Learning to Code, One Helpful Nudge at a Time

May 5, 2025November 19, 2025 ~ 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

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

April 21, 2025November 19, 2025 ~ 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

April 7, 2025November 19, 2025 ~ 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

Test-Drive Your City: How Simple Simulations Make Smarter Policies

March 24, 2025November 19, 2025 ~ Manuel Castañón-Puga

Cities are messy. Many people, rules, and surprises collide, which means even good intentions can backfire. Sandoval Félix and Castañón-Puga argue that decision-makers should “mock up” policies on a computer first, like trying a route in a map app before leaving home. These lightweight models allow people to explore what might happen if they build a new park, change bus routes, or tighten zoning—before affecting the real city. That kind of “anticipatory knowledge” helps avoid short-term fixes that create long-term problems.

The chapter explains why this matters: cities aren’t machines that can be tuned with one knob. They’re complex systems where small tweaks can trigger big, unexpected outcomes, because everything is connected. In complex systems, patterns “emerge” from many small actions—think of traffic waves or shopping streets that pop up on their own. This is why looking only at one piece often fails. The complexity lens focuses on interactions and probabilities, rather than rigid plans, allowing policies to account for side effects across different parts of the city.

To explore these interactions, the authors highlight agent-based models—small worlds filled with “agents” (such as households, shops, or buses) that follow simple rules. There’s no central boss; each agent has limited knowledge and reacts to its surroundings. When you run the simulation, their choices add up to city-scale patterns. A related technique, cellular automata, applies these rules to a grid, allowing nearby cells to influence each other—useful because, in cities, what’s next door often matters most. These tools don’t predict the future with certainty, but they help identify counterintuitive moves, path-dependent traps, and situations where individual wins don’t add up to a public win.

Getting started is less scary if you treat it like learning a creative skill. The authors suggest tinkering first, building simple blocks, keeping version notes, and borrowing small code “snippets” from similar models. Even sketching a flow diagram helps you stay focused and avoid accidental behaviors. Then, present the results clearly: use plain language, visuals, and connect the outputs to real-life steps, such as which rules or budgets would need to be changed. Communication guides, such as ODD/ODD+ D and the STRESS checklist, can help keep your work organized and understandable for non-experts. The point isn’t perfection—it’s making choices that are better informed, more transparent, and less likely to surprise everyone later.

In everyday terms, this chapter is an invitation to play “what if?” with the city you care about. Treat models like a safe sandbox where you can test ideas fast and see the ripple effects, not a crystal ball. When you understand that cities are living networks, you’re more likely to ask better questions, spot side effects early, and push for policies that work in the real world—not just on paper.

Reference:
Félix, J. S., & Castañón-Puga, M. (2019). From simulation to implementation: Practical advice for policy makers who want to use computer modeling as an analysis and communication tool. In Studies in Systems, Decision and Control (Vol. 209). https://doi.org/10.1007/978-3-030-17985-4_6

Turning a Messy To-Do List into a Project You Can Actually Finish

March 3, 2025November 19, 2025 ~ Manuel Castañón-Puga

Agile is a simple idea: build in short steps, listen to users, and be ready to change course fast. It’s used far beyond apps now, from classrooms to hospitals, because life rarely goes exactly as planned. Castañón-Puga and colleagues explain that many teams visualize work on a task board with three columns—To-Do, In Progress, Done—so everyone can see where things stand at a glance. Their study demonstrates how this setup aligns well with “earned value management” (EVM), a method for comparing what was planned with what was actually accomplished and spent. In plain terms, EVM answers: are we on time, on budget, and getting the value we expected?

Here’s the cheat sheet. Planned Value (PV) is what you expected to finish by now. Earned Value (EV) is what you truly finished. Actual Cost (AC) is the actual cost. Two quick ratios tell the story: SPI = EV ÷ PV (schedule health) and CPI = EV ÷ AC (cost health). If SPI or CPI is below 1, you’re slipping; above 1, you’re ahead. Think of a group project: if you planned to write four pages this week (PV), wrote only two (EV), and spent more hours than expected (AC), your SPI and CPI will warn you early, before the deadline panic hits.

The authors developed a simple simulator that resembles a Kanban board. Tasks move from To-Do to Done while team “agents” pick them up, work on them, and sometimes finish early or experience delays. A small dashboard displays a burndown chart of remaining tasks, a PV-EV-AC chart, and a live CPI/SPI plot, allowing you to see the project’s pulse in real-time. You don’t need fancy math to use the idea: keep a board, log the time you expected versus the time you actually spent, and watch the two indices. It’s like tracking study goals: set your plan, record actual hours, and spot slips before exam week.

What makes this practical is how small chances of “good luck” or “bad luck” add up. In 2,100 simulated runs, the team tested different conditions—namely, the number of people, the number of tasks each person juggles, and the odds of finishing early or late. A clear pattern emerges: higher chances of being delayed push CPI down, while higher chances of finishing early push CPI up. The number of people or tasks per person matters less than those delay/advance probabilities. So in everyday terms, reducing blockers and distractions (delay) and creating tiny speed-ups (advance) beats simply “throwing more people” at the work. Try time-boxing, clearer handoffs, or removing one recurring bottleneck; your CPI/SPI will thank you.

Why care? Because plans meet reality every day. Projects mix predictable steps and surprise twists, so you need flexibility and a quick feedback loop. A simple board, combined with an EVM, gives you both: you see the work, you measure progress, and you adjust quickly. Start small this week—list tasks, estimate hours, log actuals, and compute SPI and CPI. If they dip below 1, don’t stress; focus on fixing the causes you can control: fewer multitasking switches, fewer interruptions, and faster reviews. That’s how you turn a messy to-do list into a finish line you can actually reach.

Reference:
Castañón-Puga, M., Rosales-Cisneros, R. F., Acosta-Prado, J. C., Tirado-Ramos, A., Khatchikian, C., & Aburto-Camacllanqui, E. (2023). Earned Value Management Agent-Based Simulation Model. Systems, 11(2), 86. https://doi.org/10.3390/systems11020086