Undergraduate Students' Experiences in Programming: Difficulties and Obstacles

In this paper we argue that the incorporation of experiences on testing activities, in particular mutation testing, in programming courses adds valuable knowledge to the learning process. Mutation testing is centered on the idea of creating test data for uncovering seeded faults in programs that slightly differ from the original program. These faulty programs are called mutants. Through source code analysis and test case execution, testers have to identify the differences between the original program and the mutants. To do so, testers must have a sound understanding of the program's control flow and instructions. This broad understanding of programming represents a key skill for novice students in programming courses. We evaluate the effects of using mutation testing to improve the learning process of novice students in programming courses. We conclude that the introduction of mutation testing in the learning process provides students with learning experiences that go beyond traditional lectures and hands-on programming courses.

<i>Identifying the relation between the processes of programming and foreign language writing may lead to new directions for programming language and natural language focused instructional design. The present qualitative case study supported by quantitative data investigated foreign language writing experiences of computer engineering students taking an object-oriented programming course. Forty-five sophomores learning programming and academic English simultaneously in a foundation university in Ankara, Turkey, were selected purposefully for the case study. There were two data sources (students’ opinions and documents) and three data collection tools (a semi-structured interview, a short diary, and a composition. In terms of the findings of the research, four themes were obtained; however, only the use of metacognitive strategies will be explained in detail due to the length of the study. Participants stated that they feel the positive effects of programming experience on the use of self-evaluation strategy and that there are similarities and differences between the processes of programming and foreign language writing. Participants’ views on the effect of programming on foreign language writing did not differ according to their writing and programming performance scores. Participants stated that programming experience may have an effect on the use of metacognitive language learning strategies in the writing process. Upon analyzing participants’ comments, it is understood that programming experience does not hinder the use of metacognitive strategies but has a role in supporting and reinforcing their use. It is suggested that multiple case studies be done for similar views on the effects of programming and that each finding be proven by quantitative studies. </i>

Student performance modelling (SPM) is a critical step to assessing and improving students' performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual influences of hidden factors that are peculiar to students, lecturers, learning environment and the family, together with their overall effect on student performance have not been exhaustively investigated. In this paper, Student Performance Models (SPM) for improving students' performance in programming courses were developed using M5P Decision Tree (MDT) and Linear Regression Classifier (LRC). The data used was gathered using a structured questionnaire from 295 students in 200 and 300 levels of study who offered Web programming, C or JAVA at Federal University, Oye-Ekiti, Nigeria between 2012 and 2016. Hidden factors that are significant to students' performance in programming were identified. The relevant data gathered, normalized, coded and prepared as variable and factor datasets, and fed into the MDT algorithm and LRC to develop the predictive models. The developed models were obtained, validated and afterwards implemented in an Android 1.0.1 Studio environment. Extended Markup Language (XML) and Java were used for the design of the Graphical User Interface (GUI) and the logical implementation of the developed models as a mobile calculator, respectively. However, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE) and the Root Relative Squared Error (RRSE) were the metrics used to evaluate the robustness of MDT and LRC models. The evaluation results obtained indicate that the variable-based LRC produced the best model in terms of MAE, RMSE, RAE and the RRSE having yielded the least values in all the evaluations conducted. Further results obtained established the strong significance of attitude of students and lecturers, fearful perception of students, erratic power supply, university facilities, student health and students' attendance to the performance of students in programming courses. The variable-based LRC model presented in this paper could provide baseline information about students' performance thereby offering better decision making towards improving teaching/learning outcomes in programming courses.

This work proposes a study focused on Continuous Integration (CI) tools in teaching programming courses as well as for automatically grading student assignments. There are many automatic grading tools for students' programming assignments which share most of their functionalities with CI tools. We would like to provide a methodology on how to implement this tool in teaching programming languages. We will evaluate and compare common CI tools and run experiments with students in programming courses.

Computer programming skill is an essential skill for information technology and for some other natural science department students. However, teaching and learning computer programming ideas and skills has been recognized as a great challenge to both teachers and students. Therefore, the development of efficient learning strategies and environments for computer programming courses have become an important concept. This study is intended to recommend strategies for improving academic performance which can be used in teaching computer programming course effectively to undergraduate students in information technology related departments or informatics departments (including information Technology, computer science, and software engineering and information systems This concluded that there is a need for strategies for improving academic performance which requires planning and implementation to strengthen the motivation of the students to study, boost their academic performance, and uplift the quality of education in information technology departments. Finally, relevant and important workable recommendations have been suggested by the researcher.

Evaluating the learners to determine their proficiency in programming is a challenging task. In this paper, we attempt to automate this task by analyzing dynamic data collected from programming platform through sensors. Since the performance of students in programming course and their assessment depend on multiple criteria, we model the problem of finding the proficiency of student as a Multi Criteria Decision Making (MCDM) problem. We present a novel solution using Analytic Hierarchy Process (AHP) with which each student is classified into one of the expertise levels which represent their competency in a particular programming language. This competency based approach will help course instructors to grade the students. This grading helps the teachers to identify students who are facing difficulties in learning and to guide them to improve their technical skills. We validate our results by comparing them with marks scored by the students in the examination of that course.

Background: Undergraduate students often encounter significant difficulties during their research journey, which may impact the quality and completion of their projects. These challenges are typically rooted in limited scientific training, restricted research exposure, and institutional barriers. Previous literature has mostly addressed postgraduate-level complications, with limited data available on the undergraduate experience, particularly in developing countries. Understanding the nature and extent of these obstacles is essential to improve academic research support systems and foster a more research-oriented mindset at the undergraduate level. Objective: To explore the challenges faced by undergraduate physiotherapy students during their final-year research projects. Methods: A qualitative research design was employed. A total of 11 final-year undergraduate physiotherapy students from medical colleges in Lahore, Punjab, Pakistan were selected through purposive non-probability sampling. In-depth, semi-structured one-on-one interviews were conducted after informed consent. All interviews were transcribed verbatim and analyzed using NVIVO software. Thematic content analysis was performed, and recurring patterns were grouped into themes and subthemes based on the coding process. Results: Thematic analysis generated 5 core themes and 7 subthemes. Major themes included course content, supervisor interaction, research methodology, tools and scales, and data collection. Subthemes included insufficient theoretical content (reported by 8 out of 11 students), difficulty in learning SPSS (reported by 5 students), supervisor-imposed topics (4 students), unclear methodological understanding (7 students), challenges in interpreting tools and scales (6 students), data collection problems (8 students), and delays due to permissions from institutions (9 students). On average, permission-related delays ranged from 5 to 21 days. Conclusion: Undergraduate physiotherapy students face multifactorial challenges during their research projects, particularly regarding inadequate training, lack of statistical skill development, methodological uncertainty, and institutional barriers. Strengthening research curricula and improving supervisory support can enhance the undergraduate research experience.

Computer programming is considered as a difficult area of study for novices. One of the reasons is the main focus of the curriculum presented in an introductory programming (IP) course which emphasizes more on the programming knowledge (syntax and semantic) of the programming language. This study introduced a new teaching curriculum in the IP course which focuses on different skills required by the novices. We compared the IP course materials based on the traditional and new approaches against five categories. The result shows that the new approach encourages both the programming knowledge and problem solving strategies, and promotes deep learning. Furthermore, it discourages programming shortcut (Problem statement → Code), and support three-step approach (Problem statement → Solution Plans → Code) in solving a problem statement. The new approach also promotes algorithmic thinking in the IP course by paying equal attention on the problem solving strategies.

This paper describes proposed enhancements in the re-design of a senior-level Mechanical Engineering Feedback Controls course. Though several changes in course design have been made over the sequence of three successive offerings of the course, the primary focus of this paper is improving students’ ability to apply programming and computational problem-solving skills to understand and solve Controls problems. This investigation builds upon a model to use hardware to integrate programming experiences throughout the curriculum; in the model, the three learning principles deemed critical for success are student engagement, knowledge transfer and self-directed learning. In traditional Controls lecture courses, core course concepts are generally considered to be a bit abstract to a considerable percentage of students and there are often disconnects between theoretical course concepts, computational solution techniques and the behavior of real-world systems. Each of these challenges inhibit the three principles deemed critical for success in learning and it is posited in this paper that the introduction of programming involving hardware in the Controls course will enhance the three principles, resolve disconnects and improve overall student learning. The proposed re-design introduces programming experiences performed on micro-controller hardware to illustrate key topics and solution methods (e.g., system modeling and controller design) into a traditional Feedback Controls lecture course. The authors have previously taught the course numerous times, so a well-paced course schedule and solid foundation of course notes are already in place. Additionally, hybrid and problem-based learning (PBL) techniques have been incorporated into prior offerings, which enhances student engagement and allows both sufficient time to introduce programming modules and the ability of the instructor/research assistant (RA)/teaching assistant (TA) team to give necessary assistance and feedback during the programming experiences. A hands-on programming toolkit developed by Canfield and Abdelrahman for direct programming of micro-controller units (MCUs) uses MatLab as the programming environment. Using this toolkit, MCUs have been used to teach initial programming skills to engineering students in a context that matches their notions of engineering. In the Feedback Controls course, the model will be used to enhance programming skills in in a context that enhances senior students’ understanding of a somewhat abstract area of Mechanical Engineering. The overall goal is for students to be able to apply and observe the implementation of control algorithms on real-world hardware, without being hampered by significant obstacles or requirements for implementation. The MatLab-to-MCU toolbox effectively addresses this challenge, allowing students to implement their control algorithms using MatLab, a language they have previously used to simulate system response, “directly” on micro-controllers with little additional overhead requirements.

Computer literacy and programming are being taught increasingly at the K-12 level with more students than ever matriculating in college with prior programming experience. Accurately assessing student programming skills acquired in high school can inform college faculty about the range of competencies in introductory programming courses. The tool predominantly-used for assessing past CS knowledge and skills is a survey, which lacks quantitative rigor. This study aims to (1) quantify the effects of prior knowledge in entry-level programming courses and (2) compare the different measurement approaches of student prior knowledge in programming, including surveys and aptitude tests. The results of this study reveal that a discrepancy exists between the results of surveys and aptitude tests. Consistent with prior survey studies, our survey results showed that the effects of student prior programming knowledge faded gradually during the course period. In contrast, the aptitude test results indicated that the effects of student prior knowledge did not weaken over time. The accuracy of both measurements and implications for instructors were further discussed.

The college students who have relatively weak academic background feel more difficult in learning programming language grammars and programming skills in introductory course. At the end of semester, most of students had the negative attitude to programming and only a few students could write the programs for the given problems because they spent most of time to learn grammars instead of learning problem solving skills and logics. In this study, we propose to use Scratch in introductory programming course to help students to understand grammars and problem solving skills. It's necessary to educate first-time programmers how to solve the problems before they learn grammars of the programming language in their first programming language course. This paper shows that Scratch allows students not only to learn problem solving skills in programming but also to motivate students themselves in the class.

Engineers in construction and manufacturing are expected to be able to work with and lead diverse, multidisciplinary teams. Engineering students entering the workforce must be not only technically competent, but also possess skills in working with other people. These traits are frequently lacking with newly graduated engineering students due to the highly technical focus of their curriculum. The opportunity to develop Emotional Intelligence skills is limited. This research proposed that Civil and Environmental Engineering (CEE) undergraduate students may possess a higher level of Emotional Intelligence than Electrical and Computer Engineering (ECE) undergraduate students. The research also proposed that if differences were observed, that causal influences could be identified. Undergraduate engineering students at The Citadel completed The TalentSmart Emotional Intelligence Appraisal® along with demographic data and a variety of questions regarding their undergraduate experience. This research included freshman and senior students in Civil and Environmental (CEE) and Electrical and Computer Engineering (ECE) at The Citadel. These surveys suggested that undergraduate engineering students increase their EI score as they advance from Freshman to Senior year. The surveys also suggest that Electrical and ComputerEngineering (ECE) Majors do not advance in their EI scores from Freshman to Senior year as well as Civil and Environmental (CEE) majors. A positive connection was established between work experience and the impact on higher levels of EI. A positive correlation for growth in EI score was also demonstrated for students who attended high schools with smaller graduating classes. Based on the results of this research, the paper proposes CEE and ECE faculty place increased emphasis on encouraging students to pursue summer jobs, internships, and similar extracurricular programs. Both CEE and ECE faculty should evaluate their curriculum with an eye towards inculcating learning opportunities for EI into course work. Additionally, faculty and admission officials should give at least equal consideration to admitting students who graduate from smaller schools that may offer less college prep courses that may appeal to admissions officials.

Programming anxiety is a well-documented challenge in introductory computing courses, particularly among students with no prior exposure to programming. Scaffolding strategies such as pair programming, Parsons puzzles, and starter code templates are widely used to reduce anxiety and build confidence during the early stages of learning. Although starter templates often include explanatory comments and partially completed code, the relative effectiveness of these different scaffold types in mitigating anxiety has not been systematically examined. This article reports findings from a controlled experiment conducted during the first laboratory session of an undergraduate C programming course for students from different engineering and science disciplines. Pairs of students were randomly assigned to one of three conditions: a control group using blank templates, a comments-based scaffold group, and a code-based scaffold group. Coding anxiety was measured using a questionnaire with subscales related to self-efficacy, partner interaction, and peer judgement. Contrary to our expectations, the control group showed a greater reduction in coding anxiety than either scaffolded group, and students in the comments-based condition reported larger decreases in anxiety than those receiving code-based scaffolds. These findings suggest that for beginners, even well-intentioned scaffolds can temporarily increase the perceived difficulty in task completion.

Objectives . This study addresses the challenges faced by students repeating an introductory programming course (CS1) at an online university, where dropout and failure rates remain high. While programming education has been widely studied, targeted interventions for repeating students remain scarce. Our research aims to analyze the experiences of these students and develop a tailored intervention to improve support mechanisms. Participants. The study focuses on students retaking CS1, which were categorized into two groups: those who previously attempted but failed the course, and those who dropped out early with almost no follow-up of the course. Method. An observational approach was used to analyze students’ performance, behavior, and perceptions, combining data from institutional databases and questionnaires. Based on these insights, an intervention was designed, incorporating individualized feedback, structured engagement activities, and encouragement for students to reflect on their past experiences in order to foster a more supportive learning environment. Findings. Initial results indicate that the intervention increased student engagement, particularly in the early weeks of the semester. However, some students continued to struggle with course content, suggesting the need for additional instructional support. While the intervention was effective in fostering engagement, its impact on overall success rates was limited, highlighting the necessity of more personalized approaches. Conclusions. The study provides valuable insights for educators seeking to improve support for repeating students in programming courses. Key lessons include the importance of proactive engagement, tailored feedback, and structured interventions. Future research should focus on developing personalized interventions, validating a tailored questionnaire for deeper insights, and exploring adaptive learning technologies and longitudinal studies to enhance academic outcomes while reducing instructor workload.

Scholars believe that computational thinking is one of the essential competencies of the 21st century and computer programming courses have been recognized as a potential means of fostering students’ computational thinking. In tradition instruction, PFCT (problem identification, flow definition, coding, and testing) is a commonly adopted procedure to guide students to learn and practice computer programming. However, without further guidance, students might focus on learning the syntax of computer programming language rather than the concept of solving problems. This study proposes a peer-assessment-supported PFCT (PA-PFCT) approach for boosting students’ computer programming knowledge and computational thinking awareness. A quasi-experiment was conducted on a computer programming course in a high school to evaluate its influence on students’ learning achievement, computational thinking awareness, learning motivation, and self-efficacy. An experimental group of 51 students learned with the proposed approach, while a control group of 49 students learned with the traditional PFCT (T-PFCT) approach. The experimental results show that the proposed approach significantly enhanced the students’ computational thinking awareness, learning motivation, and self-efficacy, while not having significant impacts on their computer programming knowledge test scores.