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Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

A Systematic Literature Review of Empiricism and Norms of Reporting in Computing Education Research Literature

Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.

Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

A genre-based analysis of questions and comments in Q&A sessions after conference paper presentations in computer science

Gender diversity in computer science at a large public r1 research university: reporting on a self-study.

With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.

Designing for Student-Directedness: How K–12 Teachers Utilize Peers to Support Projects

Student-directed projects—projects in which students have individual control over what they create and how to create it—are a promising practice for supporting the development of conceptual understanding and personal interest in K–12 computer science classrooms. In this article, we explore a central (and perhaps counterintuitive) design principle identified by a group of K–12 computer science teachers who support student-directed projects in their classrooms: in order for students to develop their own ideas and determine how to pursue them, students must have opportunities to engage with other students’ work. In this qualitative study, we investigated the instructional practices of 25 K–12 teachers using a series of in-depth, semi-structured interviews to develop understandings of how they used peer work to support student-directed projects in their classrooms. Teachers described supporting their students in navigating three stages of project development: generating ideas, pursuing ideas, and presenting ideas. For each of these three stages, teachers considered multiple factors to encourage engagement with peer work in their classrooms, including the quality and completeness of shared work and the modes of interaction with the work. We discuss how this pedagogical approach offers students new relationships to their own learning, to their peers, and to their teachers and communicates important messages to students about their own competence and agency, potentially contributing to aims within computer science for broadening participation.

Creativity in CS1: A Literature Review

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.

Exploring students’ and lecturers’ views on collaboration and cooperation in computer science courses - a qualitative analysis

Factors affecting student educational choices regarding oer material in computer science, export citation format, share document.

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Computer science articles from across Nature Portfolio

Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.

Deep learning at the forefront of detecting tipping points

A deep learning-based method shows promise in issuing early warnings of rate-induced tipping, of particular interest in anticipating effects due to anthropogenic climate change.

• Partha Sharathi Dutta

Latest Research and Reviews

Deep Bayesian active learning using in-memory computing hardware

This study introduces an in-memory deep Bayesian active learning framework that uses the stochastic properties of memristors for in situ probabilistic computations. This framework can greatly improve the efficiency and speed of artificial intelligence learning tasks, as demonstrated with a robot skill-learning task.

• Huaqiang Wu

An enhanced classification system of various rice plant diseases based on multi-level handcrafted feature extraction technique

• Yasmin M. Alsakar
• Nehal A. Sakr
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Deep regression analysis for enhanced thermal control in photovoltaic energy systems

• Wael M. Elmessery
• Abadeer Habib
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A spatiotemporal style transfer algorithm for dynamic visual stimulus generation

The spatiotemporal style transfer (STST) algorithm enables video generation by selectively manipulating the spatial and temporal features of natural videos, fostering vision science research in both biological and artificial systems.

• Antonino Greco
• Markus Siegel

Interval variational approach for production control and waste reduction using artificial hummingbird algorithm

• Subhajit Das
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Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model

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Better data sets won’t solve the problem — we need AI for Africa to be developed in Africa

Language models developed by big technology companies consistently underperform in African languages. It’s time to focus on local solutions.

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The AI revolution is running out of data. What can researchers do?

AI developers are rapidly picking the Internet clean to train large language models such as those behind ChatGPT. Here’s how they are trying to get around the problem.

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What should we do if AI becomes conscious? These scientists say it’s time for a plan

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Join the community, trending research, ua-mpc: uncertainty-aware model predictive control for motorized lidar odometry.

kafeiyin00/ua-mpc • 18 Dec 2024

Accurate and comprehensive 3D sensing using LiDAR systems is crucial for various applications in photogrammetry and robotics, including facility inspection, Building Information Modeling (BIM), and robot navigation.

Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems

aerorobotics/sets • 15 Dec 2024

The ability of a robot to plan complex behaviors with real-time computation, rather than adhering to predesigned or offline-learned routines, alleviates the need for specialized algorithms or training for each problem instance.

Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU

We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra.

Data Structures and Algorithms Distributed, Parallel, and Cluster Computing

Empowering Robot Path Planning with Large Language Models: osmAG Map Topology & Hierarchy Comprehension with LLMs

Large Language Models (LLMs) have demonstrated great potential in robotic applications by providing essential general knowledge.

Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis

Text-to-Speech (TTS) systems face ongoing challenges in processing complex linguistic features, handling polyphonic expressions, and producing natural-sounding multilingual speech - capabilities that are crucial for future AI applications.

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MossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation

Instead of applying the recurrent neural networks (RNNs) that use traditional recurrent connections, we present a recurrent module based on a feedforward sequential memory network (FSMN), which is considered "RNN-free" recurrent network due to the ability to capture recurrent patterns without using recurrent connections.

SOAR: Simultaneous Exploration and Photographing with Heterogeneous UAVs for Fast Autonomous Reconstruction

sysu-star/soar • 4 Sep 2024

As the surface is progressively explored, we identify the uncovered areas and generate viewpoints incrementally.

AudioCIL: A Python Toolbox for Audio Class-Incremental Learning with Multiple Scenes

Deep learning, with its robust aotomatic feature extraction capabilities, has demonstrated significant success in audio signal processing.

AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario

This allows the researchers to explore different aspects in meeting processing, ranging from individual tasks such as speech front-end processing, speech recognition and speaker diarization, to multi-modality modeling and joint optimization of relevant tasks.

NanoFlow: Towards Optimal Large Language Model Serving Throughput

The increasing usage of Large Language Models (LLMs) has resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users.

Distributed, Parallel, and Cluster Computing