Due to large enrollments, undergraduate computer science (CS) courses often incorporate lectures that can scale to many students. However, there is strong evidence that students learn best through active meaning-making, particularly in collaboration with others. In this paper, we explore how students seek out opportunities to learn collaboratively during class time in a large introductory CS (CS1) course and how pedagogical decisions can create opportunities for such collaboration. We use an ethnographic approach to observe natural student interactions in a CS1 class, contributing to limited research exploring CS classroom activity through ethnographic observation. We find that students engage in frequent siding (i.e., side-talk and other backchanneling during class) to address their in-the-moment learning needs for clarification, tutoring, and support with debugging, as well as to co-construct new understandings and connect with others. We also find that students can meet some of these needs by siding with digital tools. From this, we introduce the concept of digital siding, in which a student turns to the Internet or AI to achieve a goal rather than a peer, and discuss benefits and drawbacks of peer and digital siding. Our data shows that siding happens often and serves important learning needs, providing a way for students to actively engage in learning despite the large scale of CS1. Therefore, we argue that instructors should not view siding purely as a distraction and provide design recommendations to help instructors promote siding in ways that support learning.

As part of redesigning our introductory programming sequence, the University of Washington removed formal prerequisites from each course, allowing students to self-select into whichever course they believe best fits their experience level. To help facilitate these choices, we developed a guided self-placement tool that offers course recommendations based on students' previous experience and confidence with course topics. In this report, we describe the design and implementation of the self-placement tool and reflect on its first years of use. The tool has been effective, with most students reporting that they used the tool, followed its recommendation, and are confident in their enrollment decision. The rates of students switching or dropping courses within the introductory sequence have been low. In addition, results from a preliminary interview study show that all students who followed the tool's recommendation believed the suggested course was the right choice. Most students who opted for a different course were influenced by external factors, largely related to their confidence in the course content and perception of course difficulty levels. We conclude by reflecting on what we have learned so far and laying out next steps.

Although enrollments in introductory computing courses are rising, many students still struggle to learn programming. Previous research has found that students’ perceptions of the programming process may be one factor that contributes to this problem. Students often assess their own programming abilities overly harshly when experiencing low-level programming moments that are considered normal and expected parts of learning to program. For example, many students think they are doing poorly if they need to stop coding to plan. Research has also shown that students who self-assess negatively in these moments tend to have lower self-efficacy, defined as one’s belief in their ability to achieve a particular outcome. In turn, students with lower self-efficacy tend not to persist in their computing studies. While the criteria that students use to assess their ability have been studied extensively, we have a limited understanding of the origins of these criteria and students’ reasons for adopting them. To address this gap, we conducted a total of 36 interviews with seven introductory computer science students throughout an academic quarter. In each interview, we asked students to think aloud and explain their reasoning while filling out a self-assessment survey. Through a qualitative analysis of the data, we identified the most common reasons students gave for negatively assessing their performance, including having high expectations for their abilities and feeling like they cannot overcome a struggle. We also identified common reasons why students do not negatively assess their ability in these moments, including believing an experience is “normal” or feeling like they can learn from or overcome a struggle. These findings contribute valuable new knowledge about the underpinnings of students’ self-assessments of ability, and suggest that interventions that explicitly emphasize best practices and normalize struggles in the programming learning process are needed to increase student self-efficacy and persistence in computing.

Introductory computer science for non-majors, often referred to as CS0, is a course that is designed to be more accessible and less intimidating than CS1, with the goal of alleviating barriers and fears associated with learning computer science (CS). However, despite this intention, many students still struggle in CS0 and these courses do not always successfully prepare students for future CS learning experiences. In this paper, we study the experiences of CS0 students with a particular focus on the intersection of their metacognition, affect, and behaviors. To study students’ daily learning experiences, we collected data from 20 participants who completed structured daily diaries and retrospective interviews over the course of a single homework assignment. Through a thematic analysis of the diaries and interviews, we identified three distinct patterns of engagement that highlight the importance of metacognitive knowledge of strategies, or a students’ understanding of when, why, and how to effectively use regulation and disciplinary strategies while working on tasks. The three patterns of engagement include: (1) avoidance behaviors resulting from negative emotions, negative judgements, and a lack of metacognitive knowledge of strategies, (2) persistence or re-engagement behaviors despite negative emotions and judgements aided by metacognitive knowledge of strategies, and (3) persistence behaviors with evidence that metacognitive knowledge of strategies prevented students from forming negative judgements in the first place. We contribute an initial model of the interplay of metacognition, affect, and behaviors in CS learning, showing the role of metacognitive knowledge of strategies in helping students persist in the face of struggle. In our discussion, we advocate for explicit interventions that support students in developing metacognitive knowledge of strategies while also supporting their sometimes challenging emotional experiences.

Enrollments in university-level introductory computing courses are skyrocketing [3], but many students struggle in these courses [2]. Recent research suggests that student perceptions of the programming process may contribute to this problem. Students often have inaccurate expectations of programming that may lead them to negatively assess their abilities in response to natural programming moments [6]. For example, many students believe they are doing poorly when they use resources to look up syntax, even though this is considered good practice [7]. This is important because negative self-assessments correlate with lower self-efficacy [6], or one’s belief that they can achieve a goal [1], and students with lower self-efficacy tend to exhibit lower persistence in undergraduate computing programs [9]. In this poster, we present an initial design and evaluation of an intervention that aims to reduce overly negative self-assessments and improve self-efficacy by providing real-time feedback as students program.