Computer Skills Research Articles

LLM-based collaborative programming: impact on students’ computational thinking and self-efficacy

At present, collaborative programming is a prevalent approach in programming education, yet its effectiveness often falls short due to the varying levels of coding skills among team members. To address these challenges, Large Language Models (LLMs) can be introduced as a supportive tool to enhance both the efficiency and outcomes of collaborative programming. In this shift, the structure of collaborative teams evolves from human-to-human to a new paradigm consisting of human, human, and AI. To investigate the effectiveness of integrating LLMs into collaborative programming, this study designed a quasi-experiment. To explore the effectiveness of integrating LLMs into collaborative programming, we conducted a quasi-experiment involving 82 sixth- and seventh-grade students, who were randomly assigned to either an experimental group or a control group. The results showed that incorporating LLMs into collaborative programming significantly reduced students’ cognitive load and improved their computational thinking skills. However, no significant difference in self-efficacy was observed between the two groups, likely due to the cognitive demand students faced when transitioning from graphical programming to text-based coding. Despite this, the study remains optimistic about the potential of LLM-enhanced collaborative programming, as students learning in this way exhibit lower cognitive load than those in conventional environments.

Development of a 2D Shape E-Module Based on Project-Based Learning with Scratch to Improve Computational Thinking Skills

This study aims to develop a flat shapes e-module based on project-based learning (PBL) supported by Scratch to enhance computational thinking skills. The research method used is research and development with three development phases: preliminary research, prototyping, and assessment. The research subjects consisted of 30 students from class VII at SMP High Scope in the limited trial, and 32 and 35 students from SMP Negeri 5 Denpasar in field tests 1 and 2, respectively. SMP High Scope was selected for the limited trial because its student characteristics are similar to those of SMP Negeri 5 Denpasar. In addition, SMP High Scope frequently uses Scratch in its informatics lessons, making it more suitable for identifying and addressing issues with the e-module before further testing in a more general environment. The revisions and feedback from the limited trial were then followed by field tests 1 and 2 at SMP Negeri 5 Denpasar to refine the e-module. The e-module characteristics include learning objectives, estimated time, and project-based materials. The integrated support features include Scratch, learning videos, and evaluation exercises. The e-module was assessed using validation sheets, student and teacher response questionnaires for practicality, and pre-test and post-test sheets for effectiveness. The results showed that the e-module was valid with a score of 4.00, indicating it was appropriate for use. The practicality, based on student response questionnaires, was 75.34%, and for teachers, it was 75.45%. Effectiveness was tested using a t-test with a significance value of 0.00 and an N-Gain score of 0.31, categorized as moderate.

Co-designing to develop computational thinking skills in Nigeria K-12 using scratch

Abstract The need to integrate the teaching and learning of computational thinking (CT) in K-12 education has been on the rise since it was identified as a skill for solving 21st-century problems. The co-design pedagogical approach has shown great potential in promoting effective communication of CT to both university and K-12 students with the support of different educational tools in different contexts. To ensure Nigerian secondary school (K-12) students develop CT skills, a four-day co-design CT activities workshop was organized. Co-design pedagogy and constructivism theory were deployed in this study with students co-designing COVID-19 disease spread game for learning CT. A mixed method was adopted to investigate student’s interest, attitudes, understanding of CT, and their learning experience from implementing CT-based prototype using Scratch. This study recruited 40 students from two different secondary schools in Nigeria as participants. The result revealed that student’s interest in learning CT was aroused through the use of co-design pedagogy and Scratch (μ = 4.55, σ = 0.815). Similarly, students attitude toward CT after the intervention study shows positive (μ = 4.50, σ = 0.716). This study paved way for student’s skills development in teamwork and collaborative learning, communication, idea sharing, personal skill development, game design, and understanding of programming. This study instigates thinking ideation, inspires the application of CT concepts in daily life activities, and improves problem-solving skills. This study promotes and advocates for the application of co-design pedagogy to foster the teaching and learning of CT in a Nigerian context. This study contributes to knowledge by promoting the use of Scratch as a tool for co-designing in learning CT, proposing a four-phase co-design application flow for the integration of co-design pedagogy with Scratch for learning CT in the Nigerian K-12 context and suggesting ways to implement the teaching and learning of CT in K-12 education.

Visualizing Cell Structures with Minecraft

ABSTRACT Many microscopic images and simulations of cells give results in different kinds of formats, making it difficult for people lacking computational skills to visualize and interact with them. Minecraft—known for its three-dimensional, open-world, voxel-based environment—offers a unique solution by allowing the direct insertion of voxel-based cellular structures from light microscopy and simulations into its worlds without modification. This integration enables Minecraft players to explore the ultrastructure of cells in a highly immersive and interactive environment. Here, we demonstrate several workflows that can convert images and simulation results into Minecraft worlds. Using the workflows, students can easily import and interact with a variety of cellular content, including bacteria, yeast, and cancer cells. This approach not only opens new avenues for science education but also demonstrates the potential of combining scientific visualization with interactive gaming platforms for facilitating research and improving appreciation of cellular structure for a broad audience.

A cultural adaptation study of nursing informatics competency scale.

The objective of this study is to examine the factor structure, internal consistency reliability and responsiveness of an instrument related to the informatics competency of nurses. Informatics is a scientific discipline that needs to be integrated into nursing practice. There is no current and valid tool to measure the informatics competency of nurses in Turkey. This study has a methodological design. The Self-Assessment of Nursing Informatics Competency Scale-18 was used to collect data. The original scale was translated into Turkish in a process of forward and back-translation. Data were collected in a setting of 190 respondents with a random subsample in a hospital. Internal reliability was tested using Cronbach's alpha. The factor structure of the instrument was determined through exploratory factor analysis. Factorial invariance was tested in a multi-group confirmatory factor analysis. Exploratory analysis with varimax rotation extracted three factors comprising 18 items that explained 62.1 % of the variance: applied computer skills (α=.91), role (α=.67) and basic computer skills (α=.77). The current version of the instrument is well grounded in the theoretical framework of informatics competence in nursing practice.

Scaling up Computational Thinking skills in Computer-assisted Language Learning (CTsCALL) and its fitness with language learners’ intentions to use Virtual Exchange: A bi-symmetric approach

Scaling up Computational Thinking skills in Computer-assisted Language Learning (CTsCALL) and its fitness with language learners’ intentions to use Virtual Exchange: A bi-symmetric approach

Implementing cloud computing in drug discovery and telemedicine for quantitative structure-activity relationship analysis

This work aims to use cutting-edge machine learning methods to improve quantitative structure-activity relationship (QSAR) analysis, which is used in drug development and telemedicine. The major goal is to examine the performance of several predictive modeling approaches, including random forest, deep learning-based QSAR models, and support vector machines (SVM). It investigates the potential of feature selection techniques developed in chemoinformatics for enhancing model accuracy. The innovative aspect is using cloud computing resources to strengthen computational skills, allowing for managing massive amounts of chemical information. This strategy produces accurate and generalizable QSAR models. By using the cloud's scalability and constant availability, remote healthcare apps have a workable answer. The goal is to show how these methods may improve telemedicine and the drug development process. Utilizing cloud computing equips researchers with a flexible set of tools for precise and timely QSAR analysis, speeding up the discovery of bioactive chemicals for therapeutic use. This new method fits well with the dynamic nature of pharmaceutical study and has the potential to transform the way drugs are developed and delivered to patients via telemedicine.

Designing Visual and Interactive Self-Monitoring Interventions to Facilitate Learning: Insights from Informal Learners and Experts.

Informal learners of computational skills often fi nd it difficult to self-direct their learning pursuits, which may be spread across different mediums and study sessions. Inspired by self-monitoring interventions from domains such as health and productivity, we investigate key requirements for helping informal learners better self-reflect on their learning experiences. We carried out two elicitation studies with paper-based and interactive probes to explore a range of manual, automatic, and semi-automatic design approaches for capturing and presenting a learner's data. We found that although automatically generated visual overviews of learning histories are initially promising for increasing awareness, learners prefer having controls to manipulate overviews through personally relevant filtering options to better reflect on their past, plan for future sessions, and communicate with others for feedback. To validate our findings and expand our understanding of designing self-monitoring tools for use in real settings, we gathered further insights from experts, who shed light on factors to consider in terms of data collection techniques, designing for reflections, and carrying out field studies. Our findings have several implications for designing learner-centered self-monitoring interventions that can be both useful and engaging for informal learners.

The relationship between the 21st-century skills and computational thinking skills of prospective mathematics and science teachers

The relationship between the 21st-century skills and computational thinking skills of prospective mathematics and science teachers

The development of RBL-STEM learning tools to improve students' computational thinking skills in solving plant disease classification problems using convolutional neural network segmentation

This research aims to develop Research Based Learning (RBL) and Science, Technology, Engineering and Mathematics (STEM) based learning tools to improve students' computational thinking skills. The tools include a student task design (RTM), a student worksheet (LKM) and a learning outcome test (THB). Using Thiagarajan's 4D development model (define, design, develop, disseminate), the device was tested on students using a Convolutional Neural Network (CNN) approach for citrus plant disease classification using data from quadcopter drones. The validation results showed that the device was valid with an average score of 3.85 (96.42%). The practicality of the device is very high with an implementation score of 3.85 (96.36%) and a positive student response of 90.08%. The effectiveness of the device was demonstrated by 90% of students achieving classical completeness on the post-test and an increase in computational thinking skills from 0% (high) on the pre-test to 92% on the post-test. The paired sample t-test results also confirmed the statistically significant increase. This learning tool proved to be valid, practical and effective, contributing to technology-based learning innovation in modern agriculture.

Bridging the digital divide for outpatients treated with anticancer chemotherapy: a retrospective quantitative and qualitative analysis of an adapted electronic Patient Reported Outcome program

PurposeUsing electronic patient-reported outcomes (ePRO) in clinical trial has shown benefits for patients. However, the digital divide can lead to unequal access to telehealth. We investigated whether a dedicated support program could bridge that divide.MethodsBetween February 2021 and June 2022, outpatients undergoing chemotherapy for cancer at our teaching hospital in France were given the Onco’nect® ePRO application if they were affected by the digital divide. They were also offered a dedicated support program that included the lending of a tablet, access to healthcare professionals, training, technical support, and peer-to-peer guidance. We conducted semi-structured interviews to assess the challenges they faced.ResultsWe enrolled 22 patients, of whom 10 (45%) made good use of the application and completed > 50% of the questionnaires in the application, while 5 (23%) completed > 75%. However, 12 (55%) of the 22 patients remained poor users of the application over a median participation of 4 months (IQR, 3–7). We also measured social deprivation but found no association with questionnaire completion rate. The under-use of Onco’nect® was due not only to the patients’ understanding of its clinical benefit or to their computer skills, but also to poor health literacy and strong emotional responses to using the application.ConclusionDedicated support programs help many patients make the most of telehealth. However, most of our patients in the digital divide under-used the ePRO application, primarily due to their poor health literacy.

Explore barriers to using the internet for health information access in African countries: A systematic review.

The Internet is a crucial source of health information, providing access to vast volumes of high-quality, up-to-date, and relevant healthcare information. Its impact extends beyond information access, influencing medical practice through the widespread adoption of telemedicine and evidence-based medicine. Despite the significant global increase in internet usage, Africa lags in internet penetration, particularly in utilizing the internet for health information. This study aims to systematically review the literature to explore barriers to accessing health information on the Internet in African countries. The study was conducted from January 1 to February 28, 2023. It followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline to systematically review published studies investigating the utilization of the Internet for health information in African countries. A comprehensive search was conducted across various databases, including Google Scholar, PubMed, Cochrane Library, Hinari, CINAHL, and Global Health. The inclusion criteria were applied, resulting in the selection of six studies that formed the basis for our analysis. This systematic review identifies eleven barriers to accessing health information on the internet. These include a lack of ownership of smart electronic devices, infrequent internet use, limited internet access, low E-health and computer literacy, slow internet connection, high cost of internet access, insufficient information search skills, residing in rural areas, dealing with diverse fields, and having low perceptions. Improving our understanding of barriers to accessing health information online is essential for policymakers, governments, academics, and healthcare professionals. To enhance the use of the Internet for health information and strengthen the overall health system, policymakers should prioritize increasing Internet accessibility, reducing costs, improving connections, offering basic computer skills training, and ensuring the availability of electronic devices in all institutions.

Effect of a Proposed Program Based on Artificial Intelligence in Developing Computational Thinking Skills in the Digital Skills Course for First-Grade Intermediate Female Students

The study aimed to identify the impact of a Proposed Program Based on Artificial Intelligence in Developing Computational Thinking Skills in the Digital Skills Course for First-Grade Intermediate Female Students. To achieve the study objectives, the researcher applied the experimental approach with its semi-experimental design, and she designed one data collection: The Computational Thinking skills scale in the digital skills course. The research population included all first-grade intermediate students enrolled in the first semester of the academic year (1444 AH) in the Olia district in Riyadh. The sample consisted of (48) students from (140) intermediate schools, divided into two groups: A control group and an experimental group. The results showed statistically significant differences at the level of (α ≤ 0.05) between the average scores of the experimental and control groups in the post-application of the computational thinking skills scale, favoring the experimental group. The study confirmed the positive impact of teaching using an artificial intelligence-based program in developing computational thinking skills in the digital skills course among first-grade intermediate students. The study recommended increasing the attention of digital skills course authors to incorporating AI-based educational programs in the course lessons and expanding their application to different educational stages and subjects.

Redefining computational thinking: A holistic framework and its implications for K-12 education

Abstract In the realm of K-12 Education, the growing significance of Computational Thinking has sparked extensive inquiry into its nature and instructional methodologies. Despite a wealth of literature on the subject, ongoing debates persist regarding its fundamental components. Across global educational landscapes, Computational Thinking is being incorporated into curricula with varying degrees of emphasis, ranging from robotics to computer coding to broader algorithmic problem-solving approaches. This article aims to reassess established definitions of Computational Thinking and propose a nuanced understanding that highlights the necessity and importance of data in contemporary problem-solving contexts. Additionally, we present a comprehensive framework aimed at cultivating Computational Thinking skills in students, encompassing technical competencies alongside cognitive and metacognitive processes. Subsequently, we offer (1) a refined definition emphasizing the importance of data, (2) a comprehensive framework outlining essential components to foster Computational Thinking skills in students, and (3) a discussion of these concepts within the context of K-12 education.

Economic value of HPC experience for new STEM professionals: Insights from STEM hiring managers.

The purpose of this article is to investigate particular aspects of the STEM job market in the US. In particular, we ask: could the possession of high performance computing (HPC) skills enhance the chances of a person getting a job and/or increase starting salaries for people receiving an undergraduate or graduate degree and entering the technical workforce (rather than academia)? We also estimate the value to the US economy of practical experience offered to US students through training about HPC and the opportunity to use HPC systems funded by the National Science Foundation (NSF) and accessible nationally. Interviews and surveys of employers of graduates in STEM fields were used to gauge demand for STEM graduates with practical HPC experience and the salary increase that can be associated with the possession of such skills. We used data from the XSEDE project to determine how many undergraduate and graduate students it enabled to acquire practical proficiency with HPC. People with such skills who had completed an undergraduate or graduate degree received an initial median hiring salary of approximately 7%-15% more than those with the same degrees who did not possess such skills. XSEDE added approximately $10 million or more per year to the US economy through the practical educational opportunities it offered. Practical hands-on experience provided by the US federal government, as well as many universities and colleges in the US, holds value for students as they enter the workforce. Practical training in HPC during the course of undergraduate and graduate programs has the potential to produce positive individual labor market outcomes (i.e., salary boosts, signing bonuses) as well as to help address the shortage of STEM workers in the private sector of the US.

TEACHERS' PERCEPTIONS AND CHALLENGES TOWARDS ONLINE TEACHING DURING COVID-19 AT TUTORING SCHOOLS IN BANGKOK, THAILAND

This research investigates teachers’ perceptions and challenges regarding online teaching during the COVID-19 pandemic, utilizing quantitative research methodology. Results revealed three primary factors significantly impacting teachers’ perceptions of online teaching. While teachers generally held favorable attitudes toward online teaching methods for language instruction, key challenges included limited interaction between teachers and students, internet instability, and insufficient computer skills for navigating online teaching platforms. Overall, teachers acknowledged the numerous benefits of online teaching methods compared to traditional classroom approaches. However, issues such as interaction limitations and technical difficulties considerably compromised the quality of online teaching.

Corporative governance in the literature from 2020 to 2024

The pandemic impacted knowledge management through anti-COVID-19 policies of social distancing and confinement. Educational systems went from the face-to-face classroom to the virtual classroom through the self-management of content according to technology, devices and platforms. The objective of this work was to explore the dimensions of knowledge management in the context of the health crisis. A psychometric, exploratory, correlational, and cross-sectional study was carried out with a sample of 100 students selected based on their computer skills, informational entrepreneurship, and academic innovation at a university in central Mexico. The results indicate the non-rejection of the null hypothesis regarding the differences between the theoretical structure of three dimensions with respect to the observed empirical structure. In relation to anti-pandemic policies, risk prevention and the promotion of self-management of knowledge are recommended as an extensive factor of the proposed model.

The effect of learning computational thinking skills through educational board games on students’ cognitive styles, cognitive behaviors, and learning effectiveness

The effect of learning computational thinking skills through educational board games on students’ cognitive styles, cognitive behaviors, and learning effectiveness

ScRICA: An R package for multiple-sample single-cell RNA-seq data integrative comparative analysis

Single-cell sequencing technologies offer unprecedented resolution to inspect transcriptomes and generate critical biological insights. As the number of cells and cell types increase in single-cell studies, the effort required to analyze the data surges dramatically, especially when comparative explorations need to be performed on large datasets with different cell types and various sample attributes, such as clinical samples from different age and ancestry groups. Due to the sequential nature of single-cell data analysis, many steps involving multiple method choices and parameter options need to be considered. The computational skills required for integrative and comparative analyses of large datasets with various sample attributes represent a substantial obstacle for many researchers. To address this challenge, we have developed scRICA, a systematic workflow tailored for integrative and comparative single-cell RNA sequencing (scRNA-seq) analysis. This approach streamlines the analytical process, ensuring efficient utilization of computational resources and facilitating scalability for large-scale datasets. With scRICA, researchers can conduct integrative and comparative scRNA-seq analyses with ease, empowering them to derive meaningful insights from their data in a timely manner. scRICA offers a versatile approach by allowing users to input various parameter options from a metadata table, which are inherited throughout the entire analysis workflow. This functionality greatly enhances the efficiency of programming for comparative analyses involving multiple sample attributes. As an R package, scRICA provides a user-friendly interface within the R environment, making it accessible to researchers familiar with R programming. Additionally, scRICA offers a command line execution option, allowing users to seamlessly integrate it into their computational pipelines or execute analyses on High-Performance Computing (HPC) systems. This combination of features ensures flexibility, ease of use, and scalability, making scRICA a valuable tool for comprehensive and efficient single-cell RNA sequencing analysis.

Ethical AI in HR: A Case Study of Tech Hiring

ABSTRACT The job applicant hiring process is inherently complex, encompassing various dimensions such as cultural fit, team dynamics, and individual qualifications. This paper explores the potential of machine learning techniques as analytical tools to examine the nuances of the hiring process rather than automating it. In this study, we use machine learning models to evaluate whether gender or age bias exists in hiring decisions by examining an extensive IT industry dataset comprising over 70,000 applicants. We provide practical guidelines for data preparation, bias evaluation, and assessment of AI-driven hiring outcomes, focusing on the influence of key factors on hiring probability. Our findings reveal that female applicants tend to receive lower hiring probabilities than male applicants, suggesting a gender-related bias. Additionally, we identify that proficiency in “Computer Skills,” the number of programming languages known, and familiarity with the language “TypeScript”—introduced in 2012—are among the top skills influencing hiring decisions. This study offers HR practitioners actionable insights into the AI decision-making process, highlighting how skills valued by AI models may differ from those prioritized in traditional recruitment. Through our empirical study, we demonstrate how multiple machine learning methods can be used for bias detection and the validation of critical decision factors in hiring decisions, equipping HR professionals with tools to enhance understanding and fairness in AI-driven hiring.