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Is Computer and Information Science Hard? An Honest Look at the Challenge and Reward

Is Computer and Information Science Hard? An Honest Look at the Challenge and Reward

Is Computer and Information Science Hard? An Honest Look at the Challenge and Reward

Is Computer and Information Science Hard? An Honest Look at the Challenge and Reward

Let's cut right to the chase, because if you're reading this, you're probably wrestling with that question yourself: Is Computer and Information Science (CIS) hard? The short answer, the honest answer, is yes. It absolutely can be. But that's not the whole story, not by a long shot. Like climbing a mountain, the path to mastering CIS is steep, demanding, and at times, utterly grueling. You'll question your sanity, stare at lines of code wondering if you've suddenly forgotten how to read English, and occasionally want to throw your computer out the window. Trust me, I've been there, and so has every single person who's genuinely excelled in this field. Yet, just like that mountain climber, the view from the top, the sense of accomplishment, and the profound impact you can make are incredibly, spectacularly rewarding. This isn't just a degree; it's a mental marathon, a continuous puzzle, and a relentless pursuit of innovation. So, let's peel back the layers, dissect the challenges, debunk the myths, and truly understand what makes CIS a formidable yet endlessly fascinating domain. We're going to dive deep, get real, and give you the unvarnished truth about what it takes to thrive in this dynamic world.

Understanding Computer and Information Science (CIS)

Before we can even begin to quantify "hard," we need to get crystal clear on what we're actually talking about. The term "Computer and Information Science" itself is often a catch-all, a broad umbrella that covers a lot of ground, and frankly, it's often confused with its cousins, pure Computer Science (CS) and Information Technology (IT). This distinction isn't just academic nitpicking; it's fundamental to understanding where the "hard" parts lie and what kind of intellectual muscles you'll be flexing. CIS, in its essence, is a beautiful, sometimes chaotic, blend. It’s about not just building the machines and writing the code, but also understanding why we're building them, who they're for, and how they integrate into the complex tapestry of human society and business operations. It’s where the abstract meets the application, where theoretical elegance shakes hands with practical utility.

When you're looking at a CIS program, you're not just signing up to become a code monkey, nor are you solely aiming to be an IT support guru. You're embarking on a journey that will equip you with a unique dual perspective. On one hand, you'll delve into the foundational logic and structures that underpin all computing; on the other, you'll explore how these powerful tools can be harnessed, managed, and optimized to solve real-world problems, enhance user experiences, and drive organizational success. This interdisciplinary nature is both its greatest strength and, for some, its greatest challenge. It demands a mind that can appreciate the intricate dance of algorithms while simultaneously grasping the messy, unpredictable nature of human interaction and business strategy. It’s a field where you might spend an hour debugging a complex piece of software and the next hour strategizing how that software will impact user adoption or corporate profitability.

What is Computer Science?

Ah, Computer Science. This is often what people think of when they hear "computer degree," and it's certainly a foundational pillar of CIS. At its heart, pure Computer Science is an academic discipline that delves into the theoretical foundations of information and computation, and their practical implementation and application in computer systems. Think of it as the bedrock, the deep philosophical inquiry into what makes computers tick, what their ultimate capabilities and limitations are, and how we can instruct them to perform tasks. It's less about using technology and more about creating the principles and tools that allow technology to exist and evolve.

The core of Computer Science is rooted in abstract thinking. You'll be spending a significant amount of time grappling with algorithms – not just how to write them in a specific language, but understanding their efficiency, their complexity, and their elegance. This involves a heavy dose of discrete mathematics, logic, and a way of thinking that breaks down problems into their smallest, most fundamental components. When I was a student, I remember countless hours spent on whiteboards, not coding, but drawing diagrams, flowcharts, and proving the correctness of an algorithm before a single line of code was ever written. It’s about understanding data structures, how information is organized and manipulated in memory, and the trade-offs between different approaches. This theoretical rigor is what makes CS so powerful; it teaches you to think like a computer, to design systems from the ground up, and to innovate beyond existing paradigms. It’s where you learn the "why" behind the "what," and it demands a certain intellectual curiosity for the abstract and the logical.

Moreover, Computer Science is deeply concerned with programming, but not just as a skill to write code. It's about programming as a tool for problem-solving, as a language to express your algorithmic designs. You'll learn various programming paradigms – object-oriented, functional, procedural – and understand when and why to apply each. System design is another critical component, where you learn to architect complex software systems, considering scalability, security, and maintainability. This isn't just about making something work; it's about making something work well, robustly, and for the long haul. It's a field that constantly pushes the boundaries of what's possible, from artificial intelligence and machine learning to cybersecurity and quantum computing. It's challenging because it requires an almost obsessive attention to detail, a relentless pursuit of efficiency, and a deep appreciation for the underlying mathematical and logical structures that govern our digital world.

What is Information Science?

Now, let's pivot to the "Information Science" side of CIS, which often gets less airtime in popular discourse but is absolutely vital and, dare I say, equally challenging in its own right, albeit in different ways. If Computer Science is about building the engine, Information Science is about making sure that engine powers something useful, efficient, and user-friendly within a specific context, whether that's a business, a library, or a healthcare system. It’s less about the raw theoretical power of computation and more about the practical application, management, and impact of information and technology on people and organizations. Think about how data is collected, stored, retrieved, and analyzed to make informed decisions. That's the heart of Information Science.

This field dives deep into data management, exploring database design, data warehousing, and the increasingly complex world of big data analytics. It's about understanding how to extract meaningful insights from vast oceans of information, transforming raw numbers into strategic advantages. But it's not just about the data itself; it's about the systems that manage it. You'll learn about information systems, their architecture, deployment, and how they support business processes. This often involves understanding enterprise resource planning (ERP) systems, customer relationship management (CRM) software, and other critical applications that form the backbone of modern organizations. It’s a holistic view, where the technology serves a purpose, and that purpose is usually tied to human needs or organizational goals.

Crucially, Information Science places a significant emphasis on human-computer interaction (HCI). This is where the human element truly comes into play. It's not enough to build powerful software; it needs to be intuitive, accessible, and enjoyable for people to use. You'll explore user experience (UX) design, user interface (UI) principles, and the psychology behind how people interact with technology. This requires empathy, observational skills, and a design-thinking mindset. Furthermore, Information Science often delves into the broader business applications of technology, understanding how IT strategies align with organizational objectives, how technology can drive innovation, and how to manage technology projects from conception to completion. It's a bridge between the highly technical world of CS and the strategic world of business, requiring a unique blend of technical acumen, analytical skills, and soft skills like communication and project management. The challenge here lies in bridging these worlds, translating technical jargon into business value, and designing solutions that are both technically sound and human-centric.

The Interdisciplinary Nature of CIS

Now, put it all together, and you have Computer and Information Science. This is where the magic, and frankly, some of the most profound challenges, truly happen. CIS isn't just a convenient label for a department; it represents a philosophical approach to computing education that recognizes the inseparable link between pure technical prowess and real-world application, between machine logic and human context. It's a field that actively seeks to blend technical skills with business acumen, social understanding, and human-centric design principles. You're not just learning to code; you're learning to code for a purpose, for people, and within a system.

Imagine a scenario: you're tasked with developing a new mobile application for a healthcare provider. A pure Computer Scientist might focus intensely on the backend database architecture, the efficiency of the algorithms for data retrieval, and the security protocols. An Information Scientist, however, would immediately start thinking about the user interface – is it easy for patients to navigate? Is the information presented clearly? How does this app integrate with existing hospital systems? What are the regulatory compliance issues? How will this app impact patient engagement and operational efficiency? A CIS professional is trained to consider all of these angles, often simultaneously. They possess the foundational technical skills to understand the underlying architecture and programming challenges, but also the critical thinking and analytical skills to assess the business impact, the user experience, and the ethical implications.

Pro-Tip: Embrace the Hybrid Mindset
Don't try to be just a coder or just a business analyst. The true power of a CIS degree lies in your ability to fluidly switch between these perspectives. Learn to speak both "tech" and "business." This interdisciplinary approach is what makes CIS graduates so valuable and versatile in the modern workforce. Companies aren't just looking for people who can build; they're looking for people who can think about what to build, why to build it, and how it will impact their bottom line and their users. This requires a constant mental juggling act, a willingness to step outside purely technical problems and consider the broader ecosystem. It's challenging because it demands a broader range of skills and a more holistic problem-solving approach than either pure CS or pure IT might require independently. This blend is where the real intellectual heavy lifting often occurs, making CIS a truly rigorous and comprehensive field of study.

The Core Challenges: Why CIS Can Be Perceived as Difficult

Alright, now that we've laid the groundwork for what CIS actually is, let's dive headfirst into the specific academic and intellectual hurdles that make this field so notoriously challenging. This isn't about scaring you off; it's about preparing you. Understanding these core difficulties upfront can help you develop the resilience and strategies needed to overcome them. These aren't just academic exercises; they are fundamental shifts in thinking that will redefine how you approach problems, both within and outside the digital realm.

Many students enter CIS programs with a vague notion that it involves "computers" and "coding," perhaps envisioning themselves building cool apps or designing sleek websites. While those outcomes are certainly possible, the path to get there is paved with abstract concepts, relentless debugging, and a constant demand for intellectual agility. It's not just about memorizing syntax or procedures; it's about fundamentally changing the way your brain processes information and solves problems. It requires a level of patience, persistence, and logical rigor that can be incredibly taxing, especially in the early stages. You'll find yourself wrestling with concepts that seem to defy intuition, and you'll often feel like you're learning a new language, a new way of thinking, and a new culture all at once.

Abstract Thinking and Problem-Solving

If there's one skill that underpins nearly every challenge in CIS, it's abstract thinking. This isn't just about thinking broadly; it's about conceptualizing complex systems that don't have a physical form, breaking down intricate problems into manageable, logical steps, and then reassembling those solutions into a coherent, functional whole. When you're dealing with software, you're not building with bricks and mortar; you're building with ideas, with logic, with invisible flows of data. It's like trying to describe the architecture of a dream – it exists, it has structure, but it's not tangible in the way a physical building is. This requires a mental dexterity to operate comfortably in the realm of the theoretical and the hypothetical.

Consider designing an algorithm to sort a list of a million items. You don't have a million physical items to sort. You have to conceptualize how the data behaves, how different sorting strategies would manipulate that data, and then reason about the efficiency and correctness of your chosen approach, all in your head or on a whiteboard. Debugging, too, is an exercise in abstract logical reasoning. A program isn't working. It's giving you an error, or worse, no error but incorrect output. You can't just look at it and see the problem; you have to trace the flow of execution, mentally simulate what the computer is doing at each step, and pinpoint where your logic diverged from your intention. This requires an almost surgical precision in thought, identifying the exact point of failure within a vast network of interconnected instructions.

For many, this shift to abstract problem-solving is the first major hurdle. We're often taught to think concretely, to see and touch what we're working with. CIS demands that you build mental models, that you visualize data structures, and that you understand the implications of a single line of code on a system of millions. I remember struggling immensely with recursion initially; the idea of a function calling itself felt like a paradox, a logical loop that my brain just couldn't quite grasp until I painstakingly drew out every step of the call stack. It's a fundamental retraining of your cognitive processes, and it takes time, practice, and a willingness to be uncomfortable with ambiguity until clarity emerges. The ability to abstract away unnecessary details and focus on the core logical components is what truly differentiates a competent CIS professional from a struggling one.

Mathematical Foundations

"You don't need to be a math genius!" I'll say later, and it's true, but let's be honest: a solid foundation in mathematics is absolutely non-negotiable for anyone serious about Computer and Information Science. It's not about complex trigonometry or advanced calculus for its own sake, but rather about the type of thinking mathematics fosters and the specific branches that are directly applicable to understanding computing principles. Discrete mathematics, in particular, is often the first major wake-up call for many students. This isn't the calculus you might remember from high school; it's a different beast entirely, dealing with countable, distinct elements rather than continuous ones. It covers logic, set theory, graph theory, combinatorics, and number theory – all of which are the bedrock upon which algorithms, data structures, and even computer hardware are built.

Think about it: how do you prove an algorithm will always work correctly? Through mathematical induction, a core concept in discrete math. How do you analyze the efficiency of an algorithm, determining how its performance scales with input size? That requires understanding Big O notation, which is rooted in mathematical analysis. When you're designing a database, you're essentially working with set theory. When you're optimizing network routing, you're using graph theory. These aren't just abstract concepts; they are the tools you use to reason about the behavior, performance, and correctness of computer systems. Without a grasp of discrete math, you're essentially trying to build a house without understanding the principles of physics and engineering – you might get something up, but it won't be robust or efficient.

Beyond discrete math, you'll encounter other mathematical disciplines. Calculus, while less pervasive than discrete math, is crucial for understanding continuous phenomena, especially in fields like machine learning, where gradient descent and optimization algorithms rely heavily on derivatives. Linear algebra is absolutely essential for graphics, data science, and again, machine learning, as it provides the framework for manipulating vectors, matrices, and transformations. Statistics is paramount for anyone dealing with data, from understanding probability distributions to performing hypothesis testing and building predictive models. The math isn't there to make your life harder; it's there because it is the language of computation, the rigorous framework that allows us to build complex, reliable systems. It teaches you precision, logical deduction, and the ability to articulate problems in a formal, unambiguous way. While you don't need to be a theoretical mathematician, you absolutely need to be comfortable with mathematical reasoning and its application.

Numbered List: Key Mathematical Concepts in CIS

  • Discrete Mathematics: Logic, Set Theory, Graph Theory, Combinatorics, Proof Techniques (Induction). Essential for algorithms, data structures, and theoretical computer science.

  • Linear Algebra: Vectors, Matrices, Transformations. Crucial for computer graphics, data analysis, machine learning, and scientific computing.

  • Calculus: Derivatives, Integrals. Important for optimization algorithms, machine learning (e.g., gradient descent), and understanding continuous systems.

  • Statistics and Probability: Probability distributions, hypothesis testing, regression analysis. Fundamental for data science, artificial intelligence, and any field involving data interpretation and prediction.


The Programming Learning Curve

For many, the programming aspect is the most visible and often the most daunting hurdle in CIS. It’s one thing to understand the idea of an algorithm; it's an entirely different beast to translate that idea into precise instructions that a computer can understand and execute flawlessly. The initial struggle with programming is almost universal. You're learning a new language, a new syntax, and an entirely new way of thinking about problem-solving. It's not like learning French or Spanish, where you're mapping new words to familiar concepts. Here, you're learning to communicate with a machine that has zero intuition, zero ability to infer your intent. Every single instruction must be explicitly stated, and every single comma, semicolon, and indentation error can bring your entire program crashing down.

The initial frustration stems from a steep learning curve. You start with basic concepts – variables, loops, conditional statements – and quickly move to more complex structures like functions, objects, and data structures. Each new concept builds upon the last, and if you miss a step or don't fully grasp a fundamental idea, subsequent topics become exponentially harder. Then there's the sheer breadth of languages. You might start with Python for its readability, but soon you're introduced to Java for object-oriented principles, C++ for systems programming, JavaScript for web development, and perhaps SQL for databases. Each language has its own quirks, its own paradigm, and its own set of best practices. It's a continuous effort to not just learn a language, but to become proficient in multiple languages and to understand when and why to use each one.

What makes programming truly hard isn't just the syntax; it's the logic. It's the ability to break down a complex problem into a series of smaller, solvable sub-problems, and then to orchestrate those solutions into a cohesive program. It's about designing elegant, efficient, and maintainable code. And let's not forget debugging – the infamous process of finding and fixing errors. You'll spend hours, sometimes days, staring at your code, convinced it's perfect, only to realize you missed a tiny logical flaw or a misplaced character. This process demands immense patience, meticulous attention to detail, and a systematic approach to problem-solving. It's a skill that only truly develops through relentless practice, countless errors, and the sheer grit to keep going when your code refuses to cooperate. The programming learning curve is less of a gentle slope and more of a series of vertical cliffs, each requiring a concerted effort to scale.

Rapid Technological Evolution

If you think you've learned everything you need to know after graduation, you're in for a rude awakening. One of the most defining, and often most challenging, aspects of Computer and Information Science is the blistering pace of technological evolution. This isn't a field where you can learn a set of skills and be set for life. Oh no, my friend. What's cutting-edge today can be legacy tomorrow. New programming languages emerge, existing ones get updated with new features, frameworks rise and fall, paradigms shift, and entirely new branches of technology (like AI, blockchain, quantum computing) spring up seemingly overnight. It’s a relentless treadmill, and if you stop running, you quickly fall behind.

This constant need for continuous learning can be incredibly daunting. It means that your education doesn't end when you receive your diploma; it's a lifelong commitment. You'll constantly be reading documentation, watching tutorials, experimenting with new tools, and taking online courses. It’s not just about learning new syntax; it’s about adapting your entire problem-solving approach to new paradigms. For instance, moving from traditional monolithic application development to microservices, or from imperative programming to functional programming, requires a significant mental shift. You have to be inherently curious, self-motivated, and comfortable with the idea that you will always be a student.

Insider Note: The "Imposter Syndrome" Loop
The rapid evolution often fuels imposter syndrome. You'll constantly feel like you don't know enough, that everyone else is smarter or more up-to-date. This is normal. Embrace it as a sign that you're in a dynamic field. The key isn't to know everything, but to know how to learn anything and to specialize deeply in a few areas while maintaining a broad awareness.

I remember when cloud computing started gaining serious traction. Suddenly, all my knowledge about on-premise server management felt... not obsolete, but definitely needing an upgrade. I had to learn entirely new concepts, new architectures, and new deployment models. It felt like starting from scratch in many ways, even with years of experience under my belt. This perpetual state of learning, while exciting for some, can be exhausting and intimidating for others. It demands a high degree of adaptability, a growth mindset, and a willingness to step outside your comfort zone repeatedly. The challenge isn't just keeping up; it's anticipating where technology is going and positioning yourself to be ready for the next wave, which means developing strong foundational principles that transcend specific technologies.

Debugging and Frustration Tolerance

If there's one experience that truly separates the casual dabbler from the dedicated CIS student, it's the crucible of debugging. This is where your mental resilience is tested to its absolute limits. Debugging is the systematic process of finding and fixing errors in a codebase, and it is, without exaggeration, where you will spend a significant portion of your programming life. It's not glamorous, it's often infuriating, and it demands a level of patience that many people simply don't possess. Imagine spending hours, sometimes entire days, staring at thousands of lines of code, looking for a single misplaced character, a subtle logical flaw, or an incorrect assumption that is causing your program to behave unexpectedly. The error message might be cryptic, or worse, there might be no error message at all, just incorrect output.

The frustration tolerance required for debugging is immense. You'll go through cycles of intense focus, followed by utter despair, followed by a sudden "aha!" moment that feels like a divine revelation. It's a constant battle against yourself, against your own assumptions, and against the unforgiving logic of the machine. I can vividly recall an instance early in my career where a critical bug was causing intermittent data corruption. It took me and a colleague nearly three days of non-stop work, fueled by copious amounts of coffee and mounting anxiety, to track down the culprit: an off-by-one error in a loop condition that only manifested under very specific, rare circumstances. The relief when we found it was palpable, but the journey there was pure agony.

Debugging isn't just about finding the error; it's about understanding why the error occurred. It forces you to develop an almost detective-like mindset, forming hypotheses, testing them rigorously, and systematically eliminating possibilities. You learn to use debugging tools, to print statements strategically, and to develop a mental model of how your program should be executing versus how it is executing. This mental resilience, this ability to stay calm and methodical in the face of immense frustration, is a critical skill that you will hone in CIS. It’s not just about technical aptitude; it’s about psychological fortitude. If you can push through the endless cycle of "it's not working," "why isn't it working," and "AHA! I found it," then you have what it takes to succeed in this field.

Project-Based Learning and Time Management

CIS education, perhaps more than many other fields, is heavily project-based. You're not just reading textbooks and taking exams; you're constantly building things. From small coding assignments to large, semester-long team projects, you'll be spending a significant amount of time designing, implementing, and testing software or information systems. This hands-on approach is fantastic for practical skill development, but it also introduces considerable challenges, particularly in time management and collaboration. These projects often have tight deadlines, complex requirements, and require a deep dive into problem-solving that extends far beyond classroom lectures.

The demands of these projects are extensive. They're rarely solo endeavors; you'll often be working in teams, which adds another layer of complexity. Suddenly, you're not just responsible for your own code, but for coordinating with teammates, merging code, resolving conflicts, and ensuring everyone is pulling their weight. Communication becomes paramount, and the ability to manage group dynamics, delegate tasks, and hold each other accountable is as important as your technical skills. I've seen brilliant individual coders struggle immensely in team projects because they couldn't effectively communicate their ideas or adapt to a collaborative workflow. It's a microcosm of the real-world development environment, where software is almost always built by teams.

Pro-Tip: Master Version Control Early
Seriously, learn Git and GitHub (or similar version control systems) inside and out, as early as possible. It will save you countless headaches in collaborative projects, help you track changes, and recover from mistakes. It's an indispensable tool in any developer's arsenal.

Furthermore, these projects are often open-ended, meaning there isn't always a single "right" answer. You're given a problem, and you have to design the solution, which involves making numerous architectural and implementation decisions. This requires critical thinking, creativity, and the ability to justify your choices. Balancing multiple projects, coursework, and other life commitments necessitates exceptional time management skills. You'll learn the hard way that procrastination is a luxury you cannot afford in CIS. Breaking down large projects into smaller, manageable tasks, setting realistic deadlines, and consistently chipping away at the work become vital survival skills. The pressure can be intense, but the experience gained from successfully delivering a complex project, especially as part of a team, is invaluable and deeply satisfying. It's where theory meets practice, and where you truly learn what it means to be a software engineer or an information systems professional.

Common Myths vs. Reality in CIS

The perception of CIS as "hard" isn't always rooted in reality. A lot of the apprehension, and frankly, a lot of the discouragement, comes from popular misconceptions that are widely circulated. These myths can deter intelligent, capable individuals from even considering a career in tech, or they can lead students down a path of unnecessary self-doubt. It's time to debunk some of these pervasive falsehoods and shine a light on the true nature of working and learning in Computer and Information Science. Understanding the reality can not only make the journey seem less intimidating but also highlight the diverse opportunities available within the field.

These myths often paint a picture of a monolithic, one-dimensional field, when in reality, CIS is incredibly broad and accommodating to a wide range of talents and personalities. The media often portrays programmers as solitary geniuses, hunched over keyboards in dark rooms, fueled by caffeine and an innate ability to speak in binary. While there might be some truth to the caffeine part for many of us, the rest is largely caricature. The truth is far more nuanced, collaborative, and human-centric than these stereotypes suggest. Let's dismantle these myths one by one, because your potential career shouldn't be limited by outdated or inaccurate ideas about what it means to work with computers.

Myth 1: You Must Be a Math Genius

This is perhaps the biggest, most intimidating myth that scares people away from CIS. The idea that you need to be some kind of mathematical savant, capable of solving complex equations in your head, is simply not true. While we discussed the importance of mathematical foundations earlier, the reality is that application and logical problem-solving are far more critical than raw mathematical genius. You don't need to be a theoretical mathematician; you need to be able to think mathematically. This means understanding logical reasoning, being able to follow and construct proofs, and applying mathematical concepts to computational problems.

For instance, understanding discrete mathematics is crucial, but it's not about memorizing theorems; it's about using set theory to model data relationships or graph theory to represent networks. You'll apply Big O notation to analyze algorithm efficiency, but you won't necessarily be deriving complex new mathematical formulas. The math serves as a tool, a language to describe and analyze computational processes, rather than an end in itself. What's truly essential is a logical mind, someone who can break down problems systematically, identify patterns, and reason through cause and effect. This kind of thinking is often developed through learning CIS, not necessarily a prerequisite you must possess at an elite level from day one.

Insider Note: Math as a Tool, Not a Torture Device
Think of math in CIS like a hammer to a carpenter. You don't need to be a physicist to understand how a hammer works or to use it effectively. You need to know when and how to wield it to build something useful. The same applies to mathematical concepts in computing. Focus on understanding the utility and application.

I've seen many students who initially struggled with math go on to excel in CIS because they developed a strong understanding of its practical applications. Their motivation wasn't to solve abstract mathematical puzzles, but to use math to build better software or analyze data more effectively. This shift in perspective can make all the difference. What's truly vital is a willingness to engage with mathematical concepts, to understand their underlying logic, and to appreciate their utility in solving computational problems. It's about developing mathematical literacy and reasoning, not necessarily being the next Einstein. If you can think logically and are willing to put in the effort to understand the relevant mathematical tools, you're more than capable of succeeding.

Myth 2: You Have to Be a Solitary Coder

The image of the lone hacker, coding away in isolation, is a powerful but deeply misleading stereotype. The reality of modern software development and information systems management is overwhelmingly collaborative. Very few significant projects are built by a single individual working in a vacuum. From the smallest startup to the largest enterprise, teams are the norm. This means that communication skills, teamwork, and the ability to work effectively with others are just