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Free software applications for authors for writing a research paper

Himel mondal.

1 Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India

Ayesha Juhi

Anupkumar d. dhanvijay, mohammed jaffer pinjar, shaikat mondal.

2 Department of Physiology, Raiganj Government Medical College and Hospital, Raiganj, West Bengal, India

Basic computer skills are essential for authors writing research papers as it has the potential to make the task easier for a researcher. This article provides a glimpse about the essential software programs for a novice author writing a research paper. These software applications help streamline the writing process, improve the quality of work, and ensure that papers are formatted correctly. It covers word processing software, grammar correction software, bibliography management software, paraphrasing tool, writing tools, and statistical software. All of the tools described are free to use. Hence, it would help researchers from resource-limited settings or busy physicians who get lesser time for research writing. We presume this review paper would help provide valuable insights and guidance for novice authors looking to write a high-quality research paper.

Introduction

An author is one who “writes a book, article, play, etc.” A researcher is “someone whose job is to study a subject carefully, especially in order to discover new information or understand the subject better.” However, in a broad sense, a researcher is an author first. In a research cycle, a researcher needs to become an author from the very beginning of the research (preparation of proposal) to the end of the research (writing a paper for publication).[ 1 ]

Basic computer skills are essential for authors writing research papers because computers and technology have become a fundamental part of the research and writing process. As a new author writing a research paper, there are several essential software skills that can help you streamline the writing process, improve the quality of work, and ensure that the proposal or paper is formatted correctly.[ 2 ] However, these skills are rarely taught in our formal undergraduate or postgraduate course of study.

In this context, we discuss some of the basic software skills that may enhance the the quality of an research article in this article. This includes word processing software, grammar-checking software, paraphrasing tools, statistical software, writing tools, and keyword-searching tool.

Software applications

We describe some of the free software applications that may help authors during the preparation of a research paper. All the applications described are available either for computers or can be used online without paying any fees. Relevant websites where the tools are available are shown in Table 1 .

Software application (downloadable and online) with websites and their primary use for research purposes

WPS=Writer, Presentation and Spreadsheets, MeSH=Medical Subject Headings, DOI=Digital Object Identifier, JANE=Journal/Author Name Estimator, GPT=Generative Pre-training Transformer

Computer software applications

Apache OpenOffice is a free and open-source office software suite that includes a word processor (writer), spreadsheet, presentation software, and other tools. OpenOffice Writer is similar to Microsoft Word and can be used to write and format your research article.[ 3 ] This program is capable of saving the file into its own format (.odt) and also helps in saving the file in Microsoft Word document format (.doc). Hence, any text typed in this program can easily be opened with Microsoft Word. Along with typing an article, this program can help in making a flow chart (e.g. PRISMA flow chart for systemic review and meta-analysis) for research articles. Figure 1 shows the user interface of OpenOffice writer.

A portion of a story written on OpenOffice Writer showing the user interface

There is alternative office software called WPS (an acronym for Writer, Presentation, and Spreadsheets) office. Its personal basic version is free to use. However, the full version needs a subscription. Hence, researchers who are not comfortable with OpenOffice can use this software for writing their papers.

JAMOVI is open-source software for statistical analysis, which means that it is free to download and use. This can be particularly useful for researchers on a budget who do not have access to expensive commercial software. JAMOVI has a user-friendly interface that is easy to navigate, even for beginners. JAMOVI offers a wide range of statistical analyses, including t-tests, ANOVA, regression, and factor analysis. It is particularly well suited for researchers who need to conduct statistical analyses but are not familiar with the more complex features of traditional statistical software like Statistical Package for the Social Sciences (SPSS).[ 4 ] Figure 2 shows a part of the software when we conducted a Wilcoxon signed rank test (the nonparametric equivalent of paired t -test).

Part of the application JAMOVI when a Wilcoxon signed rank test was conducted

Those who are not interested to learn the basics of the JAMOVI can refer to the “online statistics” section of this article where we provided some websites that help in conducting basic statistical tests.

Zotero allows researchers to collect and organize references from a variety of sources, including library catalogs, websites, and databases. This can help researchers keep track of their sources and ensure that they have all the necessary information to cite them correctly. Zotero allows users to store full-text articles as PDFs, web pages, or other formats, along with their corresponding bibliographic information. This can make it easier to access articles and ensure that the information is all in one place. Zotero makes it easy to create bibliographies in a variety of formats, including APA, MLA, Chicago, and many others. This can save researchers time and reduce the likelihood of errors.[ 5 ]

However, those who are not willing to manage the references by Zotero can simply use the comment option in the word processing software to easily keep the reference with the text, and after the final draft, copy those references to add them to the manuscript file.

Google drive

Google Drive is a cloud-based storage and collaboration tool that can be very useful for researchers. Google Drive allows researchers to access their work from any device with an internet connection, making it easy to work on the go and collaborate with others from anywhere in the world. Google Drive makes it easy for researchers to collaborate with colleagues by sharing documents, spreadsheets, and presentations in real time. Multiple users can work on the same document simultaneously, and changes are saved automatically. Google Drive allows researchers to organize their research materials and data in one place, making it easy to find and access them when needed. By storing research materials and data on Google Drive, researchers can ensure that their work is backed up and secure, reducing the risk of data loss due to hardware failure or other issues.[ 6 ] The drive application can be downloaded and installed [ Table 1 ] on computers that would create a separate drive in the computer and keeping any files in this folder would be synchronized online and you can access it from any device connected to the Internet. However, it is to remember that an account is provided free with 15 GB of free cloud storage.

Online software applications

Grammarly is an online grammar-checking tool that can be very helpful for writers who want to improve the accuracy and clarity of their writing. It uses advanced algorithms and artificial intelligence to analyze text and identify errors in grammar, spelling, and punctuation. In addition to catching grammar and punctuation errors, Grammarly can also suggest vocabulary enhancements improve the style and tone of your writing. This can help you avoid common writing mistakes and create more engaging content. When Grammarly identifies an error in your writing, it explains the rule that you may have violated and suggests corrections that you can make. This can help you learn from your mistakes and avoid making similar errors in the future.[ 7 ] A guide on how to use Grammarly is available elsewhere in the article by Mondal and Mondal.[ 8 ] The premium version of the software provides further enhancement of the article. However, the basic free version helps a lot in correcting grammar that is skipped by common word processing software.

Quillbot is a paraphrasing tool that uses advanced algorithms and artificial intelligence to help researchers rephrase and reword their writing. It can be very helpful for researchers who need to paraphrase content for academic or professional purposes. Quillbot can help researchers save time by automatically rephrasing and rewording content. This can be particularly useful for researchers who need to paraphrase large amounts of text or who are working under tight deadlines. Quillbot can help researchers avoid text similarity (i.e. text plagiarism) by providing a way to paraphrase the content. This can be important for researchers who need to avoid plagiarism in their academic or professional work. Quillbot can be used on a variety of platforms, including web browsers, mobile devices, and desktop applications. This makes it easy to use Quillbot on the platform of your choice and to access your writing from multiple devices.[ 8 ] Figure 3 shows an example where a paragraph of text is being paraphrased.

A paragraph of text is paraphrased by QuillBot

MeSH on demand

MeSH on Demand is a website that provides a user-friendly interface to create Medical Subject Headings (MeSH) terms, which are widely used in the biomedical literature to facilitate the indexing and retrieval of articles. It can be very useful for researchers who need to identify appropriate MeSH terms for their research articles. It generates keywords and phrases related to the text provided by the user. This can be very helpful for researchers who are unfamiliar with the MeSH vocabulary and want to ensure that their articles are indexed correctly.[ 9 ] Figure 4 shows searching MeSH terms in a paragraph of text. After getting the MeSH terms, the author needs to decide which are the most relevant keywords for their manuscript and use those. The majority of the journal has a limitation on the number of keywords.

MeSH terms were searched from a paragraph of text on MeSH on Demand web application

In addition to searching MeSH terms in an article, the search result also includes relevant articles available in PubMed. Authors can check the list if they had missed any relevant literature.

DOI stands for Digital Object Identifier, which is a unique identifier assigned to a digital object such as a research article, data set, or other types of research output. It is widely used in the scholarly publishing industry and can be very useful for researchers. Researchers can always locate and access the digital object with DOI. It also helps researchers accurately cite their sources by providing a unique identifier that can be included in the reference list. This can help ensure that the citation is accurate and can be easily located by copy editors or readers.[ 10 ] In many journals, DOI is printed as a quick response code in the printed version of the journal so that any reader can scan it and get the article online. During writing an article, authors may save the DOI number along with the reference for a quick access of the article in future. However, authors always need to check the DOI before putting it along with references as sometimes, due to technical problems, the DOI does not work. In that case, they can save the URL of the article for accessing the paper later.

JANE stands for Journal/Author Name Estimator, which is a Web-based application designed to help researchers find relevant journals and authors for their research. JANE is a free service provided by the Biosemantics Group and funded by Netherlands Bioinformatics Centre, which makes it an accessible and cost-effective tool for researchers. JANE can help researchers find relevant journals for their research by analyzing the title and abstract of their paper and comparing it to the content of thousands of journals. This can save researchers time and effort in identifying appropriate journals to submit their work. JANE can also help researchers identify potential collaborators for their research by analyzing the authors of the papers in the relevant journals. This can help researchers find other experts in their field who are working on similar research topics.[ 11 ] From the list, authors can get email address of the authors and can use those for suggesting reviewers for the article, if the journal wants some suggested peer reviewer. In Figure 5 , three buttons are shown for finding “journals,” “authors,” and “articles.”

User interface of JANE where text can be pasted or typed and journals, authors, or articles can be searched by pressing buttons below

Online statistics

There are several online free websites that provide statistical tests for researchers. These online free websites can be helpful for researchers who need to conduct statistical tests but may not have access to specialized software or support. They provide a range of statistical tests and tools that are user-friendly and can be accessed from any device with an internet connection. Table 2 is showing some of the websites. Furthermore, detailed guidelines along with practice materials are available in articles by Mondal et al .[ 12 - 15 ]

Websites for statistical analysis

This is not a comprehensive list of online calculator

There is several artificial intelligence (AI)-based writing assistance software available. In recent times, an AI language model, ChatGPT is in discussion among academicians due to its human-like conversational and writing capability. It can be a useful tool for researchers in the process of writing a research paper. Researchers can use ChatGPT to generate ideas and inspiration for their research paper by inputting a topic or question related to their research. ChatGPT can then generate relevant sentences or paragraphs that can serve as a starting point for the paper. Researchers can use ChatGPT to help them write more clearly and effectively. This would particularly be helpful for non-native speakers of English. ChatGPT can provide suggestions for improving the wording, grammar, and structure of sentences, and can also provide synonyms or related words to improve the richness of the text. ChatGPT can be used to summarize long passages of text, making it useful for summarizing articles and research papers for review and analysis. ChatGPT can assist researchers in managing their citations and references by generating citations and reference lists in the appropriate format.[ 16 ] However, many a times, ChatGPT generates fictitious references for text which is not found on the internet. Google Bard is an alternative to ChatGPT which can also help in the tasks done by ChatGPT.

An example of conversation with ChatGPT is shown in Figure 6 where the ChatGPT was asked to explain importance of family medicine in India with three references.

A conversation with ChatGPT showing the input and output

Overall, having a basic understanding of these software tools can help new authors write more efficiently, effectively, and accurately, and create a professional-looking research paper.

There are several advantages of using technology for writing a research paper. Technology can greatly increase the efficiency of the research paper writing process, enabling researchers to complete tasks faster and more accurately. For example, ChatGPT can write a portion of the manuscript within seconds and QuillBot can help paraphrase text in a very short time. Technology can facilitate collaboration among researchers by enabling them to work together remotely and share information and feedback in real time. Digital tools can help researchers organize their research materials and notes more effectively, making it easier to keep track of important information and sources. In both domains, Google Drive is of great help.[ 17 , 18 ]

There are some disadvantages to using technology for research. Overreliance on technology can lead to a loss of critical thinking and writing skills, as well as a reduced ability to solve problems independently. The Internet and other digital tools can be a source of distraction and can hinder concentration and focus, potentially leading to lower-quality research and writing. While the Internet provides access to vast amounts of information, not all of it is reliable or accurate, which can lead to lower-quality research and writing. Not all researchers have access to the necessary technology and resources to complete their research effectively, which can create barriers to entry and hinder research progress.[ 19 , 20 ]

While we use the software applications for shaping our research paper, should we acknowledge them in the research paper as we mention humans who help us for similar task? Researchers mention the software package details, acknowledge any third party editing services, or copy editing by any human. However, they usually do not acknowledge the software. When it comes to word processing software, such as Microsoft Word or OpenOffice, it is not necessary to acknowledge them in a research paper. These tools are commonly used for writing and formatting documents, and their usage is expected. Regarding specific tools like Grammarly or ChatGPT, if substantial help was obtained, then acknowledging them would be appropriate. However, specific role to be mentioned for which the help was taken.[ 21 ] For example, refer to the acknowledgement part of this manuscript to have a glimpse on how we acknowledged ChatGPT for its help in this manuscript. Similar text can be added when help is taken from other tools.

Overall, technology can greatly benefit the research paper writing process, but researchers need to be aware of its limitations and potential drawbacks. By balancing the advantages and disadvantages of using technology, researchers can use it as a tool to enhance their research and writing while maintaining the integrity and quality of their work. Primary care physicians often engage in research activities; however, busy primary care physicians hardly get time for writing. Hence, these applications can assist them in organizing research data, writing manuscripts, and formatting citations and references.

This review paper has discussed the essential software programs that are highly recommended for novice authors writing a research paper. The software programs discussed include Open Office for typing a paper, Jamovi for statistical analysis, Zotero for reference management, Google drive for data storage and accessibility, Grammarly for checking grammar, QuillBot for paraphrasing, MeSH on demand for searching keywords and related articles, DOI for searching the literature, JANE for author search, various online websites for statistical analysis, and language-based AI for generating content for a research paper. Utilizing these essential software programs and maintaining a balanced approach to technology use, novice authors can produce higher-quality research papers and contribute to the advancement of their respective fields.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgment

We would like to acknowledge the use of ChatGPT (May 24 Version), an AI language model developed by OpenAI ( https://openai.com/chatgpt ), for assisting in the language editing of this research paper. ChatGPT helped improve the clarity and readability of the manuscript.

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Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

Research areas

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

Latest news

This tiny chip can safeguard user data while enabling efficient computing on a smartphone.

Researchers have developed a security solution for power-hungry AI models that offers protection against two common attacks.

Francis Fan Lee, former MIT EECS professor and interdisciplinary speech processing inventor, dies at 97.

The former Electrical Engineering and Computer Science Professor and Research Laboratory of Electronics affiliate died on Jan. 12, 2024. He was approximately 97 years old.

Student Spotlight: Maggie Slowikowski

Today’s interviewee, Maggie Slowikowski, is a sophomore majoring in 6-9: Computation and Cognition. An undergraduate researcher in the Bioelectronics Research Group led by Prof. Polina Anikeeva, Slowikowski took time out of her schedule to answer a few questions about her experience at MIT.

Women in STEM — A celebration of excellence and curiosity

An MIT Values event showcased three women’s career journeys and how they are paving the way for the next generation.

A blueprint for making quantum computers easier to program

A CSAIL study highlights why it is so challenging to program a quantum computer to run a quantum algorithm, and offers a conceptual model for a more user-friendly quantum computer.

Upcoming events

Doctoral thesis: nonparametric high-dimensional models: sparsity, efficiency, interpretability, eecs inspiration series: spreeai ceo, doctoral thesis: designing a learner-centric future for skill-learning, doctoral thesis: machine learning for sepsis prognosis: prediction models and dissecting electronic health records, doctoral thesis: machine learning approaches for healthcare discovery, delivery, and equity, doctoral thesis: efficient deep learning with sparsity: algorithms, systems, and applications.

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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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Home » 500+ Computer Science Research Topics

500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

• Using machine learning to detect and prevent cyber attacks
• Developing algorithms for optimized resource allocation in cloud computing
• Investigating the use of blockchain technology for secure and decentralized data storage
• Developing intelligent chatbots for customer service
• Investigating the effectiveness of deep learning for natural language processing
• Developing algorithms for detecting and removing fake news from social media
• Investigating the impact of social media on mental health
• Developing algorithms for efficient image and video compression
• Investigating the use of big data analytics for predictive maintenance in manufacturing
• Developing algorithms for identifying and mitigating bias in machine learning models
• Investigating the ethical implications of autonomous vehicles
• Developing algorithms for detecting and preventing cyberbullying
• Investigating the use of machine learning for personalized medicine
• Developing algorithms for efficient and accurate speech recognition
• Investigating the impact of social media on political polarization
• Developing algorithms for sentiment analysis in social media data
• Investigating the use of virtual reality in education
• Developing algorithms for efficient data encryption and decryption
• Investigating the impact of technology on workplace productivity
• Developing algorithms for detecting and mitigating deepfakes
• Investigating the use of artificial intelligence in financial trading
• Developing algorithms for efficient database management
• Investigating the effectiveness of online learning platforms
• Developing algorithms for efficient and accurate facial recognition
• Investigating the use of machine learning for predicting weather patterns
• Developing algorithms for efficient and secure data transfer
• Investigating the impact of technology on social skills and communication
• Developing algorithms for efficient and accurate object recognition
• Investigating the use of machine learning for fraud detection in finance
• Developing algorithms for efficient and secure authentication systems
• Investigating the impact of technology on privacy and surveillance
• Developing algorithms for efficient and accurate handwriting recognition
• Investigating the use of machine learning for predicting stock prices
• Developing algorithms for efficient and secure biometric identification
• Investigating the impact of technology on mental health and well-being
• Developing algorithms for efficient and accurate language translation
• Investigating the use of machine learning for personalized advertising
• Developing algorithms for efficient and secure payment systems
• Investigating the impact of technology on the job market and automation
• Developing algorithms for efficient and accurate object tracking
• Investigating the use of machine learning for predicting disease outbreaks
• Developing algorithms for efficient and secure access control
• Investigating the impact of technology on human behavior and decision making
• Developing algorithms for efficient and accurate sound recognition
• Investigating the use of machine learning for predicting customer behavior
• Developing algorithms for efficient and secure data backup and recovery
• Investigating the impact of technology on education and learning outcomes
• Developing algorithms for efficient and accurate emotion recognition
• Investigating the use of machine learning for improving healthcare outcomes
• Developing algorithms for efficient and secure supply chain management
• Investigating the impact of technology on cultural and societal norms
• Developing algorithms for efficient and accurate gesture recognition
• Investigating the use of machine learning for predicting consumer demand
• Developing algorithms for efficient and secure cloud storage
• Investigating the impact of technology on environmental sustainability
• Developing algorithms for efficient and accurate voice recognition
• Investigating the use of machine learning for improving transportation systems
• Developing algorithms for efficient and secure mobile device management
• Investigating the impact of technology on social inequality and access to resources
• Machine learning for healthcare diagnosis and treatment
• Machine Learning for Cybersecurity
• Machine learning for personalized medicine
• Cybersecurity threats and defense strategies
• Big data analytics for business intelligence
• Blockchain technology and its applications
• Human-computer interaction in virtual reality environments
• Artificial intelligence for autonomous vehicles
• Natural language processing for chatbots
• Cloud computing and its impact on the IT industry
• Internet of Things (IoT) and smart homes
• Robotics and automation in manufacturing
• Augmented reality and its potential in education
• Data mining techniques for customer relationship management
• Computer vision for object recognition and tracking
• Quantum computing and its applications in cryptography
• Social media analytics and sentiment analysis
• Recommender systems for personalized content delivery
• Mobile computing and its impact on society
• Bioinformatics and genomic data analysis
• Deep learning for image and speech recognition
• Digital signal processing and audio processing algorithms
• Cloud storage and data security in the cloud
• Wearable technology and its impact on healthcare
• Computational linguistics for natural language understanding
• Cognitive computing for decision support systems
• Cyber-physical systems and their applications
• Edge computing and its impact on IoT
• Machine learning for fraud detection
• Cryptography and its role in secure communication
• Cybersecurity risks in the era of the Internet of Things
• Natural language generation for automated report writing
• 3D printing and its impact on manufacturing
• Virtual assistants and their applications in daily life
• Cloud-based gaming and its impact on the gaming industry
• Computer networks and their security issues
• Cyber forensics and its role in criminal investigations
• Machine learning for predictive maintenance in industrial settings
• Augmented reality for cultural heritage preservation
• Human-robot interaction and its applications
• Data visualization and its impact on decision-making
• Cybersecurity in financial systems and blockchain
• Computer graphics and animation techniques
• Biometrics and its role in secure authentication
• Cloud-based e-learning platforms and their impact on education
• Natural language processing for machine translation
• Machine learning for predictive maintenance in healthcare
• Cybersecurity and privacy issues in social media
• Computer vision for medical image analysis
• Natural language generation for content creation
• Cybersecurity challenges in cloud computing
• Human-robot collaboration in manufacturing
• Data mining for predicting customer churn
• Artificial intelligence for autonomous drones
• Cybersecurity risks in the healthcare industry
• Machine learning for speech synthesis
• Edge computing for low-latency applications
• Virtual reality for mental health therapy
• Quantum computing and its applications in finance
• Biomedical engineering and its applications
• Cybersecurity in autonomous systems
• Machine learning for predictive maintenance in transportation
• Computer vision for object detection in autonomous driving
• Augmented reality for industrial training and simulations
• Cloud-based cybersecurity solutions for small businesses
• Natural language processing for knowledge management
• Machine learning for personalized advertising
• Cybersecurity in the supply chain management
• Cybersecurity risks in the energy sector
• Computer vision for facial recognition
• Natural language processing for social media analysis
• Machine learning for sentiment analysis in customer reviews
• Explainable Artificial Intelligence
• Quantum Computing
• Blockchain Technology
• Human-Computer Interaction
• Natural Language Processing
• Cloud Computing
• Robotics and Automation
• Augmented Reality and Virtual Reality
• Cyber-Physical Systems
• Computational Neuroscience
• Big Data Analytics
• Computer Vision
• Cryptography and Network Security
• Internet of Things
• Computer Graphics and Visualization
• Artificial Intelligence for Game Design
• Computational Biology
• Social Network Analysis
• Bioinformatics
• Distributed Systems and Middleware
• Information Retrieval and Data Mining
• Computer Networks
• Mobile Computing and Wireless Networks
• Software Engineering
• Database Systems
• Parallel and Distributed Computing
• Human-Robot Interaction
• Intelligent Transportation Systems
• High-Performance Computing
• Cyber-Physical Security
• Deep Learning
• Sensor Networks
• Multi-Agent Systems
• Human-Centered Computing
• Wearable Computing
• Knowledge Representation and Reasoning
• Adaptive Systems
• Brain-Computer Interface
• Health Informatics
• Cognitive Computing
• Cybersecurity and Privacy
• Internet Security
• Cybercrime and Digital Forensics
• Cloud Security
• Cryptocurrencies and Digital Payments
• Machine Learning for Natural Language Generation
• Cognitive Robotics
• Neural Networks
• Semantic Web
• Image Processing
• Cyber Threat Intelligence
• Secure Mobile Computing
• Cybersecurity Education and Training
• Privacy Preserving Techniques
• Cyber-Physical Systems Security
• Virtualization and Containerization
• Machine Learning for Computer Vision
• Network Function Virtualization
• Cybersecurity Risk Management
• Information Security Governance
• Intrusion Detection and Prevention
• Biometric Authentication
• Machine Learning for Predictive Maintenance
• Security in Cloud-based Environments
• Cybersecurity for Industrial Control Systems
• Smart Grid Security
• Software Defined Networking
• Quantum Cryptography
• Security in the Internet of Things
• Natural language processing for sentiment analysis
• Blockchain technology for secure data sharing
• Developing efficient algorithms for big data analysis
• Cybersecurity for internet of things (IoT) devices
• Human-robot interaction for industrial automation
• Image recognition for autonomous vehicles
• Social media analytics for marketing strategy
• Quantum computing for solving complex problems
• Biometric authentication for secure access control
• Augmented reality for education and training
• Intelligent transportation systems for traffic management
• Predictive modeling for financial markets
• Cloud computing for scalable data storage and processing
• Virtual reality for therapy and mental health treatment
• Data visualization for business intelligence
• Recommender systems for personalized product recommendations
• Speech recognition for voice-controlled devices
• Mobile computing for real-time location-based services
• Neural networks for predicting user behavior
• Genetic algorithms for optimization problems
• Distributed computing for parallel processing
• Internet of things (IoT) for smart cities
• Wireless sensor networks for environmental monitoring
• Cloud-based gaming for high-performance gaming
• Social network analysis for identifying influencers
• Autonomous systems for agriculture
• Robotics for disaster response
• Data mining for customer segmentation
• Computer graphics for visual effects in movies and video games
• Virtual assistants for personalized customer service
• Natural language understanding for chatbots
• 3D printing for manufacturing prototypes
• Artificial intelligence for stock trading
• Machine learning for weather forecasting
• Biomedical engineering for prosthetics and implants
• Cybersecurity for financial institutions
• Machine learning for energy consumption optimization
• Computer vision for object tracking
• Natural language processing for document summarization
• Wearable technology for health and fitness monitoring
• Internet of things (IoT) for home automation
• Reinforcement learning for robotics control
• Big data analytics for customer insights
• Machine learning for supply chain optimization
• Natural language processing for legal document analysis
• Artificial intelligence for drug discovery
• Computer vision for object recognition in robotics
• Data mining for customer churn prediction
• Autonomous systems for space exploration
• Robotics for agriculture automation
• Machine learning for predicting earthquakes
• Natural language processing for sentiment analysis in customer reviews
• Big data analytics for predicting natural disasters
• Internet of things (IoT) for remote patient monitoring
• Blockchain technology for digital identity management
• Machine learning for predicting wildfire spread
• Computer vision for gesture recognition
• Natural language processing for automated translation
• Big data analytics for fraud detection in banking
• Internet of things (IoT) for smart homes
• Robotics for warehouse automation
• Machine learning for predicting air pollution
• Natural language processing for medical record analysis
• Augmented reality for architectural design
• Big data analytics for predicting traffic congestion
• Machine learning for predicting customer lifetime value
• Developing algorithms for efficient and accurate text recognition
• Natural Language Processing for Virtual Assistants
• Natural Language Processing for Sentiment Analysis in Social Media
• Explainable Artificial Intelligence (XAI) for Trust and Transparency
• Deep Learning for Image and Video Retrieval
• Edge Computing for Internet of Things (IoT) Applications
• Data Science for Social Media Analytics
• Cybersecurity for Critical Infrastructure Protection
• Natural Language Processing for Text Classification
• Quantum Computing for Optimization Problems
• Machine Learning for Personalized Health Monitoring
• Computer Vision for Autonomous Driving
• Blockchain Technology for Supply Chain Management
• Augmented Reality for Education and Training
• Natural Language Processing for Sentiment Analysis
• Machine Learning for Personalized Marketing
• Big Data Analytics for Financial Fraud Detection
• Cybersecurity for Cloud Security Assessment
• Artificial Intelligence for Natural Language Understanding
• Blockchain Technology for Decentralized Applications
• Virtual Reality for Cultural Heritage Preservation
• Natural Language Processing for Named Entity Recognition
• Machine Learning for Customer Churn Prediction
• Big Data Analytics for Social Network Analysis
• Cybersecurity for Intrusion Detection and Prevention
• Artificial Intelligence for Robotics and Automation
• Blockchain Technology for Digital Identity Management
• Virtual Reality for Rehabilitation and Therapy
• Natural Language Processing for Text Summarization
• Machine Learning for Credit Risk Assessment
• Big Data Analytics for Fraud Detection in Healthcare
• Cybersecurity for Internet Privacy Protection
• Artificial Intelligence for Game Design and Development
• Blockchain Technology for Decentralized Social Networks
• Virtual Reality for Marketing and Advertising
• Natural Language Processing for Opinion Mining
• Machine Learning for Anomaly Detection
• Big Data Analytics for Predictive Maintenance in Transportation
• Cybersecurity for Network Security Management
• Artificial Intelligence for Personalized News and Content Delivery
• Blockchain Technology for Cryptocurrency Mining
• Virtual Reality for Architectural Design and Visualization
• Natural Language Processing for Machine Translation
• Machine Learning for Automated Image Captioning
• Big Data Analytics for Stock Market Prediction
• Cybersecurity for Biometric Authentication Systems
• Artificial Intelligence for Human-Robot Interaction
• Blockchain Technology for Smart Grids
• Virtual Reality for Sports Training and Simulation
• Natural Language Processing for Question Answering Systems
• Machine Learning for Sentiment Analysis in Customer Feedback
• Big Data Analytics for Predictive Maintenance in Manufacturing
• Cybersecurity for Cloud-Based Systems
• Artificial Intelligence for Automated Journalism
• Blockchain Technology for Intellectual Property Management
• Virtual Reality for Therapy and Rehabilitation
• Natural Language Processing for Language Generation
• Machine Learning for Customer Lifetime Value Prediction
• Big Data Analytics for Predictive Maintenance in Energy Systems
• Cybersecurity for Secure Mobile Communication
• Artificial Intelligence for Emotion Recognition
• Blockchain Technology for Digital Asset Trading
• Virtual Reality for Automotive Design and Visualization
• Natural Language Processing for Semantic Web
• Machine Learning for Fraud Detection in Financial Transactions
• Big Data Analytics for Social Media Monitoring
• Cybersecurity for Cloud Storage and Sharing
• Artificial Intelligence for Personalized Education
• Blockchain Technology for Secure Online Voting Systems
• Virtual Reality for Cultural Tourism
• Natural Language Processing for Chatbot Communication
• Machine Learning for Medical Diagnosis and Treatment
• Big Data Analytics for Environmental Monitoring and Management.
• Cybersecurity for Cloud Computing Environments
• Virtual Reality for Training and Simulation
• Big Data Analytics for Sports Performance Analysis
• Cybersecurity for Internet of Things (IoT) Devices
• Artificial Intelligence for Traffic Management and Control
• Blockchain Technology for Smart Contracts
• Natural Language Processing for Document Summarization
• Machine Learning for Image and Video Recognition
• Blockchain Technology for Digital Asset Management
• Virtual Reality for Entertainment and Gaming
• Natural Language Processing for Opinion Mining in Online Reviews
• Machine Learning for Customer Relationship Management
• Big Data Analytics for Environmental Monitoring and Management
• Cybersecurity for Network Traffic Analysis and Monitoring
• Artificial Intelligence for Natural Language Generation
• Blockchain Technology for Supply Chain Transparency and Traceability
• Virtual Reality for Design and Visualization
• Natural Language Processing for Speech Recognition
• Machine Learning for Recommendation Systems
• Big Data Analytics for Customer Segmentation and Targeting
• Cybersecurity for Biometric Authentication
• Artificial Intelligence for Human-Computer Interaction
• Blockchain Technology for Decentralized Finance (DeFi)
• Virtual Reality for Tourism and Cultural Heritage
• Machine Learning for Cybersecurity Threat Detection and Prevention
• Big Data Analytics for Healthcare Cost Reduction
• Cybersecurity for Data Privacy and Protection
• Artificial Intelligence for Autonomous Vehicles
• Blockchain Technology for Cryptocurrency and Blockchain Security
• Virtual Reality for Real Estate Visualization
• Natural Language Processing for Question Answering
• Big Data Analytics for Financial Markets Prediction
• Cybersecurity for Cloud-Based Machine Learning Systems
• Artificial Intelligence for Personalized Advertising
• Blockchain Technology for Digital Identity Verification
• Virtual Reality for Cultural and Language Learning
• Natural Language Processing for Semantic Analysis
• Machine Learning for Business Forecasting
• Big Data Analytics for Social Media Marketing
• Artificial Intelligence for Content Generation
• Blockchain Technology for Smart Cities
• Virtual Reality for Historical Reconstruction
• Natural Language Processing for Knowledge Graph Construction
• Machine Learning for Speech Synthesis
• Big Data Analytics for Traffic Optimization
• Artificial Intelligence for Social Robotics
• Blockchain Technology for Healthcare Data Management
• Virtual Reality for Disaster Preparedness and Response
• Natural Language Processing for Multilingual Communication
• Machine Learning for Emotion Recognition
• Big Data Analytics for Human Resources Management
• Cybersecurity for Mobile App Security
• Artificial Intelligence for Financial Planning and Investment
• Blockchain Technology for Energy Management
• Virtual Reality for Cultural Preservation and Heritage.
• Big Data Analytics for Healthcare Management
• Cybersecurity in the Internet of Things (IoT)
• Artificial Intelligence for Predictive Maintenance
• Computational Biology for Drug Discovery
• Virtual Reality for Mental Health Treatment
• Machine Learning for Sentiment Analysis in Social Media
• Human-Computer Interaction for User Experience Design
• Cloud Computing for Disaster Recovery
• Quantum Computing for Cryptography
• Intelligent Transportation Systems for Smart Cities
• Cybersecurity for Autonomous Vehicles
• Artificial Intelligence for Fraud Detection in Financial Systems
• Social Network Analysis for Marketing Campaigns
• Cloud Computing for Video Game Streaming
• Machine Learning for Speech Recognition
• Augmented Reality for Architecture and Design
• Natural Language Processing for Customer Service Chatbots
• Machine Learning for Climate Change Prediction
• Big Data Analytics for Social Sciences
• Artificial Intelligence for Energy Management
• Virtual Reality for Tourism and Travel
• Cybersecurity for Smart Grids
• Machine Learning for Image Recognition
• Augmented Reality for Sports Training
• Natural Language Processing for Content Creation
• Cloud Computing for High-Performance Computing
• Artificial Intelligence for Personalized Medicine
• Virtual Reality for Architecture and Design
• Augmented Reality for Product Visualization
• Natural Language Processing for Language Translation
• Cybersecurity for Cloud Computing
• Artificial Intelligence for Supply Chain Optimization
• Blockchain Technology for Digital Voting Systems
• Virtual Reality for Job Training
• Augmented Reality for Retail Shopping
• Natural Language Processing for Sentiment Analysis in Customer Feedback
• Cloud Computing for Mobile Application Development
• Artificial Intelligence for Cybersecurity Threat Detection
• Blockchain Technology for Intellectual Property Protection
• Virtual Reality for Music Education
• Machine Learning for Financial Forecasting
• Augmented Reality for Medical Education
• Natural Language Processing for News Summarization
• Cybersecurity for Healthcare Data Protection
• Artificial Intelligence for Autonomous Robots
• Virtual Reality for Fitness and Health
• Machine Learning for Natural Language Understanding
• Augmented Reality for Museum Exhibits
• Natural Language Processing for Chatbot Personality Development
• Cloud Computing for Website Performance Optimization
• Artificial Intelligence for E-commerce Recommendation Systems
• Blockchain Technology for Supply Chain Traceability
• Virtual Reality for Military Training
• Augmented Reality for Advertising
• Natural Language Processing for Chatbot Conversation Management
• Cybersecurity for Cloud-Based Services
• Artificial Intelligence for Agricultural Management
• Blockchain Technology for Food Safety Assurance
• Virtual Reality for Historical Reenactments
• Machine Learning for Cybersecurity Incident Response.
• Secure Multiparty Computation
• Federated Learning
• Internet of Things Security
• Blockchain Scalability
• Quantum Computing Algorithms
• Explainable AI
• Data Privacy in the Age of Big Data
• Adversarial Machine Learning
• Deep Reinforcement Learning
• Online Learning and Streaming Algorithms
• Graph Neural Networks
• Automated Debugging and Fault Localization
• Mobile Application Development
• Software Engineering for Cloud Computing
• Cryptocurrency Security
• Edge Computing for Real-Time Applications
• Natural Language Generation
• Virtual and Augmented Reality
• Computational Biology and Bioinformatics
• Internet of Things Applications
• Robotics and Autonomous Systems
• Explainable Robotics
• 3D Printing and Additive Manufacturing
• Distributed Systems
• Parallel Computing
• Data Center Networking
• Data Mining and Knowledge Discovery
• Information Retrieval and Search Engines
• Network Security and Privacy
• Cloud Computing Security
• Data Analytics for Business Intelligence
• Neural Networks and Deep Learning
• Reinforcement Learning for Robotics
• Automated Planning and Scheduling
• Evolutionary Computation and Genetic Algorithms
• Formal Methods for Software Engineering
• Computational Complexity Theory
• Bio-inspired Computing
• Computer Vision for Object Recognition
• Automated Reasoning and Theorem Proving
• Natural Language Understanding
• Machine Learning for Healthcare
• Scalable Distributed Systems
• Sensor Networks and Internet of Things
• Smart Grids and Energy Systems
• Software Testing and Verification
• Web Application Security
• Wireless and Mobile Networks
• Computer Architecture and Hardware Design
• Digital Signal Processing
• Game Theory and Mechanism Design
• Multi-agent Systems
• Evolutionary Robotics
• Quantum Machine Learning
• Computational Social Science
• Explainable Recommender Systems.
• Artificial Intelligence and its applications
• Cloud computing and its benefits
• Cybersecurity threats and solutions
• Internet of Things and its impact on society
• Virtual and Augmented Reality and its uses
• Blockchain Technology and its potential in various industries
• Web Development and Design
• Digital Marketing and its effectiveness
• Big Data and Analytics
• Software Development Life Cycle
• Gaming Development and its growth
• Network Administration and Maintenance
• Machine Learning and its uses
• Data Warehousing and Mining
• Computer Architecture and Design
• Computer Graphics and Animation
• Quantum Computing and its potential
• Data Structures and Algorithms
• Computer Vision and Image Processing
• Robotics and its applications
• Operating Systems and its functions
• Information Theory and Coding
• Compiler Design and Optimization
• Computer Forensics and Cyber Crime Investigation
• Distributed Computing and its significance
• Artificial Neural Networks and Deep Learning
• Cloud Storage and Backup
• Programming Languages and their significance
• Computer Simulation and Modeling
• Computer Networks and its types
• Information Security and its types
• Computer-based Training and eLearning
• Medical Imaging and its uses
• Social Media Analysis and its applications
• Human Resource Information Systems
• Computer-Aided Design and Manufacturing
• Multimedia Systems and Applications
• Geographic Information Systems and its uses
• Computer-Assisted Language Learning
• Mobile Device Management and Security
• Data Compression and its types
• Knowledge Management Systems
• Text Mining and its uses
• Cyber Warfare and its consequences
• Wireless Networks and its advantages
• Computer Ethics and its importance
• Computational Linguistics and its applications
• Autonomous Systems and Robotics
• Information Visualization and its importance
• Geographic Information Retrieval and Mapping
• Business Intelligence and its benefits
• Digital Libraries and their significance
• Artificial Life and Evolutionary Computation
• Computer Music and its types
• Virtual Teams and Collaboration
• Computer Games and Learning
• Semantic Web and its applications
• Electronic Commerce and its advantages
• Multimedia Databases and their significance
• Computer Science Education and its importance
• Computer-Assisted Translation and Interpretation
• Ambient Intelligence and Smart Homes
• Autonomous Agents and Multi-Agent Systems.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Coding Careers and Accelerating Research

Discover how Open Source with SLU , a grant-funded initiative in Saint Louis University’s Department of Computer Science , is enhancing research efforts across the University and preparing students to enter today’s workforce with professional software development experience.

We can all recall a time when we brought an assignment home from school. Sometimes, it would take us hours, and other times, only minutes to complete before moving on to the next thing on our to-do list. This whole mentality — checking assignments off a to-do list like a routine — was something that bothered Kate Holdener, Ph.D. , and assistant professor of computer science, when she considered her courses at Saint Louis University. 

“What bothered me about [course] projects is that they were kind of toy projects or throw-away work,” Holdener said. “Students did the work, but it was not going to be used by anyone seriously or be maintained afterward. This felt like a waste because we have so many talented students that could be building something more useful.”

Holdener is a professional software engineer specializing in software engineering, software development, and evolutionary algorithms. In early 2022, she received a two-year grant from the Alfred P. Sloan Foundation to create a center that would engage graduate and undergraduate students in open-source software development for SLU research projects. She founded the program called Open Source with SLU.

“I wanted to give students a more realistic environment for software development and also support the faculty that are doing research at SLU,” Holdener said.

“This grant opportunity was looking to support various initiatives that help train a better workforce in open source because it is one of the reasons we have such rapid technological advancement [today]," she said.

Software in the Open

For every piece of technology or automation, a piece of source code is needed to make it function. Once it is ready for release, the creator of the code must answer an essential question: Will the code be closed or open to the public?

The difference between closed and open-source code is in the name itself: It’s either open or closed to public visibility and modification. Open source allows anyone to adjust the code and collaborate to improve performance. It’s a critical learning opportunity for students interested in computer science.

Holdener explained that using open source has an added benefit on university campuses where students' availability and involvement may change from semester to semester.

“We don’t have to reinvent,” she said. “Instead of writing the code from scratch [each semester], we’ll use a library, put code together, and write our own code from there.”

Open Source with SLU gives participating students practical software development experience and helps researchers with their custom software needs. Holdener structured the program to allow SLU researchers to submit a formal request for a software project, and then graduate students lead a team of undergraduate students to execute the work. Graduate students are hired to take on the role of a senior developer, overseeing all stages of the project and checking in with undergraduate developers along the way.

The program has a collaborative atmosphere. Students must develop an actionable plan that includes designing, prototyping, testing and showcasing their work to their clients — much like a corporate software development team. Yet, the greatest takeaway for students is that their capstone project can now be used as professional experience in a job interview.

“Students make contributions to these projects, and all those contributions are visible to anybody,” Holdener said. “As a potential employer, I can go and look at your profile and see these are the things you have worked on and these are the lines of code you wrote. It gives [students] a concrete foundation and proof of what they worked on."

Purpose-Driven Projects

In its first year of operation, Open Source with SLU has contributed to research projects on campus and in the St. Louis community. While each project is research-driven, they are also purpose-driven, developing software that will benefit humanity in the long term. This supports the program’s three learning outcomes that guide student work on their projects — experience, service and promotion.

Daniel Shown, program director for Open Source with SLU, explained that students are able to achieve these goals and gain professional experience as they support research that is interesting or valuable to them.     “First, we are trying to give students real-world software development experience,” he said. “Something more like what they will experience when they are out in the world and less like a class assignment. Second, we are building software that supports research, so we pick clients from across the University who are doing research. The third thing we are trying to do is promote and be a center of gravity for open-source software development and broader conversations about open scholarship on campus.”

At the time of this publication, the program is working on over 13 capstone projects that contribute to research in fields such as mathematics, statistics, chemistry, theology, history and community improvement. One community improvement project includes their recent work with an application that simplifies the process of volunteering at local homeless shelters. This project is in partnership with House Everyone STL.

“This project is a website, where volunteers can easily sign up for shifts at homeless shelters,” said Logan Wyas, SLU graduate student and developer on the project. “It has the ability to make a big impact on our world.”

The application will allow volunteers to sign up, view which shelters have open volunteer shifts, and give shelters the ability to see who is on their schedule. For students, it is the opportunity to address real-world problems in research or the local community that most excites them. When they see the skills they’ve learned in the classroom give them the power to improve the world around them, it makes the work all the more meaningful.

Another mission-driven project is called the Lived Religion Project, which wanted a digital platform that would share media and notes about people’s respective encounters with religion in their everyday life.

“Dr. Adam Parks does research in ethnography, which is how people live, and he is specifically focused on religion and the different ways people experience or engage in religion or religious artifacts,” Holdener said. “He needed a platform that would allow ethnographers to go out in the world, make notes on various religious artifacts, and come home to make more detailed notes after.”

This need developed into the idea for “Where’s Religion?” an application for both desktop and mobile that hosts a platform for collecting, organizing and sharing images, videos and sounds, along with textural notations sourced from a wide range of users. This collection of materials will not only give value to research stemming from history and religion, but also will provide a unique look at several viewpoints of American life.

Undergraduate student Massimo Evelti works on multiple projects within the program to further develop his skills, expand his knowledge of open source, and connect with classmates in a working environment. He said their roles as computer scientists and engineers within Open Source with SLU have an inherent capability to better the world now and into the future.

“Engineering is about creating things to help people, and many of these projects are for the betterment of society and its people,” Evelti said. “If one puts helping others as a priority, there will be plenty of moments to flourish in this field.”

Embracing a Collaborative Environment

Open Source with SLU requires a team effort, which is an expectation that not many of the students face in their other courses. Shown explained that the way scientists work across disciplines is more collaborative than ever, and  putting the open-source work out into the community benefits all.

Holdener built the program to foster a collaborative approach when working on open-source projects. Graduate and undergraduate students come together with their unique interests, varying experience levels, and personal skills to gain the experience they need to move into their careers beyond SLU.

Graduate student Yash Bhatia received his undergraduate degree in computer engineering in 2019 from Mumbai University in India before coming to SLU to obtain his master’s in computer science. He works at Open Source with SLU as a graduate assistant and tech lead for open-source projects, including the “Where’s Religion?” desktop and mobile applications.

Bhatia believes the program helps students gain valuable experience working in a team environment that will prepare students for a career after graduation.

“There are many students who come to the United States with no experience working on real projects or real-time group efforts, where a group leader shapes the team and helps them to become better developers,” Bhatia said. “Open Source with SLU is an excellent initiative that builds skills and makes students learn about different technologies.”

Holdener explained that the program gives students an edge in their resumes. She said most entry-level computer science positions require one to two years of experience, which makes it more challenging for a student entering the job market. This program presents a solution for students — helping them gain relevant and provable experience before entering an interview.

“Whether it's these projects or some other projects, [students] can claim some experience through it,” she said. “Also, it's not just the claim — it's something [they] can demonstrate. I tell my students to list and link their projects on their resume because it’s their development. We run this program in a professional manner just like any software development organization.”

Toward the Discovery of the New

Open Source with SLU is making an immediate impact with computer science students across campus, inviting them to dive into their chosen craft with vigor, innovation and creativity. However, it's not just for students who work in coding and engineering software, but also for students who may have an interest in learning about open-source software.

“A long-term goal for me is to involve students at different levels in the program, and not necessarily from just the computer science department,” Holdener said. “There’s many aspects to these projects that are less technical, and it could be a playground where all students can come, jump in and participate in projects.”

Current students encourage others to reach out and become involved in what projects pique their interest.

“This program is an amazing initiative,” Evelti said. “I highly recommend students to look through the projects, and if there is one that catches your eye, dive in and start with the easiest problem. From there, you will start to understand what you are good at and what you need to work on. The important thing is don’t be timid to ask for help and to finish a problem you have started!”

As Open Source with SLU continues to grow, Holdener is optimistic that this initiative is just the start of an impactful, flourishing open-source community at SLU.

“Students hear about this, and they’ll reach out to say ‘Hey, how do I get involved in this? I want to build up my skills and participate,” she said. “We’re seeing more of this now, and it is exciting to me.”

To learn more about the current projects or to submit a project request to Open Source with SLU, visit their website at https://oss-slu.github.io/ .

Story by Mary Pogue, senior copywriter, Paradigm .

This piece was written for the 2023 SLU Research Institute Annual Impact Report. The Impact Report is printed each spring to celebrate the successes of our researchers from the previous year and share the story of SLU's rise as a preeminent Jesuit research university. Design, photography, and some writing contributions are made by Paradigm . More information can be found here .