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Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programming tasks and are causing disruptions throughout the curriculum. These tools can write and explain code, enhance error messages, create resources for instructors, and even provide feedback and help for students like a traditional teaching assistant. In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. These new data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. In support of the former, a new class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time. With the literature expanding so rapidly, this report aims to summarize and explain what is happening on the ground in computing classrooms. We provide a systematic literature review; a survey of educators and industry professionals; and interviews with educators using GenAI in their courses, educators studying GenAI, and researchers who create GenAI tools to support computing education. The triangulation of these methods and data sources expands the understanding of GenAI usage and perceptions at this critical moment for our community., Comment: 39 pages, 10 figures, 16 tables. To be published in the Proceedings of the 2024 Working Group Reports on Innovation and Technology in Computer Science Education (ITiCSE-WGR 2024)

Background and Context: Some skills taught in introductory programming courses are categorized into 1) explaining code, 2) arranging lines of code in correct sequence, 3) tracing through the execution of a program, and 4) writing code from scratch. Objective: Knowing if a programming skill is a prerequisite to another would benefit teachers in properly planning the course and structuring the order in which they present activities relating to new content. Prior attempts to establish a skill hierarchy have suffered from methodological issues. Method: In this study, we used the conviction measure from association rule mining to perform pair-wise comparisons of five skills: Write, Trace, Reverse trace, Sequence, and Explain code. We used the data from four exams with more than 600 participants where students solved programming assignments of different skills for several programming topics. Findings: Our findings matched the previous finding that tracing is a prerequisite for students to learn to write code. Contradicting the previous claims, our analysis showed that using the mean threshold writing code is a prerequisite to explaining code. However, there is no clear relationship when we change the threshold to the median. Unlike prior work, we did not find a clear prerequisite relationship between sequencing code and writing or explaining code. Implications: Our research can help instructors by systematically arranging the skills students exercise when encountering a new topic. The goal is to help instructors properly teach and assess programming in a fashion most effective for learning by leveraging the relationship between skills.

Introductory programming courses often emphasize mastering syntax and basic constructs before progressing to more complex and interesting programs. This bottom-up approach can be frustrating for novices, shifting the focus away from problem solving and potentially making computing less appealing to a broad range of students. The rise of generative AI for code production could partially address these issues by fostering new skills via interaction with AI models, including constructing high-level prompts and evaluating code that is automatically generated. In this experience report, we explore the inclusion of two prompt-focused activities in an introductory course, implemented across four labs in a six-week module. The first requires students to solve computational problems by writing natural language prompts, emphasizing problem-solving over syntax. The second involves students crafting prompts to generate code equivalent to provided fragments, to foster an understanding of the relationship between prompts and code. Most of the students in the course had reported finding programming difficult to learn, often citing frustrations with syntax and debugging. We found that self-reported difficulty with learning programming had a strong inverse relationship with performance on traditional programming assessments such as tests and projects, as expected. However, performance on the natural language tasks was less strongly related to self-reported difficulty, suggesting they may target different skills. Learning how to communicate with AI coding models is becoming an important skill, and natural language prompting tasks may appeal to a broad range of students., Comment: 7 pages, 6 figures. Accepted for publication at SIGCSE Virtual 2024

Background: Introductory computer science courses use ``Explain in Plain English'' (EiPE) activities to develop and assess students' code comprehension skills, but creating effective autograders for these questions is challenging and limited to English. This is a particular challenge in linguistically diverse countries like India where students may have limited proficiency in English. Methods: We evaluate the efficacy of a recently introduced approach called Code Generation Based Grading (CGBG) in enabling language agnostic ``Explain in Plain Language'' (EiPL) activities. Here students' EiPL responses generate code that is tested for functional equivalence to the original which was being described. Objectives: We initially evaluate the correctness of code generated from correct EiPL responses provided in 10 of India's most commonly spoken languages. To evaluate the effectiveness of the approach in practice, we assess student success and perceptions of EiPL questions in a NPTEL (National Programme on Technology Enhanced Learning) course. Results: We find promising results for the correctness of code generated from translations of correct EiPL responses, with most languages achieving a correctness rate of 75% or higher. However, in practice, many students preferred to respond in English due to greater familiarity with English as a technical language, difficulties writing in their native language, and perceptions of the grader being less capable of generating code from prompts in their mother tongue.

Parsons problems are a type of programming activity that present learners with blocks of existing code and requiring them to arrange those blocks to form a program rather than write the code from scratch. Micro Parsons problems extend this concept by having students assemble segments of code to form a single line of code rather than an entire program. Recent investigations into micro Parsons problems have primarily focused on supporting learners leaving open the question of micro Parsons efficacy as an exam item and how students perceive it when preparing for exams. To fill this gap, we included a variety of micro Parsons problems on four exams in an introductory programming course taught in Python. We use Item Response Theory to investigate the difficulty of the micro Parsons problems as well as the ability of the questions to differentiate between high and low ability students. We then compare these results to results for related questions where students are asked to write a single line of code from scratch. Finally, we conduct a thematic analysis of the survey responses to investigate how students' perceptions of micro Parsons both when practicing for exams and as they appear on exams., Comment: This work is to appear in ITiCSE 2024. Both authors contributed equally to this research

The recent, widespread availability of Large Language Models (LLMs) like ChatGPT and GitHub Copilot may impact introductory programming courses (CS1) both in terms of what should be taught and how to teach it. Indeed, recent research has shown that LLMs are capable of solving the majority of the assignments and exams we previously used in CS1. In addition, professional software engineers are often using these tools, raising the question of whether we should be training our students in their use as well. This experience report describes a CS1 course at a large research-intensive university that fully embraces the use of LLMs from the beginning of the course. To incorporate the LLMs, the course was intentionally altered to reduce emphasis on syntax and writing code from scratch. Instead, the course now emphasizes skills needed to successfully produce software with an LLM. This includes explaining code, testing code, and decomposing large problems into small functions that are solvable by an LLM. In addition to frequent, formative assessments of these skills, students were given three large, open-ended projects in three separate domains (data science, image processing, and game design) that allowed them to showcase their creativity in topics of their choosing. In an end-of-term survey, students reported that they appreciated learning with the assistance of the LLM and that they interacted with the LLM in a variety of ways when writing code. We provide lessons learned for instructors who may wish to incorporate LLMs into their course., Comment: to be published in Proceedings of the 29th ACM conference on innovation and technology in computer science education (ITiCSE)

Reading, understanding and explaining code have traditionally been important skills for novices learning programming. As large language models (LLMs) become prevalent, these foundational skills are more important than ever given the increasing need to understand and evaluate model-generated code. Brand new skills are also needed, such as the ability to formulate clear prompts that can elicit intended code from an LLM. Thus, there is great interest in integrating pedagogical approaches for the development of both traditional coding competencies and the novel skills required to interact with LLMs. One effective way to develop and assess code comprehension ability is with ``Explain in plain English'' (EiPE) questions, where students succinctly explain the purpose of a fragment of code. However, grading EiPE questions has always been difficult given the subjective nature of evaluating written explanations and this has stifled their uptake. In this paper, we explore a natural synergy between EiPE questions and code-generating LLMs to overcome this limitation. We propose using an LLM to generate code based on students' responses to EiPE questions -- not only enabling EiPE responses to be assessed automatically, but helping students develop essential code comprehension and prompt crafting skills in parallel. We investigate this idea in an introductory programming course and report student success in creating effective prompts for solving EiPE questions. We also examine student perceptions of this activity and how it influences their views on the use of LLMs for aiding and assessing learning., Comment: Accepted to ITiCSE 2024

Large Language Models (LLMs) have upended decades of pedagogy in computing education. Students previously learned to code through \textit{writing} many small problems with less emphasis on code reading and comprehension. Recent research has shown that free code generation tools powered by LLMs can solve introductory programming problems presented in natural language with ease. In this paper, we propose a new way to teach programming with Prompt Problems. Students receive a problem visually, indicating how input should be transformed to output, and must translate that to a prompt for an LLM to decipher. The problem is considered correct when the code that is generated by the student prompt can pass all test cases. In this paper we present the design of this tool, discuss student interactions with it as they learn, and provide insights into this new class of programming problems as well as the design tools that integrate LLMs., Comment: accepted for CHI 2024

This article proposes a framework for content moderation based on a decentralized market where no one party, neither governments nor firms, controls the flow of information., Comment: 3 pages. This is published by CACM but the authors hold (c) and are happy to license under the listed Creative Commons CC:BY-SA license

Post-starburst (PSB), or 'E+A', galaxies represent a rapid transitional phase between major, gas-rich mergers and gas-poor, quiescent early-type galaxies. Surprisingly, many PSBs have been shown to host a significant interstellar medium (ISM), despite theoretical predictions that the majority of star-forming gas should be expelled in AGN- or starburst-driven outflows. To-date, the resolved properties of this surviving ISM have remained unknown. We present high resolution ALMA continuum and CO(2$-$1) observations in six gas- and dust-rich PSBs, revealing for the first time the spatial and kinematic structure of their ISM on sub-kpc scales. We find extremely compact molecular reservoirs, with dust and gas surface densities rivaling those found in (ultra-)luminous infrared galaxies. We observe spatial and kinematic disturbances in all sources, with some also displaying disk-like kinematics. Estimates of the internal turbulent pressure in the gas exceed those of normal star-forming disks by at least 2 orders of magnitude, and rival the turbulent gas found in local interacting galaxies, such as the Antennae. Though the source of this high turbulent pressure remains uncertain, we suggest that the high incidence of tidal disruption events (TDEs) in PSBs could play a role. The star formation in these PSBs' turbulent central molecular reservoirs is suppressed, forming stars only 10% as efficiently as starburst galaxies with similar gas surface densities. "The fall" of star formation in these galaxies was not precipitated by complete gas expulsion or redistribution. Rather, this high-resolution view of PSBs' ISM indicates that star formation in their remaining compact gas reservoirs is suppressed by significant turbulent heating., Comment: 19 pages, 9 figures, 3 tables; Accepted for publication in ApJ

The recent star formation histories (SFHs) of post-starburst galaxies have been determined almost exclusively from detailed modeling of their composite star light. This has provided important but limited information on the number, strength, and duration of bursts of star formation. In this work, we present a direct and independent measure of the recent SFH of S12 (plate-mjd-fiber for SDSS 623-52051-207; designated EAS12 in Smercina et al. 2018) from its star cluster population. We detect clusters from high resolution, $UBR$ optical observations from HST, and compare their luminosities and colors with stellar population models to estimate the ages and masses of the clusters. No clusters younger than $\sim$70 Myr are found, indicating star formation shut off at this time. Clusters formed $\sim$120 Myr ago reach masses up to $\sim \mbox{few}\times10^7~M_{\odot}$, several times higher than similar age counterparts formed in actively merging galaxies like the Antennae and NGC 3256. We develop a new calibration based on known properties for 8 nearby galaxies to estimate the star formation rate (SFR) of a galaxy from the mass of the most massive cluster, $M_{\rm max}$. The cluster population indicates that S12 experienced an extremely intense but short-lived burst $\sim$120 Myr ago, with an estimated peak of $500^{+500}_{-250}~M_{\odot}~\mbox{yr}^{-1}$ and duration of $50\pm25$ Myr, one of the highest SFRs estimated for any galaxy in the nearby universe. Prior to the recent, intense burst, S12 was forming stars at a moderate rate of $\sim 3{-}5~M_{\odot}~\mbox{yr}^{-1}$, typical of spiral galaxies. However, the system also experienced an earlier burst approximately $1{-}3$ Gyr ago. While fairly uncertain, we estimate that the SFR during this earlier burst was $\sim20{-}30~M_{\odot}~\mbox{yr}^{-1}$, similar to the current SFR in the Antennae and NGC 3256., Comment: 20 pages, 12 figures, 2 tables, accepted to ApJ

This study investigated the essential of meaningful automated feedback for programming assignments. Three different types of feedback were tested, including (a) What's wrong - what test cases were testing and which failed, (b) Gap - comparisons between expected and actual outputs, and (c) Hint - hints on how to fix problems if test cases failed. 46 students taking a CS2 participated in this study. They were divided into three groups, and the feedback configurations for each group were different: (1) Group One - What's wrong, (2) Group Two - What's wrong + Gap, (3) Group Three - What's wrong + Gap + Hint. This study found that simply knowing what failed did not help students sufficiently, and might stimulate system gaming behavior. Hints were not found to be impactful on student performance or their usage of automated feedback. Based on the findings, this study provides practical guidance on the design of automated feedback.

Dust offers a unique probe of the interstellar medium (ISM) across multiple size, density, and temperature scales. Dust is detected in outflows of evolved stars, star-forming molecular clouds, planet-forming disks, and even in galaxies at the dawn of the Universe. These grains also have a profound effect on various astrophysical phenomena from thermal balance and extinction in galaxies to the building blocks for planets, and changes in dust grain properties will affect all of these phenomena. A full understanding of dust in all of its forms and stages requires a multi-disciplinary investigation of the dust life cycle. Such an investigation can be achieved with a statistical study of dust properties across stellar evolution, star and planet formation, and redshift. Current and future instrumentation will enable this investigation through fast and sensitive observations in dust continuum, polarization, and spectroscopy from near-infrared to millimeter wavelengths., Comment: Science white paper submitted to the Astro2020 Decadal Survey

Galaxy formation depends on a complex interplay between gravitational collapse, gas accretion, merging, and feedback processes. Yet, after many decades of investigation, these concepts are poorly understood. This paper presents the argument that warm H$_2$ can be used as a tool to unlock some of these mysteries. Turbulence, shocks and outflows, driven by star formation, AGN activity or inflows, may prevent the rapid buildup of star formation in galaxies. Central to our understanding of how gas is converted into stars is the process by which gas can dissipate its mechanical energy through turbulence and shocks in order to cool. H$_2$ lines provide direct quantitative measurements of kinetic energy dissipation in molecular gas in galaxies throughout the Universe. Based on the detection of very powerful H$_2$ lines from z = 2 galaxies and proto-clusters at the detection limits of {\it Spitzer}, we are confident that future far-IR and UV H$_2$ observations will provide a wealth of new information and insight into galaxy evolution to high-z. Finally, at the very earliest epoch of star and galaxy formation, warm H$_2$ may also provide a unique glimpse of molecular gas collapse at 7 $<$ z $<$ 12 in massive dark matter (DM) halos on their way to forming the very first galaxies. Such measurements are beyond the reach of existing and planned observatories., Comment: Submitted as a science White Paper to the Astronomy and Astrophysics Astro 2020 Decadal Survey call issued by the National Academies of Sciences, Engineering and Medicine (March 11 2019)

The optimization of submodular functions on the integer lattice has received much attention recently, but the objective functions of many applications are non-submodular. We provide two approximation algorithms for maximizing a non-submodular function on the integer lattice subject to a cardinality constraint; these are the first algorithms for this purpose that have polynomial query complexity. We propose a general framework for influence maximization on the integer lattice that generalizes prior works on this topic, and we demonstrate the efficiency of our algorithms in this context.

The Small Magellanic Cloud (SMC) provides the only laboratory to study the structure of molecular gas at high resolution and low metallicity. We present results from the Herschel Spectroscopic Survey of the SMC (HS$^{3}$), which mapped the key far-IR cooling lines [CII], [OI], [NII], and [OIII] in five star-forming regions, and new ALMA 7m-array maps of $^{12}$CO and $^{13}$CO $(2-1)$ with coverage overlapping four of the five HS$^{3}$ regions. We detect [CII] and [OI] throughout all of the regions mapped. The data allow us to compare the structure of the molecular clouds and surrounding photodissociation regions using $^{13}$CO, CO, [CII], and [OI] emission at $<10$" ($<3$ pc) scales. We estimate Av using far-IR thermal continuum emission from dust and find the CO/[CII] ratios reach the Milky Way value at high A$_{V}$ in the centers of the clouds and fall to $\sim{1/5-1/10}\times$ the Milky Way value in the outskirts, indicating the presence of translucent molecular gas not traced by bright CO emission. We estimate the amount of molecular gas traced by bright [CII] emission at low A$_{V}$ and bright CO emission at high A$_{V}$. We find that most of the molecular gas is at low A$_{V}$ and traced by bright [CII] emission, but that faint CO emission appears to extend to where we estimate the H$_{2}$-to-HI transition occurs. By converting our H$_{2}$ gas estimates to a CO-to-H$_{2}$ conversion factor ($X_{CO}$), we show that $X_{CO}$ is primarily a function of A$_{V}$, consistent with simulations and models of low metallicity molecular clouds., Comment: Accepted for publication in ApJ