Prior Programming Experience Research Articles

Utilising Blended Learning for Large Classes to Deliver an Introductory Programming Course

Higher education institutions are constantly pushed to increase the student intake and produce industry-ready graduates. The pressure generally arises from the governing bodies, originating from socio-political factors. It is particularly evident in the field of Information Communication Technology (ICT) where there is a growing need for quality graduates. Undergraduate programmes in the field of ICT generally offer introductory computer programming courses that are mandatory at early stages of the degree programme. Consequently, the number of students registering for such courses can increase drastically. The students are coming from diversified backgrounds. While some students may have prior experience in computer programming, a larger majority may not have any prior knowledge or experience. Increased class size meets the constraint of the limited availability of physical classroom space and would require the class to be separated into smaller groups for face-to-face activities. Large classes are also challenged with the increased man-power requirement in terms of lecturers, instructors, and teaching assistants. Maintaining student engagement and interactivity becomes increasingly difficult in a large class. Grading of assessments becomes time-consuming and needs and requires increased manpower. Based on the observations and learning from an ongoing introductory programming course, this study evaluates the procedure for overcoming the challenges faced by the teaching staff, especially with a class size exceeding 1000. To cover the theoretical components, recorded video lectures are provided. The class is divided into groups consisting of approximately 100 students and live, online, interactive group discussions are conducted. The live, online smaller group discussions allow students to clarify doubts regarding the content. This also allows the distribution of the limited teaching and assistant staff. Students who need further assistance are encouraged to meet the teaching assistants physically. Thus, the workload is distributed effectively, and it helps ensure the dedicated support and attention is provided to the students. Auto graded programming assignments are effectively employed for formative and summative assessments, which reduces the grading workload significantly. The findings of this study provide insights on course design to effectively deliver the content and conduct assessments overcoming the challenges presented by increased class size.

Computational Thinking Integrated in School Subjects – A Cross-Case Analysis of Students’ Experiences

The integration of computational thinking (CT) into K-12 education offers substantial potential to improve digital literacy and deepen students’ understanding across various subject areas. By embedding computational procedures and solution-oriented approaches into traditional curricula, students can develop essential skills that are critical for thriving in an increasingly digital world. However, the effective integration of CT into subject-specific learning poses challenges for educators, largely due to a lack of training and experience in implementing interdisciplinary instructional strategies. This study explores the impact of integrating CT into K-12 education through diverse instructional approaches, including modelling, storytelling, and unplugged activities. The research examines how students' subject knowledge, CT concepts, and practices evolved under each CT integration practice, as well as their acceptance toward CT integrated lessons. The study was conducted in different subjects, including language arts, biology, and physics, and at different grade levels. The technology acceptance model was used as a theoretical framework to understand students' adoption towards the different integration practices. The study revealed that different CT integration practices yield varying impacts on students' attitudes, particularly in relation to their prior programming experiences. Namely, primary school students with prior programming experience display a higher behavioural intention due to a lack of early biases toward programming. Conversely, secondary school students, less familiar with interdisciplinary approaches, initially show less enthusiasm for future engagement. These insights underscore the need for tailored instructional strategies that consider students' prior experiences and the broader goal of preparing them to contribute to a technologically driven society.

Developing and validating an artificial intelligent empowerment instrument: evaluating the impact of an artificial intelligent literacy programme for secondary school and university students

Artificial intelligence (AI) is rapidly transforming various sectors of society, requiring a new form of literacy: AI literacy. This study validated a new instrument designed to measure students’ AI empowerment conceptualised as consisting of four components: impact, self-efficacy in AI, creative self-efficacy in AI, and meaningfulness. Confirmatory factor analysis was used to validate the proposed components of the AI empowerment instrument. The sample comprised 224 secondary school and university students who completed an 18-hour AI literacy programme. The results showed that the students’ AI empowerment was significantly increased by the AI literacy programme. Specifically, the AI literacy programme was found to narrow the gender gap in AI empowerment. Furthermore, the results highlighted that prior programming experience did not significantly affect AI empowerment, indicating that AI literacy can be achieved regardless of programming experience. This study provides a theoretical framework for understanding and quantifying the extent to which individuals feel empowered after engaging with AI activities for its conceptual understanding. It provides educators with a tool to measure students’ understanding and confidence in their AI abilities. The study also suggests directions for future research.

Learning game design while playing games

This course investigated game design competencies of graduate students as they engaged in gameplay and learned about game-related concepts, including gamified approaches, game-based learning (GBL), design thinking, maker technologies, and game designs. Students were introduced to the week's topic beforehand and collaborated on design projects during class sessions. Students played a different game each week, focusing mainly on game mechanics. Through the utilization of foundational readings, video tutorials, discussions, assignments, and guidance from the instructor and a guest speaker, students developed a comprehensive understanding of game design principles. This understanding ultimately led them to design both board and video games. No prior programming or game design experience was necessary.

Quantification of collective signalling in time-lapse microscopy images.

Live-cell imaging of fluorescent biosensors has demonstrated that space-time correlations in signalling of cell collectives play an important organisational role in morphogenesis, wound healing, regeneration, and maintaining epithelial homeostasis. Here, we demonstrate how to quantify one such phenomenon, namely apoptosis-induced ERK activity waves in the MCF10A epithelium. We present a protocol that starts from raw time-lapse fluorescence microscopy images and, through a sequence of image manipulations, ends with ARCOS, our computational method to detect and quantify collective signalling. We also describe the same workflow in the interactive napari image viewer to quantify collective phenomena for users without prior programming experience. Our approach can be applied to space-time correlations in cells, cell collectives, or communities of multicellular organisms, in 2D and 3D geometries.

Abstract 7430: Development and demonstration of a user-friendly application to explore single-cell atlas for prostate cancer (PCa) and analyze newly generated single-cell and spatial PCa datasets

Abstract Background: Single-cell atlases provide a high-resolution and tissue-specific depiction of cellular state at the transcriptomic level. Newer technologies like spatial transcriptomics, MALDI, and long-read sequencing introduce orthogonal modalities to single-cell data, making the analyses of large atlases even more complex. RShiny package in R is used to develop user-friendly GUI applications that could enable users to use analysis tools in an interactive manner, thus minimizing the requirement of prior programming experience. Methods: Publicly available scRNA-seq datasets were collected for a wide range of prostate-related pathologies. The datasets were evaluated for their quality based on cell count and availability of the appropriate markers in each compartment. Finally, 11 datasets out of 16 datasets were selected for building integrated datasets. These datasets were filtered for cell types with <10% mitochondrial RNA, and cells with <500 genes were removed from the datasets. The datasets were then clustered to evaluate the effects of cell cycle, dissociation-induced stress, and the presence of doublets. The quality-controlled datasets were batch-corrected using the Harmony package and the harmonized principal components were used in Seurat for clustering the integrated data. Tools used for the analysis of single-cell datasets, beginning from raw counts, have been developed in R with a focus on modularity. These tools are packaged into a simple and intuitive RShiny application which follows the workflow shown in Figure 1a. Results: The atlas obtained include samples from healthy prostate, benign prostate hyperplasia, primary prostate tumor, ICC/IDC, castration-resistant prostate cancer (CRPC), and Neuroendocrine prostate cancer (NEPC). The dataset includes 72 patients and ~320k cells with a median number of ~1600 genes expressed. We were able to distinctly identify Luminal, Hillock, Club, Basal, Neuroendocrine, Endothelial, Fibroblast, Smooth Muscle, Myeloid, T, B, NK, Mast, and Plasma cells in the integrated along with some unknown cellular subtypes in the epithelial compartment. The integrated data was manually annotated using cell type-specific markers. The single-cell atlas obtained was weaved with the workflow tools into an RShiny app. Conclusions: Besides being a useful reference for PCa, the atlas would find extensive use in understanding the transcriptomic landscape of PCa biology. The integrated dataset will also prove crucial in developing bulk RNA sequencing deconvolution and single-cell imputation algorithms. Finally, we aim to provide a user-friendly interface to the prostate cancer community for using the single-cell PCa atlas and analysis tools with minimal computational requirements. Citation Format: Shivang Sharma, Eugene Shenderov. Development and demonstration of a user-friendly application to explore single-cell atlas for prostate cancer (PCa) and analyze newly generated single-cell and spatial PCa datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7430.

Using students’ cognitive, affective, and demographic characteristics to predict their understanding of computational thinking concepts: a machine learning-based approach

ABSTRACT Logistic regression models have traditionally been used to identify the factors contributing to students’ conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected cognitive, affective, and demographic data from 4,142 primary students as features to predict their understanding of computational thinking (CT) concepts. We identified and used five popularly used machine learning models. All five machine learning models outperformed the logistic regression model, with the extreme gradient boosting (XGBoost) model achieving the highest predictive accuracy. We used these features and the K-means algorithm of the unsupervised clustering technique to identify four optimal clusters of students. By comparing the representative students from each of the four clusters, selected by the t-distributed stochastic neighbour embedding algorithm, we found that each cluster had its characteristics. For example, one cluster consisted of students who outperformed students in the other three clusters in mathematics, scored highest in prior experience in programming, and used computers and the Internet most frequently. By identifying the characteristics of these clusters, pedagogical design, and resource support can be proposed to support students’ learning of CT concepts.

Don’t say “modal analysis:” A backdoor introduction to vibrations via programming and numerical methods

As an undergraduate degree requirement, The Walker Department of Mechanical Engineering at the University of Texas at Austin teaches a lower-division, introductory course on computer programming and numerical methods. The enrollment consists primarily of second-year mechanical engineering (ME) students, approximately half of whom enter the course with no prior programming experience. Due to its relatively early position in the ME curriculum, the course presents the unique challenge of teaching students to apply numerical methods to ME-relevant problems, with little background about the physics involved or the contexts in which they are likely to occur in practice. This talk presents an end-of-term project in which students use two numerical methods discussed earlier in the course, singular value decomposition and Runge-Kutta methods, to construct, simulate, and benchmark a reduced-order, dynamic model of a vibrating guitar string. In addition to reinforcing course outcomes associated with programming, mathematics, and data visualization, this project provides an introduction to acoustics, vibration, and modal analysis at a level that belies its position in the overall curriculum.

African American Students’ Academic and Web Programming Self-Efficacy, Learning Performance, and Perceptions towards Computer Programming in Web Design Courses

Computer programming has been included in computer literacy education in many countries in the last decade. This study examined the effects of gender and the prior programming experience of computer programming on academic and web programming self-efficacy and learning performance in the web design course among African American students, as well as their perceptions towards computer programming. This study’s 14-week web design course taught African American students multiple web programming languages, including HTML, CSS, and JavaScript, in order. A one-group pretest–posttest design was adopted in the experiment. The quantitative method was primarily used in data analysis. This study revealed that African American students’ academic and web programming self-efficacy significantly increased after the web design course. Most of the African American students’ perceptions of computer programming became positive after attending the web design course. This study also found that male African American students had a significantly higher level of web programming self-efficacy than female students before the web design course. Interestingly, this difference disappeared after the course. Additionally, both gender and prior experience in computer programming did not significantly affect students’ learning performance in the web design course. The findings of this study not only contribute to the understanding of the feasibility of teaching multiple programming languages in web programming courses for African American students, they also provide evidence of the positive influence of web programming on African American students’ perceptions of computer programming.

SENTIMEN ANALISIS CHATGPT DENGAN ALGORITMA NAÏVE BAYES DAN OPTIMASI PSO

Abstract— ChatGPT which is an OpenAI technology that responds to conversations between humans and machines. enabling users of all ages and backgrounds to communicate naturally in multiple languages ​​without having prior knowledge or experience in programming or the computer world. However, a technology will always be at odds and has flaws on the human side, various assumptions about chatGPT are formed from many sides, such as in the world of education, chatGPT creates parallels for teachers and lecturers. When giving assignments, students/students can use chatGPT as material in answering assignments from teachers/lecturers. And that results in students/students not carefully reading the answers to these assignments, if that continues to happen, students/students will find it too easy to get something and then will lose interest in solving problems with their own efforts. This article aims to analyze sentiment analysis whose data is taken from Twitter using the keyword "CahtGPT OpenAI". With 2,000 data calculated using the naive Bayes algorithm and optimized using PSO, it is found that sentiment analysis for chatGPT itself has an accuracy of 69.23% with a positive class of 0.503 and a negative of 0.497 and obtains an AUC curve value of 0.68 +/- 0.55..

Engineering Students’ Experiences of Assessment in Introductory Computer Science Courses

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This study evaluates the generalizability of previously identified perceptions among engineering students of assessments in introductory programming (CS1). The students’ perceptions of their instructors’ and teaching assistants’ (TAs) roles in these assessments are also studied, and differences based on prior programming experience, gender, and course explored. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> Basic programming skills are desirable also for students who are not majoring in computer science (CS). Students’ experience of assessments has not been fully explored. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Questions:</i> 1) How do engineering students experience the assessment (lab assignments, midterm exam, and project) in their CS1 courses? 2) What are the students’ perceptions of the TAs and instructors in relation to these assessment situations? 3) What significant differences can be found based on students’ prior experience in programming, gender, and course? <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> Previously identified themes from an interview study worked as a framework for the formulation of 25 statements used in a survey among 137 students in six CS1 courses (second part of a mixed-method study). Descriptive statistics, Mann–Whitney U Test, and Kruskal–Wallis tests with Bonferroni corrections, were used to analyze the data. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> Laboratory assignments were experienced as an opportunity to learn while the exams were viewed as predictable. The projects were perceived as authentic, although varying in difficulty, and as a huge leap from the lab assignments. Students perceived the instructors to put their touch on the course but viewed their TAs as carrying out the assessments, and experienced variations between TAs. Female students experienced these variations to a larger extent and perceived received feedback as less useful.

Automatic detection of spatio-temporal signaling patterns in cell collectives.

Increasing experimental evidence points to the physiological importance of space-time correlations in signaling of cell collectives. From wound healing to epithelial homeostasis to morphogenesis, coordinated activation of biomolecules between cells allows the collectives to perform more complex tasks and to better tackle environmental challenges. To capture this information exchange and to advance new theories of emergent phenomena, we created ARCOS, a computational method to detect and quantify collective signaling. We demonstrate ARCOS on cell and organism collectives with space-time correlations on different scales in 2D and 3D. We made a new observation that oncogenic mutations in the MAPK/ERK and PIK3CA/Akt pathways of MCF10A epithelial cells hyperstimulate intercellular ERK activity waves that are largely dependent on matrix metalloproteinase intercellular signaling. ARCOS is open-source and available as R and Python packages. It also includes a plugin for the napari image viewer to interactively quantify collective phenomena without prior programming experience.

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.

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.

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.

CS1: Intrinsic Motivation, Self-Efficacy, and Effort

This research investigates university students’ success in their first programming course (CS1) in relation to their motivation, mathematical ability, programming self-efficacy, and initial goal setting. To our knowledge, these constructs have not been measured in a single study before in the Finnish context. The selection of the constructs is in line with the statistical model that predicts student performance (“PreSS”) (Quille and Bergin, 2018). The constructs are compared with various demographic and background variables, such as study major, prior programming experience, and average weekly working hours. Some of the main results of this study are as follows: (1) students generally entered with a high interest in programming and high motivation, but these factors did not increase during the course, i.e., interest in programming did not increase. (2) Having prior experience yielded higher initial programming self-efficacy, grade expectations, and spending less time on tasks, but not better grades (although worse neither). While these results can be seen as preliminary (and alarming in some parts), they give rise to future research for investigating possible expectation–performance gaps in CS1 and later CS studies. As our dataset accumulates, we also hope to be able to construct a valid success prediction model.

Student and teacher co-agency when combining CT with arts and design in a cross-curricular project

The technological development has raised awareness for the importance of digital competence and computational thinking (CT) to understand the digital world and has resulted in revised curricula in many countries. In Finland, a new curriculum for grades 1–9 came into force in 2016 introducing digital competence (including programming) to be integrated in other subjects. Most teachers lack prior experience in programming and there is a need for suitable instructional models. This article presents a cross-curricular teaching sequence and the results from a case study conducted in four Finnish schools. Students in grades 4–6 collaboratively worked on a project combining arts, design and CT with other subjects. The results show that students demonstrated several CT abilities while working on their projects, in particular creativity, tinkering and debugging. The findings also indicate that teachers and students learned together (co-agency) and suggest that models like the teaching sequence can help and encourage teachers to integrate programming and CT in a cross-curricular manner. Still, the teachers’ knowledge, ambition level and understanding of the task at hand, as well as the organizational support appear to play a notable role when planning and carrying out projects of this kind. While CT is commonly seen as developed through programming, the teaching sequence seems to have fostered CT abilities through the project as a whole, with programming playing the role of a tool or a glue depending on the time available, and the students’ skill and ambition level.

Adopting distributed pair programming as an effective team learning activity: a systematic review.

As online learning has become an inevitable trend in the post-peak era of the COVID-19 pandemic, distributed pair programming (DPP) is gaining momentum in both education and industry. DDP serves as a collaborative programming approach and also benefits the development of computational thinking, a fundamental skill in today's world. This study conducted a systematic review of studies on DPP published after 2010 to understand the themes and factors that impact the team effectiveness of DPP and thus inform future research and practices on how to better leverage this approach for teaching and learning. The results showed that individual characteristics attracted major investigations in the selected 23 studies, including prior programming experience, actual skill, perceived skill, gender, personality, time management, confidence, and self-esteem, with pair compatibility identified as a critical team design factor that significantly affects programmers' satisfaction. Although the feel-good factor in the team process was investigated, no significant impact was found. Under the team environment theme, we compared different opinions on the orientation (e.g., scripted roles) and the use of technology (e.g., integrated development environment tools). Future research should investigate how task structure influences team effectiveness of DPP and relates to computational thinking education. Additionally, because most studies were conducted in higher education contexts, more research in primary and secondary educational contexts is also needed.

Student satisfaction with R vs. Excel in Data Mining and Business Analytics: A Herzberg's motivation‐hygiene theory perspective

AbstractApplying Herzberg's motivation‐hygiene theory, we studied the determinants of student satisfaction in using R in a Decision Support Systems course that previously used Excel to teach Data Mining and Business Analytics (DMBA). The course is a degree requirement, and prior programming experience is not a prerequisite. We hypothesized that motivators for student satisfaction with R would include: (i) an intrinsic interest in DMBA, (ii) a perception that R is a better tool than Excel for DMBA, and (iii) a favorable view of R as a facilitator of career advancement. We postulated that the hygiene factor was the compulsory course requirement to learn R, a new and challenging language to students in this study. Data from 120 students, analyzed using Principal Component Analysis (PCA) and Generalized Additive Modeling (GAM), showed that motivators were positively related to satisfaction while hygiene factors were neutral. Students showed willingness to take on a harder challenge in exchange for intrinsic and future career benefits. The model can be useful in curriculum design or career advisement to increase student satisfaction in learning new software skills while meeting market demands.

WebART: Web-Based Augmented Reality Learning Resources Authoring Tool and Its User Experience Study Among Teachers

Augmented reality (AR) has been incorporated into the educational context for years. Numerous studies have demonstrated the effectiveness of AR in different disciplines, learning styles, and learning environments. However, few studies have focused on the problem of generating AR applications in authentic educational contexts. In this study, an AR learning resource authoring tool called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WebART</i> was designed and developed to help teachers with no prior programming experience create and share AR learning resources. This study collected feedback and opinions on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WebART</i> from 51 K-12 teachers through questionnaires and interviews. Multiple linear regression was used to measure the relationships between teachers’ intentions to use technology and the user experience of the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WebART</i> tool. The questionnaire results revealed that multiple variables in teachers’ intentions are predictive of user experience. Interview results showed that teachers had a positive attitude toward the AR learning resources authoring tools inspiring novel ways of teaching and learning.