Teaching computer science (CS) to non-majors poses unique pedagogical challenges as students often approach the subject with limited technical background and diverse learning needs. This paper explores the best practices for teaching computer science to non-majors, emphasizing the importance of active learning, interdisciplinary relevance, contextualization, and fostering student engagement. By utilizing project-based learning, pair programming, and problem-solving tasks relevant to students’ fields of study, educators can demystify technical concepts and improve student retention and success rates. This research identifies effective strategies from existing literature to enhance the teaching experience and the overall learning outcomes for non-majors.
The growing ubiquity of technology in virtually every field of work and study has driven an increasing demand for digital literacy, even among students pursuing degrees outside of computer science. Non-majors, individuals who do not plan to specialize in computer science, represent a significant portion of students enrolled in introductory programming courses. These students typically come from diverse academic backgrounds and may have varied motivations for studying computer science, ranging from understanding basic digital literacy to applying computing principles in their fields of study. While teaching computer science to non-majors is critical for building a digitally literate society, it presents specific challenges. Many non-majors enter computer science courses with a lack of prior technical knowledge or may view the subject as difficult or irrelevant to their careers. This requires an adaptive approach that emphasizes engagement, reduces anxiety, and connects computer science concepts to their real-world applications. This paper aims to investigate and provide recommendations on best practices for teaching computer science to non-majors through a review of relevant literature, discussion of pedagogical strategies, and analysis of their impact on student outcomes.
1. Teaching Challenges in Computer Science for Non-Majors
Teaching non-majors differs significantly from teaching computer science majors due to the differences in student motivations, backgrounds, and goals. Research shows that non-majors often feel intimidated by technical subjects such as computer science, viewing them as difficult or irrelevant [1]. A lack of prior exposure to coding, computational thinking, and technical problem-solving can make the learning curve steeper for non-majors, leading to higher rates of frustration and disengagement [2].
Furthermore, non-majors might not have the same intrinsic interest in computing as computer science majors, and their career trajectories may not directly depend on mastering programming. This lack of perceived relevance can impact their motivation, making it essential to show how computer science knowledge applies across various disciplines and industries [3]. Educators need to address these challenges by designing curricula that is accessible, engaging, and applicable to a wide range of student needs.
2. Active Learning and Engagement
The benefits of active learning in computer science education have been well-documented, particularly for students with little to no background in the subject. Active learning strategies, such as collaborative problem-solving, peer instruction, and real-time coding exercises, have been shown to improve student performance, engagement, and retention of course material [4]. Studies indicate that non-majors are more likely to succeed in courses that use interactive, hands-on learning activities rather than traditional lecture-based formats [5].
Pair programming, where two students work together to solve coding problems, is one of the most effective strategies for promoting active learning in computer science education. Research shows that pair programming not only enhances student engagement but also improves learning outcomes, reduces the stress of working on complex tasks, and helps build problem-solving skills [6]. Peer collaboration also allows non-majors to learn from each other and develop confidence in their coding abilities.
3. Project-Based and Contextual Learning
Project-based learning (PBL) is another highly effective approach for teaching computer science to non-majors. PBL allows students to work on practical, real-world projects that demonstrate the relevance of computer science to their field of study. For example, a biology student might work on a project analyzing genetic data, while a business major could use programming to develop a financial model. Research shows that contextualizing computer science within the framework of other disciplines increases student interest and retention [7].
PBL also allows students to apply theoretical knowledge in a practical setting, which can enhance their understanding and long-term retention of computer science concepts. Instructors can further enrich the learning experience by incorporating interdisciplinary projects that draw from students’ existing knowledge in their major fields, helping them see the broader applications of computer science.
4. Scaffolding and Support for Non-Majors
Since non-majors often begin with little experience in computing, providing additional support through scaffolding is crucial. Scaffolding refers to the instructional techniques used to guide students from simple to more complex tasks, helping them build confidence and skills incrementally [8]. This is especially important for students who may feel overwhelmed by programming or computer science concepts early on in the course.
Studies suggest that instructors should employ methods such as frequent formative assessments, step-by-step tutorials, and personalized feedback to help non-majors progress at their own pace. Providing structured, low-pressure learning environments—such as coding exercises that gradually increase in difficulty—helps reduce anxiety and enables students to build a solid foundation of knowledge [9].
DISCUSSION:
1. Making Computer Science Accessible
One of the most effective ways to engage non-majors is by making computer science accessible and relatable. Instructors should strive to demystify the subject by explaining complex concepts in simple, everyday terms and using analogies from students’ fields of study. For example, when teaching algorithms, instructors might compare them to step-by-step processes in business or science, helping students relate new concepts to familiar ideas [10].
Visual programming languages like Scratch or block-based coding environments can also be helpful in introducing beginners to programming concepts without overwhelming them with complex syntax. These tools allow students to focus on logical problem-solving and computational thinking, which are foundational skills in computer science [11].
2. Fostering Engagement Through Real-World Applications
Another key strategy is to show non-majors how computer science can be applied to solve real-world problems in a variety of fields. When students understand the practical applications of programming, they are more likely to engage with the material and appreciate its relevance to their future careers. For example, students in the social sciences might use programming to analyze data trends, while those in the arts could learn to create digital art using code [12].
Incorporating interdisciplinary case studies and guest lectures from professionals who use computer science in their work can also help bridge the gap between abstract concepts and practical applications. This approach helps students see how computer science is not limited to the tech industry but is instead a versatile tool that can enhance work in many domains.
3. Building Confidence in Problem-Solving
For many non-majors, the most significant barrier to success in computer science is a lack of confidence in their ability to learn technical subjects. Research shows that non-majors often experience “impostor syndrome,” where they feel they do not belong in a computer science class or are not “tech-savvy” enough to succeed [13]. Overcoming this barrier requires creating a supportive learning environment that encourages experimentation and resilience.
Instructors can help students build confidence by providing positive reinforcement and focusing on growth rather than perfection. Emphasizing the iterative nature of programming—where trial and error is a normal part of the process—can also help reduce students’ fear of failure. Providing opportunities for small successes early in the course, such as simple coding exercises or low-stakes projects, helps students develop a sense of accomplishment and motivates them to tackle more complex tasks [14].
4. Assessment Strategies for Non-Majors
Effective assessment in computer science courses for non-majors should focus not only on technical proficiency but also on problem-solving skills and the ability to apply computer science concepts in real-world contexts. Traditional assessments, such as exams and quizzes, can be supplemented with project-based assessments, peer evaluations, and reflective writing assignments that encourage students to think critically about what they have learned [15].
Regular feedback is essential in helping students track their progress and improve their understanding. Instructors should provide timely, constructive feedback that highlights students’ strengths and areas for improvement, encouraging them to build on their knowledge and skills incrementally [16].
RESULTS:
Implementing the best practices outlined in this paper has shown to improve student outcomes in computer science courses for non-majors. Studies demonstrate that students who engage in active learning, project-based learning, and interdisciplinary coursework are more likely to persist in computer science courses and report higher satisfaction with their learning experience [17].
For instance, courses that use pair programming and real-world projects have seen increased student participation, improved problem-solving skills, and better retention rates. Furthermore, non-majors who receive personalized support and scaffolding throughout the course tend to report lower levels of anxiety and greater confidence in their ability to succeed in computer science [18].