Online Programming Research Articles

Comparative Analysis of Machine Learning Models for Predicting Student Success in Online Programming Courses: A Study Based on LMS Data and External Factors

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.

Artificial intelligence-enabled multipurpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics

An active-matrix electrowetting-on-dielectric (AM-EWOD) system integrates hundreds of thousands of active electrodes for sample droplet manipulation, which can enable simultaneous, automatic, and parallel on-chip biochemical reactions. A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps. In this work, we demonstrated an artificial intelligence (AI)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks. We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods. We used the YOLOv8 model to monitor the droplet-splitting process online. A 97.76% splitting success rate was observed with 18 different AM-EWOD chips. A 99.982% model precision rate and a 99.980% model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets. Compared with manual verification, the model achieved 99.260% and 99.193% precision and recall rates, respectively. In addition, single-cell droplet sorting and routing experiments were demonstrated. With an AI-based smart detection system, AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.

Injecting competition into online programming and Chinese- English translation classrooms.

The introduction of competition has the potential to enhance the efficacy of students' learning performance. Nevertheless, there have been contradictory findings about the impact of intergroup competition on students' learning performance and engagement. Therefore, further comprehensive investigations for this problem are necessary. In order to bridge this gap, the present study seeks to ascertain the efficacy of intergroup competition in relation to students' academic performance and motivation. Consequently, we present the concept of intergroup competition and implement it within the context of an online programming course and an online Chinese-English translation course. The participants of this study consist of sophomore students majoring in Computer Science and English. Initially, a total of 108 sophomore students majoring in Computer Science participated. Then, a total of 100 sophomore students majoring in English participated. A quasi-experimental study was subsequently undertaken to compare students from two courses, which are online programming and Chinese-English translation, assigning them to an experimental group and a comparison group, respectively. Then, we conducted independent samples t-tests to measure the difference between the academic performance of the two group of students from two courses. The results indicate that both groups of students who were exposed to the intergroup competition mechanism demonstrated considerably higher levels of academic performance and engagement compared to the other group of students. The findings indicate that the competition mechanism, has the potential to be a beneficial instrument for enhancing both students' learning performance and motivation.

Static Scheduling of Weight Programming for DNN Acceleration with Resource Constrained PIM

Most existing architectural studies on ReRAM-based processing-in-memory (PIM) DNN accelerators assume that all weights of the DNN can be mapped to the crossbar at once. However, these studies are over-idealized. ReRAM crossbar resources for calculation are limited because of technological limitations, so multiple weight mapping procedures are required during the inference process. In this article, we propose a static scheduling framework which generates the mapping between DNN weights and ReRAM cells with minimum runtime weight programming cost. We first build a ReRAM crossbar programming latency model by simultaneously considering the DNN weight patterns, ReRAM programming operations, and PIM architecture characteristics. Then, the model is used in the searching process to obtain an optimized weight-to-OU mapping table with minimum online programming latency. Finally, an OU scheduler is used to coordinate the activation sequences of OUs in the crossbars to perform the inference computation correctly. Evaluation results show the proposed framework significantly reduces the weight programming overhead and the overall inference latency for various DNN models with different input datasets.

Revisiting design principles and guidelines for online makerspace services and programming in public libraries

Purpose The purpose of this paper is to revisit previous design principles and guidelines for online makerspaces in public libraries (Kim et al., 2020) and expand the design principles with more updated implications and examples from the literature published during and after the COVID-19 pandemic. Design/methodology/approach The authors reviewed recently published papers about online transitions of makerspaces, especially during and after the COVID-19 pandemic, summarized their implications and deduced applicable design principles and guidelines. Findings This paper proposes updated design principles and guidelines based on four key areas: Program and service design; Tools and materials; Facilitation; and Logistic support. These updated design principles considered a wider range of patrons that public library makerspaces may serve, the digital divide issues and logistic concerns that should be addressed beyond the scope of a single makerspace. Originality/value This paper compiles various lessons learned and strategies regarding online makerspaces and maker programming for public libraries and provides helpful design principles and guidelines for the continued use of online components for makerspace services and programs.

Leveraging Language Models and Automatic Summarization in Online Programming Learning Environments

Leveraging Language Models and Automatic Summarization in Online Programming Learning Environments

Sustaining Undergraduate Students’ Metacognitive Regulatory Actions During Online Flipped Programming Course

This research explores how metacognitive strategies influence the metacognitive awareness of undergraduate students enrolled in an online flipped programming course. It specifically focuses on regulatory actions crucial for success in programming instruction and distance education settings. The primary objective is to contribute to the existing literature by investigating the implementation of online flipped programming courses that integrate metacognitive-oriented approaches to support students' metacognitive regulatory actions. The study employed an explanatory sequential mixed methods design. A total of 29 university students enrolled in programming courses participated in the study, engaging with instructional videos provided before each 10-week lesson. They were administered the Metacognitive Awareness Scale and supplementary forms designed to assess their metacognitive awareness and regulatory actions. A detailed coding scheme was developed to analyze students' metacognitive regulation activities during programming lessons. The study also evaluated the impact of supportive activities on students' metacognitive awareness. While no statistically significant difference was found in the students' metacognitive awareness through quantitative analysis, qualitative data revealed that activities supporting metacognition significantly enhanced students' comprehension of the programming content.

Use of basic programming tools to foster programming logic in university students with school preparation other than computer science

Teaching programming logic to students who do not have a background in computer science is challenging, as the instructor has to awaken problem-solving, critical thinking, and logical reasoning skills Several programming tools have been created to teach coding concepts to computer science students of different ages. However, these tools are not well designed to meet the challenge of teaching programming to new developers who come with school training in other areas such as accounting, management, etc. Therefore, this research focused on analyzing the importance of the application of online programming tools to students starting college who come with a school background in an area other than technology. A pre/post experimental design was carried out with 82 first-level students of the Technical University of Ambato in the careers of Systems and Electronics. The results revealed that 45% of the students increased their levels of application and analysis in programming processes. In addition, the research revealed that students who come from a background other than computer science agree with the integration of online programming tools from the first level of university entrance since this method helps to improve their learning capacity.

Using an Online Assessment Tool to Teach Authentic Assessment to Early Childhood Teacher Candidates

Early childhood teacher candidates (TCs) need preparatory experiences using authentic assessments to inform their instructional practices with rural learners and their families. The shift to virtual field placements in response to COVID-19 restrictions pushed faculty in teacher preparation programs to reimagine how to engage TCs in meaningful experiences using authentic assessments with children and families. This study explores how TCs experienced authentic assessment practices using an online assessment management system, Assessment, Evaluation, and Programming System Interactive (AEPS i), during virtual field experiences. Fifty-five undergraduate and graduate TCs from 11 universities (10 U.S. & 1 international) completed a 45-item survey about their experience. Teacher candidates articulated knowledge and skills aligned with best practices underpinning authentic assessments. The virtual learning environment created opportunities for faculty to reimagine practices for promoting TCs’ understandings of assessment in early childhood special education contexts. We discuss implications for training TCs in rural areas to use authentic assessment.

Audience Preferences for Extension Forestry Zoom Webinars

The Washington State University Extension Forestry program switched to all online programming in 2020 in response to the COVID-19 pandemic. Over 2,000 people participated in our webinars, providing an opportunity to survey a large audience about their webinar preferences. We found that people prefer webinars that are approximately an hour long and offered in the evening or late morning. Participants placed high importance on having a recording available, but they had mixed views on including video of the instructor speaking. Participants found online delivery to be successful and had a strong preference for online programming in the future.

Active visual continuous seam tracking based on adaptive feature detection and particle filter tracking

Welding seam tracking based on online programming is the future trend of intelligent production. However, most of the existing image processing methods have certain limitations in the adaptability, accuracy, and robustness of weld feature point detection. The online welding method of gas metal arc welding (GMAW) based on active vision sensing is studied in this paper. The Steger sub-pixel detection method is used to guarantee the accuracy of feature point extraction, and a self-adaptive search window and self-adaptive slope extraction are proposed on this basis. The self-adaptive window is generated according to the linear information of the weld area, and the scale factor and range threshold constraint are added to realize the real-time detection of the weld feature information. Screening the center pixel of the laser stripe in the self-adaptive window of the current frame by the initial slope or the self-adaptive slope of the previous frame, the linear information of the weld area is obtained. The self-adaptive slope of the current frame is fitted by the random sampling consistency method, and the pixel margin is retained to adapt to the linear detection of different continuous welds. When arc light and other serious interference make it difficult to obtain weld information, a particle filter is used to make the best prediction of the weld position. Finally, the welding robot platform based on laser vision sensing was built to test various continuous welds of the butt weld, fillet weld, and lap weld. Experimental results show that the detection speed is 27 ms, and the accuracy of detection and tracking can respectively reach 0.03 mm and 0.78 mm, which meets the requirements of weld detection and tracking.

Empowering Learning: A Review of Online Programming Tools and their Evolving Landscape

Abstract: The emergence of online programming tools has significantly impacted computer programming education. This review paper delves into the current research landscape surrounding these tools, analyzing their influence on learning outcomes, collaboration, and the potential of AI-powered personalized learning. The review explores the foundational role of online compilers and collaborative environments. It then examines recent advancements in AI, including adaptive coding tutors and AIpowered code completion.

Implementation of an Online Programming Software to Explore the Computational Thinking Ability of Biological Education Students

This study aimed to explore the Computational Thinking (CT) ability of biology education students in artificial intelligence courses through code.org programming, an online programming software. Students were required to solve problems systematically and design systems with coding for beginners. The research method used a qualitative approach because it was based on observational data from computational media produced in accordance with artificial intelligence learning for education and based on CT indicators. The subjects of this study were 73 students of the Biology Education Study Program in 2021. How students responded to using the code.org platform was captured through a questionnaire distributed via a Google form. The results showed that students could follow each stage of educational game projects through the code.org platform to practice computational thinking skills in accordance with the success of the stages of doing simple projects through good, systematic and problem-solving coding. Student responses to the code.org platform as an online program tool that can hone computational thinking can provide creativity and enthusiasm for student learning. As evidenced by the questionnaire results, 63.3% of students strongly agree that this increases creativity.
 Keywords: online programming, computational thinking, biological education, students

Boosting Spectrum-Based Fault Localization via Multi-Correct Programs in Online Programming

Boosting Spectrum-Based Fault Localization via Multi-Correct Programs in Online Programming

User Study to Validate the Performance of an Offline Robot Programming Method That Enables Robot-Independent Kinesthetic Instruction through the Use of Augmented Reality and Motion Capturing

The paper presents a novel offline programming (OLP) method based on programming by demonstration (PbD), which has been validated through user study. PbD is a programming method that involves physical interaction with robots, and kinesthetic teaching (KT) is a commonly used online programming method in industry. However, online programming methods consume significant robot resources, limiting the speed advantages of PbD and emphasizing the need for an offline approach. The method presented here, based on KT, uses a virtual representation instead of a physical robot, allowing independent programming regardless of the working environment. It employs haptic input devices to teach a simulated robot in augmented reality and uses automatic path planning. A benchmarking test was conducted to standardize equipment, procedures, and evaluation techniques to compare different PbD approaches. The results indicate a 47% decrease in programming time when compared to traditional KT methods in established industrial systems. Although the accuracy is not yet at the level of industrial systems, users have shown rapid improvement, confirming the learnability of the system. User feedback on the perceived workload and the ease of use was positive. In conclusion, this method has potential for industrial use due to its learnability, reduction in robot downtime, and applicability across different robot sizes and types.

Understanding Real-time Collaborative Programming: a Study of Visual Studio Live Share

Real-time collaborative programming (RCP) entails developers working simultaneously, regardless of their geographic locations. RCP differs from traditional asynchronous online programming methods, such as Git or SVN, where developers work independently and update the codebase at separate times. Although various real-time code collaboration tools (e.g., Visual Studio Live Share , Code with Me , and Replit ) have kept emerging in recent years, none of the existing studies explicitly focus on a deep understanding of the processes or experiences associated with RCP. To this end, we combine interviews and an email survey with the users of Visual Studio Live Share , aiming to understand (i) the scenarios, (ii) the requirements, (ii) and the challenges when developers participate in RCP. We find that developers participate in RCP in 18 different scenarios belonging to six categories, e.g., pair programming , group debugging , and code review . However, existing users’ attitudes toward the usefulness of the current RCP tools in these scenarios were significantly more negative than the expectations of potential users. As for the requirements, the most critical category is live editing , followed by the need for sharing terminals to enable hosts and guests to run commands and see the results, as well as focusing and following , which involves “following” the host’s edit location and “focusing” the guests’ attention on the host with a notification. Under these categories, we identify 17 requirements, but most of them are not well supported by current tools. In terms of challenges, we identify 19 challenges belonging to seven categories. The most severe category of challenges is lagging followed by permissions and conflicts . The above findings indicate that the current RCP tools and even collaborative environment need to be improved greatly and urgently. Based on these findings, we discuss the recommendations for different stakeholders, including practitioners, tool designers, and researchers.

The ‘woeful’ state of administrative support for online programmes: A critical discourse analysis

AbstractThis study used critical discourse analysis to explore how higher education administrators in the United States talk about how they assess and support online programmes. Specifically, we hoped to analyse administrators' perceptions of their responsibilities over online programmes, faculty and students, to attain where they may need more training. Therefore, we explored the perspectives of 11 administrators at both the mid‐level administrative and campus senior administrative levels who oversee online programmes in U.S. higher education. Our findings suggest that mid‐level administrators hold pivotal roles in communicating needs, administrators are not viewing their online faculty holistically, current online programmes assessment is insufficient and concern for student engagement is often neglected. Implications for research and practice include additional investigation of the online faculty experience and the development of administrative training specifically focused on the needs of online programming and online faculty support.

An extended study of a cloud-based recommender system for competitive programming platforms with machine and deep learning

Nowadays, online judges are very important to improve programming skills for education and technology companies. For this reason, there are many online judges that include large sets of programming challenges. This creates an information overload problem that affects students due to their lack of expertise in choosing the correct challenge to solve, resulting in frustration and a loss of interest in this topic. To solve this scenario, recommender systems appear, but programming judges have not delved much into it. Consequently, this research aims to evaluate the performance of six selected collaborative filtering techniques via a cloud-based software architecture. To validate our experiments we used real online programming judges like CodeChef and NinjaCoding using cloud based architecture with Amazon Web Services, evaluated through Friedman and Wilcoxon statistical tests. The results indicated that Singular Value Decomposition is the best model evaluated with RMSE metric and the fastest in execution time with big datasets.

Using multimodal learning analytics to understand effects of block‐based and text‐based modalities on computer programming

AbstractBackgroundWith the development of computational literacy, there has been a surge in both research and practice application of text‐based and block‐based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education settings.ObjectivesTo further compare programming effects between and within text‐based and block‐based modalities, this research conducted a quasi‐experimental research in China's secondary school.MethodsAn online programming platform, Code4all, was developed to allow learners to program in text‐based and block‐based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag‐sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two modalities.Results and ConclusionsThe results indicated that learners in text‐based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block‐based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural patterns.ImplicationsThree major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process‐oriented and summative analysis to reveal learners' programming learning quality.

Work-in-Progress Do Not Cite Without Author Approval Article Forthcoming in Spring 2024 with South Dakota Law Review Fostering a Law Student’S Professional Identity to Becoming a Rural Lawyer Using Law School Field Placements and Online Programming

Work-in-Progress Do Not Cite Without Author Approval Article Forthcoming in Spring 2024 with South Dakota Law Review Fostering a Law Student’S Professional Identity to Becoming a Rural Lawyer Using Law School Field Placements and Online Programming