As K–12 computer science expands in the United States, students encounter a growing array of programming tools. Many introductory experiences use block-based environments, where programs are assembled by snapping together visual blocks instead of typing code. While these tools can support learning, high school students often perceive them negatively, even when they support the same underlying logic as text-based coding. Using a constructivist grounded theory approach, we interviewed 17 high school students to trace how early experiences, tool design, peer discourse, and cultural framings shape these views. We find that students develop informal folk theories: that computer science is about accumulating languages, that block-based programming is for young children, and that limitations in programming activities stem from the block modality itself—beliefs that can shift when students encounter counterexamples. Our findings call for more deliberate design and sequencing of tools that are attentive to the meanings students construct as they progress, and that promote more expansive notions of programming beyond modality.

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.

Authentic learning, characterized by engagement with real-world problems and tools, has long been of interest in education due to its impact on student motivation and learning outcomes [2, 7]. In computer science (CS) education, however, students and teachers face the challenge of balancing the desire to teach and learn "real" programming with the need for a gentle and scaffolded introduction to this highly abstract and cognitively demanding discipline [4]. As a tool-dependent discipline, the tension between authentic and scaffolded is particularly evident in the perceived in-authenticity of educational programming tools. While scaffolded blocks-based programming tools are approachable [14] and beneficial for learning [3, 10], they are often perceived as less authentic by high school students [4, 14], which can be demotivating. Conversely, "real" text-based programming, while authentic, can be difficult and intimidating, creating a barrier to learning and engagement [10, 14]. This dichotomy exemplifies a challenge in CS education: how can we provide an authentic learning experience through tools that are both approachable and representative of authentic programming practice?

Programming can be an emotional experience, particularly for undergraduate students who are new to computer science. While researchers have interviewed novice programmers about their emotional experiences, it can be difficult to pinpoint the specific emotions that occur during a programming session. In this paper, we argue that electrodermal activity (EDA) sensors, which measure the physiological changes that are indicative of an emotional reaction, can provide a valuable new data source to help study student experiences. We conducted a study with 14 undergraduate students in which we collected EDA data while they worked on a programming problem. This data was then used to cue the participants’ recollections of their emotions during a retrospective interview about the programming experience. Using this methodology, we identified 21 distinct events that triggered student emotions, such as feeling anxiety due to a lack of perceived progress on the problem. We also identified common patterns in EDA data across multiple participants, such as a drop in their physiological reaction after developing a plan, corresponding with a calmer emotional state. These findings provide new information about how students experience programming that can inform research and practice, and also contribute initial evidence of the value of EDA data in supporting studies of emotions while programming.

As the number of conversational programmers grows, computing educators are increasingly tasked with a paradox: to teach programming to people who want to communicate effectively about the internals of software, but not write code themselves. Designing instruction for conversational programmers is particularly challenging because their learning goals are not well understood, and few strategies exist for teaching to their needs. To address these gaps, we analyze the research on programming learning goals of conversational programmers from survey and interview studies of this population. We identify a major theme from these learners' goals: they often involve making connections between code's real-world purpose and various internal elements of software. To better understand the knowledge and skills conversational programmers require, we apply the Structure Behavior Function framework to compare their learning goals to those of aspiring professional developers. Finally, we argue that instructional strategies for conversational programmers require a focus on high-level program behavior that is not typically supported in introductory programming courses.

In introductory computer science courses (CS1), students who are programming for the first time will inevitably need help in order to overcome programming challenges. To accommodate large course sizes, most CS1 courses provide this help in the form of office hour sessions led by undergraduate teaching assistants (TAs). However, we have little understanding of how students go about seeking help during office hours, and what help they receive in return. In this poster, we present current findings from a grounded theory analysis of eight different CS1 office hours observations, and 16 interviews with the students and TAs who participated in them.

Undergraduate computer science (CS) programs often suffer from high dropout rates. Recent research suggests that self-efficacy -- an individual's belief in their ability to complete a task -- can influence whether students decide to persist in CS. Studies show that students' self-assessments affect their self-efficacy in many domains, and in CS, researchers have found that students frequently assess their programming ability based on their expectations about the programming process. However, we know little about the specific programming experiences that prompt the negative self-assessments that lead to lower self-efficacy. In this paper, we present findings from a survey study with 214 CS1 students from three universities. We used vignette-style questions to describe thirteen programming moments which may prompt negative self-assessments, such as getting syntax errors and spending time planning. We found that many students across all three universities reported that they negatively self-assess at each of the thirteen moments, despite the differences in curriculum and population. Furthermore, those who report more frequent negative self-assessments tend to have lower self-efficacy. Finally, our findings suggest that students' perceptions of professional programming practice may influence their expectations and negative self-assessments. By reducing the frequency that students self-assess negatively while programming, we may be able to improve self-efficacy and decrease dropout rates in CS.

While problem solving is a crucial aspect of programming, few learning opportunities in computer science focus on teaching problem-solving skills like planning. In this paper, we present Pyrus, a collaborative game designed to encourage novices to plan in advance while programming. Through Pyrus, we explore a new approach to designing educational games we call behavior-centered game design, in which designers first identify behaviors that learners should practice to reach desired learning goals and then select game mechanics that incentivize those behaviors. Pyrus leverages game mechanics like a failure condition, distributed resources, and enforced turn-taking to encourage players to plan and collaborate. In a within-subjects user study, we found that pairs of novices spent more time planning and collaborated more equally when solving problems in Pyrus than in pair programming. These findings show that game mechanics can be used to promote desirable learning behaviors like planning in advance, and suggest that our behavior-centered approach to educational game design warrants further study.

Undergraduate programs in computer science (CS) face high dropout rates, and many students struggle while learning to program. Studies show that perceived programming ability is a significant factor in students' decision to major in CS. Fortunately, psychology research shows that promoting the growth mindset, or the belief that intelligence grows with effort, can improve student persistence and performance. However, mindset interventions have been less successful in CS than in other domains. We conducted a small-scale interview study to explore how CS students talk about their intelligence, mindsets, and programming behaviors. We found that students' mindsets rarely aligned with definitions in the literature; some present mindsets that combine fixed and growth attributes, while others behave in ways that do not align with their mindsets. We also found that students frequently evaluate their self-efficacy by appraising their programming intelligence, using surprising criteria like typing speed and ease of debugging to measure ability. We conducted a survey study with 103 students to explore these self-assessment criteria further, and found that students use varying and conflicting criteria to evaluate intelligence in CS. We believe the criteria that students choose may interact with mindsets and impact their motivation and approach to programming, which could help explain the limited success of mindset interventions in CS.

Online resources can help novice developers learn basic programming skills, but few resources support progressing from writing working code to learning professional web development practices. We address this gap by advancing Readily Available Learning Experiences, a conceptual approach for transforming all professional web applications into opportunities for authentic learning. This article presents Isopleth, a web-based platform that helps learners make sense of complex code constructs and hidden asynchronous relationships in professional web code. Isopleth embeds sensemaking scaffolds informed by the learning sciences to (1) expose hidden functional and event-driven relationships, (2) surface functionally related slices of code, and (3) support learners manipulating the provided code representations. To expose event-driven relationships, Isopleth implements a novel technique called Serialized Deanonymization to determine and visualize asynchronous functional relationships. To evaluate Isopleth, we conducted a case study across 12 professional websites and a user study with 14 junior and senior developers. Results show that Isopleth’s sensemaking scaffolds helped to surface implementation approaches in event binding, web application design, and complex interactive features across a range of complex professional web applications. Moreover, Isopleth helped junior developers improve the accuracy of their conceptual models of how features are implemented by 31% on average.

While many online resources teach basic web development, few are designed to help novices learn the CSS concepts and design patterns experts use to implement complex visual features. Professional webpages embed these design patterns and could serve as rich learning materials, but their stylesheets are complex and difficult for novices to understand. This paper presents Ply, a CSS inspection tool that helps novices use their visual intuition to make sense of professional webpages. We introduce a new visual relevance testing technique to identify properties that have visual effects on the page, which Ply uses to hide visually irrelevant code and surface unintuitive relationships between properties. In user studies, Ply helped novice developers replicate complex web features 50% faster than those using Chrome Developer Tools, and allowed novices to recognize and explain unfamiliar concepts. These results show that visual inspection tools can support learning from complex professional webpages, even for novice developers.

Student retention is a central challenge in systems for learning at scale. It has been argued that educational video games could improve student retention by providing engaging experiences and informing the design of other online learning environments. However, educational games are not uniformly effective. Our recent research shows that player retention can be increased by using a brain points incentive structure that rewards behaviors associated with growth mindset, or the belief that intelligence can grow. In this paper, we expand on our prior work by providing new insights into how growth mindset behaviors can be effectively promoted in the educational game Refraction. We present results from an online study of 25,000 children who were exposed to five different versions of the brain points intervention. We find that growth mindset animations cause a large number of players to quit, while brain points encourage persistence. Most importantly, we find that awarding brain points randomly is ineffective; the incentive structure is successful specifically because it rewards desirable growth mindset behaviors. These findings have important implications that can support the future generalization of the brain points intervention to new educational contexts.

Interactive learning environments such as intelligent tutoring systems and software tutorials often teach procedures with step-by-step demonstrations. This instructional scaffolding is typically authored by hand, and little can be reused across problem domains. In this work, we present a framework for generating interactive tutorials from an algorithmic representation of the problem-solving thought process. Given a set of mappings between programming language constructs and user interface elements, we step through this algorithm line-by-line to trigger visual explanations of each step. This approach allows us to automatically generate tutorials for any example problem that can be solved with this algorithm. We describe two prototype implementations in the domains of K-12 mathematics and educational games, and present results from two user studies showing that educational technologists can author thought-process procedures and that generated tutorials can effectively teach a new procedure to students.

Video games have great potential to motivate students in environments for learning at scale. However, little is known about how to design in-game incentive structures to maximize learning and engagement. In this work, we expand on our previous research that introduced a new "brain points" incentive structure designed to promote the growth mindset, or the belief that intelligence is malleable. We replicate our original findings, showing that brain points increase student persistence and use of strategy. We also explore how brain points impact students from different demographic groups. We find that brain points are less engaging for low-income students, and discuss methods of improving our design in the future.

Educational technology requires a delivery mechanism to scale. One method that has not yet seen widespread use is the educational campaign: large-scale, short-term events focused on a specific educational topic, such as the Hour of Code campaign. These are designed to generate media coverage and lend themselves nicely to collaborative or competitive goals, thus potentially leveraging social effects and community excitement to increase engagement and reach students who otherwise would not participate. In this article, we present a case study of three such campaigns that we ran to encourage students to play an algebra game—DragonBox Adaptive: the Washington, Norway, and Minnesota Algebra Challenges. We provide several design recommendations for future campaigns based on our experience, including the effects of different incentive schemes, the insertion of “tests” to fast-forward students to levels of appropriate difficulty, and the strengths and weaknesses of campaigns as a method of collecting experimental data.

There is great interest in leveraging video games to improve student engagement and motivation. However, educational games are not uniformly effective, and little is known about how in-game rewards affect children's learning-related behavior. In this work, we argue that educational games can be improved by fundamentally changing their incentive structures to promote the growth mindset, or the belief that intelligence is malleable. We present "brain points," a system that encourages the development of growth mindset behaviors by directly incentivizing effort, use of strategy, and incremental progress. Through a study of 15,000 children, we show that the "brain points" system encourages more low-performing students to persist in the educational game Refraction when compared to a control, and increases overall time played, strategy use, and perseverance after challenge. We believe that this growth mindset incentive structure has great potential in many educational environments.

Video games are increasingly recognized as a compelling platform for instruction that could be leveraged to teach students at scale. Hint systems that provide personalized feedback to students in real time are a central component of many effective interactive learning environments, however little is known about how hints impact player behavior and motivation in educational games. In this work, we study the effectiveness of hints by comparing four designs based on successful hint systems in intelligent tutoring systems and commercial games. We present results from a study of 50,000 students showing that all four hint systems negatively impacted performance compared to a baseline condition with no hints. These results suggest that traditional hint systems may not translate well into the educational game environment, highlighting the importance of studying student behavior to understand the impact of new interactive learning technologies.

One of the key challenges of video game design is teaching new players how to play. Although game developers frequently use tutorials to teach game mechanics, little is known about how tutorials affect game learnability and player engagement. Seeking to estimate this value, we implemented eight tutorial designs in three video games of varying complexity and evaluated their effects on player engagement and retention. The results of our multivariate study of over 45,000 players show that the usefulness of tutorials depends greatly on game complexity. Although tutorials increased play time by as much as 29% in the most complex game, they did not significantly improve player engagement in the two simpler games. Our results suggest that investment in tutorials may not be justified for games with mechanics that can be discovered through experimentation.