Computer Science Disciplines Research Articles

SUSTAINABILITY OF E-LEARNING IN HIGHER EDUCATION

The present study examines the sustainability of e-learning strategies integrated into higher education, with a particular focus on computer science disciplines. As part of a pedagogical experiment conducted at the Technical University of Moldova (TUM), 259 students were evaluated post-experimentally to analyze the effectiveness and long-term impact of these strategies on academic performance and the development of professional skills. The results demonstrated that the implementation of the e-learning-centered pedagogical model led to significant improvements in students' cognitive, emotional, and behavioral engagement, thus confirming the sustainability and relevance of these instructional methods. The contribution of this study lies in highlighting the academic and scientific value of effectively integrating e-learning strategies into the university educational process, as well as in formulating well-founded recommendations for optimizing e-learning strategies to meet the current demands of higher education.

Solving the Dual Generalized Commutative Quaternion Matrix Equation AXB = C

Dual generalized commutative quaternions have broad application prospects in many fields. Additionally, the matrix equation AXB=C has important applications in mathematics and engineering, especially in control systems, economics, computer science, and other disciplines. However, research on the matrix equation AXB=C over the dual generalized commutative quaternions remains relatively insufficient. In this paper, we derive the necessary and sufficient conditions for the solvability of the dual generalized commutative quaternion matrix equation AXB=C. Furthermore, we provide the general solution expression for this matrix equation, when it is solvable. Finally, a numerical algorithm and an example are provided to confirm the reliability of the main conclusions.

Penerapan Metode Cased Based Reasoning dalam Mendiagnosa Penyakit TB Paru Berbasis Web

Collaboration of computer science disciplines with other disciplines has been widely carried out, for example, medical science. Expert systems for disease diagnosis are one of the many computer programs used by doctors to assist them in their work and provide good results. Researchers in the field of artificial intelligence are working to improve existing systems to cover their shortcomings. Expert systems are used in many applications for disease diagnosis. Expert systems have been used in various industries and have had a significant impact. An innovative method for diagnosing diseases is the application of case-based reasoning systems, compared to expert systems. The solution to diagnosing and treating diseases can be found in the expert system for diagnosing pulmonary tuberculosis. Because this web-based system is based on a web application, all pulmonary tuberculosis patients can access it. A rule-based system that applies the CBR method can identify various types of diseases through the use of weighting techniques and offer treatment recommendations.

ChatGPT as a Digital Assistant for Archaeology: Insights from the Smart Anomaly Detection Assistant Development

The introduction of generative AI has the potential to radically transform various fields of research, including archaeology. This study explores the potential of generative AI, specifically ChatGPT, in developing a computer application for analyzing aerial and satellite images to detect archaeological anomalies. The main focus was not on developing the application itself but on evaluating ChatGPT’s effectiveness as an IT assistant for humanistic researchers. Starting with a simple prompt to analyze a multispectral orthophoto, the application was developed through successive iterations, improved through continuous interactions with ChatGPT. Various technical and methodological challenges were addressed, leading to the creation of a functional application with multiple features, including various analysis methods and tools. This process demonstrated how the use of large language models (LLMs) can break down the barriers between humanities and computer science disciplines, enabling researchers without programming skills to develop complex applications in a short time.

Pathways to Belonging: Understanding How University Entry Routes Shape the Sense of Belonging of Undergraduate Computing Students

Sense of belonging, or belongingness in academia, is an individual’s personal conviction as to their acceptance as a valued member of an academic community. The importance of belongingness lies in correlations with motivation, persistence, mental health and well-being. Prior work has shown that belongingness can be lower in students who are minoritised due to their gender, race, nationality, socio-economic status, religion and/or disability. However, there is limited research on how a student’s university entry route impacts their sense of belonging in Computer Science and Other Science disciplines. In Ireland’s higher education system, university entry is primarily managed through the Central Applications Office (CAO) process, which predominantly serves students transitioning directly from secondary school to university. Additionally, alternative access routes are available for students from socio-economically disadvantaged backgrounds, those with disabilities, and mature students. At University College Dublin (UCD), entry is facilitated through both the traditional direct-entry school leaving route and combined access routes, each serving distinct student demographics. To investigate how these varying entry routes influence students’ sense of belonging, we utilised a survey adapted from the validated ‘Math Sense of Belonging Scale’ to examine the belongingness of undergraduate Science students, including Computer Science students. We examined how the belongingness of students varies by university entry route, and how the belongingness of students in Computer Science varies by university entry route compared to Other Science students. Our results reveal a significant difference in belongingness between students entering university through the direct-entry school leaving route, compared to those who entered via combined access routes (e.g. university admissions schemes for school leavers from socio-economically disadvantaged backgrounds, with disabilities, and mature students). Specifically, within Computer Science, students entering university through the direct-entry school leaving route had significantly higher belongingness than combined access route students. These results provide insight that may help others improve the belongingness of undergraduate Computer Science students.

Cultural heritage preservation in the digital age, harnessing artificial intelligence for the future: a bibliometric analysis

PurposeThis study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.Design/methodology/approachThis study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.FindingsThe performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.Practical implicationsThe cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.Social implicationsThis study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.Originality/valueSome similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in glaucoma from 2013 to 2022.

To conduct a bibliometric analysis of research on artificial intelligence (AI) in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies. Relevant articles on the application of AI in the field of glaucoma from the Web of Science Core Collection were retrieved, covering the period from January 1, 2013, to December 31, 2022. In order to assess the contributions and co-occurrence relationships among different countries/regions, institutions, authors, and journals, CiteSpace and VOSviewer software were employed and the research hotspots and future trends within the field were identified. A total of 750 English articles published between 2013 and 2022 were collected, and the number of publications exhibited an overall increasing trend. The majority of the articles were from China, followed by the United States and India. National University of Singapore, Chinese Academy of Sciences, and Sun Yat-sen University made significant contributions to the published works. Weinreb RN and Fu HZ ranked first among authors and cited authors. American Journal of Ophthalmology is the most impactful academic journal in the field of AI application in glaucoma. The disciplinary scope of this field includes ophthalmology, computer science, mathematics, molecular biology, genetics, and other related disciplines. The clustering and identification of keyword nodes in the co-occurrence network reveal the evolving landscape of AI application in the field of glaucoma. Initially, the hot topics in this field were primarily "segmentation", "classification" and "diagnosis". However, in recent years, the focus has shifted to "deep learning", "convolutional neural network" and "artificial intelligence". With the rapid development of AI technology, scholars have shown increasing interest in its application in the field of glaucoma. Moreover, the application of AI in assisting treatment and predicting prognosis in glaucoma may become a future research hotspot. However, the reliability and interpretability of AI data remain pressing issues that require resolution.

412 Awardee Talk: Implementing student-centered teaching and learning strategies in undergraduate animal science

Abstract Student-centered teaching and learning strategies enhance active engagement, foster critical thinking, and promote the practical application of knowledge gained from undergraduate animal science courses. The scholarship of teaching and learning (SOTL) has evolved within the animal science community, with a recent focus on adopting evidence-based teaching activities in college classrooms. We discuss documented SOTL approaches utilized in our program for the development of student-centered teaching and learning. In addition to traditional internships and student research projects, capstone projects utilizing a design thinking approach to solving animal industry problems have proven significantly effective. This problem-based learning program enables students to develop industry networks, understand problems within the industry, and develop creative ways to address the problems. Non-conventional and innovative designs of course assignments can foster student interest and enhance learning experiences. For example, by incorporating engaging and novel resources such as animal industry webinars and podcasts as classroom materials, instructors can reinforce course content and captivate students’ attention to the topic. Specifically, students have appreciated the opportunity to learn from experts not available locally at the university. Developing and utilizing real-world case studies exposes students to farm practices and related decision-making. Involving students in competitive team activities like the Dairy Challenge and Academic Quadrathlon contests develops marketable skills for students while training them to implement the knowledge acquired in the classrooms on a commercial farm. Field trips, short courses, and summer schools provide hands-on training opportunities, enhancing students’ exposure to the industry and opportunities after graduation. Students have identified that animal science topics are relevant beyond the classroom walls in everyday life including grocery store and kitchen, as demonstrated by a significant increase in enrollment of non-animal science major students in the animal science courses. The student-centered programs like Cross-training students in data-driven decision support tools help to develop strong day-one competency of graduates through training and networking within and outside the discipline. With the advent of new technologies and tools, training programs surrounding data management and interpretation should integrate systems knowledge across plant science, animal science, and computer science disciplines. These interdisciplinary and cross-disciplinary training activities promote the exchange of ideas while developing systems-level understanding. Furthermore, initiatives like Collaborative Online International Learning (COIL) provide students with exposure to diverse thoughts and situations, broadening their horizons and perspectives. For students not able to participate in study abroad and international programs due to time and financial constraints, COIL delivers cost-effective virtual opportunities to develop global understanding and cultural competency. By incorporating these student-centered teaching and learning strategies, undergraduate animal science programs can effectively prepare students for real-world challenges for the next generation of agriculture leaders.

Interstellar computation their challenges and approaches in interplanetary cloud systems and data transmission

With the advancement of human civilisation beyond the confines of Earth's atmosphere, there has been an increasing need for efficient data processing and transmission in extraterrestrial environments. The article explores innovative strategies for resolving intricate computational challenges encountered in the context of interplanetary distances. The purpose of this study is to investigate the impact of issues such as latency, data corruption, and power consumption on classical computing paradigms in space. We examine the concept of "interplanetary cloud systems", which use distributed computation and data storage to tackle these issues. The article also covers advanced techniques for sending data, including communication systems using quantum entanglement and deep space networks. The article integrates the disciplines of astronomy, computer science, and space exploration to provide a holistic perspective on the issue. It is crucial to emphasise that the implementation of fault-tolerant systems, adaptive algorithms, and robust networking protocols is essential for the success of extrasolar computing programs. The article thoroughly explores the potential and risks that arise when space travel, astronomy, and computer science intersect. We explore the potential future of interplanetary data processing and communication technologies via the examination of fault tolerance, adaptive algorithms, and resilient networking. The importance of resolving computational issues in space increases as humanity extends its presence in the cosmos. The study results have significant ramifications for the development of forthcoming technology enabling interplanetary data transfer, hence facilitating human colonisation and scientific exploration beyond Earth.

An assessment framework for creative production in computing and engineering disciplines

With the increasing expansion of creative computing courses among higher education institutions, there is a growing demand for more objective and efficient assessment frameworks that cater to the multidisciplinary skills that students engage in during the production of such work. This research addresses persistent gaps for a systematic approach of assessing creative content in technical courses. Existing theories are reviewed including creative requirements framework and computing production model, which leads to the establishment of a newly proposed assessment framework. A mixture of research methods is employed combining both inductive and deductive approaches, evidence- based analysis, and case study, based on data collected over three academic years at a British university. The proposed assessment framework incorporates three factors of production (function, usability, and management) and two attributes of creativity (core and optional requirements), which is evaluated to determine its suitability for accurately reflecting the work undertaken by students. Creativity is quantified in the assessment based on technical contribution. The proposed framework can be applied in the delivery of courses involving creative production in the computer science and engineering disciplines in which teaching usually focuses on technical content rather than contextual design, creativity, or aesthetics. The findings reveal positive feedback and a number of pertinent outcomes, including promoting creativity, student satisfaction, and efficient assessments. The framework may be adopted by educators for similar courses that are underpinned by technical skills and creativity.

Comparing methods for creating a national random sample of twitter users

Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to create a random sample of Twitter users in the US: 1% Stream, Bounding Box, Location Query, and Language Query. Then, we compare these methods according to their tweet- and user-level metrics as well as their accuracy in estimating the US population. Our results show that users collected by the 1% Stream method tend to have more tweets, tweets per day, followers, and friends, a fewer number of likes, are younger accounts, and include more male users compared to the other three methods. Moreover, it achieves the minimum error in estimating the US population. However, the 1% Stream method is time-consuming, cannot be used for the past time frames, and is not suitable when user engagement is part of the study. In situation where these three drawbacks are important, our results support the Bounding Box method as the second-best method.

Prospects of the Application of Data Science in the Financial Field

The application of data science in the financial field has become an important trend in the financial industry. This article reviews the current application status of data science in the financial field, including application scenarios in risk management, credit assessment, market forecasting, etc. Through the analysis of existing research, this article summarizes the benefits brought by data science in the financial field, such as improving decision-making efficiency, reducing risks, optimizing customer experience, etc. In addition, this article also looks forward to the future development trends of data science in the financial field, including the application prospects of new technologies such as machine learning and deep neural networks in the financial field. Finally, this article calls on financial institutions to strengthen the training and technical investment in data science talents to adapt to the new trends in the development of financial technology and achieve digital transformation and innovative development in the financial industry. This article provides a relatively comprehensive introduction to the application of data science in multiple financial fields, reflecting the integration of mathematics, finance, computer science, and other disciplines. Most of the literature on academic websites starts from a specific aspect and introduces a certain field in depth. Although it is quite specific, it lacks an overall perspective.

Technology at the Table: Incorporating AI into Contemporary Food Industry Operations

Abstract The discipline of computer science known as artificial intelligence (AI) mimics human thought processes, learning capacities, and knowledge stores. There are many different algorithms available in AI, including fuzzy logic (FL), artificial neural networks (ANNs), etc., which offers myriad opportunities to solve approaching problems in food and agricultural sectors, including global food demand. AI has been used in supply chain management, manufacturing improvement, food quality enhancement, and in maintenance of industrial hygiene. By employing a computerized system, the industry can assess and then deliver the product in the most favourable conditions at every stage, for example, by keeping an eye on all seed selection, crop monitoring, agricultural operations, such as watering, and temperature monitoring, new product development, and maintaining food industry safety standards which could enhance the quality of the products. Nevertheless, there are obstacles in the way of AI’s implementation in the food industry like data privacy issues, job displacement through automation, flaws in algorithms used in making decisions and difficulties in compliance with laws and regulations. The ethical implications of food safety and sustainability make the application of AI much more challenging. The food industry’s future depends on a holistic approach that takes advantage of AI’s potential while maintaining ethical and environmentally friendly operations. Information © CAB International 2024

Bias and ethics of AI systems applied in auditing - A systematic review

The integration of artificial intelligence into auditing shows great potential in enhancing automation and gaining insights from complex data. However, it also presents significant ethical challenges, including algorithmic biases, transparency, accountability, and fairness. This study aimed to investigate the sources of bias and risks posed by AI systems applied in auditing and the complex downstream interactions and effects they have. The study also explored the technical and ethical guardrails proposed and recommendations for translating principles into auditing practice. A systematic methodology was employed to acquire relevant studies across scientific databases. This involved a three-step process, including a targeted search query using Boolean operators and snowballing to yield 310 preliminary publications. A systematic review process was then conducted to identify 123 relevant articles focused on AI's implications for auditing, accounting, finance, or assurance contexts. Finally, screening and filtering on research quality distilled 83 high-quality publications from the year 2018 to 2023 spanning computer science, accounting, management science, and ethics disciplines. The analysis revealed five primary sources driving technical and human biases: data deficiencies, demographic homogeneity, spurious correlations, improper comparators, and cognitive biases. It also highlighted wider issues, such as trade-offs between efficiency and diligence, erosion of human skills and judgement, data dependence risks, and privacy violations from uncontrolled personal data exploitation. The study found promising remedies, including causal modeling to enable auditors to uncover subtle biases, representative algorithmic testing to evaluate fairness, periodic auditing of AI systems, human oversight alongside automation, and embedding ethical values like fairness and accountability into system design. The study concludes that auditors play a crucial role in assessing and ensuring AI's reliable and socially beneficial integration. It recommends governance, risk assessment before deployment, ongoing performance monitoring, and policies fostering trust and collaboration to responsibly translate principles into auditing practice.

Artificial intelligence in healthcare: an Italian perspective on ethical and medico-legal implications.

Artificial intelligence (AI) is a multidisciplinary field intersecting computer science, cognitive science, and other disciplines, able to address the creation of systems that perform tasks generally requiring human intelligence. It consists of algorithms and computational methods that allow machines to learn from data, make decisions, and perform complex tasks, aiming to develop an intelligent system that can work independently or collaboratively with humans. Since AI technologies may help physicians in life-threatening disease prevention and diagnosis and make treatment smart and more targeted, they are spreading in health services. Indeed, humans and machines have unique strengths and weaknesses and can complement each other in providing and optimizing healthcare. However, the healthcare implementation of these technologies is related to emerging ethical and deontological issues regarding the fearsome reduction of doctors' decision-making autonomy and acting discretion, generally strongly conditioned by cognitive elements concerning the specific clinical case. Moreover, this new operational dimension also modifies the usual allocation system of responsibilities in case of adverse events due to healthcare malpractice, thus probably imposing a redefinition of the established medico-legal assessment criteria of medical professional liability. This article outlines the new challenges arising from AI healthcare integration and the possible ways to overcome them, with a focus on Italian legal framework. In this evolving and transitional context emerges the need to balance the human dimension with the artificial one, without mutual exclusion, for a new concept of medicine "with" machines and not "of" machines.

How Many Videos are Cited in Academic Research

This research aims to measure the occurrence of videos cited in the research publications by Indian researchers. More specifically, we investigated the videos associated with scholarly publications and analysed the usefulness of those videos using computational methods. The bibliographic metadata was gathered from Scopus using advanced search techniques. A total of 2,121 publications cited 3,191 online videos during 2008-2022, but 2,736 videos were considered for further analysis due to discrepancies in video IDs. The free Google API for YouTube was used to extract video metadata from YouTube, and the raw data was edited and extracted using OpenRefine, an opensource data carpentry tool. The diagrams have been made using R Studio. The result indicates a linear growth of cited videos from 2008-2018; an exponential increase is observed from 2019 onwards. Most videos are cited from the Computer Science and Social Science disciplines, and journal articles are the most preferred document types that mention videos. The Education category videos are cited the most, and the channel TEDx Talks produced the maximum number of cited videos in research publications. The overall study shows that YouTube is emerging as a significant source of information for research and educational purposes.

Data-Centric Machine Learning in Nursing: A Concept Clarification.

The ubiquity of electronic health records and health information exchanges has generated abundant administrative and clinical healthcare data. The vastness of this rich dataset presents an opportunity for emerging technologies (eg, artificial intelligence and machine learning) to assist clinicians and healthcare administrators with decision-making, predictive analytics, and more. Multiple studies have cited various applications for artificial intelligence and machine learning in nursing. However, what is unknown in the nursing discipline is that while greater than 90% of machine-learning implementations use a model-centric strategy, a fundamental change is occurring. Because of the limitations of this approach, the industry is beginning to pivot toward data-centric artificial intelligence. Nurses should be aware of the differences, including how each approach affects their engagement in designing human-intelligent-like technologies and their data usage, especially regarding electronic health records. Using the Norris Concept Clarification method, this article elucidates the data-centric machine learning concept for nursing. This is accomplished by (1) exploring the concept's origins in the data and computer science disciplines; (2) differentiating data- versus model-centric machine learning approaches, including introducing the machine-learning operation life cycle and process; and (3) explaining the advantages of the data-centric phenomenon, especially concerning nurses' engagement in technological design and proper data usage.

Career Paths of Doctoral Recipients in Engineering and Computer Science: Trends by Sex, Race, Citizenship, and Discipline with an Emphasis on Biomedical Engineering

We summarize national-scale data for Ph.D. earners in engineering or computer science from 2015 to 2019 whose post-graduate school employment is known, highlighting outcomes for biological/biomedical/biosystems engineering students. We use NSF’s Survey of Earned Doctorates (SED), which has collected information from Ph.D. recipients in the USA since 1957. The data are collected at the time of degree completion and constitute a greater than 90% response rate. Compared to all engineering and computer science disciplines, biological/biomedical/biosystems engineering has a higher proportion going to 4yr/med/research institutions (52% vs. 33%) and non-profit (3.6% vs. 2.9%) and lower proportion going to industry (33% vs. 48%), government (4.3% vs. 8.4%), and is similar for non-US positions (6.1% vs. 5.7%). Compared to 2010–2014 biological/biomedical/biosystems engineering Ph.D. recipients, more 2015–2019 recipients are going to industry (25% to 33%) and fewer to 4yr/med/research institutions (59% to 52%) and governmet (5.3% to 4.3%). Across all engineering and computer science disciplines, a smaller proportion of females entered industry (43%) compared to males (49%), while a larger proportion of females entered 4yr/med/research institutions (37%) compared to males (32%). Over half of Asian doctoral recipients entered industry, as compared to 38% of Hispanic doctoral recipients. In contrast, a higher proportion of Hispanic individuals (37%) entered 4yr/med/research institutions after their doctoral programs, as compared to 31% of Asian doctoral recipients. Black doctoral recipients had the highest proportion enter positions in government (14%) and non-profit (4%) sectors. Our results are situated in the broader literature focused on postdoctoral career, training, and employment sectors and trends in STEM. We discuss implications for graduate programs, policymakers, and researchers.

Computer Networking: Fundamentals, Models, and Security Considerations

Computer networking is an emerging and fascinating field of study. A computer network allows communication and exchange of information between two or more independent computers and other devices. Computer networking is based on the related discipline of electrical engineering, computer engineering, and computer science. Nodes and Links constitute the basic building blocks of the computer network. An equipment for data communication or equipment of data terminal that connects two or more computers such as modem, router etc. can be exemplified as a network node. Links include wires, cables or free spaces of wireless networks. Working of computer networks relies on a set of defined rules or protocols. The network structure can be composed of various patterns known as network topology. Various networking models are developed that relies on various networking layers made of different protocols. In modern times, the importance of computer networks is remarkable in almost every areas including education, entertainment, business, military, healthcare, insurance, transportation and others. With so much reliance on computer networking, it becomes important to ensure a safe and secure networking system and thus network security system was developed such includes firewall, VPNs, IDS and IPS, etc

Contextualization, Authenticity, and the Problem Description Effect

In recent years, computing education researchers have investigated the impact of problem context on students’ learning and programming performance. This work continues the investigation motivated, in part, by cognitive load theory and educational research in computer science and other disciplines. The results of this study could help inform computing assessment design. If the context and authenticity of a programming problem aid student performance then, instructors’ time in creating appropriately contextualized programming problem descriptions is time well-spent. On the other hand, if the context of a programming problem hinders performance, then instructors should leave it out of programming problems. Recent studies investigating the impact of programming problem context on student success have arrived at different conclusions. Presented here is a series of experiments, conducted over 3 years, investigating the impact of context on novice programmers’ success in algorithmic programming assignments using three contextualized tasks and their generic counterparts. This experiment series also looked into the possibility of “authenticity” as a factor affecting performance. Common sense would suggest that a student would perform better on a problem if they understood or cared about it. Contextualization could provide authenticity and authenticity could provide interest. Research suggests that perceiving a problem as authentic has a positive effect on engagement and learning. Alternatively, if a problem is “just an abstract set of numbers”, it may be harder to make sense of the details and the lack of context could consequently contribute additional cognitive load to an already challenging assignment. The results of this study show that assignment context and problem context authenticity have no effect on the performance of novice programmers. We think, however, that contextualization could be worth investing in to support students’ interest in computing. Additional implications of the results suggest that instructors can assign equivalent versions of the same problem in varied contexts to suit their students’ interests without worrying if the context will hinder performance.