Teaching for Success in Computer Science: Research-Backed Strategies
Dr. Kai Dupe • December 15, 2025
Computer science has often been portrayed as a field where only a select few can succeed.
Computer science has often been portrayed as a field where only a select few can succeed. Yet decades of research in computer science education suggest something far more encouraging: student success in computing is strongly influenced by how we teach, how students are supported, and how learning environments are designed. When instruction aligns with research-based practices, student learning and persistence increase across experience levels.
One of the most consistent findings across STEM education is the effectiveness of active learning. A large meta-analysis by Freeman et al. found that students in active-learning environments performed better and were significantly less likely to fail than students in traditional lecture-only courses (Freeman et al., 2014: https://www.pnas.org/doi/10.1073/pnas.1319030111). In computer science, active learning includes structured labs, guided coding exercises, peer discussion, and opportunities for students to reason through problems. Programming is a skill developed through practice, iteration, and feedback—not passive observation.
Research also emphasizes the role of self-efficacy, or students’ belief in their ability to succeed. Albert Bandura’s foundational work shows that confidence influences persistence, motivation, and academic performance (Bandura, 1997: https://www.uky.edu/~eushe2/Bandura/BanEncy.html). In computing, studies indicate that students who believe success comes from effort and effective strategies—rather than innate talent—are more likely to continue in the major (Lewis et al., 2016: https://dl.acm.org/doi/10.1145/2839509.2844593). Early, well-scaffolded successes and explicit normalization of struggle, particularly around debugging, help reinforce this belief.
Collaborative learning further supports student success. Research on peer instruction and pair programming demonstrates improvements in conceptual understanding and student engagement, especially in introductory computer science courses (Porter et al., 2013: https://dl.acm.org/doi/10.1145/2445196.2445248). Working with peers helps students articulate their thinking, learn from alternative approaches, and develop communication skills central to professional computing practice.
From a cognitive perspective, worked examples and scaffolding are especially effective for novice programmers. Cognitive Load Theory shows that learners benefit from studying annotated or partially solved examples before tackling problems independently (Sweller, 1988: https://link.springer.com/article/10.1007/BF00375144). As students gain expertise, these supports can be gradually removed.
Finally, real-world and challenge-based projects increase motivation by helping students see computing as relevant and impactful. When students understand why their code matters, they engage more deeply and persist longer.
The message from the research is clear and hopeful: computer science students thrive when instruction is intentional, supportive, and evidence-based.
The truth is that most software applications exist to manage information.
Artificial intelligence has become the newest member of nearly every software development team.
The true promise of computing is not found in the machines we build, but in the human potential we unlock when knowledge becomes accessible to everyone.
One of the reasons I value the MCT program so highly is that it represents more than technical knowledge.
Software engineering is not simply coding.
Companies often promote the idea that technical skill alone determines success.
Hackathons also strengthen teamwork and communication skills.
Stepping onto the campus of Morehouse College this past weekend for Admitted Students Day was more than a visit—it was a moment of reflection. As I watched young Black men walk with purpose across the yard, I found myself asking a simple but profound question: What would it have been like for me to study computer science here? My journey into computing was shaped in environments where I was often the only Black man in the room. That reality brings with it an unspoken weight—the need to prove you belong, the awareness of being watched, and sometimes, the quiet isolation that comes with underrepresentation. Standing at Morehouse, I realized that this burden is not a given. It is a condition of the environment. At Morehouse, the environment is different by design. Here, Black men are not anomalies—they are the standard. I imagined what it would feel like to learn algorithms, data structures, and software development in a space where my identity was not questioned but affirmed. Where excellence is expected, not in spite of who you are, but because of it. As a computer science professor, I understand the academic rigor required to succeed in this field. There is no shortcut through recursion, no bypass around debugging, no substitute for disciplined problem-solving. But what struck me during my visit is how much context matters. When students are free from the psychological burden of proving they belong, they can redirect that energy toward mastering the material. They can collaborate more openly, ask questions more freely, and take intellectual risks without fear. I also thought about legacy. At Morehouse, students walk the same grounds as Martin Luther King Jr.. That kind of history does something to a person. It raises the bar—not just academically, but personally. It invites students to see their education not just as a pathway to a career, but as preparation for impact. Leaving campus, I felt inspired—but also reflective. I cannot rewrite my journey, but I can appreciate what spaces like Morehouse offer the next generation. For a Black male pursuing computer science, it is more than a degree. It is an opportunity to develop skill, confidence, and identity in alignment. And that combination is powerful.
If you walk into most computer science classrooms today, you might assume that computing has always been a male-dominated field. As someone who has spent decades in the industry and now teaches the next generation of developers, I can tell you—that assumption is not only common, it’s historically inaccurate. In the early days of computing, many of the first programmers were women. Ada Lovelace is widely recognized as the first computer programmer, having written what we would now call an algorithm for Charles Babbage’s Analytical Engine. Fast forward to the 1940s, and women were programming some of the first electronic computers, including ENIAC. These were not peripheral roles. These women were solving complex computational problems, often inventing programming techniques as they went (Abbate, 2012). So what happened? From a systems perspective, the answer is not mysterious—it’s structural. In its early stages, programming was considered clerical work. It required precision, patience, and attention to detail—qualities that, at the time, were socially assigned to women. But as computing became more central to business, government, and innovation, its status changed. What was once seen as routine work became prestigious and lucrative. And when that shift happened, the demographics shifted with it. By the 1980s, we see a clear inflection point. Personal computers entered the home—but they were marketed primarily to boys. This created an early access gap that translated into confidence, experience, and eventually career pathways. At the same time, hiring practices and workplace cultures began to favor men, reinforcing a feedback loop that pushed women out of the field (Hicks, 2017). Over time, the narrative changed. Computing was no longer something women had built—it became something they were seen as entering late. But that narrative is not just incomplete—it’s a distortion. Understanding this history is not about nostalgia; it’s about accuracy. When students learn that women were foundational to computing, it reshapes how they think about the field. Diversity is no longer framed as a modern intervention—it is recognized as part of computing’s original DNA. In my classroom, I’ve seen what happens when students encounter this truth. It disrupts assumptions. It broadens participation. And perhaps most importantly, it changes who students believe belongs in this space. So, if women were the original programmers, what happened? Part of the answer lies in systems—education, marketing, hiring, and culture. But another part lies in storytelling. The stories we tell about computing shape who feels invited to participate in it. As educators, technologists, and leaders, we have an opportunity—and a responsibility—to tell that story more accurately. References Abbate, J. (2012). Recoding Gender: Women’s Changing Participation in Computing. MIT Press. Hicks, M. (2017). Programmed Inequality: How Britain Discarded Women Technologists and Lost Its Edge in Computing. MIT Press. Evans, C. L. (2018). Broad Band: The Untold Story of the Women Who Made the Internet. Portfolio. Shetterly, M. L. (2016). Hidden Figures. HarperCollins.
During Women's History Month, we often celebrate women who have made groundbreaking contributions to science and technology. What many people do not realize, however, is that in the earliest years of the computing industry, programming was largely done by women. This overlooked chapter of history reveals how deeply women helped shape modern computing—and how cultural shifts later obscured their contributions. One of the most famous examples comes from the development of the ENIAC, one of the first general-purpose electronic computers built during World War II. When the machine was unveiled in 1946, public attention focused primarily on the hardware and the male engineers who built it. Yet the individuals responsible for programming the computer were six women mathematicians: Jean Bartik, Kathleen McNulty, Betty Jennings, Frances Bilas, Ruth Lichterman, and Marlyn Wescoff. Their work was extraordinarily complex. Programming ENIAC did not involve writing code in a modern programming language. Instead, they programmed the machine by rewiring plugboards, configuring switches, and designing logical sequences of operations to compute ballistic trajectories for the U.S. military. In effect, they were inventing the practice of programming as they went along. Despite the significance of their work, these women were largely left out of the early historical record. Photographs from the ENIAC project sometimes showed them standing beside the machine, but they were often misidentified as models rather than as the programmers who made the system function. For decades, their contributions were largely forgotten. Women continued to play critical roles in computing in the years that followed. One notable figure was Grace Hopper, who helped develop one of the first compilers and contributed to the development of the programming language COBOL. Her work helped transform programming from a hardware-focused task into the software-driven discipline we know today. Ironically, programming was not initially considered a prestigious profession. In the early decades of computing, it was sometimes viewed as routine clerical work, and organizations often hired women to perform it. However, as software became more central to the technology industry in the 1960s and 1970s, the status of programming began to change. Companies started redefining the role as a highly technical and prestigious occupation. At the same time, hiring practices began emphasizing personality profiles and educational pathways that favored men, while early home computers were heavily marketed to boys. These cultural and institutional shifts gradually pushed many women out of the field. By the 1980s, computer science programs had become overwhelmingly male-dominated, creating the demographic pattern that still exists in much of the technology industry today. Understanding this history reminds us that the gender imbalance in computing is not inevitable or inherent to the field. Women were present at the very beginning of modern computing and were instrumental in building the foundations of programming itself. For readers interested in learning more, two excellent resources are Recoding Gender by Janet Abbate, which examines the historical role of women in computing, and Broad Band by Claire L. Evans, a narrative history of women’s contributions to the development of the internet and computing culture.