Programming Expertise Research Articles

Reference Sequence Browser: An R application with a user-friendly GUI to rapidly query sequence databases.

Land managers, researchers, and regulators increasingly utilize environmental DNA (eDNA) techniques to monitor species richness, presence, and absence. In order to properly develop a biological assay for eDNA metabarcoding or quantitative PCR, scientists must be able to find not only reference sequences (previously identified sequences in a genomics database) that match their target taxa but also reference sequences that match non-target taxa. Determining which taxa have publicly available sequences in a time-efficient and accurate manner currently requires computational skills to search, manipulate, and parse multiple unconnected DNA sequence databases. Our team iteratively designed a Graphic User Interface (GUI) Shiny application called the Reference Sequence Browser (RSB) that provides users efficient and intuitive access to multiple genetic databases regardless of computer programming expertise. The application returns the number of publicly accessible barcode markers per organism in the NCBI Nucleotide, BOLD, or CALeDNA CRUX Metabarcoding Reference Databases. Depending on the database, we offer various search filters such as min and max sequence length or country of origin. Users can then download the FASTA/GenBank files from the RSB web tool, view statistics about the data, and explore results to determine details about the availability or absence of reference sequences.

Perencanaan Strategi Bisnis Usaha Online: UMKM

The development of micro, small and medium enterprises (MSMEs) requires a joint role in the environment from both regulators and other companies. The Business Competition Supervisory Commission (KPPU) together with the Ministry of Cooperatives and SMEs signed a memorandum of understanding to form a task force to monitor partnerships established between SMEs and large industries. This article explores the programs organized by the Ministry of Cooperatives and Small and Medium Enterprises for the development of MSMEs. Through literature and online research about company programs, researchers can compare them with government policy programs to analyze the accuracy of expert programs. The research results show that the joint Business Competition Supervisory Commission (KPPU) program can fulfill the policies of the Ministry of Cooperatives and SMEs stipulated in the 2020-2024 Strategic Plan.

The Effect of Cooperative Learning Model Type Think Pair Share (TPS) on Student Learning Outcomes

This study aims to examine the effect of the Think Pair Share (TPS) Cooperative Learning Model on the learning outcomes of Class X students in the Accounting Expertise program at SMK Negeri 1 Gowa. The research employs an experimental design with a posttest-only control group, involving two groups: an experimental group utilizing the TPS model and a control group receiving conventional instruction. The sample consists of 68 students, divided equally between the two groups, selected through purposive sampling. Data collection techniques include observation, documentation, and tests, while data analysis is performed using SPSS 27.0 for Windows, encompassing descriptive statistics, validity tests, and hypothesis testing. The findings indicate that the TPS Cooperative Learning Model was implemented effectively, with a high average score of 91.2% in the experimental group. Descriptive analysis reveals that the learning outcomes in the experimental group averaged 86.24, significantly higher than the control group’s average of 61.76. Regression analysis shows a significant positive effect of the TPS model on learning outcomes, with a regression equation of Y' = 40.312 + 0.499X and a determination coefficient of 44.1%, indicating that the TPS model accounts for 44.1% of the variance in learning outcomes. The t-test results confirm that the TPS model significantly enhances learning outcomes (p < 0.05). These results align with previous studies and support the theoretical framework that cooperative learning models, particularly TPS, enhance student engagement and academic performance.

SCUBA implements a storage format-agnostic API for single-cell data access in R

While robust tools exist for the analysis of single-cell datasets in both Python and R, interoperability is limited, and analysis tools generally only accept one object class. Considerable programming expertise is required to integrate tools across package ecosystems into a comprehensive analysis, due to their differing languages and internal data structures. This complicates validation of results and leads to inconsistent visualizations between analysis suites. Conversion between object formats is the most common solution, but this is difficult and error-prone due to the rapid pace of development of the analysis suites and their underlying data structures. To address this, we created SCUBA (Single-Cell Unified Backend API), an R package that implements a unified data access API for all common R and Python single-cell object formats. SCUBA extends the data access approach from the widely used Seurat package to SingleCellExperiment and anndata objects. SCUBA also implements new data-specific access functions for all supported object types. Performance scales well across all SCUBA-supported formats. In addition to performance, SCUBA offers several advantages over object conversion for the visualization and further analysis of pre-processed single-cell data. First, SCUBA extracts only data required for the operation at hand, leaving the original object unmodified. This process is simpler, less error prone, and less memory intensive than object conversion, which operates on the entire dataset. Second, code written with SCUBA can use any supported object class as input, with simple and consistent syntax across object formats. This allows a single analysis script or package (like our interactive single-cell browser, scExploreR) to work seamlessly with multiple object types, reducing the complexity of the code and improving both readability and reproducibility. Adoption of SCUBA will ultimately improve collaboration and reproducible research in single-cell analysis by lowering the barriers between package ecosystems.

Clustering of Student Expertise Fields Using the K-Means Algorithm (Case Study: STMIK Kaputama Binjai)

Grouping students' fields of expertise in higher education is an important issue that can provide significant benefits for students and educational institutions. STMIK Kaputama is one of the universities that has students with various fields of expertise, but the absence of data that informs the field of expertise of students is very unfortunate. Research data was obtained through questionnaires distributed to students, which included information about study programs, Grade Point Average (GPA), and areas of expertise. Clustering analysis was conducted using Matlab software to validate and implement the clustering results. The results show that the K-Means algorithm is effective in grouping students into clusters that have similar characteristics. The first cluster consists of students with expertise in programming and database, the second cluster focuses on students with networking expertise, and the third cluster includes students with various combinations of expertise.This study also found a tendency that students with certain expertise have a higher GPA than students with other expertise.

Web Scrapping Tools used in Healthcare Sector

Web scraping has become essential in the healthcare sector and provides vast opportunities for data access and analysis. This abstract outlines several key web scraping tools commonly used in healthcare:: Scrapy: A Python-based framework for structured data extraction from various medical sources, offering adaptability and scalability. Beautiful Soup: Facilitates seamless data extraction from medical websites and research articles with its user-friendly interface. Selenium: Particularly useful for extracting data from dynamic healthcare websites driven by JavaScript. Octoparse: Empowers healthcare professionals to extract data without programming expertise through an intuitive interface. ParseHub: Simplifies data extraction from complex healthcare websites with scheduling capabilities. Import.io: A visual API-based scraping solution for integrating and analyzing data from web pages and APIs. WebHarvy: Streamlines data retrieval from medical websites, journals, and forums with automatic configuration and diverse export options. Web scraping tools are invaluable for accessing and analyzing health information, enhancing assessment, decision-making, and patient outcomes. However, adherence to legal and ethical guidelines, including data privacy and consent considerations, is crucial for responsible use in the healthcare industry.

GVAF: generalized, flexible filtering software for annotated variant files.

In the rapidly advancing field of genomics, many tools have been developed to interpret genetic variants using next-generation sequencing (NGS) data. However, these tools often produce annotated variant files in different formats, which require specific software or programming skills to filter and analyze. To provide a filtering tool that can be used with diverse variant annotation tools without requiring specific software or programming skills. We developed Germline Variant Annotation and Filtering (GVAF), a command-line software tool that can handle annotated variant files in any table-shaped format. GVAF offers powerful filtering operations without the need for additional software or programming expertise. Built on the Java framework and bash scripts, it provides extensive features, including flexible filtering rules, recognition of genotype-related fields from variant call format (VCF) files, and customizable result generation. GVAF also integrates easily into existing data analysis pipelines. Compared to other tools, GVAF offers a broader range of functionalities, making it more flexible and intuitive for managing annotated variant files. This GVAF software and online manual is publicly available at https://www.sysbiolab.org/gvaf for academic users and is designed to streamline the variant interpretation process, aiding researchers in producing meaningful results.

Optimasi Metode Microwave-Assisted Extraction (MAE) untuk Menentukan Kadar Flavonoid Total Ekstrak Etanol Kulit Pisang Kepok (Musa Paradisiaca.L)

Indonesia is a country that has a lot of banana diversity. One of the banana plants that is widely found in Indonesia is the Kepok banana (Musa Paradisiaca.L). Kepok banana skin has a very high content of flavonoids and phenols. Flavonoids are known as good antioxidants because they can find reactive oxygen species with the phenolic hydroxyl group that flavonoids have. The aim of this research was to determine the results of the optimization of the Microwave-Assisted Extraction (MAE) method on the total flavonoid content of kapok banana peel ethanol extract. Microwave-Assisted Extraction (MAE) was done to extraction flavonoid from ethanol extract of kepok banana peel (Musa Paradisiaca.L). The sample collected from Widoro village, Karangsambung sub-district, Kebumen regency. Optimum extraction condition was determined by the Response Surface Methodology (RSM). The Box- Behnken design (BBD) was used to evaluate the influence of 3 factors with 3 levels extraction that is power level (10%, 30%, 50%), ethanol concentration (50, 70, 96%), and extraction time (3, 5 and 7 minutes) with 15 different runs. The research showed that optimum extraction condition was at 24,14 % of power level, 66,26% ethanol concentration, and time extraction of 3,52 minutes with calculations from the Design Expert program, the total flavonoid content response was 8.491 Mg QE/g. The total flavonoid content value obtained experimentally was 8,246 Mg QE/g under condition of 30 % power level, 70% ethanol concentration, and time extraction of 5 minutes. Based on the result, total flavonoid content can be significantly increased by optimizing the MAE process use RSM.

CalTrig: A GUI-Based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.

Advances in in vivo Ca2+ imaging using miniatured microscopes have enabled researchers to study single-neuron activity in freely moving animals. Tools such as MiniAN and CalmAn have been developed to convert Ca2+ visual signals to numerical information, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying Ca2+ transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing. CalTrig integrates multiple data streams, including Ca2+ imaging, neuronal footprints, Ca2+ traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient Ca2+ transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for Ca2+ transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.

Emergency Knowledge Translation, COVID-19 and indoor air: evaluating a virtual ventilation and filtration consultation program for community spaces in Ontario

BackgroundAn October, 2021 review of Public Health Ontario's COVID-19 guidance for congregate settings such as shelters and long-term care homes demonstrated that this guidance did not include references to ventilation or filtration. In April 2022, an interdisciplinary team with expertise in indoor air quality (IAQ), engineering, epidemiology, community programming and knowledge translation launched a virtual ventilation and filtration consultation program for community spaces in Toronto, Ontario. The program gives people working in community spaces direct access to IAQ experts through 25-min online appointments. The program aims to help reduce the risk of COVID-19 transmission in community spaces, and was designed to help compensate for gaps in public health guidance and action.MethodsRepresentatives from participating organizations (n. 27) received a link to an online survey via email in April 2023. Survey questions explored the impacts of the program on topics such as: purchase and use of portable air filters; maintenance and use of bathroom fans; and, maintenance and modification of HVAC systems. Survey participation was anonymous, and no demographic information was collected from participants.ResultsRepresentatives from 11 organizations completed the survey (40%). Of those who responded, nine (82%) made changes as a result of the program, with eight (73%) making two or more changes such as purchasing portable air filters and increasing routine maintenance of HVAC systems.ConclusionsWhen presented with brief access to expert support and tailored plain language guidance, people working in community spaces increased their use of ventilation and filtration strategies for COVID-19 infection prevention and control.

Adulthood dietary and lifestyle patterns and risk of breast cancer: Global Cancer Update Programme (CUP Global) systematic literature review

BackgroundAn increasing number of studies in recent years investigate various dietary and lifestyle patterns and associated breast cancer (BC) risk. ObjectivesThis study aimed to comprehensively synthesize and grade the evidence on dietary and lifestyle patterns and BC risk. MethodsDatabases were systematically searched up to 31 March, 2022, for evidence from RCTs and prospective cohort studies on adherence to a dietary pattern alone or in combination with lifestyle behaviors and incidence of or mortality from primary BC in adult females. Findings in all, premenopausal, and postmenopausal females were descriptively synthesized instead of meta-analyzed due to patterns heterogeneity. An independent Global Cancer Update Programme Expert Panel graded the strength of the evidence. ResultsA total of 84 publications were included. Results for patterns reflecting both a healthy diet and lifestyle were more consistent than for patterns that included diet only. There was strong-probable evidence that a priori World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) and American Cancer Society (ACS) dietary and lifestyle scores may reduce BC risk in all and postmenopausal females, whereas in premenopausal females, less evidence was found contributing to limited-suggestive grade. There was also a limited-suggestive evidence that adherence to the Healthy Lifestyle Index and other diet and lifestyle scores may reduce BC risk in postmenopausal females; a posteriori Western/meat/alcohol dietary patterns may increase BC risk in postmenopausal females; and prudent/vegetarian/Mediterranean dietary patterns may reduce BC risk in all females. For the remaining patterns, evidence was graded as limited-no conclusions. ConclusionsAdvice to adopt combined aspects of a healthy diet and lifestyle according to WCRF/AICR and ACS scores, encouraging a healthy weight, physical activity, alcohol and smoking avoidance, and a healthy diet rich in fruits, vegetables, (whole)grains and cereals and discouraging red and processed meat, can be proposed to females to lower BC risk.This review was registered at PROSPERO as ID CRD42021270129 (https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021270129) on 28 August, 2021, and further updated on 4 May, 2022, in order to extend the search period.

SEISMICgraph: a web-based tool for RNA structure data visualization.

In recent years, RNA has been increasingly recognized for its essential roles in biology, functioning not only as a carrier of genetic information but also as a dynamic regulator of gene expression through its interactions with other RNAs, proteins, and itself. Advances in chemical probing techniques have significantly enhanced our ability to identify RNA secondary structures and understand their regulatory roles. These developments, alongside improvements in experimental design and data processing, have greatly increased the resolution and throughput of structural analyses. Here, we introduce SEISMICgraph, a web-based tool designed to support RNA structure research by offering data visualization and analysis capabilities for a variety of chemical probing modalities. SEISMICgraph enables simultaneous comparison of data across different sequences and experimental conditions through a user-friendly interface that requires no programming expertise. We demonstrate its utility by investigating known and putative riboswitches and exploring how RNA modifications influence their structure and binding. SEISMICgraph's ability to rapidly visualize adenine-dependent structural changes and assess the impact of pseudouridylation on these transitions provides novel insights and establishes a roadmap for numerous future applications.

Design and Optimization of Solid Lipid Nanoparticles for Enhanced Therapeutic Activity of Linagliptin.

To augment oral bioavailability, linagliptin (LGP) loaded solid lipid nanoparticles (SLNs) have been formed using the solvent evaporation technique of emulsification. Utilizing the Box Behken design (BBD), examining the relation between independent and dependent variables was possible. Particle size (PS), entrapment efficiency (EE), and in-vitro drug release (DR) studies were considered response factors, whereas the various concentrations of lipid carrier, surfactant, and co-surfactant were considered process variables for optimization. The design expert program was used to obtain the optimized batch. At 24 hours, it was discovered that the optimized batch expected responses for PS, %EE, and %DR were 105.83 nm, 81.19, and 96.69%, respectively. The observed PS, %EE, and %DR responses of the optimized batch at 24 hours were determined to be 101.36 nm, 80.56, and 96.31%, respectively. There is no interaction seen in the basic physical mixing of drug and polymer, according to the FTIR spectra of pure drugs, polymers, and drug and polymer mixtures. The optimized formulation’s average size was found to be 138 nm by the particle size analysis. Similarly, a value of 0.255 was found by the polydispersity index (PDI). This suggests that the SLN in the formulation has a homogeneous size dispersion. With a zeta value of -32.8 mV, stability is indicated. Linagliptin was effectively developed as a drug with a 24-hour sustained release. Wistar rats were used in the in-vivo evaluation of linagliptin SLN formulations. The drug bioavailability increased five times (a highly significant fivefold increase) in LGP-loaded SLNs (AUC0-t ≈ 134.5 μg*h/mL) compared to raw LGP (AUC0-t = 27.23 μg*h/mL).

Design of a Tool Capable of Assessing Environmental Sociocultural Physical Factors Influencing Women's Decisions on When and Where to Toilet Within Real-World Settings: Protocol for the Build and Usability Testing of a Mobile App for Use by Community-Dwelling Women.

Although surveys and apps are available for women to report urination and bladder symptoms, they do not include their decisions regarding toileting. Real-world factors can interfere with toileting decisions, which may then influence bladder health. This premise lacks data per want of a robust data collection tool. The Prevention of Lower Urinary Tract Symptoms (PLUS) research consortium engaged a transdisciplinary team to build and test WhereIGo, a mobile data collection app for Android and iOS. The design goal was a comprehensive reporting system for capturing environmental, sociocultural, and physical factors that influence women's decisions for toileting. Aims include having (1) an innovative feature for reporting physiologic urge sensation when "thinking about my bladder" and shortly before "I just peed," (2) real-time reporting along with short look-back opportunities, and (3) ease of use anywhere. The development team included a plain language specialist, a usability specialist, creative designers, programming experts, and PLUS scientific content experts. Both real-time and ecological momentary assessments were used to comprehensively capture influences on toileting decisions including perceived access to toileting, degree of busyness or stress or focus, beverage intake amount, urge degree, or a leakage event. The restriction on the maximal number of taps for any screen was six. PLUS consortium investigators did pilot-testing. Formal usability testing relied on the recruitment of community-dwelling women at four PLUS research sites. Women used the app for 2 consecutive days. Outcome measures were the system usability scale (SUS; 0-100 range) and the functional Mobile Application Rating Scale (1-5 range). These scales were embedded at the end of the app. The estimated a priori sample size needed, considering the SUS cut point score set at ≥74, was 40 women completing the study. Funding was provided by the National Institute of Diabetes and Digestive and Kidney Diseases since July 2015. The integrity of the build process was documented through multiple 5-minute videos presented to PLUS Consortium and through WhereIGo screenshots of the final product. Participants included 44 women, with 41 (93%) completing data collection. Participants ranged in age from 21 to 85 years, were predominantly non-Hispanic White (n=25, 57%), college-educated (n=25, 57%), and with incomes below US $75,000 (n=27, 62%). The SUS score was 78.0 (SE 1.7), which was higher than 75% of the 500 products tested by the SUS developers. The mean functional Mobile Application Rating Scale score was 4.4 (SE 0.08). The build and informal acceptability testing were completed in 2019, enrollment for formal usability testing completed by June 2020, and analysis was completed in 2022. WhereIGo is a novel app with good usability for women to report toileting decisions, urination, and fluid intake. Future research using the app could test the influence of real-time factors on bladder health. RR1-10.2196/54046.

Early insights into SLA with chatGPT: Navigating CS teachers and student perspectives in an opinion-based exploration

This study explores the integration of ChatGPT into Social Learning Analytics (SLA) to support programming education among computer science students at Mustapha Stambouli University, Mascara, Algeria. Utilizing a mixed-methods approach, the research combines quantitative surveys and qualitative analysis of recorded interactions of one example and interviews to examine the effectiveness, challenges, and perceptions of ChatGPT’s use in programming tasks across Arabic, French, and English. The study involved 57 students and five teachers, providing a comprehensive view of ChatGPT’s impact on learning experiences, engagement patterns, and programming performance. Results indicate that ChatGPT is frequently used as a supplementary tool, especially for programming-related queries, debugging, and last-minute assistance before deadlines. The tool’s adaptability to students’ needs, combined with its ease of use, enhances its perceived value in supporting independent learning. However, the limitations of the free version—such as restricted access, slower response times, and occasional inaccuracies—were frequently cited as barriers to consistent, effective use. Teachers acknowledged ChatGPT’s role in easing instructional burdens but emphasized the need for critical oversight to prevent over-reliance on AI-generated content. Ethical concerns regarding data privacy, academic integrity, and the quality of AI feedback were highlighted as key issues requiring attention. Interestingly, a significant portion of students expressed the belief that AI, including ChatGPT, could potentially replace human programmers in the near future, reflecting both optimism and concern about the evolving role of AI in the field. Despite this, educators maintained that while ChatGPT can augment programming education, human intuition, creativity, and contextual understanding remain irreplaceable. The study concludes that ChatGPT’s integration into SLA offers substantial opportunities to enhance educational support and enrich data on student learning behaviors. However, addressing accessibility issues, enhancing multilingual support, and mitigating ethical challenges are critical for maximizing the tool’s effectiveness. The findings underscore the importance of a balanced approach that leverages AI’s strengths while maintaining the essential role of human expertise in education and programming.

Challenges in Applying System Dynamics to Address Scoping and Estimating Problems

System dynamics (SD) is a fundamental model that facilitates research by defining and modeling a project’s scope and estimation. Scoping and estimation is a set of approaches that ensure engineering managers that a project’s accuracy and cost estimation are properly specified and mapped. Engineering management’s primary project planning responsibilities include project scoping and estimating. Insufficient project scoping and estimation criteria significantly fail the project plan. The study authors collaborated with five distinctive journal institutions to address project scoping and estimation challenges. The authors thoroughly analyzed one core research issue and five secondary research questions from each article. In terms of core issues, most project managers dislike SD modeling because of its complexity, especially if they lacked engineering, programming, mathematical analysis, or IT expertise. The first secondary question revealed that project managers’ awareness of scoping and estimating problems ranges from 78.3333% to 91.6666%. The secondary question revealed that 83.3333% to 90% of managers are familiar with SD, which can help resolve scoping and estimation issues in project management. The third secondary question showed that 83.3333% to 91.6666% of managers are aware of SD during the project’s scoping and estimating phases. The fourth secondary question found that 83.333% to 91.6666% of managers can use mathematical equations and programming for SD design during the scoping and estimating phases. The authors developed a novel SD scoping and estimating model since the last question indicated that there were no downloadable models from the five journals.

Evaluating the implementation of the mechanical engineering student skills competency

This study aims to evaluate the implementation of the Expertise Competency Test (UKK) for students of Vocational High Schools (SMK) in the Machining Engineering Expertise Program implemented by the First Party Professional Certification Agency (LSP P1) in Banten Province. The evaluation was conducted using the CIPP model (Context, Input, Process, Product, and Outcome). This study is useful for assessing the performance of LSP P1 in implementing the UKK and evaluating the impact of UKK graduates on their acceptance in the industrial world. The research was conducted in three vocational schools in Banten Province with the research subjects including the head of LSP P1, the head of the machining engineering expertise programme, and competency assessors. The research instruments used were questionnaires, observations, documents and interviews, which had been validated by expert judgement. The data analysis technique used was descriptive quantitative. The results showed that the implementation of UKK by LSP P1 in Banten vocational schools received the following evaluations: 1) Context aspect scored 3.36 (very good category), 2) Input aspect scored 3.47 (very good category), 3) Process aspect scored 3.36 (very good category), 4) Product aspect scored 3.26 (very good category), and 5) Outcome aspect scored 2.94 (good category). In conclusion, the implementation of the UKK by LSP P1 in the Machining Engineering Expertise Programme at SMK Banten Province has been running very well in most aspects, although there is room for improvement in the Outcome aspect related to the impact of graduates in the industrial world. The contribution of this research is to provide an overview of the effectiveness of expertise competency certification in SMKs as well as input to improve the quality of graduates who are better prepared to compete in the world of work.

Increasing creativity, production innovation and commercialization through the new teaching factory model based on life skills

This research aims to implement and develop a new teaching and learning factory model based on life skills in Vocational Secondary Education (SME) Creative and Production Crafts Expertise Program. This model is designed to support the needs of the Industrial Revolution 4.0 by integrating project-based learning through collaboration between the school curriculum and the industrial world. This research usesmixed methods, with a quasi-experimental design involving control and experimental groups using pre-test and post-test. The results showed that the new teaching and learning factory model was valid and reliable based on expert validation tests. The implementation of this model successfully increased students' creativity in product design, production, and commercialisation, and it developed social skills such as critical thinking, communication, collaboration, and interpersonal skills. In conclusion, the implementation of the life skills-based teaching factory model is effective in preparing students to face the challenges of the world of work, improving innovation skills, and supporting production processes that are relevant to industry needs. The contribution of this research is in the development of an innovative learning model that integrates the education curriculum with the world of work, thus producing graduates who are better prepared to face the challenges of the Industrial Revolution 4.0.

Characterizing teacher support of debugging with physical computing: Debugging pedagogies in practice

Objectives . Physical computing systems are increasingly being integrated into secondary school science and STEM instruction, yet little is known about how teachers, especially those with little background and experience in computing, help students during the inevitable debugging moments that arise. In this paper, we describe a framework, comprising two dimensions, for characterizing how teachers support students as they debug a physical computing system called the Data Sensor Hub (DaSH). The DaSH enables students to program sensors to measure, analyze, and visualize data as they engage in science inquiry activities. Participants . Five secondary school teachers implemented an inquiry-oriented instructional unit designed to introduce students to working with the DaSH as a tool for scientific inquiry. Study Method . Findings drew on video analysis of the teachers’ classroom implementations of the unit. A review of the data corpus led to the selection of 23 moments where the teachers supported an individual or small groups of students engaged in debugging. These moments were analyzed using a grounded perspective based on Interaction Analysis to characterize the teachers' varied interactional approaches. Findings . Our analysis revealed how teachers’ moves during debugging moments fell along two dimensions. The first dimension characterizes teachers’ positioning during the debugging interactions, ranging from a positioning for teacher understanding to a positioning for student understanding of the bug. The second dimension characterizes the inquiry orientation of the teachers’ questions and guidance, ranging from focusing on the debugging process to focusing on the product – or fixing the bug. Further, teachers’ moves often fell along different points on these dimensions in any given interaction given nuances in the instructional context. Conclusions . The framework offers a first step towards characterizing teachers’ debugging pedagogy as they support students during debugging moments. It also calls attention to how teachers do not necessarily need to be programming experts to effectively help students learn independent and generalizable debugging strategies. Further, it illustrates the variety of expertise that teachers can bring to debugging moments to support students learning to debug. Finally, the framework provides implications for the design of professional learning and supports for teachers as they increasingly are asked to support students in computing – and debugging – activities across a range of disciplines.

The Influence of Covid-19 on Industrial Work Practices of Building Engineering Students of SMKN 1 West Sumatra

<span lang="EN-US">This research was conducted to determine the magnitude of the influence of COVID-19 on the implementation of Industrial Work Practices for Building Engineering students of SMKN 1 West Sumatra. This research includes quantitative research of associative approaches. The population of this study was students of class XI Building Engineering, which numbered 50 students. Data collection is carried out using questionnaires. Questionnaires are used to measure COVID-19 variables and Industrial Work Practices. Instrument testing was carried out at SMKN 1 West Sumatra in class XI students of the Mechatronics Expertise Program, totaling 30 students. The data analysis techniques used are the prerequisite analysis tests, namely the normality test and linearity test, and the hypothesis test used is a simple regression analysis. The results of the research showed that there was a positive and significant influence of COVID-19 on the implementation of Industrial Work Practices for Building Engineering Students of SMKN 1 West Sumatra where the value of r xy was 0.499, the coefficient of determination (r2xy) was 0.249 and t count (3.771) > t table (1,677). Based on the coefficient of determination (r2xy), 0.249 means that 24.9% of students ' Industrial Work Practices are affected by COVID-19, while other factors influence the remaining 75.1%.</span>