As AI-assisted coding becomes standard in software development, computer science educators need a clearer understanding of how Large Language Models (LLMs) can support the learning process. Recent work has examined how students can benefit from using LLMs in their courses, but most studies rely on self-reported usage or controlled experiments with short, isolated programming tasks. To complement these approaches, this paper investigates how students organically leverage LLMs in an advanced computer science course where assignments reflect real-world complexity. We analyze 448 LLM chat logs from 147 students across two offerings of a senior-level web programming course at a large U.S. research university. Through open coding, we identified 14 distinct prompt–response pair types that cluster into three categories: to generate code, debug code, and explain programming concepts. Our analysis reveals that how students interact with LLMs correlates with academic performance. High-effort detailed specifications for code generation positively correlated with final grades (r = 0.25, p < 0.01), whereas low-effort behaviors such as pasting raw error messages showed negative correlations (r = −0.34, p < 0.01). We also observed a temporal shift toward explanation-oriented interactions, suggesting that students increasingly use LLMs as conceptual tutors and not just as code generators.

Interaction mining is a popular technique for capturing design and interaction data while a mobile app is being used. Over the years, researchers have leveraged interaction mining systems to build large repositories of interaction data, enabling novel, ML-based tools for UX practitioners, designers, and programmers. Existing interaction mining systems range from simple screen recorders — which are easy to use but capture sparse, unstructured data — to complex installations requiring dedicated hardware and custom OS forks — which yield rich, high-fidelity traces but are difficult to deploy outside of a lab environment. This paper presents ODIM, an on-device framework for mobile interaction mining that produces detailed trace metadata. The framework is reified in an Android implementation based on a simple APK that users can install on their personal devices. The paper outlines ODIM’s design principles, describes its implementation, evaluates the system on traces collected from 100 popular apps on the Google Play Store, and discusses future avenues for scaling the utility and impact of interaction mining systems. The ODIM software, source code, and online trace repository are all freely available at interactionmining.org, for anyone to use and contribute to.

Sorting items by user rating is a fundamental interaction pattern of the modern Web, used to rank products (Amazon), posts (Reddit), businesses (Yelp), movies (YouTube), and more. To implement this pattern, designers must take in a distribution of ratings for each item and define a sensible total ordering over them. This is a challenging problem, since each distribution is drawn from a distinct sample population, rendering the most straightforward method of sorting --- comparing averages --- unreliable when the samples are small or of different sizes. Several statistical orderings for binary ratings have been proposed in the literature (e.g., based on the Wilson score, or Laplace smoothing), each attempting to account for the uncertainty introduced by sampling. In this paper, we study this uncertainty through the lens of human perception, and ask "How do people sort by ratings?" In an online study, we collected 48,000 item-ranking pairs from 4,000 crowd workers along with 4,800 rationales, and analyzed the results to understand how users make decisions when comparing rated items. Our results shed light on the cognitive models users employ to choose between rating distributions, which sorts of comparisons are most contentious, and how the presentation of rating information affects users' preferences.

Ranking users in social networks is a well-studied problem, typically solved by algorithms that leverage network structure to identify influential users and recommend people to follow. In the last decade, however, curation --- users sharing and promoting content in a network --- has become a central social activity, as platforms like Facebook, Twitter, Pinterest, and GitHub drive growth and engagement by connecting users through content and content to users. While existing algorithms reward users that are highly active with higher rankings, they fail to account for users' curatorial taste. This paper introduces CuRank, an algorithm for ranking users and content in social networks by explicitly modeling three characteristics of a good curator: discerning taste, high activity, and timeliness. We evaluate CuRank on datasets from two popular social networks --- GitHub and Vine --- and demonstrate its efficacy at ranking content and identifying good curators.

Advances in data mining and knowledge discovery have transformed the way Web sites are designed. However, while visual presentation is an intrinsic part of the Web, traditional data mining techniques ignore render-time page structures and their attributes. This paper introduces design mining for the Web: using knowledge discovery techniques to understand design demographics, automate design curation, and support data-driven design tools. This idea is manifest in Webzeitgeist, a platform for large-scale design mining comprising a repository of over 100,000 Web pages and 100 million design elements. This paper describes the principles driving design mining, the implementation of the Webzeitgeist architecture, and the new class of data-driven design applications it enables.

Design patterns have proven useful in many creative fields, providing content creators with archetypal, reusable guidelines to leverage in projects. Creating such patterns, however, is a time-consuming, manual process, typically relegated to a few experts in any given domain. In this paper, we describe an algorithmic method for learning design patterns directly from data using techniques from natural language processing and structured concept learning. Given a set of labeled, hierarchical designs as input, we induce a probabilistic formal grammar over these exemplars. Once learned, this grammar encodes a set of generative rules for the class of designs, which can be sampled to synthesize novel artifacts. We demonstrate the method on geometric models and Web pages, and discuss how the learned patterns can drive new interaction mechanisms for content creators.

The Web provides a corpus of design examples unparalleled in human history. However, leveraging existing designs to produce new pages is often difficult. This paper introduces the Bricolage algorithm for transferring design and content between Web pages. Bricolage employs a novel, structured-prediction technique that learns to create coherent mappings between pages by training on human-generated exemplars. The produced mappings are then used to automatically transfer the content from one page into the style and layout of another. We show that Bricolage can learn to accurately reproduce human page mappings, and that it provides a general, efficient, and automatic technique for retargeting content between a variety of real Web pages.

Procedural representations provide powerful means for generating complex geometric structures. They are also notoriously difficult to control. In this article, we present an algorithm for controlling grammar-based procedural models. Given a grammar and a high-level specification of the desired production, the algorithm computes a production from the grammar that conforms to the specification. This production is generated by optimizing over the space of possible productions from the grammar. The algorithm supports specifications of many forms, including geometric shapes and analytical objectives. We demonstrate the algorithm on procedural models of trees, cities, buildings, and Mondrian paintings.

Enabling ordinary people to create high-quality 3D models is a long-standing problem in computer graphics. In this work, we draw from the literature on design and human cognition to better understand the design processes of novice and casual modelers, whose goals and motivations are often distinct from those of professional artists. The result is a method for creating exploratory modeling tools, which are appropriate for casual users who may lack rigidly-specified goals or operational knowledge of modeling techniques. Our method is based on parametric design spaces, which are often high dimensional and contain wide quality variations. Our system estimates the distribution of good models in a space by tracking the modeling activity of a distributed community of users. These estimates drive intuitive modeling tools, creating a self-reinforcing system that becomes easier to use as more people participate. We present empirical evidence that the tools developed with our method allow rapid creation of complex, high-quality 3D models by users with no specialized modeling skills or experience. We report analyses of usage patterns garnered throughout the year-long deployment of one such tool, and demonstrate the generality of the method by applying it to several design spaces.

Grid-based methods have difficulty resolving features on or below the scale of the underlying grid. Although adaptive methods (e.g. RLE, octrees) can alleviate this to some degree, separate techniques are still required for simulating small-scale phenomena such as spray and foam, especially since these more diffuse materials typically behave quite differently than their denser counterparts. In this paper, we propose a two-way coupled simulation framework that uses the particle level set method to efficiently model dense liquid volumes and a smoothed particle hydrodynamics (SPH) method to simulate diffuse regions such as sprays. Our novel SPH method allows us to simulate both dense and diffuse water volumes, fully incorporates the particles that are automatically generated by the particle level set method in under-resolved regions, and allows for two way mixing between dense SPH volumes and grid-based liquid representations.

A long-standing difficulty in the development of introductory courses in computer graphics is balancing the educational necessity of ensuring mastery of fundamental graphical concepts with the highly desirable goal of exciting and inspiring students to further study by enabling them to produce visually interesting programming projects. Recently, we have developed a modified curriculum predicated on the extensive integration of the OpenGL Shading Language with a more traditional pedagogical approach. We utilized this curriculum in the quarter-long, upper-division introductory graphics course taught in the Department of Computer Science at the University of California, Santa Cruz. Our experience indicates that making shading an integral part of the entry-level curriculum inculcates students with a comprehensive understanding of the algorithms and mathematical concepts that underlie modern graphical systems, while simultaneously equipping them with the tools necessary to produce complex projects with state-of-the-art technology.

This paper describes Scavenger Hunt, a team-based orientation activity for incoming freshmen in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Like many large research universities, Illinois has struggled with the high attrition rate of first-year students in computing disciplines. Scavenger Hunt, which has been held each of the past three years, is intended to foster a sense of community within the department and acclimate new students to undergraduate life as computer science majors. A unique aspect of the activity is the use of handheld computers to manage most aspects of the competition, which enables physically handicapped students to participate on an equal footing with their able-bodied peers. Thus far, Scavenger Hunt has been directly responsible for the increased participation of new students in departmental activities and organizations. In addition, we have observed a significant correlation between student participation in Scavenger Hunt and retention rates in the department. Along with other retention-oriented activities, Scavenger Hunt is measurably improving the attitude of new students towards computer science.