Understanding what students expect from undergraduate teaching assistants (tutors) is essential to improving the effectiveness of student-tutor interactions. Building on the work of Lim et al. (2023), this study replicates and extends prior research by conducting 23 semi-structured interviews across four institutions to further examine the expectations students have on tutors in university CS2 courses. The original study was scoped within a single institution; by expanding beyond a single-institution sample, we examine the generalizability of previously identified student expectations and explore new perspectives on the tutor's role during tutoring hours. Our findings reveal a large set of roles tutors are expected to play. These roles can be categorized into tutors as solutionists, diagnosticians, and facilitators. Tutors are expected to play these roles, switch between them, or at times take on multiple roles at once. We detail the expectations associated with each category and surface both confirmed and novel findings relative to Lim et al.'s work. Our discussion highlights the emotional labors involved in tutoring, identifies implicit and sometimes unreasonable student expectations, and provides a practical framework for supporting tutors in aligning their efforts with student needs. Implications and limitations of our categorization of roles are discussed alongside directions for future research.

How do we help undergraduates master the rigorous material of Theory of Computing and Algorithms courses while keeping them engaged and confident, especially in the era of Generative AI? Additionally, what goals do educators of these courses believe are important? This panel's goal is to further the discussion of these questions. The panel consists of four educators from distinct institution types who will share evidence-based, classroom-tested strategies for these courses. After the panel gives their position statements, the moderator will guide a structured discussion on motivating abstract topics, assessment and feedback at scale, integrating contemporary tools, and aligning theory/algorithms courses with varied curricula. Specifically, the panel will discuss Generative AI and Large Language Models' place within these courses, the pedagogical implications of autograder usage in these courses, and broader learning goals educators should strive for in these courses.

Peer code review activities, like their industry-proven counterpart code reviews, have had many benefits reported: they enhance programming ability, conceptual understanding, and community, while improving students' debugging ability and code quality. Problems, however, can include lack of engagement and poor review quality; therefore, motivating students to engage with code reviews is essential.

Concept inventories are standardized assessments that evaluate student understanding of key concepts within academic disciplines. While prevalent across STEM fields, their development lags for advanced computer science topics like dynamic programming (DP)---an algorithmic technique that poses significant conceptual challenges for undergraduates. To fill this gap, we developed and validated a Dynamic Programming Concept Inventory (DPCI). We detail the iterative process used to formulate multiple-choice questions targeting known student misconceptions about DP concepts identified through prior research studies. We discuss key decisions, tradeoffs, and challenges faced in crafting probing questions to subtly reveal these conceptual misunderstandings. We conducted a preliminary psychometric validation by administering the DPCI to 172 undergraduate CS students finding our questions to be of appropriate difficulty and effectively discriminating between differing levels of student understanding. Taken together, our validated DPCI will enable instructors to accurately assess student mastery of DP. Moreover, our approach for devising a concept inventory for an advanced theoretical computer science concept can guide future efforts to create assessments for other under-evaluated areas currently lacking coverage.

There is both great hope and concern about the future of Computer Science practice and education concerning the recent advent of large language models (LLMs).

Algorithms courses are a core part of many CS programs, but have received little focus in computing education, lacking statistical data about how they are generally taught. To remedy this, we present the results of the first large-scale comprehensive survey of undergraduate introductory algorithms courses at four-year institutions in the United States. Questions in the survey targeted instructor information, course concepts, the ways students are evaluated, challenges instructors encountered, and instructor envisioned improvements. We received 87 responses from 34 different states, across a wide variety of 4-year institutions. The results indicate that algorithms courses vary dramatically in most surveyed areas.

Clustering is a popular problem with many applications. We consider the k-means problem in the situation where the data is too large to be stored in main memory and must be accessed sequentially, such as from a disk, and where we must use as little memory as possible. Our algorithm is based on recent theoretical results, with significant improvements to make it practical. Our approach greatly simpli(cid:173) fies a recently developed algorithm, both in design and in analysis, and eliminates large constant factors in the approximation guarantee, the memory requirements, and the running time. We then incorporate approximate nearest neighbor search to compute k-means in o(nk) (where n is the number of data points; note that com(cid:173) puting the cost, given a solution, takes 8(nk) time). We show that our algorithm compares favorably to existing algorithms - both theoretically and experimentally, thus providing state-of-the-art performance in both theory and practice.

One of the central problems in data-analysis is k-means clustering. In recent years, considerable attention in the literature addressed the streaming variant of this problem, culminating in a series of results (Har-Peled and Mazumdar; Frahling and Sohler; Frahling, Monemizadeh, and Sohler; Chen) that produced a (1 + ε)-approximation for k-means clustering in the streaming setting. Unfortunately, since optimizing the k-means objective is Max-SNP hard, all algorithms that achieve a (1 + ε)-approximation must take time exponential in k unless P=NP. Thus, to avoid exponential dependence on k, some additional assumptions must be made to guarantee high quality approximation and polynomial running time. A recent paper of Ostrovsky, Rabani, Schulman, and Swamy (FOCS 2006) introduced the very natural assumption of data separability: the assumption closely reflects how k-means is used in practice and allowed the authors to create a high-quality approximation for k-means clustering in the non-streaming setting with polynomial running time even for large values of k. Their work left open a natural and important question: are similar results possible in a streaming setting? This is the question we answer in this paper, albeit using substantially different techniques. We show a near-optimal streaming approximation algorithm for k-means in high-dimensional Euclidean space with sublinear memory and a single pass, under the same data separability assumption. Our algorithm offers significant improvements in both space and running time over previous work while yielding asymptotically best-possible performance (assuming that the running time must be fully polynomial and P ≠ NP). The novel techniques we develop along the way imply a number of additional results: we provide a high-probability performance guarantee for online facility location (in contrast, Meyerson's FOCS 2001 algorithm gave bounds only in expectation); we develop a constant approximation method for the general class of semi-metric clustering problems; we improve (even without σ-separability) by a logarithmic factor space requirements for streaming constant-approximation for k-median; finally we design a “re-sampling method” in a streaming setting to convert any constant approximation for clustering to a [1 + O(σ2)]-approximation for σ-separable data.