As enrollment increases, teaching assistants (TAs) need help prioritizing responding to students' posts in on-line forums. In a graduate-level Artificial Intelligence forum, three instructors rank the urgency of posts which is compared to the ratings of 13 course TAs; correlation with the instructors' scores have an r=0.55, with a TA inter-rater reliability of 35%. However, when TAs used a codebook containing seven dimensions created by the instructors to define urgency levels, correlation increased to r=0.73 and reliability to 53%. The instructor rankings are also compared to cognitive presence ratings from the Community of Inquiry framework. Cognitive presence correlates with urgency with r=-0.68. These results suggest that recommendation agents that prioritize posts based on cognitive presence or the urgency codebook may be beneficial for TAs.

Embedding a post recommendation system in online course forums improves students' ability to find relevant course content. Yet, there is limited research quantifying how these interventions impact forum interactions and students' class performance. We randomly divide an on-line class in a Masters degree-seeking program into control and experimental sections. Midway through the class, for the experimental section, we introduce an information retrieval system that suggests relevant posts to students while they compose their new posts. The average number of initial posts in the discussion forum dropped by 55% for the experimental group compared to 21% in the control group. In post-hoc analysis, we classify students as having an active or passive (i.e., mostly lurking) forum participation style. Posts per student per assignment by passive participants in the experimental group decreased by 15%, while passive participants' posts in the control group increased by 200%. The number of answers given by instructors in the experimental group decreased twice as much as the control group after intervention, though the difference was not statistically significant. The reduction of posts was not associated with a decrease in academic performance (grades) for the experimental group. This line of research convinced the popular forum Piazza to implement a post recommendation system similar to that used in these experiments.

Plagiarism in computer science programs remains a problem at many universities. Programming assignments lend themselves to unpermitted collaboration: students can share code or find solutions from past semesters through simple internet searches. JackMarker helps address this problem by embedding a hidden, traceable, and unique token within documents. GitDown extends this work by locating available online solutions and requesting that they be removed.

Examinator compares pairs of take-home exams to select which should be manually checked for plagiarism. Examinator also generates a report with evidence for these cases using its metrics and those generated as a by-product of the commercial grading tool Gradescope. Examinator supports degree-seeking graduate programs (both online and on-campus) at a top computer science graduate institute in the United States. Since Spring 2019, Examinator has compared over 2 million pairs of exams from a popular Artificial Intelligence course, resulting in 56 cases being referred for discipline. Iterative development has improved the percentage of referrals of suggested cases from 15% to 25%.

As enrollment numbers in online courses increase, students, instructors, and teaching assistants have difficulty finding needed information in online forums because of the number of posts, resulting in duplicate posts that exacerbate the problem. We introduce PARQR, a recommendation tool that suggests relevant contributions as participants compose their posts. We investigate the use of PARQR in five online degree-seeking courses. We survey 74 students and interview five teaching assistants to understand their experience with online forums and PARQR. We compare the differences between using and not using PARQR for an online course assignment. PARQR users found the tool to be useful for navigating online forums, and PARQR was effective in reducing the number of posts (0.291 vs. 0.506 posts per active student) and duplicate posts (17.8% vs. 25.6%) in an online course. These results suggest that PARQR makes on-line forums more efficient for users to find needed information.

As online educational programs scale, monitoring peer collaboration in platforms like BlueJeans for plagiarism becomes difficult. Recent studies indicate that students are less likely to cheat if presented with direct warning messages prior to engaging in online activities. In this work, we present Bluejeans codE Leak deTection (BELT), a system that monitors online BlueJeans meetings for shared code and sends timely warning messages to meeting participants. To test BELT's robustness as an online proctor, we evaluate its code-text disambiguation, code detection from images of varying quality, and code detection from videos of varying resolution. We conclude this work by pinpointing areas of improvement and briefly discuss possible extensions for future work.

Game-based second language (L2) learning represents an ideal alternative to foreign language classroom instruction. However, despite a diverse player demographic, the design of L2 games is often not informed by players representative of marginalized populations, especially women of color (i.e., Black women). Such oversight in the design process contributes to games that perpetuate gendered and racist stereotypes, and therefore, are less appealing to women of color. In response this dilemma, we utilize Black Feminist Thought (BFT) as a critical framework to engage Black women, a marginalized and understudied population within the gaming subculture, and more broadly, the Computer Supported Cooperative Work (CSCW) community in game design. Twenty-five Black women take on multiple roles as game designers, foreign language instructor, and informants who represent both producers and consumers throughout the conceptualization phase of a L2 mobile game prototype. Applying BFT principles, we leverage Black women\textquotesingle s lived intersectional experiences to transform the traditional game design process into a more inclusive design experience for Black women. In the context of games that support L2 learning, our findings reveal that Black women appreciate games that: 1. provide authentic cultural experiences; 2. feature intersectional game characters that reflect real life experiences; 3. accurately portray the diversity of Black women's bodies; and 4. provide opportunities for players to customize game assets. As a disruptor to traditional game design, BFT makes salient oppressive design practices within the gaming culture that also extend to the larger CSCW community, signifying the need to embrace more inclusive design practices that benefit Black women and other marginalized populations.

We introduce PARQR, a tool for online education forums that reduces duplicate posts by 40% in a degree seeking online masters program at a top university. Instead of performing a standard keyword search, PARQR monitors questions as students compose them and continuously suggests relevant posts. In testing, PARQR correctly recommends a relevant post, if one exists, 73.5% of the time. We discuss PARQR's design, initial experimental results comparing different semesters with and without PARQR, and interviews we conducted with teaching instructors regarding their experience with PARQR.

African American women who have little if any experience in computer programming prior to matriculation into college often struggle in introductory CS courses that emphasize programming from the outset. These same students feel underprepared and disadvantaged, oftentimes choosing to withdraw from such courses which contributes to the underrepresentation of African American women in CS. The challenge lies in creating equitable learning environments that bridge students' everyday experiences to fundamental CS concepts. In this experience report, we introduce the Dessert Wars Challenge, an alternative pedagogical strategy that leverages students' everyday experiences with food to forge meaningful connections to CS. We examine how African American women's participation in the Dessert Wars Challenge supports the development of their Computational Algorithmic Thinking (CAT) capabilities, the ability to design, implement, assess and adapt algorithms, and contributes to their retention in a college-level introductory CS course.

Though toddlers and preschoolers are regular touchscreen users, relatively little is known about how they learn to perform unfamiliar gestures. In this paper we assess the responses of 34 children, aged 2 to 5, to the most common in-app prompting techniques for eliciting specific gestures. By reviewing 100 touchscreen apps for preschoolers, we determined the types of prompts that children are likely to encounter. We then evaluated their relative effectiveness in teaching children to perform simple gestures. We found that children under 3 were only able to interpret instructions when they came from an adult model, but that children made rapid gains between age 3 and 3-and-a-half, at which point they were able to follow in-app audio instructions and on-screen demonstrations. The common technique of using visual state changes to prompt gestures was ineffective across this age range. Given that prior work in this space has primarily focused on children's fine motor control, our findings point to a need for increased attention to the design of prompts that accommodate children's cognitive development as well.