Search for: All records

Introductory computer science courses for non-majors (CS0) aim to increase diversity and highlight the relevance of computing across disciplines. To enhance the accessibility and engagement of CS0, researchers have explored contextualized computing, where computing is integrated with another subject, to teach course content. While research has explored various designs for contextualized courses, we know less about how contextualized computing tasks impact students’ learning experiences. Through the lens of metacognition and affect, we conducted a secondary qualitative analysis on daily diary and retrospective interview data from 20 students in a CS0 course that applied coding to different contexts. Our findings demonstrate that students’ feeling of knowing and their perception of the task are two central themes that shape their affect and interest in the course. We conclude with design suggestions for contextualized computing in CS0 to better support students. 

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. 

Teens often encounter cyberbullying on social media. One promising way to reduce cyberbullying is through empowering teens to stand up for their peers and cultivating prosocial norms online. While there is no shortage of bystander interventions that have shown potential, little research has explored designing chatbots with users to provide a contextualized and embedded “learning at the moment” experience for bystanders. This study involved teens and educators in two design sessions: an in-depth interview to identify the barriers that prevent upstanding behaviors, and interaction with the “social media co-pilot'' chatbot prototype to identify design guidelines to empower teens to overcome these barriers. Qualitative analysis on the conversations from the two design sessions revealed three factors that curb teens' upstanding behaviors: a) inadequate knowledge about social norms, appropriate language, and consequences, b) inhibitive emotions such as fear of retaliation and confrontation; c) lack of empathy toward their peers. Key parameters were also identified to shape chatbot responses to encourage upstanding behaviors, such as a) adopting voices representing multiple roles, b) empathetic, friendly and encouraging tone, c) reflective, specific and relatable language and d) appropriate length. These insights inform the design of personalized and scalable education programs and moderation tools to combat cyberbullying. 

Abstract Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high‐quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators' workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open‐source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.