Programming Course Research Articles

Qualitative analysis of peer reviews of a large introductory programming course

ABSTRACT Background and Context Getting timely feedback is important for learning. However, providing individual feedback is a problem in large courses. Peer code review can address this issue and has shown to offer various advantages such as enhanced collaboration among students, improved coding skills, and seeing and criticizing different solution strategies. Objectives The goal is to gain a comprehensive understanding of the contents of voluntary peer reviews and students’ review skills in a first-semester university-based introductory programming course with about 900 enrolled students and no special support or training for conducting reviews. Method A qualitative analysis was conducted on 215 randomly sampled peer reviews as well as the associated code submissions. Qualitative factors such as frequencies of corrections, coding tips, questions, “empty” submissions/feedback, and the sentiment are analyzed. Furthermore, errors in the submissions and mentioned errors in the reviews are investigated exploratively. Findings The analysis results indicate that, in general, students are better in detecting incorrect solutions as incorrect than correct submissions as correct. The feedback is neutral to positive contains a lot of praise, but is rather short and uncertainty is expressed quite often. Students seem to be very nice to each other. Submissions without intensive solution attempts and “empty” reviews are quite rare. Students can correct errors and provide coding tips, however, often do not see subtle errors such as partly incorrect algorithms, typos in method names, or output formatting errors. Implications The results help to train students to write better reviews and to inform educators on how to provide better instruction or support for peer review.

Effectiveness of the Flipped Classroom Learning Model in Improving Students' Mathematical Reasoning Abilities in the Application of School Mathematics Course

This research aims to measure the effectiveness of the flipped classroom learning model in improving students' mathematical reasoning abilities in the Application of School Mathematics course at the Universitas Terbuka. The research method used was quasi-experimental with a pretest-posttest control group design. The research sample consisted of 60 students, who were divided into two groups: an experimental group that used the flipped classroom (n=30) and a control group that used the conventional method (n=30). The results of data analysis show that the average score of students' mathematical reasoning ability in the experimental group increased from 58.2 (SD = 8.7) in the pretest to 81.4 (SD = 7.2) in the posttest, with an average increase (N -gain) of 0.64 which is included in the medium category. In contrast, the control group experienced an increase from 57.6 (SD = 9.1) to 70.3 (SD = 8.5), with an N-gain of 0.38 which is in the low category. The t test showed that the increase in mathematical reasoning ability in the experimental group was significantly higher than in the control group (t = 4.12, p < 0.001). These results indicate that the flipped classroom is an effective learning model in improving students' mathematical reasoning abilities. These findings provide an important contribution to the development of distance learning, especially at the Universitas Terbuka. This research recommends implementing the flipped classroom integrated with active learning strategies for other courses in mathematics education programs.

HUBUNGAN LITERASI MEDIA DENGAN TINGKAT AKSES KURSUS PROGRAM PENDIDIKAN ONLINE LINKEDIN LEARNING MAHASISWA FAKULTAS ILMU KOMUNIKASI NON-PASCASARJANA UNIVERSITAS PADJADJARAN

This study aims to see if there is a relationship between media literacy and the level of access to LinkedIn Learning courses. The concept of digital literacy used in the research is the Individual Competence Framework by the European Commission which consists of 3 indicators, namely technical usage skills, critical understanding, and communication skills. The media literacy variable is further divided into 3 (three) ability categories namely Basic, Medium, and Advanced. Similarly, the LinkedIn Learning course access variable is divided into 3 (three) levels namely “Low”, “Medium”, “High”. The method used in this research is a quantitative method with a correlational approach, which tries to see the relationship between two or more variables and the strength of the relationship. This research was conducted on 65 students of the Faculty of Non-Postgraduate Communication Sciences class of 2019, 2020, 2021 and 2023 at Padjadjaran University, who were selected based on simple random sampling technique. The data analysis techniques used in this study are descriptive and inferential using the Chi-Square test. The results showed that there was no correlation between students' media literacy and the level of access to LinkedIn Learning online education program courses by students of the Faculty of Non-Postgraduate Communication Sciences, Universitas Padjadjaran. From the three sub-variables of media literacy based on the Individual Competence Framework, namely critical understanding, and communication skills also have a significant relationship with media literacy, but the sub-variable of technical usage skills has a significant relationship with media literacy.

Coding Analogy

This study aimed to examine the pre-service teachers’ experiences in preparing and using analogies within a programming course to better understand and evaluate the concepts. The research design of this study was identified as a case study. The 21 participants of the study took an elective introductory programming (Python) course at a state university over 14 weeks. They prepared analogies during the course, and 11 of them attended a focus group interview at the end of the course. The data collection tools used in the study included a questionnaire in which learners indicated the topics they found challenging within the programming course, the analogies they created based on programming education, and interview questions that explored their views at the end of the course. These data were analyzed descriptively. The results indicated that although learners experienced difficulties in understanding algorithms, programming logic, and learning loops, the use of analogies in programming education offered positive contributions.

The effects of machine-learning programming simulator on university students’ engagement in learning programming

A computer programming course is essential for students pursuing a computer science major. The college offers a course that imparts essential knowledge to undergraduate computer science students. After finishing their degree, individuals may utilize their experience in entrepreneurial pursuits. Students without competency in the fundamental concepts of computer programming may face challenges in obtaining employment, particularly in positions such as developers or software developers. Enrollment in the programming course is compulsory for computer science students. A decline in employment for computer programmers is a recent issue nowadays, while growing demand for skilled programmers happened in certain regions, such as Indonesia. While it is true that many simpler duties can be automated, there will be a growing demand for positions that require strategic decision-making. To remain competitive, programmers must increase their abilities to satisfy these obligations. The study emphasizes the need for integrating technology, including gamification and simulation tools, in programming education to enhance student engagement and learning outcomes. A quasi-experimental study was conducted to investigate the impact of a machine learning (ML) programming simulator on student engagement. The study involved 120 university students, divided into two groups: one using the ML programming simulator and the other using a non-ML simulator. The results showed that students using the ML simulator had higher engagement levels compared to those using the non-ML simulator, suggesting that ML-based tools can significantly improve student participation and learning in programming courses. The findings underscore the potential benefits of using advanced technological tools to foster better learning experiences in computer programming education.

INTEGRATING BLENDED LEARNING IN PROGRAMMING COURSE: A COMPREHENSIVE SYSTEMATIC REVIEW

This systematic review examines the integration of blended learning in programming courses, addressing the increasing diversity of student backgrounds and the consequent challenges in teaching programming effectively. The study synthesizes findings from recent research to identify effective strategies and best practice outcomes associated with blended learning approaches in this context. A comprehensive methodology was employed, involving the selection and analysis of relevant articles from databases such as Scopus and Web of Science, focusing on studies published between 2020 and 2024. The study was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework. The database found (n=30) final primary data was analysed. Numerical results from the chosen literature reveal three main themes: (1) Effectiveness of Blended Learning in K-12 Education, (2) Best Practice of Blended Learning in Higher Education and Professional Development, and (3) Students’ Understanding on Blended Learning in Programming and Computer Science Education. This review concludes that integrating blended learning in programming courses not only addresses diverse learning needs but also fosters improved academic outcomes and student engagement. Future research should continue to explore innovative blended learning strategies and their long-term impacts on programming education.

Gut Microbiota Interacts with Dietary Habits in Screenings for Early Detection of Colorectal Cancer.

Gut microbiota interacts with nutrients, which may be relevant to assigning a microbial signature to colorectal cancer (CRC). We aim to evaluate the potential of gut microbiota combined with dietary habits in the early detection of pathological findings related to CRC in the course of a screening program. The colonoscopy performed on 152 subjects positive for fecal occult blood test showed that 6 subjects had adenocarcinoma, 123 had polyps, and 23 subjects had no pathological findings. Gut microbiota was analyzed by 16S metagenomic. Caret package was used to build the classification models in R. Random forest (RF) classifier models were used to test the potential of gut microbiota alone or combined with dietary habits as a biomarker to discern between individuals with CRC-related lesions (polyps or adenocarcinoma) versus individuals without pathological findings. RF classifier models yielded an area under the curve of 0.790 using gut microbiota data, 0.710 using dietary habits data, and 0.804 in the combined model including gut microbiota and dietary habits data. The abundance of Suterella, Oscillospirales, Proteobacteria, and Burkholderiales was highly discriminant between groups, together with the consumption of fruit and vegetables and the consumption of carbonated and/or sweetened beverages. Our results suggest that the interaction between gut microbiota and dietary habits is relevant when a microbial signature is used as a marker in CRC. Moreover, gut microbiota signature and information about the dietary habits of the individuals seem to be important for improving screening programs for the early detection of CRC.

TRAINING OF COMPUTER SCIENCE TEACHERS FOR THE FORMATION OF CYBERSECURITY SKILLS IN STUDENTS: ACTUALIZATION OF PROBLEMS AND THEIR POSSIBLE SOLUTIONS

The article presents an analysis of the problem of preparing computer science teachers for the formation of cybersecurity skills in students. The generalization of the available scientific results gives an idea of the general trends and problems in this field: the need for constant updating of the curriculum in computer science and cybersecurity is emphasized to keep pace with the development of technologies and standards; emphasizes the need to combine practical skills with fundamental knowledge in cybersecurity education; stresses the importance of pedagogical and methodological knowledge along with subject knowledge in the training of computer science teachers; mastering innovative digital strategies for teaching and learning computer science; a mixed way of teaching courses (face-to-face, hybrid or online) and a variety of teaching methods; development of methodological competence of computer science teachers through different approaches to such development (special courses, internship programs, mentoring, joint lesson planning and reflective practice, etc.). Based on the results of the analysis, recommendations were formulated for the modernization of teacher training programs for the formation of students' cybersecurity skills: integration of content modules on cybersecurity into courses on methods of teaching computer science; development and implementation of specialized cybersecurity courses in educational and professional teacher training programs; use of digital tools and simulation technologies; promoting the continuous professional development of teachers in the field of cybersecurity; promoting cooperation and knowledge exchange with cybersecurity practitioners; bringing computer science teacher training curricula in line with international standards in the field of cybersecurity. We consider further research to be promising in studying the content of educational programs for training computer science teachers and analyzing the effectiveness of various teaching methods for developing cybersecurity skills in future computer science teachers.

Multimodal Learning Data Analysis and Algorithmic Teaching Effectiveness Evaluation Model Construction

Introduction:A combination of internal characteristics, such as motivation, personality, beliefs, and dispositions that combine with external circumstances to affect student results is referred to as teaching efficacy. The English-speaking environment becomes a major problem as students learn by interaction. To communicate effectively, they do not acquire the speaking and listening abilities necessary in English. Objective:This study examines the impact of computer programming courses and learning analytics on student’s computer programming skills. Methods:In this study, we proposed a novel Dung Beetle Optimized Flexible Random Forest (DBO-FRF) to evaluate the teaching effectiveness. In this study, 175 students’ data were collected for teaching effectiveness. Using student data, prediction model was constructed based on the attributes of the students, their past academic records, their interactions with online resources, and their advancement in laboratory work related to programming. The proposed method is compared to other traditional algorithms. Results and conclusion:The proposed method is implemented using Python software. The expected performance in the course and, in the instance that any submitted programs failed to meet the requirements, a programming recommendation from one of the class’s top students. The result shows the proposed method achieved better performance in terms of accuracy, precision, recall, and [Formula: see text]1-score. This decreased the performance gap between students who performed lower and those who performed higher, enabling students who adjusted their programs to learn more.

Assessing student perceptions and use of instructor versus AI‐generated feedback

AbstractDespite the growing research interest in the use of large language models for feedback provision, it still remains unknown how students perceive and use AI‐generated feedback compared to instructor feedback in authentic settings. To address this gap, this study compared instructor and AI‐generated feedback in a Java programming course through an experimental research design where students were randomly assigned to either condition. Both feedback providers used the same assessment rubric, and students were asked to improve their work based on the feedback. The feedback perceptions scale and students' laboratory assignment scores were compared in both conditions. Results showed that students perceived instructor feedback as significantly more useful than AI feedback. While instructor feedback was also perceived as more fair, developmental and encouraging, these differences were not statistically significant. Importantly, students receiving instructor feedback showed significantly greater improvements in their lab scores compared to those receiving AI feedback, even after controlling for their initial knowledge levels. Based on the findings, we posit that AI models potentially need to be trained on data specific to educational contexts and hybrid feedback models that combine AI's and instructors' strengths should be considered for effective feedback practices. Practitioner notesWhat is already known about this topic Feedback is crucial for student learning in programming education. Providing detailed personalised feedback is challenging for instructors. AI‐powered solutions like ChatGPT can be effective in feedback provision. Existing research is limited and shows mixed results about AI‐generated feedback. What this paper adds The effectiveness of AI‐generated feedback was compared to instructor feedback. Both feedback types received positive perceptions, but instructor feedback was seen as more useful. Instructor feedback led to greater score improvements in the programming task. Implications for practice and/or policy AI should not be the sole source of feedback, as human expertise is crucial. AI models should be trained on context‐specific data to improve feedback actionability. Hybrid feedback models should be considered for a scalable and effective approach.

Assessment of the Success of the Independent Campus Independent Learning Program Through a Web-Based Integration System at the Participating University

This study explores the implementation of the Merdeka Belajar Kampus Merdeka (MBKM) program in private universities in Banten Province, Indonesia, emphasizing the importance of a well-structured curriculum for effective learning outcomes. The policy enables students to engage in various activities outside their majors, enhancing both technical and soft skills essential for workforce readiness. Despite its positive reception, challenges in the assessment conversion process have been identified, particularly the lack of involvement from study program leaders and course lecturers in evaluating student performance. This study employs the R&D method using the ADDIE model to develop a web-based integration system aimed at streamlining and improving the evaluation of student achievements in the MBKM program. The findings indicate that this system enhances monitoring, transparency, and accountability in assessment, fostering better collaboration among lecturers, students, and partner institutions. Moreover, the web-based system alleviates administrative burdens, allowing lecturers to focus on enriching student learning experiences. This research underscores the necessity for continuous evaluation of the MBKM program to align competencies with academic curricula, ultimately contributing to the quality of higher education and the successful integration of students into the workforce. Recommendations for further development of the integration system and enhanced training for lecturers are provided to optimize its impact on educational outcomes.

Developing A Data Driven Framework To Improve Feedback For Educators

This paper presents a framework for the use of weekly reflections to drive iterative course improvement throughout a term. This data includes weekly student reflections, where students anonymously self-assess both their behavior (through attendance and use of course resources) and their comprehension of course concepts. Results from a pilot of this framework for a large first-year introduction to computer programming course are used to illustrate the use case. Improvements to this framework from a previous term are made and discussed. Results demonstrate student reflections can offer actionable insights for targeted interventions in lectures and tutorials. Key observations include the impact of incoming experience, patterns of students falling behind, and differences across cohorts. By automating a portion of this data collection and analysis, student feedback can be integrated with minimal administrative burden. The authors plan to further automate this framework and develop predictive models based on specific course factors.

Supporting learning and motivation with knowledge mapping in programming courses

Supporting learning and motivation with knowledge mapping in programming courses

Self-efficacy as students’ motivation factor in learning programming

In learning, one of the fundamental motivating factors is self-efficacy. Therefore, it is crucial to understand the level of students’ self-efficacy in learning programming. This article presents a quantitative study on undergraduate students’ perceived programming self-efficacy. 110 undergraduate computing students took part in this survey to assess programming self-efficacy. Before being given to the respondents, the survey instrument, which included a 28-item self-efficacy assessment and 30 multiple-choice programming questions, was pilot-tested. The survey instrument had a reliability of 0.755. The study results show that the students’ self-efficacy was low when they solved complex programming tasks independently. However, they felt confident when there was an assistant to guide them through the tasks. From this study, it could be concluded that self-efficacy is an essential achievement component in programming courses and can avoid education dropouts.

A língua Nakado’tu no cotidiano das aldeias e no contexto escolar

This article presents the sociolinguistic situation of the Nakado'tu-Negarotê people, focusing on the use of their mother tongue in the villages and in the Vale do Guaporé Indigenous Municipal School, located in the Central Negarotê village, within the Vale do Guaporé Indigenous Land, in the municipality of Comodoro-MT. It was produced as a requirement for the subject “Language Policies”, offered in the Professional Master's course of the postgraduate program in “Teaching in an Intercultural Indigenous Context” -PPGECII/UNEMAT. The main objective is to show how the Nakado'tu language is used, both in the communities and in the school environment. The research follows a qualitative approach, based on interviews with elders, leaders, teachers and young people, in order to understand the linguistic dynamics and challenges faced by the Nakado'tu people in the current context.

The factors that influence the entrepreneurial decision-making model moderated by Entrepreneurship education

In implementing Presidential Decree No. 02 of 2022 and Presidential Decree No.08 of 2012, universities as one of the educational institutions in Indonesia include the topic of Entrepreneurship as a compulsory subject in the curriculum. This study aims to provide empirical evidence of the factors that influence the entrepreneurial decision-making model moderated by Entrepreneurship education. This study uses a survey approach, data collection using questionnaires distributed via Google Form. The analysis method used is descriptive analysis, Multiple Regression, and Moderate Regression Analysis. The sample was collected using Purposive Sampling, namely students majoring in Accounting at Politeknik Negeri Padang who have taken Entrepreneurship courses in both D3 and D4 study programs. The results showed that partially the Entrepreneurial Decision was influenced by Profit Expectations, Use of Accounting Information Systems, and Entrepreneurship Education, but was not influenced by the Use of e-commerce. Entrepreneurship Education is able to strengthen entrepreneurial decision making. This research can provide recommendations and evaluations for the government, especially the academics, and entrepreneurs that the importance of Entrepreneurship Education in increasing the growth and development of the economy of Indonesia.

Investigating Aptitude in Learning Programming Language Using Machine Learning and Natural Language Processing

This study investigates the relationship between prerequisite courses and skill acquisition in programming education. It proposes a case study examining cognitive, natural language, and mathematical aptitude indicators as predictors of programming performance. Analyzing data from 1238 undergraduate students at Riphah International University, the research employs Machine Learning models to predict outcomes, achieving high R2 scores and low Mean Squared Error rates. A zero-shot text classification model identifies required aptitude skills: 62% cognitive, 24% natural language, and 14% mathematical. These skills are mapped to predicted programming course scores, offering a new approach to understanding programming language aptitude. The study aims to bridge the gap between prerequisite courses and subsequent skill development, contributing valuable insights to computing education curriculum design.

DEVELOPING LANGUAGE ASSESSMENT LITERACY OF FILIPINO PRE-SERVICE TEACHERS: REFLECTIONS FROM A LANGUAGE ASSESSMENT COURSE

Many educational and language scholars in the field have remarked the need to strengthen assessment and testing academic courses in the undergraduate teacher education programs, more specifically in highlighting the importance of language assessment literacy (LAL). With several findings indicating insufficient or impractical assessment components in many of these programs, academics and teachers alike agreed with the significance of developing pre-service teachers’ LAL to reinforce their theoretical knowledge and practical skills of assessment which would enable them to effectively construct and administer language assessments. In order to contribute to the discussions on LAL, this qualitative case study reports on the reflections made by the pre-service teachers (PSTs) of English language education (n=13) on their learning process during a university-based academic course on language test development in the Philippine setting. Guided by an assessment literacy framework, this study aims to explore PSTs’ LAL development and the contribution of the assessment course to this development. Findings revealed that the course produced extensive modifications in understanding the definition and principles of assessment, the various strategies and tools of assessment, and purposes and construction of language assessments. Additionally, the current research offers discussions into the influence of language assessment training on PSTs’ LAL development.

GRAD-AI: An automated grading tool for code assessment and feedback in programming course

GRAD-AI: An automated grading tool for code assessment and feedback in programming course

The Effect Of Peer-Assisted Learning (PAL) Method On Visual Programming Knowledge Outcomes In Educational Technology Students

This study aims to evaluate the effect of the Peer-Assisted Learning (PAL) method on differences in student knowledge outcomes in the Visual Programming course in the Educational Technology Study Program. This study used a quasi-experimental design with two groups, namely the experimental class that applied the PAL method vertically and the control class that used conventional learning methods. The research participants totaled 50 students, drawn from the four class offerings randomly and divided into 2 class groups of 25 learners each. Data were collected through learning motivation questionnaire and knowledge test measured by Likert scale and Bloom's Taxonomy-based test on cognitive domain. Results of statistical analysis with Two-Way Anova test showed that there was no significant difference in knowledge outcomes with a value of 0.331>0.05 based on learning motivation. However, the study showed a value of 0.036 <0.05, meaning that there was a significant difference between the PAL method and conventional learning on student knowledge outcomes. In addition, the value of 0.972>0.05 means that there is no significant interaction between the level of student learning motivation and learning methods on knowledge outcomes. This study found good responses from students where they prefer and are enthusiastic about learning with the PAL method compared to the conventional method, because the method is considered more flexible and comfortable to use for learning. This study suggests further evaluation related to the use of the PAL method by improving tutor skills and setting the duration of learning to increase effectiveness in achieving student learning outcomes