Eleven researchers from the University of Michigan School of Information will be leading papers, talks and presentations at the 2024 Association for Computing Machinery’s Special Interest Group on Computer Science Education Technical Symposium. The symposium takes place in Portland, Oregon and online from March 20-23. 

The SIGCSE Technical Symposium addresses problems common among educators working to develop, implement and evaluate computing programs, curricula and courses. The symposium provides a forum for sharing new ideas for syllabi, laboratories, and other elements of teaching and pedagogy, at all levels of instruction.

UMSI researchers presenting at SIGSE:

Papers

Confidence vs insight: Big and Rich Data in Computing Education Research

Neil Brown, Mark Guzdial

There are now many large datasets available for programming education research. They tend to be very large-scale, but often lack detailed participant information, or context. This “big data” is in contrast to the “rich data” that has generally been collected from smaller, qualitative studies, with detailed context and participant information. Big data is often criticised for its lack of context, and rich data is often criticised for its small sample size which makes generalizable conclusions dubious. In this position paper we examine the constraints, advantages, and disadvantages of each type of data, and discuss how they can provide differing information on phenomena in programming education research. We argue that both types of data are useful and that we should value the potential findings of each, as well as encourage their combination in order to provide a complete picture.

Undergraduate Student Attitudes towards a Social Justice Context in a Programming Project

Aadarsh Padiyath, Kyle Ashburn, Barbara Ericson

Amid increasing calls for critical and anti-oppressive approaches to computer science (CS) education, educators are exploring how to create justice-centered teaching material. Additionally, broadening participation in justice-centered computing requires an understanding of students’ relationship with social justice and their CS education. In this study, we created and distributed a programming project with a social justice context and critical thinking reflection questions as a probe for an intermediate programming class. We conducted a thematic analysis of 11 semi-structured interviews and distributed a short survey (N=86) with students of this class at a large public research university in the American Midwest. Our findings showed that these students support social justice contexts and content within their computer science education. Students requested deeper dives and discussions into social justice programming that would challenge their preconceived notions, incorporate calls to action, and direct action. However, we also found an interesting tension forming: many students described how their homework problem-solving mindset clashed with the critical thinking reflection questions.

Investigating Student Mistakes in Introductory Data Science Programming

Anjali Singh, Anna Fariha, Christopher Brooks, Gustavo Soares, Austin Z. Henley, Ashish Tiwari, Chethan M, Heeryung Choi, Sumit Gulwani

Data Science (DS) has emerged as a new academic discipline where students are introduced to data-centric thinking and generating data-driven insights through programming. Unlike traditional introductory programming education, which focuses on program syntax and core Computer Science (CS) topics (e.g., algorithms and data structures), introductory DS education emphasizes skills such as studying the data at hand to gain insights and making effective use of programming libraries (e.g., re, NumPy, pandas, scikit-learn). To better understand learners’ needs and pain points when they are introduced to DS programming, we investigated a large online course on data manipulation designed for graduate students who do not have a CS or Statistics undergraduate degree. We qualitatively analyzed incorrect student code submissions for computational notebook-based programming assignments in Python. We identified common mistakes and grouped them into the following themes: (1) programming language and environment misconceptions, (2) logical mistakes due to data or problem-statement misunderstanding or incorrectly dealing with missing values, (3) semantic mistakes from incorrect usage of DS libraries, and (4) suboptimal coding. Our work provides instructors valuable insights to understand student needs in introductory DS courses and improve course pedagogy, along with recommendations for developing assessment and feedback tools to better support students in large courses.

Workshops and Talks

Re-making CS Departments for Generation CS

Kathleen Lehman, Carla Brodley, Mark Guzdial, Paul Tymann, Aman Yadav

In response to the enrollment surge that started in many CS departments around 2006, the Computing Research Association published a report on “Generation CS”, which named a pervasive theme in computing education: more and a greater diversity of students are seeking computing education, even if not as traditional CS majors. However, our curricula and departments have stayed much the same. We still mostly prepare students for software development jobs in the technology industry, while we rarely identify the damage that same industry has caused in our democratic societies. How do we do better? How do we change to meet the needs of a changing society? What strategies should we apply? We know that large-scale change will require structural shifts, but such shifts are likely to be slow and expensive, whereas smaller, “boots on the ground” initiatives can positively impact individuals but do little to change the systems that underlie the deeper-seated problems in computing. Navigating this paradox is imperative to our success as a field. Our panel will address the big questions about how to make structural changes in computing education in order to meet the greater needs of Generation CS.

Tracing Participation Beyond Computing Careers: How Women Reflect on Their Experiences in Computing Programs

Melissa Perez, Patricia Garcia

Norms and values in computing education are constantly changing as dominant narratives about the role of computing in society evolve over time. Within the current evolving landscape of computing education, researchers and practitioners have advocated for ensuring people from all backgrounds, and particularly women, non-binary, and Black, Indigenous, and Latinx people, are able to participate equitably within the field of computing. Yet, the values of computing educational experiences are narrowly framed within career outcomes, such as securing a career in computing, leaving many important experiences and ways of participating in the field out of the picture. To address this, we conducted reflective interviews with women who participated in broadening participation in computing (BPC) programs to understand their perceptions of computing and how it aligns (or not) with what they value about their experiences in computing learning environments. We investigate the following research questions: (1) How do women who participated in BPC programs describe their perceptions of computing? (2) How do those perceptions align or misalign with the program outcomes they valued? The findings from our study call attention to tensions arising from centering “computing careers” in BPC work and highlight the outcomes of participation valued by the women in our study, such as developing communities and relationships, gaining communication skills, and expanding perspectives on skills computer scientists should possess.

Computing as a University Graduation Requirement

Zachary Dodds, Yuan Garcia, Vidushi Ojha, Mark Guzdial, Tamara Nelson-Fromm, Valerie Barr, Stephanos Matsumoto

Computing is everywhere, and it’s here to stay. Computing is crucial in many disciplines, and it influences every discipline. It’s unlikely we’ll willingly return to a society unmediated by computing. How do our institutions proceed?

This BoF asks, “Should computing be a requirement for all college and university students?”

Some say yes, citing potential for improving equity-of-access, for expanding students’ capabilities, for diversifying the people who understand and critique computing, and for increasing the diversity of computing participation. Some say no, citing the lack of equity-of-outcomes, the infeasibility of teaching all students equitably, and students’ need for freedom in choosing what they study. The wisest say, “Let’s consider the full spectrum of possibilities… .”

This session will discuss these possibilities, as expressed and constrained by 2024’s forces. Is computing’s value saturated - or soon to be? Or, is computing a meta-skill, whose practice in learning-to-learn amplifies individual efficacy along all paths? Is Computing1 too gate-kept to be as equitable a GenEd as Composition1? Or does requiring computing, in fact, help dismantle those gates? Can students adequately learn about core computing concepts via non-CS courses that use computing? What might a required computing course entail?

We invite and welcome all with an interest in computing-as-degree-requirement, program-requirement, or GenEd offering. The session’s seed materials will highlight evidence against the idea, for the idea, and across its vast, uncertain middle.

Our BoF proposers include researchers and educators, both non-CS-requiring and CS-requiring, as well as non-CS-required and CS-required “educatees.” Join us!

Creating an on-ramp to programming for arts and humanities students with teaspoon languages and custom block languages

Mark Guzdial

Programming is a useful medium for artists. Learning to program provides a useful set of skills and concepts for critical computing scholars and conversational programmers. Introductory computing classes (at both secondary and post-secondary) level focus on job skills in software development or data science, which are different than what arts and humanities students need. We have been developing new introductory computing courses for arts and humanities undergraduate students, explicitly including programming. The courses were designed using participatory methods with faculty who focus on creative expression and justice. The goal was to make them engaging with no requirement for prior programming background or mathematics past algebra. We use teaspoon (task-specific programming) languages and custom blocks in Snap to create a highly-scaffolded on-ramp into programming. These include supports for image processing, language recognition and generation, digital sound manipulation, creating Web pages, building chatbots, and manipulating databases. The workshop will include a presentation of the learning goals that emerged from our participatory design sessions. Participants will use our teaspoon languages and context-specific Snap blocks during the workshop, and also try out the ebooks we use to help students transfer their knowledge to mainstream tools like Python, Processing, and SQL. These activities might also be useful in secondary school computing classes. (Links to all of the tools will be provided, though only some will be used hands-on in the workshop.)

Posters

Supporting Instructors Adoption of Peer Instruction

Xingjian Gu, Barbara Ericson, Zihan Wu

Peer Instruction (PI) is a learning activity that lets students solve a difficult multiple-choice question individually, submit their answer, discuss with peers to solve the problem collaboratively, and then submit the answer again.

Despite plentiful evidence to support its effectiveness, PI has not been widely adopted by undergraduate computing instructors due to low awareness of PI, the effort needed to create PI questions, the limited instructional time needed for PI activities during lectures, and potential adverse reactions from students.

We hypothesized that we could allay some of these concerns by hosting a three-day summer workshop on Peer Instruction for instructors and building and sharing a free tool and a question bank that supports PI in an open-source ebook platform. We invited eighteen instructors to attend an in-person three-day workshop on PI in the summer of 2022. We collected their feedback by using pre- and post-surveys and conducting semi-structured interviews. We report on the effect of the three-day summer workshop on instructor attitudes towards and knowledge of PI, the barriers that prevented instructors from adopting the free tool, and feedback from instructors who used the tool.

The results show that most workshop attendees are willing to adopt the tool, but less than half did after a semester. Responses from both users and non-users yield insights about the support instructors need to adopt new tools. This research informs future professional development workshops, tool development, and how to better support instructors interested in adopting Peer Instruction.

Integrating Personalized Parsons Problems with Multi-Level Textual Explanations to Scaffold Code Writing

Xinying Hou, Barbara Ericson, Xu Wang

Novice programmers often face difficulties when writing code independently. To assist struggling students in writing textual code in Python, we have recently implemented personalized Parsons problems as a pop-up scaffolding. These personalized Parsons problems require lower cognitive load to solve in contrast to the matched write-code problems. Students found them to be engaging and helpful for learning. However, a drawback of using Parsons problems as hints is that students may be able to put the code blocks back in place without fully understanding the rationale of the correct solution. As a result, the learning benefits of such hints are compromised. In this work, we aim to augment the benefits of using personalized Parsons problems as hints. In this poster, we present a design to add multi-level textual explanations to code blocks in Parsons problems generated by a large language model. This design will serve as a foundation for subsequent classroom experiments where we investigate the effectiveness of incorporating textual explanations in Parsons problems to enhance their instructional benefits when used as scaffolding opportunities.