Pedagogical Tips for the Start of the Semester

The first weeks of the semester are a unique window to shape not only what students will learn, but how they will learn. In STEM courses, where concepts can be abstract, skill levels vary wildly, and technologies evolve quickly, intentional, evidence-based practices can help you set students up for long-term success.

Below are a few strategies with examples and tools you can implement immediately.

Design an Inclusive, Transparent Syllabus

Evidence base: Transparent teaching research (Winkelmes et al., 2016) shows that when students understand the purpose, tasks, and criteria for success, they perform better.

Implementation tips:

• Purpose statements: For every major assignment, include a short note on why it matters and how it connects to industry or future coursework.
  Example: “This database schema project builds skills in relational modeling, which are directly relevant to backend software engineering interviews.”

• Clear expectations: Break down grading policies, late work policies, and collaboration guidelines into plain language, avoiding overly technical or legalistic phrasing.

• Accessibility & flexibility: Link to tutoring labs, office hours, online learning resources, and note-taking tools. Indicate whether assignments can be resubmitted after feedback.

• Create a one-page “Quick Reference” sheet covering key policies (late work, collaboration, grading)

• Norm-setting: Add a “Community Norms” section that covers respectful code reviews, how to ask questions in class, and expectations for group work. In large classes, it’s vital to set expectations for respectful online discussions, effective use of the Q&A forum (e.g., checking if a question has already been asked), and guidelines for group work if applicable (e.g., conflict resolution strategies).

Establish Psychological Safety Early

Evidence base: Google’s Project Aristotle (2015) and Edmondson’s (1999) work on team learning show that psychological safety, where students feel safe to take intellectual risks, is essential for high performance.

Implementation tips:

• Low stakes start: In week one, run short, open-ended coding challenges that allow multiple solutions. Make it clear that mistakes are part of the process.

• Start with anonymous polls about programming experience to acknowledge the diversity of backgrounds in the room.

• Instructor vulnerability: Share a personal example of a bug or failed project you learned from. This normalizes challenges in programming. In a large lecture, you can briefly mention common misconceptions students often have with a new concept, and how to navigate them.

• Model Constructive Feedback: When providing feedback on early assignments (even low-stakes ones), focus on growth and learning. When addressing common errors in a large class, frame it as an opportunity for collective learning rather than pointing out individual mistakes.

• Multiple communication channels: Set up a Q&A platform (InScribe) where students can post questions anonymously.

Use Early Analytics for Intervention

Evidence base: Freeman et al. (2014) found that early course engagement strongly predicts later success, allowing for timely support.

Implementation tips:

• Student Engagement Roster (SER): https://ser.indiana.edu/faculty/index.html During the first week of class,  consider explaining the SER to your students and tell them how you will be using it. If students are registered for your class and miss the first class, report them as non-attending in SER.  It will allow outreach that can help clarify their situation. Here’s a sample text you could put into your syllabus:
  This semester I will be using IU’s Student Engagement Roster to provide feedback on your performance in this course. Periodically throughout the semester, I will be entering information on factors such as your class attendance, participation, and success with coursework, among other things. This information will provide feedback on how you are doing in the course and offer you suggestions on how you might be able to improve your performance.  You will be able to access this information by going to One.IU.edu and searching for the Student Engagement Roster (Faculty) tile.

• Use Canvas Analytics:

• Identify struggling students. “Submissions” allows you to view if students submit assignments on-time, late, or not at all.

  1. See grades at a glance. “Grades” uses a box and whisker plot to show the distribution of grades in the course.

  2. See individual student data. “Student Analytics” shows page view, participations, assignments, and current score for every student in the course.

• Track early submissions: Note which students complete the first assignments or attend early labs

• Personal outreach: Email or meet with students who are slipping to connect them with tutoring, peer mentors, or study groups.

• Positive nudges: Celebrate early wins (e.g., “I noticed you submitted the optional challenge problem. Great initiative!”).

• Proactive Outreach (with TA Support): If you identify students who are struggling, send personalized emails offering support and directing them to available resources (e.g., tutoring, office hours with TAs). Consider delegating some of this outreach to TAs in large courses.

• Announcements Highlighting Resources: Regularly remind the entire class about available support resources, study strategies, and upcoming deadlines through announcements.

Key Implementation Strategies for Success

• Start Small and Build Don’t attempt to implement all strategies simultaneously. Choose 2-3 that align with your teaching style and course structure, then gradually incorporate additional elements.

• Leverage Your Teaching Team In large courses, TAs are essential partners. Invest time in training them on consistent feedback practices, student support strategies, and early intervention protocols.

• Iterate Based on Data Use student feedback, performance analytics, and your own observations to refine your approach throughout the semester. What works in one context may need adjustment in another.

• Maintain Connection at Scale Even in large courses, students need to feel seen and supported. Use technology strategically to maintain personal connection while managing the practical demands of scale.

Conclusion

By implementing these research-backed strategies, faculty can create learning environments where diverse students thrive, engagement remains high, and learning outcomes improve significantly.

The investment in implementing these practices pays dividends not only in student success but also in teaching satisfaction and course sustainability. As you prepare for the new semester, consider which strategies best align with your course goals and student population, then take the first step toward transforming your large enrollment course into a dynamic, supportive learning community.

Remember: even small changes, consistently applied, can create significant improvements in student learning and engagement. Start where you are, use what you have, and do what you can to create the best possible learning experience for your students.

References

1. Winkelmes, M. A., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., & Weavil, K. H. (2016). A teaching intervention that increases underserved college students’ success. Peer Review, 18(1/2), 31–36. Association of American Colleges and Universities.

2. Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999

3. Google Inc. (2015). Project Aristotle: Understanding team effectiveness. Retrieved from https://rework.withgoogle.com/intl/en/guides/understanding-team-effectiveness

4. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111

The Tech Faculty Imperative: Leading with Inclusive Design and Dual Title II Compliance

This article was written in collaboration with:

Michele Kelmer, MS Ed.
Director of Faculty Engagement and Outreach
UITS Learning Technologies

Michael Mace, MS Ed.
Manager
UITS Assistive Technology and Accessibility Centers

Cara Reader, PhD
University ADA Coordinator
Director of Compliance, Training, and ADA
Indiana University – Office of Civil Rights Compliance

As technological advancements reshape education, faculty in computing, engineering, data science, and information technology sit at the intersection of innovation and inclusion. But with this influence comes a responsibility: ensuring the digital environments we create are accessible for all learners.

This is more than compliance—it’s about shaping a future where every student, regardless of ability or background, can thrive. Two federal statutes—Title II of the Americans with Disabilities Act (ADA) and Title II of the Higher Education Act (HEA)—along with recent executive orders provide a powerful framework for technology faculty to lead transformative change in education.

Why Accessibility? Because There Are Students in Your Classes with Disabilities.

The data makes this clear:

• According to 2022 data from the Centers for Disease Control and Prevention (CDC), around 28% of the US public reports having one or more disabilities, including physical, mental, and emotional disabilities. This includes 23.8% of individuals ages 18-44 and 34% of military veterans.

• In a 2019-2020 survey of college students by the National Center for Education Statistics, 21% of undergraduates and 11% of graduate students reported having a disability. These percentages were similar for traditional and adult students and across disciplines of study, and they increase each year.

There are four main reasons why you may not know who your students with disabilities are:

1. Most disabilities are invisible. You can’t always look at someone and know they have a mental health, learning, chronic health, physical, hearing, vision, or neurological disability.

2. Students don’t disclose. Less than 50% of students report their physical disabilities, and less than 30% report mental health, learning, or neurological disabilities. Most students who do not disclose cite the fear of stigma from peers, pushback on accommodation requests by instructors, and the general hassle of documentation.

3. Students may have a disability but don’t have documentation. They may not have been formally diagnosed due to the cost of testing, lack of adequate health care, or cultural norms. ADHD and autism, for example, can be diagnosed later in life.

4. Students with new acute or chronic health conditions or injuries may not consider themselves as having a disability, even if it impairs their learning for a semester or more. Being diagnosed and treated for conditions like cancer, multiple sclerosis, or major injuries can significantly impact a student’s ability to manage coursework.

Based on 2024 data, any given 100 college students could include:

• 30% diagnosed with anxiety and/or depression

• 20% with sleep difficulties like insomnia or sleep apnea

• 12% attention deficit hyperactivity disorder (ADHD)

• 10% who experience migraines or other severe headaches

• 4% with specific learning disabilities including dyslexia and dyscalculia

• 4% with autism

• 2% who are blind or have low vision

• 2% with a trauma-related disability including post-traumatic stress disorder (PTSD)

• 2% who are Deaf or hard of hearing

It’s common for people to have overlapping disabilities, so while this isn’t to say everyone has a disability, the point is that it’s extremely unlikely that no one in your classes has a disability.

Understanding Title II: ADA + HEA

Accessibility isn’t just the right thing to do for your students; digital accessibility, like physical accessibility provided by ramps and curb cuts, is now the law.

Title II of the ADA (1990, updated 2024): Prohibits discrimination by public entities, including public colleges and universities. In April 2024, the Department of Justice released new rules requiring digital content and services to be accessible to people with disabilities. This includes:

• Course content in Canvas (your Learning Management System (LMS))

• Department websites and internal platforms

• Educational technologies used in class

• Videos, documents, and simulations

• Social Media Posts

Key Deadline:

April 2026 for institutions serving >50,000 

The purpose of this update is to help ensure that people with any of a wide range of disabilities can easily access the same web content and online services provided by state and local government and public educational institutions that those without a disability can. Your online courses and anything you put within your LMS are considered web content.

This web content must meet the new accessibility standards if:

• students or the public can access it online,

• it’s currently being used (not archival content), and

• it’s part of the work you do for your institution.

For something to be considered accessible, it must be:

• Equally integrated: provided at the same time and not separate.

• Equally effective: provides equal opportunity or outcome.

• Substantially equivalent in ease of use: should not be more difficult.

According to the Title II update, content in Spring 2026 courses and beyond must be accessible, whether or not you have a student with an accommodation request. There will no longer be an option to wait for an accommodation request to make your course site meet basic accessible guidelines. Accommodations apply when the basics of accessibility are insufficient to meet the specific need of the student. You will still receive accommodation requests for extended time on assessments or specialized accommodations such as a sign language interpreter, a Braille textbook, or tactile graphics as needed.

Title II of the HEA: Requires teacher preparation programs (and increasingly, faculty across disciplines) to use evidence-based pedagogical practices and report on outcomes like teaching effectiveness and alignment with workforce demands.

What Tech Faculty Can Do: Inclusive Teaching in Action

Here’s how you can align your pedagogy with Title II ADA, Title II HEA, and federal priorities—with real-world examples to guide you.

1. Design Digitally Accessible Content from the Start

• Use alternative text (alt-text) for all images, charts, and graphs:

• Example: In a software engineering course, use: “UML diagram showing user login process, including ‘Enter Credentials’, ‘Verify’, and ‘Authenticate’.” This applies to images embedded in presentations, documents, and web pages.

• Caption all video and transcribe all audio content:

• Example: A data structures professor records weekly screencasts with auto-captioning, edited for accuracy and posted with transcripts on Canvas. For students who are deaf or hard of hearing, this is essential. Providing a full transcript also benefits students who prefer to read or who need to quickly search for specific information within the content.

• Structure documents for readability and navigation: When creating lecture notes, assignments, or syllabi in Word, PowerPoint, or PDF, use proper heading structures (e.g., H1, H2, H3), bullet points, and numbered lists—not just bold or color. This allows screen readers to navigate the document logically and helps all students process information more easily. Avoid using color alone to convey meaning (e.g., “red text indicates a critical warning”) as this can be inaccessible to color-blind individuals.

• Use accessibility checkers in Word, Adobe Acrobat, or Google Docs. IU recommends this practice across all digital materials.

1. Evaluate the Accessibility of Tools and Platforms

• Check for WCAG 2.1 AA compliance before adopting new software, simulations, or online learning platforms:

• Example: Before adopting a new online code editor, the faculty requests a VPAT (Voluntary Product Accessibility Template) and only proceeds after reviewing it with IT accessibility staff. If a vendor cannot provide evidence of compliance, consider alternative solutions or work with your institution to ensure reasonable accommodations can be made.

• Test for keyboard navigation and screen reader compatibility:

• Example: In a web development course, the professor makes part of the final project require full keyboard navigation and ARIA labels.

• Leverage built-in LMS accessibility tools like Canvas Accessibility Checker or Anthology Ally.

• Example: When uploading a new module to Canvas, a professor runs the accessibility checker to identify any images without alt-text or poorly contrasted text, rectifying these issues before publishing.

1. Implement Inclusive Pedagogical Practices (Title II HEA + ADA)

• Use Universal Design for Learning (UDL) to offer multiple means of engagement and representation: Provide information in various formats (e.g., video, text, simulation) and allow students to demonstrate their learning in diverse ways (e.g., flexible assessment like a prototype + presentation or GitHub repo + write-up).

• Example: In an IoT capstone project, students can present via slide deck, interactive demo, or video walkthrough—with guidelines for accessibility built into the rubric. This accommodates different learning styles and abilities.

1. Track Outcomes and Improve with Data

• Align assignments to real-world certifications (e.g., AWS, CompTIA, Python Institute), and track student success to inform redesigns.

• Use learning analytics in GitHub, Jupyter Notebooks, or Canvas to see where engagement or comprehension gaps occur.

Moving Forward: Build a Culture of Accessibility

Implementing Title II effectively isn’t a one-time project; it’s an ongoing commitment that requires a cultural shift towards proactive accessibility. For technological faculty, this means:

• Continuous Improvement: Regularly audit your courses with accessibility in mind each semester. Ask students for anonymous feedback on digital barriers.

• Collaborate: Partner with your institution’s accessibility services office and instructional designers. Join or form a cross-departmental working group on inclusive STEM teaching.

• Educate Yourself and Others: Complete self-paced training or attend workshops on accessibility and UDL. Share accessible templates with your colleagues.

Tech Faculty: You Are Equity Catalysts

By aligning your teaching with Title II of the ADA and HEA, you’re doing more than following the law. You’re building a future where every student—regardless of disability, background, or learning style—can succeed in STEM and computing fields.

Additional resources:

IU Knowledgebase documents:

• Create Inclusive Documents in 7 Simple Steps

• The true cost of free resources

• Minimum digital accessibility guidelines for IU courses

• About TidyUp for Canvas

• Ally instructor guide

• How to link to Library Resources in Canvas

• Download and edit captions or transcripts in Kaltura

• Create accessible math in Pressbooks and Canvas

IU Expand Training Courses

• ADA Basics for Faculty

• 7 Simple Steps for Creating More Inclusive Digital Content

• Improving Course Accessibility

Web resources

• IU Tech Tool finder

• Fact Sheet: New Rule on the Accessibility of Web Content and Mobile Apps Provided by State and Local Governments

• Canvas accessibility for instructors

• Accessibility at IU

• The UDL Guidelines

Supporting non-majors in introductory computer courses

The article titled “Exploring Relations between Programming Learning Trajectories and Students’ Majors” https://dl.acm.org/doi/fullHtml/10.1145/3674399.3674497 investigates how students from various academic disciplines learn programming in a compulsory introductory programming course consisting of 75 students, with 40 majoring in CS and 35 in non-CS majors. “They were all freshmen without prior programming experience. Considering their similar scores of entrance exam to this university, it can be assumed that their levels of mathematical logic and computational thinking were roughly comparable”.

The authors note that “an increasing number of non-computer science students are now engaging in programming learning. However, they often struggle in early programming courses. The researchers analyzed data from students’ learning processes to understand how their major influences their learning journey in programming.

The study found that students’ backgrounds and areas of study can affect how they approach and progress in learning programming. They suggest:

1. Making Programming Relevant: When teaching programming to students who aren’t majoring in computer science, it’s important to connect the lessons to things that are important to them. For example, showing how programming can be used in art, music, or business can make the subject more interesting, especially at the start of the course.

2. Paying Extra Attention to Struggling Students: Teachers should keep a close eye on students who are not doing well or aren’t very interested in the course. These students might need extra help to keep up, so they don’t fall behind. Connecting them with teaching assistants, Luddy tutors, and additional resources early in the semester could be helpful.

3. Using Tests to Track Progress: For computer science students, instructors can use quizzes and smaller tests throughout the semester to see how well they’re doing. This helps faculty know if they’re learning. However, for non-CS students who are doing well, these smaller tests might not show their full abilities, so the teacher needs to be extra careful when evaluating their skills. These students might be good at memorizing facts or completing basic tasks in the test, but that doesn’t mean they fully grasp the deeper concepts or could apply the knowledge in real-world situations. So, instructors need to be extra thoughtful and consider other ways to evaluate their skills, not just based on these smaller tests.

Example:

Imagine a student in a business major who is acing the quizzes in a programming class. They might be good at solving problems that are simple and similar to what they’ve studied, but the quizzes might not show how well they can use programming to solve real business problems. Faculty might need to look at other work, like projects or group activities, to better understand the student’s true abilities.

Implications for Teaching and Learning:

• Tailored Instruction: Educators can design programming courses that consider the diverse backgrounds of students, offering different learning paths or support based on their major.Example: In a programming class, students from a data science major might already have some knowledge of coding, so the instructor could offer them more advanced challenges while giving students from a humanities background more basic programming tasks. This ensures that all students are working at a level that matches their prior knowledge, making learning more effective.

• Early Support: Providing additional resources or guidance early in the course can help students who might struggle due to their major’s focus, ensuring they keep up with the material.Example: In the first few weeks of a programming course, an instructor might offer extra study sessions or online tutorials for students from non-technical majors (like business or social sciences). These students may find programming challenging, so additional support would help them catch up and build their confidence early in the course.

• Encouraging Diverse Majors: Encouraging students from various disciplines to engage with programming can enrich their learning experience and broaden their skill set.Example: A university might organize workshops to show students from creative fields (like art or design) how programming can help them bring their ideas to life, such as creating interactive websites or digital art. Encouraging students from these fields to explore programming opens new possibilities for their careers and learning.

By understanding the relationship between a student’s major and their programming learning trajectory, educators can create more effective and supportive learning environments.

“A Map Makes You Smarter. GPS Does Not.”: A Story About AI, Work, and What Comes Next with Jose Antonio Bowen

Jose Antonio Bowen is introduced as a Renaissance thinker with a jazz soul. His background includes leadership roles at Stanford, Georgetown, and SMU, as well as being the president of Johnstreet College. He is also a jazz musician who has played with legends, a composer with a Pulitzer-nominated symphony, and the author of “Teaching Naked,” 30% off with the code TNT30 at Wiley “Teaching Change,” and “Teaching with AI.” 30% off Teaching Change or Teaching with AI with Code HTWN at JH.

He provided a workshop for us on AI Assignment and Assessments, where he mentioned:

“A map makes you smarter. GPS does not.”

It was such a small, quiet moment, but it cracked open something bigger. Because this wasn’t just about directions. It was about how we’re all starting to think less, remember less, and—if we’re not careful—become less, all thanks to the technology we depend on.

The Decline of Entry-Level Everything

Dr. Bowen shared that Shell, a global energy giant, had laid off nearly 38% of a particular workforce group. Internships? Vanishing. Entry-level jobs? Replaced.

Replaced by what?

Artificial Intelligence

Tasks that used to belong to interns or fresh graduates—writing reports, creating slide decks, analyzing data—are now handled by machines that don’t take lunch breaks or need supervision.

And that’s where the real twist came in: the people who still have jobs? They’re not the ones who can do the task better than AI. They’re the ones who can think better than AI. Who can improve, refine, and oversee what AI produces.

If AI is writing the first draft, the humans left in the room better know how to write the final one—with nuance, clarity, and insight.

Offloading Our Minds, One Task at a Time

Back to that GPS quote. Dr. Bowen called it “cognitive offloading”—how we gradually stop using certain mental muscles because tech is doing the lifting.

We used to memorize phone numbers, navigate with paper maps, even mentally calculate tips at restaurants. Now? We ask Siri.

The scary part isn’t that we’re forgetting how to do these things. It’s what happens when we offload creativity, problem-solving, and thinking itself.

Because if AI can be creative—can write poems, code apps, design marketing plans—what do we do? What’s left for us?

Creativity, Reimagined

But here’s where things got interesting. Dr. Bowen isn’t anti-AI. In fact, he practically gushed about it.

He showed how AI can be used to spark creativity, not stifle it.

He explained how students could upload a 700-page textbook and have the AI turn it into a podcast. A nine-minute podcast. With baseball analogies, if that’s what helps them learn.

He talked about using AI to create personalized assignments: instead of a generic math problem about trains, give a politics student a question about voter turnout rates. Suddenly, they care. Suddenly, they’re engaged.

Because AI isn’t replacing the teacher—it’s becoming the chalk, the blackboard, the entire toolset that a smart educator can use to make learning come alive.

Prompt Like a Pro

Here’s another nugget that stuck with me: prompting isn’t coding. It’s storytelling.

Don’t just ask the AI to “fix your proposal.” Ask it to “transform your proposal into something your provost will love.”

Use emotion. Use intent. Give context. AI, it turns out, responds best when it knows what you’re really trying to say.

The 70% Problem

Still, AI isn’t perfect. Dr. Bowen introduced what he called the “70% problem.”

AI can do a lot of things—but only up to a C-level standard. That’s fine for a rough draft. It’s dangerous for a final product.

If students rely on AI to do the work, and they can’t take it past that 70% mark, then what happens when employers expect more?

The solution? Raise the bar.

What used to be acceptable for a B or C should now earn an F—unless the student can make the AI’s work better, smarter, more human.

From Tools to Teaching Assistants

The future of education, the he argued, is not about banning AI—it’s about designing with it.

He showed how teaching assistants could use AI notebooks filled with chemistry texts to answer student questions on the fly.
How AI can test business plans, simulate presidential decisions, or offer critiques from the perspective of a political opponent.
How students can train AI to “be” Einstein and ask it about thermodynamics at their own pace, in their own language.

AI isn’t replacing teachers—it’s becoming part of the classroom, like textbooks once were.

The Arms Race

Of course, there’s a darker side. AI can cheat. It can take online courses for students, fake typing patterns, even simulate human error.

Dr. Bowen called it an “arms race” between those building smarter AI and those trying to prevent it from being misused.

But even in this, he saw hope.

If educators embrace AI—not as an enemy but as a creative partner—they can design assignments AI can’t complete alone. They can build simulations, storytelling challenges, and editing tasks that require a human mind.

Because at the end of the day, that’s what this moment demands: humans who think more deeply, ask better questions, and create things worth remembering.

Final Words

The session ended with a simple truth:

“AI raises the floor. You must raise the ceiling.”

Whether you’re a student, a teacher, a manager, or a job-seeker, AI is now the baseline.

It will write the first draft, sketch the first idea, solve the first problem.

But it’s still up to us to bring the brilliance.

AI can produce work at a “C” level, which is problematic if students can only perform at that level. Instructors need to raise their standards and expectations. Assignments that would have been considered a “C” should now be evaluated as an “F” if they only meet the level of quality that AI can produce.

Implications

Students need to surpass AI capabilities to be competitive in the job market, especially in fields like coding and writing.

And maybe—just maybe—it’s time we all learned to read the map again.

Bridging the Gap: What Tech Practitioners Really Want from Computer Science Education

In the spring of 2024, the Computing Research Association (CRA) asked a simple but powerful question: What do industry professionals think about the way we teach computer science today?  as part of a “Practitioner to Professor (P2P)‘ survey that the CRA-Education / CRA-Industry working group is doing.

The response was overwhelming. More than 1,000 experienced computing practitioners—most with over two decades of experience—shared their honest thoughts on how well today’s CS graduates are being prepared for the real world.

These weren’t just any professionals. Over three-quarters work in software development. Many manage technical teams. Most hold degrees in computer science, with Bachelor’s and Master’s being the most common. Half work for large companies, and a majority are employed by organizations at the heart of computing innovation.

So, what did they say?

The Call for More—and Better—Coursework

One of the loudest messages was clear: students need more coursework in core computer science subjects. Respondents recommended about four additional CS courses beyond what’s typical today. Algorithms, computer architecture, and theoretical foundations topped the list.

But it wasn’t just CS classes that practitioners wanted more of. They also suggested expanding foundational courses—especially in math, writing, and systems thinking. It turns out that the ability to write clearly, think statistically, and understand how complex systems interact is as critical as knowing how to code.

It’s Not Just About Programming

When it came to programming languages, the responses painted a nuanced picture. Practitioners agreed: learning to code isn’t the end goal—learning to think like a problem-solver is.

They valued depth over breadth. Knowing one language well was seen as more important than dabbling in many. But they also stressed the importance of being adaptable—able to pick up new languages independently and comfortable working with different paradigms.

Familiarity with object-oriented programming? Definitely a plus. But what mattered most was a student’s ability to approach problems critically, apply logic, and build solutions—regardless of the language.

The Soft Skills Shortfall

One of the most striking critiques was aimed not at technical training, but at the lack of soft skills being taught in undergraduate programs.

Soft skills, they argued, can be taught—but many universities simply aren’t doing it well. Oral communication courses were highlighted as a critical need. And interestingly, several respondents felt that liberal arts programs were doing a better job than engineering-focused ones in nurturing communication, collaboration, and leadership.

Asked to identify the most important communication skills, respondents pointed to the ability to speak confidently in small technical groups, write solid technical documentation, and explain ideas clearly to leaders and clients—both technical and non-technical.

Math Is Still a Must

Despite the rise of high-level frameworks and automation, the industry’s love affair with math is far from over. In fact, 65% of respondents said they enjoyed or pursued more math than their degree required.

Why? Because math is the backbone of emerging fields like AI, machine learning, and data science. It sharpens analytical thinking, cultivates discipline, and builds a foundation for lifelong adaptability.

The most important math subjects? Statistics topped the list, followed by linear algebra, discrete math, calculus, and logic.

Foundations First

The survey didn’t just surface high-level trends—it got specific.

In algorithms, the emphasis was on conceptual thinking, not just implementation. Students should deeply understand how algorithms work, why they matter, and how to analyze them.

In computer architecture, digital logic and memory hierarchy were considered essential. These are the building blocks that enable students to understand modern computing systems, from the ground up.

And when it came to databases? Practitioners wanted a balance: students should learn both the theory (like relational algebra and normalization) and the practice (like SQL and indexing). Real-world readiness depends on both.

Toward a Better Future for CS Education

What makes this survey so impactful is its timing and intent. As technology continues to reshape every industry, there’s a growing urgency to close the gap between academia and the workforce. The P2P Survey is part of a broader movement to do just that.

Endorsed by leading organizations—ABET, ACM, CSAB, and IEEE-CS—this initiative creates a powerful feedback loop between universities and the industry they serve.

So, what’s next? A full report is expected later this year. But the message is already loud and clear: today’s students need a curriculum that not only teaches them how to code, but prepares them to lead, adapt, and thrive in a complex, evolving world.