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In computer science, students could benefit from more opportunities to learn important, high-level concepts and to improve their learning skills. Peer review is one method to encourage this by providing students with the opportunity to…
Causal representation learning (CRL) models aim to transform high-dimensional data into a latent space, enabling interventions to generate counterfactual samples or modify existing data based on the causal relationships among latent…
Classifying journals or publications into research areas is an essential element of many bibliometric analyses. Classification usually takes place at the level of journals, where the Web of Science subject categories are the most popular…
Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating…
Scientific papers do more than report results $-$ they advance $\textit{claims}$ that later work supports, extends, or sometimes refutes. Yet existing methods for citation and claim analysis capture only fragments of this dialogue. In this…
Over the past decade, an explosion in the availability of education-related datasets has enabled new computational research in education. Much of this work has investigated digital traces of online learners in order to better understand and…
This report synthesizes findings from the November 2024 Community Workshop on Practical Reproducibility in HPC, which convened researchers, artifact authors, reviewers, and chairs of reproducibility initiatives to address the critical…
Reproducibility is a cornerstone of scientific research, enabling independent verification and validation of empirical findings. The topic gained prominence in fields such as psychology and medicine, where concerns about non - replicable…
As Engineering Education Research (EER) develops as a discipline it is necessary for EER scholars to contribute to the development of learning theory rather than simply being informed by it. It has been suggested that to do this effectively…
Many research groups aspire to make data and code FAIR and reproducible, yet struggle because the data and code life cycles are disconnected, executable environments are often missing from published work, and technical skill requirements…
With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks,…
Reinforcement Learning (RL) is an important machine learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in this field due to the rapid development of deep neural networks.…
Background: Research software is crucial for enabling research discoveries and supporting data analysis, simulation, and interpretation across domains. However, evolving requirements, complex inputs, and legacy dependencies hinder the…
Undergraduates are unlikely to even consider graduate research in Computer Science if they do not know what Computer Science research is. Many programs aimed at introducing undergraduate to research are structured like graduate research…
This first "Bibliometric-enhanced Information Retrieval" (BIR 2014) workshop aims to engage with the IR community about possible links to bibliometrics and scholarly communication. Bibliometric techniques are not yet widely used to enhance…
In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small subset of past data is stored and replayed alongside new data,…
Science communication forms the bridge between computer science researchers and their target audience. Researchers who can effectively draw attention to their research findings and communicate them comprehensibly not only help their target…
Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields, thereby driving advancements in other domains of interest. As the field continues to evolve, it is crucial to understand the…
Computational reproducibility, the possibility for independent researchers to exactly reproduce published empirical results, is fundamental to science. Despite its importance, the proportion of research articles aiming for reproducibility…
With the rapid development of deep learning, the implementation of intricate algorithms and substantial data processing have become standard elements of deep learning projects. As a result, the code has become progressively complex as the…