Speaker 1: Welcome to the deep dive. We're here to help you
Speaker 1: get the essential knowledge from sources.
Speaker 2: Fast yep, straight to the good stuff.
Speaker 1: Today, we're plunging into a really interesting space where software
Speaker 1: engineering meets games. But you know, not just for fun.
Speaker 1: We're talking serious applications.
Speaker 2: Exactly, think education, training, maybe tackling real world problems. It's
Speaker 2: about more than high scores.
Speaker 1: So if you're listening, you're probably looking for a let's say,
Speaker 1: quick but solid grasp of this, trying to get those
Speaker 1: aha moments without wading through tons of jargon.
Speaker 2: That's the idea and our main guide here is the
Speaker 2: book Software Engineering for Games in Serious Contests Theories, Methods,
Speaker 2: tools and Experiences. It's edited by Kendrick Cooper and Antonio
Speaker 2: Book your Own.
Speaker 1: And what's really impressive, I think is the sheer range
Speaker 1: of voices in this book. It's not just one or
Speaker 1: two perspectives.
Speaker 2: No, not at all. We're talking forty five authors from
Speaker 2: nine different countries.
Speaker 1: Wow. So that gives you a really broad view right
Speaker 1: on how game design ideas and solid software practices are
Speaker 1: well coming together in some quite important ways.
Speaker 2: Yeah, a really rich picture.
Speaker 1: So our mission today is basically to dig into the
Speaker 1: core concepts, understand the challenges, you know, in using software
Speaker 1: engineering to build serious games that actually work and keep
Speaker 1: people hooked.
Speaker 2: Okay, sounds good. Where should we start maybe with just
Speaker 2: how complex these things are to.
Speaker 1: Build good idea? Yeah, because it's not just coding, is it.
Speaker 1: It's inherently interdisciplinary.
Speaker 2: Absolutely not just coding. The book really lays it out.
Speaker 2: You've got inputs from well, the arts, behavioral sciences, business, education,
Speaker 2: engineering itself, humanities, even physical sciences.
Speaker 1: And math, and then domain specific stuff like healthcare too.
Speaker 2: It's a real mix, and it's worth thinking about what
Speaker 2: each brings. Like the arts give you the visuals, the sound,
Speaker 2: the feel right two D, three D assets, music, even
Speaker 2: performance informs, animation, and the.
Speaker 1: Humanities that brings in things like ethics, game addiction maybe
Speaker 1: or storytelling from literature, you know, crafting the narrative of
Speaker 1: the setting.
Speaker 2: The behavioral sciences are huge. Anthropology helps with understanding social norms,
Speaker 2: communication theory for getting the message across, sociology for multiplayer.
Speaker 1: Stuff, and psychology for engagement motivation, how rewards work the
Speaker 1: whole gameplay experience.
Speaker 2: Really exactly. It underlines that building a good serious game
Speaker 2: rests on this incredibly diverse base of knowledge, which.
Speaker 1: You know, must make collaboration tricky sometimes compared to more
Speaker 1: traditional software projects. You need different ways to talk to
Speaker 1: each other.
Speaker 2: Yeah, communication is key and connecting it back. This complexity
Speaker 2: shows why having strong software engineering practices underneath it all
Speaker 2: is so vital. You're juggling so much.
Speaker 1: Okay, so complex landscape check. But the influence of games
Speaker 1: goes beyond just making serious apps look like games, doesn't it.
Speaker 2: Oh definitely. The book talks about game full engineering and gamification,
Speaker 2: but applied within software engineering itself.
Speaker 1: Right. I saw that table table one point one. It
Speaker 1: lists areas like requirements engineering, architecture testing, even project management
Speaker 1: and QA getting gamified.
Speaker 2: It's kind of surprising, isn't it, seeing these, you know,
Speaker 2: seemingly fun techniques popping up in the core processes of
Speaker 2: building software.
Speaker 1: Yeah, really is.
Speaker 2: And there's this idea of directly transferring strategies. Chapter Sun
Speaker 2: by Vanisawanik and her colleagues digs into this. They look
Speaker 2: at it from three angles. Yeah, player agency giving players
Speaker 2: meaningful choices. Then there's serious game modification or modding, and
Speaker 2: also the role of AI emotion modeling to design better games.
Speaker 1: Interesting. So the stuff that makes regular games fun and
Speaker 1: Sticky isn't just for entertainment anymore. It's being used strategically,
Speaker 1: even behind the scenes in software.
Speaker 2: Creation, which naturally leads to the question, how do we
Speaker 2: know if these serious games are actually well working? How
Speaker 2: do you measure the user experience effectively?
Speaker 1: Ah good transition that brings us to chapter two by
Speaker 1: soteririst kre Genus. He points out that game ux depends
Speaker 1: on things.
Speaker 2: Like flow, that feeling of being totally absorbed.
Speaker 1: Right, an immersion, maybe frustration or tension with the right
Speaker 1: amount of psychological absorption, and even the social setting.
Speaker 2: And there's this breakdown by Roto into three phases of experience,
Speaker 2: what you expect beforehand, what happens during play, and the
Speaker 2: overall feeling after and he argues.
Speaker 1: The during interaction part is the most crucial for making improvements.
Speaker 2: Right exactly, And then Lalaman adds more detail to that
Speaker 2: during phase. He says it's influenced by the person playing,
Speaker 2: their mood, motivation.
Speaker 1: Needs, okay, the human factor.
Speaker 2: Yep, then the system itself, it's complexity, purpose, usability, and
Speaker 2: finally the context of the environment, whether it feels meaningful voluntary.
Speaker 1: So loads of factors how do they suggest evaluating all this,
Speaker 1: especially if you want quick and thorough insights.
Speaker 2: Well, the book covers several methods. There's expert evaluation, basically
Speaker 2: getting designers or ux pros to give feedback. It's usually quick,
Speaker 2: cost effective.
Speaker 1: Class mentions two ways for that, right, experts as evaluators
Speaker 1: or evaluators supervising experts.
Speaker 2: 's then you've got focus groups, more qualitative, subjective, good
Speaker 2: for exploring perceptions, figuring out what questions to ask, finding unexpected.
Speaker 1: Issues, kind of exploratory.
Speaker 2: Yeah, and surveys. Of course, you can gather objective stuff
Speaker 2: like demographics, playtime, and subjective things like attitudes, emotions.
Speaker 1: Pretty standard but useful, especially combined with other data like
Speaker 1: game analytics.
Speaker 2: Maybe absolutely. Surveys are easy to deploy and can be
Speaker 2: powerful when triangulated. But then it gets really interesting. They
Speaker 2: talk about physiological.
Speaker 1: Measurements ah, okay, like biometrics.
Speaker 2: Sort of electrodermal activity or EDA that measures sweat gland
Speaker 2: activity basically indicating arousal or mental effort, hum.
Speaker 1: And cardiovascular heart rate YEP, heart rate variability and so on.
Speaker 1: Gives insight into overall physical and emotional response, and even
Speaker 1: electro biography EMG which measures muscle tension.
Speaker 2: Muscle tension, what does that tell you?
Speaker 1: It can indicate things like anxiety or even cognitive load
Speaker 1: or strain.
Speaker 2: Wow, So really getting under the hood of the player's experience.
Speaker 1: Definitely, And it's important to distincish between this objective data
Speaker 1: from machines and subjective data what the player reports.
Speaker 2: Right, there's always a trade off there in reliability versus richness.
Speaker 1: Perhaps kind of ideally you want both. The book also
Speaker 1: talks about formative versus summative evaluation. Okay, formative is during development,
Speaker 1: right to guide design like think clouds or interviews.
Speaker 2: Exactly, it's more dialogue based. Summative is at the end
Speaker 2: assessing overall performance against goals, often using those psychophysiological measures
Speaker 2: or self assessments.
Speaker 1: Got it now, shifting gears a bit? How about assessing
Speaker 1: what someone's learning in the game, but without stopping the
Speaker 1: game for a test.
Speaker 2: Ah, Yes, that's the idea behind stealth assessment.
Speaker 1: From chapter three. Sealth assessment sounds intriguing. It is the.
Speaker 2: Goal is to measure those harder to pin down twenty
Speaker 2: first century skills, creativity systems, thinking, persistence, things traditional tests
Speaker 2: often miss.
Speaker 1: And crucially, without breaking that flow that engagement we talked
Speaker 1: about precisely.
Speaker 2: It provides feedback without interrupting the experience.
Speaker 1: And they link this to something called CAGE, content agnostic
Speaker 1: game engineering.
Speaker 2: Right. CAGE tries to solve a common problem with older
Speaker 2: serious games. They were too tied to specific educational content,
Speaker 2: made them hard to reuse sometimes less engaging.
Speaker 1: So CAGE aims to make the core game mechanics and
Speaker 1: the assessment reusable across different subjects.
Speaker 2: That's the goal. Build the assessment into the framework in
Speaker 2: a content agnostic way so you can potentially reuse it,
Speaker 2: maybe speed up development for new topics.
Speaker 1: How do they actually do that stealth assessment within CAGE?
Speaker 2: They mentioned methods like mouse tracking, analyzing cursor movements, and
Speaker 2: emotion tracking software things like Visa, just DK or aft
Speaker 2: dex which look at facial.
Speaker 1: Expressions facial expressions yea to detect boredom or frustration.
Speaker 2: Yeah potentially, though with mouse tracking there's a performance consideration.
Speaker 2: Track too much and it might slow the.
Speaker 1: Game down, a classic trade off.
Speaker 2: All this data stealth or not feeds into educational data
Speaker 2: mining EDM looking for patterns.
Speaker 1: Okay, and they also mentioned indogenous versus exogenous games.
Speaker 2: Ah, Right, Indogenous means the game mechanics and the learning
Speaker 2: content are tightly linked, intrinsically connected. Exogenous is where they're separate,
Speaker 2: Like playing a fun game than getting a quiz.
Speaker 1: Old chocolate covered broccoli problem kind of.
Speaker 2: Yeah. Endogenous games generally lead to better learning and more enjoyment.
Speaker 2: It feels more integrated.
Speaker 1: Makes sense. And this all feeds into a.
Speaker 2: Student model exactly. It's like a profile that accumulates data, emotions, skills,
Speaker 2: knowledge based on how the player interacts. It helps understand behavior,
Speaker 2: give feedback, and this is key. Dynamically adapt the.
Speaker 1: Game, adapt the game on the fly, Yeah.
Speaker 2: To try and create an optimal learning zone, keep it
Speaker 2: challenging but not frustrating. The book mentions validation work on
Speaker 2: cage showing benefits for reuse and speed, and that adaptation
Speaker 2: helps with boredom, though maybe less so for learners who
Speaker 2: are already knew the material well. They even use dynamic
Speaker 2: basion networks for the adaptation part.
Speaker 1: Complex stuff. Yeah, Okay, let's move from the player experience
Speaker 1: and assessment to the actual bones of the game. The
Speaker 1: software architecture Chapter four right.
Speaker 2: Chapter four tackles architecture, especially for mobile serious.
Speaker 1: Games, mobile ads, constraints, doesn't it memory processing.
Speaker 2: Power definitely, So how you structure the game the components,
Speaker 2: how they talk to each other is critical for handling
Speaker 2: data efficiently and distributing resource as well. Design patterns can
Speaker 2: really help here.
Speaker 1: So thinking about non functional requirements from the start, things
Speaker 1: like complexity, coupling, performance, reusability absolutely crucial.
Speaker 2: Interestingly, research by Mizutani mentioned in the chapter suggests that
Speaker 2: while data driven design is common, things like test driven
Speaker 2: development and applying standard design patterns are less frequent in
Speaker 2: serious games than you might expect.
Speaker 1: HU wonder why that is.
Speaker 2: Could be various reasons, maybe team composition, project pressures. The
Speaker 2: chapter uses an example Android game to illustrate points, even
Speaker 2: mentioning how UI color choices might impact usability like blue.
Speaker 1: For calm, subtle things matter. How do they suggest evaluating
Speaker 1: the architecture itself?
Speaker 2: They look at metrics complexity like cyclomatic complexity CCM and
Speaker 2: coupling between objects CBO. Basically how complicated and interconnected the
Speaker 2: code is?
Speaker 1: Okay, standard software metrics yeah, and.
Speaker 2: They use statistical correlation Spearman correlation to see how these
Speaker 2: relate and to derive a sort of quality factor for
Speaker 2: code paths. And the main point is evaluating the design
Speaker 2: early saves time and money later by catching potential problems
Speaker 2: like defects and helping prioritize testing efforts.
Speaker 1: Makes sense, find problems before they get baked in. Did
Speaker 1: they find patterns helped?
Speaker 2: Yes, they found that implementing patterns like wrapper and MBC
Speaker 2: tended to result in code sequences with better quality scores
Speaker 2: based on those complexity and coupling metrics.
Speaker 1: Okay, so good architecture matters. Now let's look at a
Speaker 1: very specific, quite high stakes application Chapter five and this
Speaker 1: en Trust platform.
Speaker 2: Ah. Yes, en Trust. This is a serious game platform
Speaker 2: specifically designed for assessing clinical decision making in medical residents.
Speaker 1: Right, judging their readiness for entrustment, basically being trusted to
Speaker 1: perform tasks independently.
Speaker 2: Exactly. Medical education is shifting more towards competency based assessment.
Speaker 2: It's not just did you pass the test? But can
Speaker 2: you actually do this safely and effectively?
Speaker 1: And traditional observation has limits, right, depends on the case
Speaker 1: that they happen to see faculty time potential bias.
Speaker 2: Precisely, Virtual patient simulations like interruest offer objectivity, reproducibility. You
Speaker 2: can create standardized scenarios measure performance consistently potentially reduced bias.
Speaker 1: What does en trust actually do? What are its features?
Speaker 2: It's pretty comprehensive. There's a case library for creating scenarios,
Speaker 2: specifying how interventions affect vials, scoring actions, a virtual patient
Speaker 2: generation tool for customization age, sex, BMI, even physical abnormalities.
Speaker 1: Really detailed customization for diversity.
Speaker 2: Yeah, you can set starting vital signs and choose algorithms
Speaker 2: for how they change realistically, like simulating shock or sepsis.
Speaker 2: Specify physical exam findings, what orders are available labs, meds
Speaker 2: procedures and their outcomes and scoring effects.
Speaker 1: It's very granular, sounds powerful. Did they test it? Was
Speaker 1: there a pilot study?
Speaker 2: Yes, and the pilot study showed promising results. It was
Speaker 2: able to discriminate between surgical residents at different training levels
Speaker 2: PGI levels when assessing their management of inguinal hernias.
Speaker 1: So higher PGI level generally meant better score on entrust.
Speaker 2: That's what they found a positive correlation. It also seemed
Speaker 2: to capture performance on critical decision points in complex cases
Speaker 2: like strangulated hernias.
Speaker 1: So it suggests feasibility and some initial validity for using
Speaker 1: this kind of platform for objective assessment, potentially informing those
Speaker 1: crucial entrustment decisions exactly.
Speaker 2: It's a really interesting application.
Speaker 1: Building on that idea of assessment and adaptation. Chapter six
Speaker 1: dies into using machine learning mL in serious games.
Speaker 2: Right, this is a growing trend using mL not just
Speaker 2: for assessment like we touched on with Stealth assessment, but
Speaker 2: also to automatically adapt the game.
Speaker 1: Itself, personalizing the experience basically to reduce frustration keep players engaged.
Speaker 2: That's the aim. The chapter lays out a kind of
Speaker 2: general four phase methodology if you want it to take
Speaker 2: an existing non adaptive game and add mL to it.
Speaker 1: Okay, what are the steps?
Speaker 2: First, identify a game that could actually benefit from adaptation. Second,
Speaker 2: model the gameplay tasks and how they relate to the
Speaker 2: learner's ability or the competencies you're targeting.
Speaker 1: Makes sense, Know what you want to adapt and why.
Speaker 2: Third is the technical part, Actually build the mL models
Speaker 2: and integrate them into the game's code. And fourth, critically
Speaker 2: evaluate if the adaptive version is actually better than the
Speaker 2: original does it improve learning or engagement?
Speaker 1: That evaluation part sounds crucial, But are their challenges in
Speaker 1: doing this?
Speaker 2: Oh?
Speaker 1: Yes?
Speaker 2: The chapter highlights five common ones first, selecting the right
Speaker 2: data to feel the mL model garbage in.
Speaker 1: Garbage out right, always it Else.
Speaker 2: Choosing which game elements to adapt what makes the most impact.
Speaker 2: Then there's the.
Speaker 1: Cold star problem, like a recommendation engine with no.
Speaker 2: History exactly, how do you personalize when you have no
Speaker 2: data yet? Maybe use existing data or generate synthetic data
Speaker 2: to start Tricky. Another is figuring out how often to
Speaker 2: adapt between sessions levels during tasks. There are trade offs
Speaker 2: in computational cost and impact on.
Speaker 1: The player, right, Adapting too much could.
Speaker 2: Be jarring and maybe the biggest challenge proving that adaptation
Speaker 2: actually improves learning, not just engagement. Sometimes more fun doesn't
Speaker 2: equal more effective education a.
Speaker 1: Really important distinction to make. So evaluate both the engagement
Speaker 1: and the learning value.
Speaker 2: Definitely you need evidence for both.
Speaker 1: Okay, so we've covered complexity, evaluation, adaptation. Where does the
Speaker 1: field go from here? What are the big future directions
Speaker 1: or challenges?
Speaker 2: Yeah?
Speaker 1: Chapter seven and thirteen touch.
Speaker 2: On this, right they do. Chapter seven revisits that idea
Speaker 2: of t ansfering strategies from entertainment games. They give examples
Speaker 2: like games tackling serious themes effectively hell Blade and Psychosis
Speaker 2: as mentioned.
Speaker 1: Or those simulator's truck driving PC building that are surprisingly
Speaker 1: engaging but also teach real world skills sort of exactly.
Speaker 2: They also talk about emergent possibilities, how game design can
Speaker 2: lead to unexpected player experiences, and modding again as a
Speaker 2: way to modify and transfer serious games. Emphasizing contexts and.
Speaker 1: AI emotion modeling comes up again too.
Speaker 2: Yes, the idea that understanding player emotion can lead to
Speaker 2: better design. They introduce this concept of personality vectors for
Speaker 2: NPC's non player characters.
Speaker 1: Personality vectors like giving AI characters more nuanced behavior.
Speaker 2: Kind of They give examples like patrolling guards or poker
Speaker 2: players behaving more realistically. This could be huge for intelligent
Speaker 2: tutors or health scenarios, making interactions feel more genuine.
Speaker 1: That sounds potentially very powerful. What about the grand challenges
Speaker 1: laid out in chapter thirteen?
Speaker 2: Chapter thirteen summarizes several big ones, like the need for
Speaker 2: software engineering methods, specifically for serious games. We need tailored approaches.
Speaker 1: Not just borrowing from general software engineering right.
Speaker 2: Also establishing common terminology, a shared ontology, so everyone speaking
Speaker 2: the same language. Improving reusability and maintainability always a challenge
Speaker 2: in software, but maybe more so here.
Speaker 1: Quality assurance and testing too, I imagine have unique aspects.
Speaker 2: Definitely, and developing better ways to monitor and adapt gameplay
Speaker 2: safely avoiding unwanted side effects, enabling truly player centric design.
Speaker 2: They stress needing uniform data, logues and iterative design.
Speaker 1: Consistent data is key for that monitoring and adaptation.
Speaker 2: Absolutely, and finally, a big opportunity creating effective serious games
Speaker 2: for teaching software engineering itself, using gamification and AI for
Speaker 2: personalized learning paths for developers.
Speaker 1: Turning the tools back on ourselves essentially.
Speaker 2: Pretty much So. Yeah, lots of challenges, but also lots
Speaker 2: of opportunities for research and advancement.
Speaker 1: It really sounds like a dynamic field with a lot
Speaker 1: still to figure out.
Speaker 2: No question, these challenges are where the next breakthroughs will
Speaker 2: likely happen.
Speaker 1: Okay, let's try and wrap this up. We've covered a
Speaker 1: lot of ground in this deep dive. We started with
Speaker 1: that inherent complexity pulling from so many disciplines.
Speaker 2: Right then, how gamification is sneaking into software development itself
Speaker 2: and the crucial need for solid user experience evaluation using
Speaker 2: various methods, even physiological ones.
Speaker 1: We looked at stealth assessment measuring learning without interrupting flow
Speaker 1: and dynamic adaptation using machine learning, plus the architectural nuts
Speaker 1: and bolts, especially for mobile.
Speaker 2: Don't forget the specific applications like the Entrust platform for
Speaker 2: assessing medical decision making showing real world impact true.
Speaker 1: And finally, we touched on those future directions transferring ideas
Speaker 1: from entertainment, AI personality, and the big challenges ahead like
Speaker 1: specialized methodologies and better adaptation.
Speaker 2: It's clear that blending rigorous engineering with engaging game design
Speaker 2: gives us some incredibly powerful tools for education, training, maybe
Speaker 2: much more so.
Speaker 1: The final thought for you, the listener, think about those
Speaker 1: principles of engagement and motivation from games. How could they
Speaker 1: apply elsewhere in your life? Maybe in learning something new,
Speaker 1: or even making mundane tasks feel a bit more well
Speaker 1: game like and rewarding.
Speaker 2: What game mechanics could make tricky things more approachable? Definitely
Speaker 2: some interesting food for thought there.
Speaker 1: Absolutely If this sparked your interest, do check out the
Speaker 1: book Software Engineering for Games and Serious Contexts and the
Speaker 1: related research for a much deeper look. Thanks for joining
Speaker 1: us on the deep dive.