Education Science

Education science (or the science of education) is the systematic study of how people learn, how teaching works, and how educational systems can be designed, managed, and improved. It combines insights from psychology, sociology, neuroscience, philosophy, and data science to understand and enhance learning at all levels — from individuals to institutions to societies.

Why Your Group Chat Acts Like an Ecosystem (and What That Says About Real-World Organizations)

~ Manuel Castañón-Puga

Think of a complex adaptive system as many small players, all doing their thing at the same time, reacting to each other, and shaping the big picture without a single boss running the show. Ahumada-Tello and Castañón-Puga describe it as a decentralized network of “agents” (people, teams, apps, even companies) whose combined choices create the overall result. When these agents interact, local moves—such as a few friends changing plans—can ripple into larger patterns, like the overall vibe of a whole club or community. That’s why studying interaction (who talks to whom, and how often) often explains more than just listing everyone’s individual traits.

Four ideas make this easier to picture in daily life. First, there are many layers: your team sits inside a class, a school, a city, and so on; each level influences the others. Second, it’s nonlinear: small inputs can snowball—one rumor or meme can flip a whole plan. Third, connectivity matters: the links between people can be more important than any one person. And fourth, agents adapt: we sense what’s going on, choose, act, and adjust again, which lets the group self-organize without a central controller.

From these interactions, patterns “emerge.” No one plans every step, but simple local rules still add up to smooth outcomes—such as a supply chain functioning or a club event coming together. Good systems keep a balance: not frozen, not chaotic, but near the “edge of chaos,” where creativity and options are highest. They also benefit from variety (different viewpoints), simple rules that everyone understands, and numerous quick feedback loops where small tweaks can lead to significant improvements. Your world is full of nested systems, too—your part-time job is inside a local market that sits inside a national economy—so expecting change and adjusting fast is a winning habit.

What does this mean for organizations you’re part of—student groups, startups, workplaces? They work best when they avoid two traps: a rigid “perfect balance” that kills innovation, and total rule overload that tips into chaos. Aim for limited instability where people can experiment safely. Over-centralizing with too many procedures can suffocate initiative, so use a few clear rules, strong communication, and trust teams to adapt. As Ahumada-Tello and co-authors note, organizations thrive through emergence, self-organization, and evolution—so design your projects to learn quickly, share information widely, and let good ideas spread.

Reference:
Ahumada-Tello, E., & Castañón-Puga, M. (2019). Sistemas adaptativos complejos: un método de análisis organizacional. In I. Plascencia López & J. Ramos (Eds.), Teorías de la complejidad en las ciencias económico-administrativas: Una aproximación (1st ed.). Universidad Autónoma de Baja California.

Learning to Code, One Helpful Nudge at a Time

~ Manuel Castañón-Puga

If you’ve ever opened a coding tutorial and felt lost by line two, you’re not alone. Hurtado et al. describe a simple idea that helps: teaching beginners with a platform that guides you step by step, provides clear feedback, and recommends the next thing to learn based on your progress. Their tool, Protoboard, suggests learning materials by combining teacher input with intelligent rules about difficulty, and it adapts to each student rather than presenting the same content to everyone. Think of it like a friendly playlist for studying Java: it starts with easier “tracks,” then levels you up as you demonstrate your readiness. The system uses fuzzy-rule recommendations tied to beginner, medium, and advanced learning objects, along with basic metadata such as audience and format, to determine what you should see next.

When you open a unit, Protoboard prompts you to read the short lesson first and then try two types of practice: one where you fill in missing code and another where you start from a blank page. This order matters because it builds confidence before throwing you into the deep end. The app also checks for good habits—clear variable names, proper use of brackets, clean structure—and points out exactly what went wrong when you slip. That means your mistakes turn into quick lessons instead of long detours on Stack Overflow. In plain terms: you see what to fix, why it matters, and what “good” looks like.

Does this approach actually help? Hurtado et al. tested it with 112 students across two universities, focusing on classic control structures like if/else, switch, while, do-while, and for. After studying a topic, each student completed a pair of exercises (one “complete the code,” one “from scratch”). On average, students needed roughly one to three tries to get programs right—evidence that the feedback and structure were doing their job. The trickiest bits were usually the if/else cases, which makes sense for beginners; still, most learners landed the solution in just a few attempts.

Why should you care if you’re just starting out? This study suggests a smoother and less frustrating way to learn. A tool that nudges you to read first, practice right after, and adopt clean habits can save you time and make your code easier to grow later. Teachers benefit too—they can see how many attempts a task takes and adjust lessons or add new examples where people stumble. For you, that means clearer instructions, more tailored practice, and faster progress. If you’re curious about coding, look for resources that copy these ideas: short lessons, immediate practice, precise feedback, and gradual difficulty. Small wins stack up, and with the right nudges, you’ll go from “What is this bracket doing?” to “I’ve got this” much faster than you think.

Reference:
Hurtado, C., Licea, G., García-Valdez, M., Quezada, A., & Castañón-Puga, M. (2020). Teaching computer programming as well-defined domain for beginners with protoboard. Advances in Intelligent Systems and Computing, 1160 AISC, 262–271. https://doi.org/10.1007/978-3-030-45691-7_25

How Tech Can Read the Room—and Make Your Experience Better

~ Manuel Castañón-Puga

Ever felt like a game, app, or museum exhibit “just gets you” and reacts at the right moment? Rosales et al. explain a simple idea behind that feeling: measure how you’re interacting, then use that to adapt what you see. They lean on a classic set of eight clues about your behavior—presence, interactivity, control, feedback, creativity, productivity, communication, and adaptation—to describe your “level of interaction.” Think of them as vibes the system watches for: Are you engaged? Are you trying new things? Are you getting responses when you press buttons? These signals help the system learn what to show next, so you don’t get bored or lost.

To test this in real life, the team visited an interactive science museum in Tijuana, where people—especially children and teenagers—play to learn. They tracked everyday details, such as how long someone stayed, where they moved, whether they read information labels, and if they returned to the same spot. That may sound small, but together, those bits tell a story about attention and curiosity, helping designers make labels clearer, stations easier to use, and activities more enjoyable. Imagine a driving or flight station that notices you’re stuck and gives a quick tip, or speeds things up when you’re clearly nailing it—that’s the goal.

Under the hood, Rosales et al. use a fuzzy logic system—don’t worry, it’s just math that handles “in-between” values instead of only yes/no. Each of the eight clues gets a score between 0 and 1, and the system groups those scores into levels from “very bad” up to “excellent.” Then it determines your overall interaction level, ranging from 0 to 5, much like a skill tier in a game. If your level is near the next tier, it nudges you upward and updates its knowledge of you for the next step. In plain terms, the exhibit watches what you do, estimates your current mood, and adapts so you can keep learning without zoning out.

Does it work? They tried it with data from 500 visitors. The team split the group in half—one half to set up the tool and the other half to test it—and compared the system’s calls with human judgments. The results were close most of the time, with about 76% accuracy, which is decent for a first pass. For everyday life, that means smarter exhibits, apps, and games that can sense when to give you hints, when to challenge you, and when to switch things up. It’s the same idea you can use yourself: notice your own signals—am I engaged, getting feedback, learning something new?—and tweak your setup, whether that’s changing a study app’s difficulty, turning on captions, or picking a different mode in a game. Small cues add up to a better experience.

Suggested by Gayesky and Williams’ level idea and brought to life by Rosales et al., this approach is about meeting you where you are and moving with you. The more systems pay attention to those eight everyday clues—and the more they adjust in the moment—the more tech feels like a helpful guide, rather than a hurdle. Next time a tool feels smooth and responsive, there’s a good chance it’s quietly reading the room and adapting to keep you in the zone.

Reference:
Rosales, R., Ramírez-Ramírez, M., Osuna-Millán, N., Castañón-Puga, M., Flores-Parra, J. M., & Quezada, M. (2019). A fuzzy inference system as a tool to measure levels of interaction. In Advances in Intelligent Systems and Computing (Vol. 931). https://doi.org/10.1007/978-3-030-16184-2_52

Why Software Engineering Matters for Your Next Ten Years

~ Manuel Castañón-Puga

Software is behind almost everything you use each day, from your phone to your favorite apps, so it’s no surprise that building software has become one of the most important careers of our time. Candolfi et al. explain that software development continues to grow as more devices and services rely on it, and this trend is expected to persist for years. The field has matured significantly: early work copied ideas from hardware, then shifted to better planning, design before code, and later to faster, more flexible methods used to build the web and mobile apps you use daily. Today’s hot areas—such as mobile apps, Internet of Things devices in the home, big data, and artificial intelligence—are all powered by software skills.

If you’re in Mexico, there are real opportunities. Programs like PROSOFT encouraged universities to update their courses and connect students with industry, enabling more people to acquire practical skills that companies need. In Baja California, the local tech scene is represented by the IT@BAJA cluster and spaces like the BIT Center, where more than a hundred companies develop software for various applications, including government systems, websites, and call centers—proof that there’s a homegrown market for talent. Companies say they need people for things you can picture in your daily life: apps for small businesses, finance and HR tools, e-commerce, online learning, logistics, and even games.

The career outlook is strong. In the United States, roles like full-stack developer and data scientist have topped “best jobs” lists thanks to high pay and demand—signals that also matter for anyone collaborating with U.S. teams from this side of the border. Industry reports reviewed by Candolfi et al. predict more cloud services, microservices (think apps built from small, easy-to-update pieces), edge computing, and AI in products you’ll use, which means more teams will need people who can build and improve them. This isn’t just for big tech firms; it affects hospitals, schools, shops, and factories as they transition into “Industry 4.0,” where software connects machines, data, and people to work more efficiently.

So what should you focus on? The experts Candolfi et al. gathered point to a balanced toolset: learn to solve real problems with code, understand data, and try areas like AI or mobile—but don’t skip soft skills. Being able to communicate ideas, work with others, and learn fast is what helps you grow when tech changes. If you start now—take a course, join a local project, or build a small app—you’ll be stepping into a field that is set to stay relevant for at least the next two decades.

Reference:
Candolfi Arballo, N., Licea Sandoval, G., Navarro Cota, C., Mejía Medina, D. A., Castañón Puga, M., Velázquez Mejía, V., & Caraveo Mena, C. (2021). Ingeniería de Software. Necesidades y prospectiva de la profesión en Baja California. In C. A. Figueroa Rochín & E. I. Santillán Anguiano (Eds.), Software libre educativo en una cultura digital (1st ed.). Qartuppi, S. de R.L. de C.V. https://doi.org/10.29410/QTP.21.03

From Taps to Talk: How Smart Exhibits Learn From You

~ Manuel Castañón-Puga

When you visit a museum or use an app, you’re not just a spectator—you’re part of a conversation. Rosales et al. show that the quality of that conversation depends on a few simple things: Are you actually there and paying attention? Do you get to control anything? Do you receive feedback that helps you keep going? Can you adapt what you’re doing, be creative, or even “talk” back to the system? Those ideas—presence, interactivity, control, feedback, creativity, productivity, communication, and adaptation—can be observed and visualized to create a picture of how engaged you are. The goal is to use that picture to serve you better in the moment, not after the fact.

To make this practical, the authors describe six easy-to-grasp “levels” of interaction, ranging from simply being present (Level 0) to full-on back-and-forth interactions where you create, choose, adapt, and receive instant responses (Level 5). Imagine the difference between glancing at a welcome screen and actually steering what happens next. At the high end, you can pick what you see, change the order, give input, and get tailored replies—more like a game than a poster. Thinking in levels helps designers ask: what tiny tweak would move someone up one step—add a button, a hint, a quick “nice job,” or a way to choose the next challenge?

The team tested these ideas in a Mexican science museum called “El Trompo,” watching 500 visitors try a four-screen exhibit where you control a car, plane, bike, or balloon. Their system treated each exhibit and each visitor like “agents” that sense what’s happening and adjust the experience. Think of it as a low-key guide that notices if you’re lost, bored, or excited and then nudges the content—more instructions if you’re stuck, more freedom if you’re cruising. This isn’t sci-fi; it’s built with rules that translate fuzzy, human behavior into clear decisions about what to show you next.

What’s the practical takeaway for your everyday tech life? If you’re building a school project, a club website, or a small app, aim to lift people one level at a time. Offer quick feedback so they know a tap or swipe “worked.” Offer small choices so they feel in control, such as picking the next topic or difficulty level. Let them adapt the path, not just the pace. And if you’re the user, look for tools that “listen”—ones that react when you linger, explore, or ask for more. In the study, a learning approach called a neuro-fuzzy system performed the best in recognizing the level of people, which helped the system respond more accurately. In plain terms, the tech learned to read the room and act accordingly, which made the experience smoother and more enjoyable.

Reference:
Rosales, R., Castañón-Puga, M., Lara-Rosano, F., Flores-Parra, J. M., Evans, R., Osuna-Millan, N., & Gaxiola-Pacheco, C. (2018). Modelling the interaction levels in HCI using an intelligent hybrid system with interactive agents: A case study of an interactive museum exhibition module in Mexico. Applied Sciences (Switzerland), 8(3), 1–21. https://doi.org/10.3390/app8030446