In an increasingly globalized and service-oriented economy, people need to engage in computer-mediated collaborative problem solving (CPS) with diverse teams. However, teams routinely fail to live up to expectations, showcasing the need for technologies that help develop effective collaboration skills. We take a step in this direction by investigating how different dimensions of team diversity (demographic, personality, attitudes towards teamwork, prior domain experience) predict objective (e.g. effective solutions) and subjective (e.g. positive perceptions) collaborative outcomes. We collected data from 96 triads who engaged in a 30-minute CPS task via videoconferencing. We found that demographic diversity and differing attitudes towards teamwork predicted impressions of positive engagement, while personality diversity predicted learning outcomes. Importantly, these relationships were maintained after accounting for team makeup. None of the diversity measures predicted task performance. We discuss how our findings can be incorporated into technologies that aim to help diverse teams develop CPS skills.

With the rise of Computational Thinking (CT) instruction at the elementary level, it is imperative that elementary computing instruction support a variety of learners. A popular pedagogical approach for this age group is Use->Modify->Create, which introduces a concept through a more scaffolded, guided instruction before culminating in a more open-ended project for student engagement. Yet, there is little research on student learning during the Use->Modify step, nor strategies to promote learning in this step. This paper introduces TIPP&SEE, a metacognitive learning strategy that further scaffolds student learning during this step. Results from an experimental study show statistically-significant performance gains from students using the TIPP&SEE strategy on nearly all assessment questions of moderate and hard difficulty, suggesting its potential as an effective CS/CT learning strategy.

As many school districts nationwide continue to incorporate Computer Science (CS) and Computational Thinking (CT) instruction at the K-8 level, it is crucial that we understand the factors and skills, such as reading and math proficiency, that contribute to the success of younger learners in a computing curriculum and are typically developed at this age. Yet, little is known about the relationship between reading and math proficiency, and the learning of key CS concepts at the elementary level. This study focused on 4th-grade students (ages 9-10) who were taught events, sequence, and repetition through an adaptation of the Creative Computing Curriculum. While all students benefited from access to such a curriculum, there were statistically-significant differences in learning outcomes, especially between students whose reading and math proficiency are below grade-level, and students whose proficiency are at or above grade-level. This performance gap suggests the need for curricular improvement and learning strategies that are CS specific for students who struggle with reading and math.

The CS community has struggled to assess student learning at the K-8 level, with techniques ranging from one-on-one interviews to written assessments. While scalable, automated techniques exist for analyzing student code, a scalable method for assessing student comprehension of their own code has remained elusive. This study is a first step in bridging the gap between the knowledge gained from interviews and the time efficiency and scalability of written assessments and automated analysis. The goal of this study is to understand how student answers on various types of questions differ depending on whether they are being asked about their own code or generic code. We find that while there were no statistically-significant differences in overall scores, questions about generic and personalized code of comparable complexity are far from equivalent. Our qualitative analyses revealed interesting patterns in student responses, inviting further research into this assessment technique. In particular, students answered differently from students with generic code when presented with individual blocks from their code taken out of context and placed into different code snippets, and students answered in a way that demonstrates a functional, instead of structural, understanding on Explain in Plain English (EiPE) questions.

Efforts to broaden participation in computing have led to gender-focused interventions intended to increase the number of women in the field of computing. However, such efforts have failed to significantly increase the percentage of Black women in computing. For example, only 1% of the 28,884 bachelor's degrees in computing were awarded to Black women in 2018. Moreover, too few empirical studies have intentionally explored the lived experiences of Black women, an often overlooked and understudied population in the computing ecosystem. In this paper, we introduce intersectionality - the complex overlap of socially constructed identities such as race, gender, class, sexuality, etc. - as a theoretical framework and springboard for exploring the lived experiences of Black women in computing. We interview 14 Black women in various stages of the computing ecosystem (undergraduate students, graduate students and early career professionals) to understand how intersectionality influences their ability to persist in computing. Preliminary findings from the analysis of the 14 interviews provides insights into how the interlocking systems of oppression (i.e., gendered racism) play out in computing education and negatively impact the recruitment and retention of Black women in the field of computing.

With many school districts nationwide integrating Computer Science (CS) and Computational Thinking (CT) instruction at the K-8 level, it is crucial researchers closely inspect the relationship between program expression and student understandings. In this study, we propose and report on our use of Scratch Charades, a game in which students act out Scratch scripts while others build them. The purpose of Scratch Charades is to familiarize students with scripts and blocks without the cognitive overhead of the complex user interface. However, in this study, we also used it to elicit student understandings about Scratch blocks and scripts to design mnemonics to help students debug their code. We propose two building and/or debugging strategies based on our observations.

The computer science community has struggled to assess student learning via Scratch programming at the primary school level (ages 7-12). Prior work has relied most heavily on artifact (student code/projects) analysis, with some attempts at one-on-one interviews and written assessments. In this paper, we explore the relationship between artifact analysis and written assessments. Through this study of a large-scale introductory computing implementation, we found that for students who had code in their projects, student performance on specific questions on the written assessments is only very weakly correlated to specific attributes of final projects typically used in artifact analysis as well as attributes we use to define candidate code (r < 0.2, p < 0.05). In particular, the correlation is not nearly strong enough to serve as a proxy for understanding. © 2020 ACM.

Investigating student knowledge transfer and metacognitive activities at college CS departments and at coding bootcamps. © 2020 ACM.

Polyglot programming, the use of multiple programming languages during the development process, is common practice in modern software development. This study investigates this practice through a randomized controlled trial conducted under the context of database programming. Participants in the study were given coding tasks written in Java and one of three SQL-like embedded languages. One was plain SQL in strings, one was in Java only, and the third was a hybrid embedded language that was closer to the host language. We recorded 109 valid data points. Results showed significant differences in how developers of different experience levels code using polyglot techniques. Notably, less experienced programmers wrote correct programs faster in the hybrid condition (frequent, but less severe, switches), while more experienced developers that already knew both languages performed better in traditional SQL (less frequent but more complete switches). The results indicate that the productivity impact of polyglot programming is complex and experience level dependent. © 2020 ACM.

With the rise of Computational Thinking (CT) instruction at the elementary level, it is imperative for elementary computing instruction to support a variety of learners. TIPP&SEE is a meta-cognitive learning strategy that scaffolds student learning when learning from example code. Results from a previous study show statistically-significant performance differences favoring students using the TIPP&SEE strategy on a written assessment [43]. In this work, our goal is gain insight as to why such dramatic learning differences may have occurred. We analyze the students' computational artifacts and TIPP&SEE worksheets. Artifact analysis reveals that students in the TIPP&SEE group are more thorough in their work, completing more elements of the required tasks. In addition, they build open-ended projects with longer scripts that utilize more learned blocks. Worksheet analysis shows that students were highly accurate on some types of questions but largely skipped others. Despite these positive behaviors, there was little statistical correlation between student worksheet correctness, project completion, and written assessment performance. Therefore, while students in the TIPP&SEE group performed actions we believe lead to more success, no individual actions directly explain the results. Like other meta-cognitive strategies, the value of TIPP&SEE may lie in cognitive processes not directly observable, and may vary based upon individual student differences.