CIS4020 is a community-engaged classroom. Specifically, throughout the semester you will work with a partner from our community while you master the learning outcomes of the course. For most of you, this will be your first time working with a community partner, and perhaps your first time working with an actual client.

This process – where your discipline-based learning occurs in tandem with solving a problem provided by the community is referred to as community-engaged teaching & learning.

What is Community-Engaged Teaching & Learning (CETL)?

While there are several definitions of community-engaged learning, we will use the definition from the University of Guelph’s Community Engaged Scholarship Institute:

“Community-Engaged Teaching and Learning (CETL) is an active and critically reflective form of experiential learning which is regarded as a high-impact educational practice.

Community Engaged Learning (CEL) is a teaching and learning pedagogy that meaningfully integrates community engagement and curricular programming with intentional alignment between course learning outcomes and community-identified needs. CEL involves mutually beneficial collaboration for the purposes of co-learning and co-creating relevant scholarship or scholarly activity that strengthens academic inquiry, personal & professional development and contributes to positive social change/justice.”

Further, “CEL enhances teaching and learning while strengthening the relevance of higher education in broader society through critically examining and addressing complex issues we face in the 21st century.”

But what are the complex issues of the 21st century?

To answer this question, we look to the United Nations General Assembly, and its list of 17 Sustainable Development Goals that are described here as “a universal call to action to end poverty, protect the planet, and ensure that by 2030 all people enjoy peace and prosperity.”

Each of the 17 Sustainable Development Goals are big challenges; complex, interdisciplinary, and multi-sectoral. Moreover, each of them will require the collective efforts of people with different disciplinary training, lived experiences, and ways of understanding the world if we are to solve them. In other words, it’s highly unlikely that any one discipline will solve even one of these challenges.

In CIS3750, you’ll be tasked with mastering the learning outcomes of the course while developing a solution to address the needs of a local community partner. To date, every single community partner challenge could be described as a broad social challenge, or as previously stated, a “complex issue we face in the 21st century”. This has included challenges pertaining to food insecurity, education, and improving health and wellness, to name but a few. You should recognize these challenges from the list of 17 Sustainable Development Goals.

For example, the 2014 CIS3750 cohort spent their semester developing the Garden Fresh Box online ordering system for the SEED Community Food Project; a solution that addressed (on some level) the broad social challenges of zero hunger, good health and well-being, and decent work and economic growth.

Did the 2014 cohort of students solve these challenges? No. But they did use their skills as computer scientists and software engineers while working with a community partner to develop a prototype of a solution that was used to address these issues locally.

In short, our community partners bring challenges that are typically interdisciplinary; challenges that require the thinking of not just one, but many disciplines.

Why Social Challenges In The Classroom?

On the surface, computer science and broad social challenges such as food insecurity and health and wellness are not obvious bedfellows. So why include broad social challenges in the classroom? Why is this important to students in general, and computer science students in particular?

As previously stated, broad social challenges are not the domain of a single discipline so we can’t expect that their solutions will be borne from the contributions of one single discipline. Computer Science will not likely solve hunger, mitigate biodiversity loss, or improve our climate. However, computer science combined with other disciplines can make a huge difference.

Of course, higher education is more often than not taught in a siloed fashion. Undergraduate students studying discipline X are likely to spend most of their academic conversations with students from discipline X.

This isn’t necessarily a bad thing, as the siloed approach is meant to focus and hone the skills of the students within their discipline of choice. However, it can and often has the drawback that students never consider other ways of knowing the world. This is the geographical equivalent of having the opportunity to travel the world to explore and understand different cultures and different points of view, but opting to stay home in a place that is known and comfortable.

Since social challenges are naturally interdisciplinary, they provide an opportunity for computer science students to work with folks who fall outside the domain. But we can’t expect a computer scientist to meaningfully solve any challenge if we don’t first provide them space to:

1. understand and explore the complexities of the challenge in a safe setting that allows for experimentation and failure, and
2. develop the skills to communicate effectively within and between teams composed of seemingly unrelated disciplines.

That is, we need to provide students with the means and skills to travel the broader academic world. Building curricula that demand students achieve learning outcomes of a particular course while applying their knowledge and discipline-specific skills to a real-world broad social challenge is essential to achieving 1 and 2 above. Incorporating community partners, and introducing topics that sit outside the particulars of a course provides a space for students to begin exploring these challenges while building real-world skills.

Beyond this, it’s important not only for educators to provide discipline-specific knowledge but to encourage and foster our students’ sense of civic-mindedness and engagement. Being a contributing member of society is more than just getting up for the 9 to 5 job. By initiating students with broad social challenges, we ask them to begin considering the world outside of the academic bubble that we too often find ourselves in. It also provides the opportunity to challenge computer science students to consider how their skills might be used beyond a Google, Microsoft, Facebook, or Amazon job (not that there is anything wrong with these jobs).

Working With A Community Partner

Of course, working in an interdisciplinary setting, and in particular, with a community partner, requires a unique set of skills. These skills include the ability to:

• communicate effectively (with your teammates, and with your community partner – who likely doesn’t speak computer science or data science),
• effectively translate, transfer, and mobilize knowledge in a way that the intended recipient can not only receive it, and understand it, but use it,
• clearly identify a problem and its root causes,
• critically evaluate all available information and data, including knowledge gleaned from lived experience or outside the formal academic setting,
• work with a team to develop solutions that address the problem at hand,
• recognize our personal and discipline-based biases that might affect our ability to develop solutions,
• reflect on the process, knowledge, and outcomes,
• work as a contributing and active member of a team,
• lead when necessary, and
• remain open to other ways of understanding the world.

You’ll notice that all of these skills are foundational skills; and more specifically, the foundational learning outcomes of CIS4020.

As we work through the course, you will have opportunities to explore and develop each of these foundational skills while working with our partner. In some cases, this will involve activities that you can do on your own. In other cases, this will include some seemingly weird individual and group activities in the class or lab. Each of them has been curated to help you develop your foundational skills.

For now, however, we will focus on a few key things to keep in mind when working with a community partner.

Respect Our Community Partner

Our community partner is giving up a lot of their time to work with our class; a lot of time that they don’t have to spare. With this in mind, it’s absolutely critical that we use their time as wisely as we possibly can. This means being prepared, being polite, and being flexible.

Whenever our community partner is in the classroom, or has offered to answer questions, we need to come prepared with a list of questions or concerns; we should have answers to any questions they provide us; and we need to be able to let them know where in the process we are in terms of development. This is part of our professional duty.

Finally, because our community partners are working full-time jobs and life happens, there may be times when they need to reschedule a meeting or aren’t as quick to give us feedback as we’d like. We need to remain flexible to these challenges. If you are worried that their delay might affect your ability to complete an assignment or a lab demo, speak with the instructor. In some cases, the instructor will make a judgement call so that your ability to master the learning outcomes of the course isn’t hindered.

Whatever the case, the community partner should never be used as a reason why you haven’t done your job. They also should never be blamed for the success or failure of your solution.

Listen, Then Speak

Active listening is a skill that everyone needs to practice. We all have a tendency to start thinking of questions or replies even before the person to whom we are speaking has finished their thought. This can stunt our ability to get quality information from our community partners and can be considered disrespectful.

In our classroom and in our labs, we will explore various methods to help us listen first, and then speak (if necessary) later.

Be Open To Other Ways Of Knowing

While our community partner is not likely to be a computer science or data science expert, they are, however, an expert in the challenge area that we are trying to address in CIS4020. Our community partner understands the broad social challenge, and how the system works or doesn’t work to support folks affected by the social challenge. They also understand the needs of the community and the potential opportunities that might exist. All of this is essential information that you need to develop a good software solution. It is critical that we remain judgement-free and open to the information that our community partner provides.

It’s also important that we don’t elevate ourselves or our discipline above our community partner or their challenge. It’s not uncommon to hear certain STEM (Science, Technology, Engineering, and Mathematics) disciplines claim superiority over, for example, the arts and social sciences. It’s not uncommon to hear the same from arts and social science disciplines about STEM disciplines. No discipline is the best. No discipline will solve a broad social challenge on its own. And not all disciplines fit within the walls of a university. We need to respect that other disciplines provide different tools and methods to understand the world. We need to stop thinking that our tools are the only tools to solve a problem.

So, for example, if our community partner is explaining a challenge that they’ve already overcome, we need to avoid thinking that’s not how I would have done that. Instead, we should ask ourselves what is this the method they’ve used? Why have they used it? And how can I incorporate it into the work that I’m doing to develop a solution that is more than just a computer science solution?

Ask Good Questions

This might sound like an easy thing to do, but there are different ways to ask questions, and each of those methods will provide you with very specific types of data. It’s important to know whether or not your question is going to allow our community partner to provide broad answers or very specific and focused answers.

Consider, for example, how we might ask our community partner about the intended end-users of the analyses we are conducting in CIS4020. We could ask:

• Are your users 18 years of age or older?

Or we could ask

• Could you describe the user that you imagine will work with the results of the analyses we will eventually conduct?

In the former case, the answer would likely be yes or no, but the question doesn’t invite the community partner to provide more information. And even if we can say that their answer to this question is accurate and precise, it limits our ability to do anything with it.

In the latter case, however, the answer is likely to be a short description of their users, which may, in turn, trigger more questions and a discussion. The answer isn’t as tidy as the yes/ no response to the first question, but it is richer and potentially more informative.

We will explore this further in class.

Beware Of Language

If you’ve ever been in a situation where you’ve been approached by someone who speaks a different language than you, then you know how difficult communication can be. But just because you might speak the same language as someone does not mean that you understand them. Language can be extremely tricky and can be used to obfuscate meaning. Consider the following English sentences:

• Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo.
• James, while John had had “had”, had had “had had”; “had had” had had a better effect on the teacher.
• Is this a ship-shipping ship, shipping shipping ships?

How easy are these to understand?

In science, each discipline has its own set of words to help experts share ideas quickly and accurately amongst members of the discipline. Data science is no different. However, those words – even if they are simple words – aren’t understood in the same way outside of that discipline. Consider the following:

While most of us will recognize the individual words, gleaning any sort of meaning takes time (and possibly a good dictionary or Google). Although there may be a few of us in the class who can understand this sentence (which comes from a colleague in the Department of Political Science who studies intersectionality and citizen participation; northern wellbeing; youth engagement; local politics; and community-engaged scholarship), that understanding requires a very specific type of training that allows those who are in the know to extract meaning.

Adding to language complexities is that different disciplines (both on and off campus) have developed different meanings, uses, and/or contexts for the same word. Consider, for example, what you think of when you think of the words bit, boot, class, client, data type, memory, root, stack, or tree. Do you immediately assume a specific context when you hear these words? Do folks outside of computer science think about these words in the same way? Might the use of these words cause challenges to understanding if definitions aren’t first identified?

A good general rule to follow is to avoid disciplinary jargon. Our community partner might not be familiar with terms and phrases that we use freely, such as back end, front end, LAMP stack, Java, Angular, CSS, PHP, SQL, agile, scrum, object-oriented, IDE, API, etc. This doesn’t mean you can’t ever use these (and other terms), but be aware that your language might confuse our partners. Worse still, it might act as a barrier to developing the best solution we can for them.

Of course, your community partner will also come to class with their own jargon. If you don’t understand a word or phrase they use, ask them to explain it to you. And once they have, be sure you can explain it back to them. The goal is not to become an expert in their disciplinary domain but to build communication pathways that lead to good analyses using the most appropriate tools given the data at hand. As such, you are expected to learn and use their jargon.