Programming Experience Research Articles

Tutorial: Power Analyses for Interaction Effects in Cross-Sectional Regressions

Interaction analyses (also termed “moderation” analyses or “moderated multiple regression”) are a form of linear regression analysis designed to test whether the association between two variables changes when conditioned on a third variable. It can be challenging to perform a power analysis for interactions with existing software, particularly when variables are correlated and continuous. Moreover, although power is affected by main effects, their correlation, and variable reliability, it can be unclear how to incorporate these effects into a power analysis. The R package InteractionPoweR and associated Shiny apps allow researchers with minimal or no programming experience to perform analytic and simulation-based power analyses for interactions. At minimum, these analyses require the Pearson’s correlation between variables and sample size, and additional parameters, including reliability and the number of discrete levels that a variable takes (e.g., binary or Likert scale), can optionally be specified. In this tutorial, we demonstrate how to perform power analyses using our package and give examples of how power can be affected by main effects, correlations between main effects, reliability, and variable distributions. We also include a brief discussion of how researchers may select an appropriate interaction effect size when performing a power analysis.

Investigating Evolutionary Pressures on Protein Sequence: Applications to Drug Resistance in Flatworm Parasites

Abstract: The aim of This report is that it focuses on a particular flatworm called S mansoni and the disease it causes in humans and cattle, called schistosomiasis. This disease causes a million deaths annually across the globe. The drug for this disease is praziquantel, which is quite effective. Though lately, S mansoni is showing resistance towards this drug. In particular, The aim of this project is to find the regions showing evolutionary pressure in its protein structure and, hence, know how the protein structure mutates. The methods used in this project, amongst others, are SNAP, rasmol, clustal omega mainly. They are supported by using fundamentals from PDB, Biomart, blast. These methods are very helpful to biologists or people with less programming experience. This report will explore each method in detail before using them, and the steps needed to properly execute them are discussed as well. Along with the methods, this will use raw data in the form of protein sequence/ FASTA sequence from biomart. For the results, 12 different types of flatworms are chosen but narrowed down to 3-4, which closely resemble the structure of S mansoni. These flatworms are combined into a multiple sequence alignment. They are later compared using SNAP tool. Using the data from SNAP, the dn/ds ratio for each codon number is computed and the type of selection is determined. {dn/ds ratio and selection type are defined in further sections} For discussion and conclusion, the selection type, if positive, shows evolutionary pressure and the protein structure mutates and discusses its inference. It is shown visually through RASMOL tool. This tool helps in locating the binding sites where mutation takes place, so, focusing on those will help future researchers in finding a universal vaccine for this and some other viruses as well. The main finding of this experiment is the detection of regions of positive selection in the protein structure of the flatworm, using dn/ds. Also, the amino acid residues are explored as well. As with every experiment, this report covers the limitations of the process and the future works being carried on. The referencing style is APA Harvard, and the appendix section covers all the raw data used.

Game-Based Learning for Students’ Computational Thinking: A Meta-Analysis

Computational thinking (CT) education has drawn increasing attention from educators and researchers. This study conducted a meta-analysis of 27 empirical studies to examine the effectiveness of game-based learning (GBL) for fostering students’ CT. The effects of various factors on the learning process for acquiring CT were also examined. The results showed that (a) conducting GBL can foster students’ CT, and the overall effect was at the upper-middle level (Hedges’ g = .600, 95% CI [.465, .735], p < .001). (b) Furthermore, conducting GBL can improve students’ CT concepts (Hedges’ g = .916, 95% CI [.410, 1.423], p < .001), CT skills (Hedges’ g = .494, 95% CI [.389, .600], p < .001), and CT perspectives (Hedges’ g = .927, 95% CI [.039, 1.816], p < .05). (c) Additionally, game mode, teaching context, and participant characteristics have positive effects on CT. Based on the findings, it is suggested that using more unplugged games and video games, designing collaborative game activities, and tailoring approaches according to gender difference and programming experience can effectively promote CT. The results have significance for fostering students’ CT in GBL; it is further suggested that instruction processes be rationally designed.

Understanding In-Situ Programming for Smart Home Automation

Programming a smart home is an iterative process in which users configure and test the automation during the in-situ experience with IoT space. However, current end-user programming mechanisms are primarily preset configurations on GUI and fail to leverage in-situ behaviors and context. This paper proposed in-situ programming (ISP) as a novel programming paradigm for AIoT automation that extensively leverages users' natural in-situ interaction with the smart environment. We built a Wizard-of-Oz system and conducted a user-enactment study to explore users' behavior models in this paradigm. We identified a dynamic programming flow in which participants iteratively configure and confirm through query, control, edit, and test. We especially identified a novel method "snapshot" for automation configuration and a novel method "simulation" for automation testing, in which participants leverage ambient responses and in-situ interaction. Based on our findings, we proposed design spaces on dynamic programming flow, coherency and clarity of interface, and state and scene management to build an ideal in-situ programming experience.

Looking “Under the hood” of learning computer programming: the emotional and cognitive differences between novices and beginners

ABSTRACT Background and Context Current programming learning research often compares novices and experienced programmers, leaving early learning stages and emotional and cognitive states under-explored. Objective Our study investigates relationships between cognitive and emotional states and learning performance in early stage programming learners with varying expertise (12 novices and 11 beginners; N = 23). Method Measures such as electrodermal activity, automated facial emotion recognition, and pupil diameter were collected to study cognitive and emotional states during a programming task. We examined how these states correlated with performance and the influence of expertise on these correlations. Findings Multiple regression analysis on panel data (n = 690 observations) suggested fear negatively impacts programming performance, irrespective of expertise level. Meanwhile, programming expertise positively moderated cognitive effort applied to the task. Implications Our findings highlight the importance for educators to design early programming experiences that anticipate and reduce fear and cognitive overload, promoting better learning outcomes.

Using Visual Programming Games to Study Novice Programmers

Enabling programmers to write correct and efficient parallel code remains an important challenge, and the prevalence of on-chip accelerators exacerbates this challenge. Novice programmers, especially those in disciplines outside of Computer Science and Computer Engineering, need to be able to write code that exploits parallelism and heterogeneity, but the frameworks for writing parallel and heterogeneous programs expect expert knowledge and experience. More effort must be put into understanding how novice programmers solve parallel problems. Unfortunately, novice programmers are difficult to study because they are, by definition, novices. We have designed a visual programming language and game-based framework for studying how novice programmers solve parallel problems. This tool was used to conduct an initial study on 95 undergraduate students with little to no prior programming experience. 71% of all volunteer participants completed the study in 48 minutes on average. This study demonstrated that novice programmers could solve parallel problems, and this framework can be used to conduct more thorough studies of how novice programmers approach parallel code.

Abstract 79: The Impact of COVID-19 on HPV Immunization Programs in Countries With High Cervical Cancer Burdens: Global and National Perspectives

Abstract Purpose: The COVID-19 pandemic has had a considerable impact on HPV immunization efforts within low-and-middle-income countries (LMICs). This is a cause for concern as LMICs account for the global burden of deaths attributable to cervical cancer; yet the direct and indirect impacts of COVID-19 on HPV immunization programs have not been well described in the literature. Our study explored COVID-19 specific challenges in HPV vaccine introduction and scale-up in LMICs through interviewing stakeholders with context specific knowledge in cervical cancer prevention efforts within LMICs. Methods: This qualitative study involved a stakeholder analysis coupled with semi-structured in-depth interviews with n=31 global stakeholders (i.e. academic institutions, global immunization partners) and national stakeholders (i.e. ministry of health officials, non-governmental organizations). Purposive sampling was used to select participants with content knowledge and experience in HPV vaccine programming. Data was analyzed using qualitative content analysis. Inductive codes were categorized into broader themes describing challenges HPV vaccination programs faced during the COVID-19 pandemic. Results: Five broad themes emerged from the analysis: HPV vaccine prioritization, funding considerations, supply chain challenges, HPV vaccine delivery platforms and catch-up dosing, and vaccine hesitancy. The amplified focus of COVID-19 vaccines resulted in decreased prioritization and funding towards HPV vaccination efforts. In certain settings, HPV vaccines were found to be the least prioritized antigen. Decreased demand, coupled with global supply chain shortages due to COVID-19, resulted in sizeable challenges for HPV vaccine introduction and scale-up. Nearly all stakeholders described how anti-COVID-19 vaccine sentiments magnified hesitancy towards HPV vaccines. Particularly revealing, were delivery challenges as a consequence of school closures. This resulted in stakeholders’ acknowledgment of the need to establish alternative delivery platforms and strategies for catch-up dosing. Conclusion: Understanding how emerging infectious disease outbreaks impact HPV vaccination efforts in countries with high cervical cancer burdens is critical. Increasing HPV vaccine prioritization within countries, developing strategies to overcome existing shortfalls, along with addressing vaccine hesitancy will be key in generating momentum towards HPV immunization programs. Citation Format: Dominique Guillaume, Due-e-Nayab Waheed, Mieke Schlieff1, Kirthini Muralidharan, Alex Vorsters, Rupali Limaye. The Impact of COVID-19 on HPV Immunization Programs in Countries With High Cervical Cancer Burdens: Global and National Perspectives [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 79.

A Comprehensive, Affordable, Open-Source Hardware-Software Solution for Flexible Implementation of Complex Behaviors in Head-Fixed Mice.

Experiments that take advantage of head-fixed behavioral tasks have been a staple of systems neuroscience research for half a century. More recently, rodents came to the forefront of these efforts, primarily because of the rich experimental possibilities afforded by modern genetic tools. There is, however, a considerable barrier to entering this field, requiring expertise in engineering, hardware and software development, and significant time and financial commitment. Here, we present a comprehensive, open-source hardware and software solution to implement a head-fixed environment for rodent behaviors (HERBs). Our solution provides access to three frequently used experimental frameworks (two-alternative forced choice, Go-NoGo, or passive sensory stimulus presentation) in a single package. The required hardware can be built at a relatively low cost compared with commercially available solutions, from off-the-shelf components. Our graphical user interface-based software provides great experimental flexibility and requires no programming experience for either installation or use. Furthermore, an HERBs takes advantage of motorized components that allow the precise, temporal separation of behavioral phases (stimulus presentation, delays, response window and reward). Overall, we present a solution that will allow laboratories to join the growing community of systems neuroscience research at a substantially lower cost of entry.

MATLAB GUI Application for Processing the Remote Sensing Images

Spatial Data Analyst is important for image processing and accuracy assessment. To adjust a better image, we need to do some processes and steps in order to achieve this goal. MATLAB has been commonly used in image processing. MATLAB provides a Graphical Use Interface (GUI) designer "Guide" that is easy to use and does not require any programming experience. In this research, accuracy assessment, and image processing tools are built using MATLAB GUI. This straightforward GUI will also contain a Correlation Coefficient and Composite Band part. GUI is easier and more convenient for running the MATLAB functions and getting the results.

Spatial- and Fourier-domain ptychography for high-throughput bio-imaging.

First envisioned for determining crystalline structures, ptychography has become a useful imaging tool for microscopists. However, ptychography remains underused by biomedical researchers due to its limited resolution and throughput in the visible light regime. Recent developments of spatial- and Fourier-domain ptychography have successfully addressed these issues and now offer the potential for high-resolution, high-throughput optical imaging with minimal hardware modifications to existing microscopy setups, often providing an excellent trade-off between resolution and field of view inherent to conventional imaging systems, giving biomedical researchers the best of both worlds. Here, we provide extensive information to enable the implementation of ptychography by biomedical researchers in the visible light regime. We first discuss the intrinsic connections between spatial-domain coded ptychography and Fourier ptychography. A step-by-step guide then provides the user instructions for developing both systems with practical examples. In the spatial-domain implementation, we explain how a large-scale, high-performance blood-cell lens can be made at negligible expense. In the Fourier-domain implementation, we explain how adding a low-cost light source to a regular microscope can improve the resolution beyond the limit of the objective lens. The turnkey operation of these setups is suitable for use by professional research laboratories, as well as citizen scientists. Users with basic experience in optics and programming can build the setups within a week. The do-it-yourself nature of the setups also allows these procedures to be implemented in laboratory courses related to Fourier optics, biomedical instrumentation, digital image processing, robotics and capstone projects.

Increasing Women's Participation in CS at Large Public Universities: Issues and Insights

Despite rapidly growing interest in Computer Science (CS), CS has the second lowest participation rate for women of all science and engineering degrees, according to a recent report by the US National Science Board [16]. The reasons for women's underrepresentation and their experiences may differ from university to university. Universities are very different in terms of types of students, student profiles, students' socio-economic status, race, number of students, admission requirements, and resources. For example, some universities can create gender balance in introductory courses through selective admission. However, typically, admission practices in large public universities are not able to achieve gender balance for CS majors. As a result, there are lower percentages of women in the CS major in large public universities than in selective schools. In addition, large public universities tend to have a larger number of CS students when compared to private universities. To understand women students' experiences and struggles in computer science at a large public university in the United States and to find ways to intervene in favor of gender equity in computer science, we conducted in-depth interviews with women students in introductory programming courses. In this article, we present women students' experiences, struggles, expectations and offer potential interventions based on the findings to encourage women to consider CS as their major and stay in the CS major. Our interview data shows there is a persistent effect of lack of prior programming experience and gender bias that leads to a negative experience for women students in introductory CS courses. This article presents the challenges faced by women in CS and provides their recommendations for attracting and retaining women students in CS at large public universities.

Studying Developer Eye Movements to Measure Cognitive Workload and Visual Effort for Expertise Assessment

Eye movement data provides valuable insights that help test hypotheses about a software developer's comprehension process. The pupillary response is successfully used to assess mental processing effort and attentional focus. Relatively little is known about the impact of expertise level in cognitive effort during programming tasks. This paper presents a quantitative analysis that compares the eye movements of 207 experts and novices collected while solving program comprehension tasks. The goal is to examine changes of developers' eye movement metrics in accordance with their expertise. The results indicate significant increase in pupil size with the novice group compared to the experts, explaining higher cognitive effort for novices. Novices also tend to have a significant number of fixations and higher gaze time compared to experts when they comprehend code. Moreover, a correlation study found that programming experience is still a powerful indicator when explaining expertise in this eye-tracking dataset among other expertise variables.

Understanding Latinx Farmers in Pennsylvania to Meet Their Needs for Non-Formal Education

Latinx/Hispanic farmers are an underrepresented group with the largest number of farm operators in the US. However, agricultural educators have encountered challenges in identifying Latinx farmers in Pennsylvania and consequently, in meeting their needs. This study aims to contribute to improved agricultural programming by offering an exploratory overview of Latinx farmers in the Commonwealth, and to bring to light the experiences of Extension educators when working with Latinxs in the agricultural context. Qualitative research interviews were conducted with seventeen Latinxs at different stages of their farming journey, and with twelve educators with experience in agriculture programming and outreach to Latinxs. Latinx farmers discussed the characteristics, goals, challenges, and solutions of their farming operations. Educators discussed their motivations to serve Latinx farmers, the challenges they face in meeting their needs, and the implementation of targeted strategies for successful programming. Both groups discussed challenges they believe Latinx farmers face due to their ethnicity and provided recommendations to better serve the Latinx farming population. Based on the findings, agricultural educators could support Latinx farmers by developing statewide programming, including programming in Spanish, having employers allocate a percentage of educators’ responsibilities to connecting with this audience, creating a Latinx farmers’ network, partnering with organizations connected with Latinx farmers, participating in cultural competency training, and promoting stories of success. Our methodology and findings can be adapted to educators in other locations working with minority populations.

To What Extent the ARIMNet Transnational Programme Has Contributed to the Implementation of the Euro-Mediterranean Research and Innovation Area?

The Mediterranean countries face common challenges in adapting their agro-systems to climate change and demographic threats. It is on this basis that ARIMNet (Agricultural Research in the Mediterranean Network) programme was implemented by 15 Euro-Mediterranean partners to foster agricultural research and innovation with the support of two successive grants from the European Commission. Our work aims to evaluate the impact of this collaborative work of 10 years over the period (2008–2017) on the implementation of a EURO-MED Research and Innovation Area. Considering ARIMNet as the first regional experience of joint programming and co-funding, it has allowed the funding of 46 research projects in the Mediterranean region with a budget of €25 M. The carried survey with coordinators shows that ARIMNet activities have a positive effect on regional networking, mobility, scientific production, innovation and the construction of a regional common vision regarding societal challenges regarding food security and sustainable agriculture. This transnational programme has consolidated collaboration between EU-MED policymakers and impacted as well national research governance in South Mediterranean countries, as stated by the questionnaire sent to partners from these countries. The ARIMNet experience inspired the current PRIMA initiative (Partnership for Research and Innovation in the Mediterranean Area) which represents a step ahead for the reinforcement of the scientific Euro-Mediterranean collaboration in agriculture. The lessons learned from ARIMNet can benefit the Mediterranean and other regions in developing future common science and technology-based approaches to achieve Sustainable Development Goals.

A Flexible and General-Purpose Platform for Heterogeneous Computing

In the big data era, processing large amounts of data imposes several challenges, mainly in terms of performance. Complex operations in data science, such as deep learning, large-scale simulations, and visualization applications, can consume a significant amount of computing time. Heterogeneous computing is an attractive alternative for algorithm acceleration, using not one but several different kinds of computing devices (CPUs, GPUs, or FPGAs) simultaneously. Accelerating an algorithm for a specific device under a specific framework, i.e., CUDA/GPU, provides a solution with the highest possible performance at the cost of a loss in generality and requires an experienced programmer. On the contrary, heterogeneous computing allows one to hide the details pertaining to the simultaneous use of different technologies in order to accelerate computation. However, effective heterogeneous computing implementation still requires mastering the underlying design flow. Aiming to fill this gap, in this paper we present a heterogeneous computing platform (HCP). Regarding its main features, this platform allows non-experts in heterogeneous computing to deploy, run, and evaluate high-computational-demand algorithms following a semi-automatic design flow. Given the implementation of an algorithm in C with minimal format requirements, the platform automatically generates the parallel code using a code analyzer, which is adapted to target a set of available computing devices. Thus, while an experienced heterogeneous computing programmer is not required, the process can run over the available computing devices on the platform as it is not an ad hoc solution for a specific computing device. The proposed HCP relies on the OpenCL specification for interoperability and generality. The platform was validated and evaluated in terms of generality and efficiency through a set of experiments using the algorithms of the Polybench/C suite (version 3.2) as the input. Different configurations for the platform were used, considering CPUs only, GPUs only, and a combination of both. The results revealed that the proposed HCP was able to achieve accelerations of up to 270× for specific classes of algorithms, i.e., parallel-friendly algorithms, while its use required almost no expertise in either OpenCL or heterogeneous computing from the programmer/end-user.

RWCVP: a companion R package for the World Checklist of Vascular Plants.

The World Checklist of Vascular Plants (WCVP) is an extremely valuable resource that is being used to address many fundamental and applied questions in plant science, conservation, ecology and evolution. However, databases of this size require data manipulation skills that pose a barrier to many potential users. Here, we present rWCVP, an open-source R package that aims to facilitate the use of the WCVP by providing clear, intuitive functions to execute many common tasks. These functions include taxonomic name reconciliation, geospatial integration, mapping and generation of multiple different summaries of the WCVP in both data and report format. We have included extensive documentation and tutorials, providing step-by-step guides that are accessible even to users with minimal programming experience. rWCVP is available on cran and GitHub.

GatorByte – An Internet of Things-Based Low-Cost, Compact, and Real-Time Water Resource Monitoring Buoy

Conventional water resource monitoring systems are usually expensive, have a low-temporal resolution, and lack spatial dimension entirely. These systems are typically available as stations or handheld devices. Pinpointing sources of pollution using these systems can be difficult. This project involves developing a high-resolution free-flowing monitoring buoy that records spatiotemporal water-quality data in flowing stream environments. The system is highly customizable, and even users with limited experience in programming or electronics can tailor GatorByte to their needs. The platform includes a data logger, a cloud-based server, and visualization tools. The data logger uses low-cost sensors, electronic peripherals, a 3D-printed enclosure, and printed circuit boards, with a total cost per unit under $1,000 USD. The data logger uses an NB-IoT-capable Arduino for real-time reporting and visualizing sensor data. The GatorByte records physiochemical water metrics – pH, temperature, dissolved oxygen, electroconductivity, and the current location of the buoy using a GPS module. The data logger also includes micro-SD storage and a Bluetooth module for on-field diagnostics. Using the GatorByte buoy, the collection of variations in water quality data in temporal as well as spatial dimensions can be achieved cost-effectively and reliably, enabling quick detection and resolution of pollution events.

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

Due to the everchanging and evergrowing nature of programming technologies, the gap between the programming industry’s needs and the educational capabilities of both formal and informal educational environments has never been wider. However, the need to learn computer programming has never been greater, regardless of the motivation behind it. The number of programming concepts to be taught is increasing over time, while the amount of time available for education and training usually remains the same. The objective of this research was to analyze the source codes used in many educational systems to teach fundamental programming concepts to learners, regardless of their prior experience in programming. A total of 25 repositories containing 3882 Python modules were collected for the analysis. Through self-organization of the collected content, we obtained very compelling results about code structure, distribution, and differences. Based on those results, we concluded that Self-Organizing Maps are a powerful tool for both content and knowledge management, because they can highlight problems in the curriculum’s density as well as transparently indicate which programming concepts it has successfully observed and learned to recognize. Based on the level of transparency exhibited by Self-Organizing Maps, it is safe to say that they could be used in future research to enhance both human and machine learning of computer programming. By achieving this level of transparency, such an Artificial Intelligence system would be able to assist in overall computer programming education by communicating what should be taught, what needs to be learned, and what is known.

An Experimental Study of the Impact of Co-Curricular First-Year Experience Programming

The paper uses an experimental approach to investigate whether co-curricular first-year experience programming can have a positive impact on student success related attitudes, skills, and behaviors for first-year university students. We argue that co-curricular first-year experience training in first-year seminars are comparable to stand-alone first year seminars. Using an experimental study design, we found that students who receive a co-curricular first year feel they have more success in understanding the course material, academic performance, managing time, working in groups, and relating to their professors, compared to their counterparts in the control group. Interestingly, we also found that these students achieved a higher level of academic performance during the semester when learning transitioned from in person to online learning. These results suggest that co-curricular training not only helps students develop attitudes, skills, and behaviors associated with student success, but also helps students to work more effectively in online learning environments.

Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey

Objectives: Applications of artificial intelligence (AI) have the potential to improve aspects of healthcare. However, studies have shown that healthcare AI algorithms also have the potential to perpetuate existing inequities...