know, if you can not look directly there’s ways to, you know, get a synthetic look, for example, at Northeastern, we developed a tool for which we actually have a patent that allows us to look at heat maps in a dynamic way, and to create Boolean operations across different heat maps so that you can ask complex questions.
Alessandro (21m 28s):
For example, when and where did players reload and then get shot by an enemy, whether you can create complex queries. And then you can see the visualization as a dynamic heat map that plays out over the map of the level. So observing players in all the different ways or the different meanings of observing, either directly or through these enhanced observing tools, like the start mapper as we call it.
Rob (21m 58s):
Absolutely fantastic. That sounds like a very, very good practice. Definitely like looking at the players directly or indirectly, if that is not possible. I think that will be a fantastic way of figuring out whatever it is you’re trying to see because sometimes we sort of getting stuck in hypothesizing and thinking and, you know, stuck in our own heads or in the heads of our team and we’re discussing and talking about it. And sometimes you have to sort of go out to the field again, physically or synthetically. You have to go into the field and figure out and see what people are actually doing.
Rob (22m 30s):
So I think it’s a very, very good practice, absolutely Alessandro. And after hearing these things, and actually this is something that we sort of comment on before we got into the interview on the pre-interview chat. Is there somebody that you would like to listen to that, That we interview here in this, in this podcast, in Professor Game?
Alessandro (22m 45s):
Well, I would like to listen to professor Jichen Zhu from Drexel University, Philadelphia, because she works with games, but she also has a special interest in explainable AI. So I think I would like to hear from her and the professor Julian Togelius from New York University,
Rob (23m 6s):
Those sound like very, very, very interesting profiles as well. And we’ll, we’ll probably get into contact with them to have them on the podcast. And in that same sense of recommendation’s, is there a book that you would recommend our audience, again, the Engagers people who are looking at, you know, understanding the players, creating things for these players, you know, and, and creating these games or using them for some sort of purpose. Is there, is there something that comes to mind?
Alessandro (23m 30s):
Oh, I hate to be that guy, but I have to mention that the book that we’re finishing now, it’s going to be called the game data science and a published by Oxford university press probably is going to be out early next year.
Rob (23m 43s):
Nice. That sounds very interesting. And of course, looking at it right. And imagine you’re seeing your book in a bookshelf, is there a book right next to it that you would like to recommend as well?
Alessandro (23m 55s):
So I’m just looking at it from a purely functional point of view. Our previous book was called Game Analytics. And so while the second volume is going to be fully written by the authors, and it is a manual on how to do game analytics, the first one was an edited collection of contributions from people from the industry and academia. And what is the state of the art in terms of analytics, how to look at player-generated data and how to make sense of it
Rob (24m 26s):
Sounds good. That makes a lot of sense. That makes functional sense. As you, as you mentioned, then again, we’re, we’re getting into recommendations. So I would like to know, and this might be a difficult question you’re you’re into games. Seems like you’re, you’re, you’re talking about games and doing games and research and games all the time. So you’re probably exposed to quite a few of them. And what would you say in that sense is your favorite game
Alessandro (24m 47s):
Oh, well, historically speaking, the game that opened my eyes in terms of what are the expressive potential for the players to express themselves was actually Deus Ex. I think it was the first time that I felt that I could choose my own way to solve challenge the first Deus Ex from like, I think 99 or 2000. It was like 20 years ago. But I have to say recently against my better judgment, I enjoyed a lot more than I expected Death Stranding by Hideo Kojima.
Alessandro (25m 18s):
I don’t know if you heard about this game, you basically have to cart gigantic cargo on your back, walking across the Plains of Iceland and you do that for like 30, 40 hours. It sounds a bit trite and boring, but I had a lot of fun optimizing the distribution of the weight of my cargo to see how many frequencies I could bring across the plains of Iceland. But there’s something about the way they rendered the aesthetics of the place with an incredible pairing of music.
Alessandro (25m 51s):
And this response created by the enemies of the game. They just made that particularly aesthetic experience. Meditative, almost
Rob (26m 0s):
Interesting, interesting. And talking about these things and probably in, in player and in, in-game or research, What would you say is your superpower that thing that, you know, you feel great at. Doing that you probably do better as well than most of the other people.
Alessandro (26m 14s):
Okay. So I don’t think I have it because when I look, I think there are like much better than me. That’s why I often pair up with partners and experts that help me with their… Maybe I have a, yeah. Okay. So I can actually see a pattern where many cases, where there was a difficult analysis that I didn’t know how to solve, not knowing a lot about any particular discipline I’m actually able to serve through different. For example, some, a couple of years ago, we were trying to model the sequence of actions that players taking into the games, not looking at an aggregation of their actions throughout the whole play session, but looking at the order in which they do them, I’ll give you an example.
Alessandro (27m 1s):
If you engage in a fight and then open the vent or you regroup and heal yourself is a very different approach than somebody that opens the inventory first plans, which weapon to use and which tools to deploy and then enters a fight. So the same action. If you look at them, if you don’t look at the order in which they happen, you lose a lot of information. So we wanted to be able to model the order in which players do their actions as well. And then, in order to do that, I started looking at molecular biology because sequencing is what people do when they do molecular and DNA analysis.
Alessandro (27m 38s):
So I borrowed algorithms from biology basically to make sense of the actions of the players. And we had quite a lot of success. And so being able to look out of my domain and see how other domains have solved similar problems. I, yeah, I’d say that that’s my superpower. I’ve done something similar to social network analysis, trying to apply it to game communities. And again, with a certain that level of success, we got an honorable mention at Chi last year for this social network analysis paper.
Rob (28m 11s):
Nice, nice. That sounds very, very exciting as well. And congratulations, of course. And Alessandro you know, we’ve been, we’ve been talking about very interesting things and I don’t want to lose this opportunity to ask you a question coming from the audience. So we will find a random question from the menu though, that we’ve had before and we will, you know, take it directly from our Engagers. So, okay. This one could be an interesting one. And I think, I, I think with you, with your background on player engagement and their motivations, it might be one of the right questions to ask you.
Rob (28m 45s):
So when you are going to create a game, right? So this person I’m guessing that they’re starting, or they’re going to start creating a game, how do you decide if you should go ahead or drop the idea? I’m guessing that this has a lot to do as well with, do you think your players will like it? How will you do you figure out that they will engage with it? Can you help this person understand how, you know, how to, how to, or at least a path to it, to how to figure this out?
Alessandro (29m 9s):
Okay. It’s a, it’s interesting because so at Ubisoft, there is a procedure to vet different ideas than to see them to the completion. And so I, I can ensure it from the industry point of view, what are the requirements to be able to make it through the green lighting process? But I don’t know how informative it is because a lot of the checks are rooted in the culture of the company more than in a guarantee success.
Alessandro (29m 42s):
If you know what I mean,
Rob (29m 43s):
Of course, guarantee for success. I don’t even think Ubisoft where there all the success that they’ve had can actually guarantee success. That’s really, really hard, especially in such a competitive industry. What, what would be your thoughts around this? Again, you can take from some, from Ubisoft, from what you’ve observed, maybe like what would you, what would be your thoughts around something like this?
Alessandro (30m 2s):
So at this point, I guess, Amount of personal preferences and likes and dislikes comes in my answer basically. So I’m not saying that this is the only way, but I’m telling you, these are how I make sure that the games ever, the games that either my students or I make have a future, I kind of require for the games to be able to allow the players, to express themselves, even in a simple game, for example, like the tower defense, there are ways where you can develop your own style and your own way to do like play it.
Alessandro (30m 38s):
So I think games like limbo are absolutely stellar. There are masterpieces. I really liked the work that played that is doing a lot, but those are not the games that I would do because in a way you play the game that was designed for you and you have zero expressive power, you receive a lot, but you don’t give out.
Rob (30m 58s):
Interesting. So Alessandro thank you very much for all the insights, all the experience you’ve brought in today. Is there any final words you want to give the Engagers before you let us know where we can find you
Alessandro (31m 9s):
a well, I mean, stay curious. Yeah. Steve jobs, words stay hungry. I think he’s probably can give to anyone.
Rob (31m 18s):
Absolutely. Absolutely. So Alessandro, where can we find you in the world of the internet? I’m sure many people are now curious about your work, about what you’re doing. Want to maybe ask you more questions? Where can we, is there a way that we can interact with you? I don’t own social media, a web, a website. Where can we find more about Alessandro?
Alessandro (31m 34s):
I checked out of social media a couple of years ago, so I don’t engage Twitter nor with Facebook nor with Instagram. Yeah. It’s pretty old school, but I think I kind of needed it. And so I guess the best way is to the official channel at my email at the design school. It’s A L E C at K A K dot D K.
Rob (31m 60s):
Interesting. Thank you very much for that. Alessandro, giving your email is a very generous move as well. So thank you for that. Thank you for all your insights. I’m sure that the Engagers have gotten a lot from this because there’s a lot of value in what you are offering today. However, at least for now and for today, it is time to Say that it’s Game Over Engagers. It is fantastic to have you here because you know, this podcast makes sense because we have you, so let’s connect on Twitter. And that way you can let me know what things you want, what questions you have.
Rob (32m 35s):
If you have any guests you like to have on what, I don’t know if there’s anything we can help with, if there are any doubts, anything exciting coming up, you can, you can let us know. You can, you can find my Twitter account in professorgame.com/twitter. Always, always very frequently sharing content on game-based solutions, gamification, game-based learning, especially around education and learning, which is as you know, my main jazz. So before you click continue, please, please go ahead and subscribe using your favorite podcast app and listen to the next episode of Professor Game.
Rob (33m 14s):
See you there.
End of transcription