Introductory Programming Research Articles

Changes in integration and wellbeing by educational background among migrants in Kristiansand

Abstract Background Integration levels and wellbeing of newly arrived migrants change according to their everyday activities in their new home country. Although migrants have very different educational backgrounds and expectations regarding work in Norway, the compulsory educational activities are the same for all, disregarding their educational background. Aims To examine the changes in integration and wellbeing among newly arrived migrants participating in a compulsory integration program in Norway, according to their educational background. Methods As part of the Integration for Health project (I4H) we collected information from participants in Kristiansand municipality through a self-filled questionnaire in 2023 (N = 102) repeated six months apart, including questions from the Immigrant Policy Lab Index and the WHO-5 Well Being Index. For both screening tools, higher scores indicate better integration and wellbeing. Information on educational background was dichotomized into either health or non-health (natural and technical) sciences, using health sciences as the reference. We used generalized estimating equations to calculate relative risks (RR) with 95% confidence intervals (CI) adjusted for age and gender. Results The mean age was 34 years (SD 8.3) and 59% were women. Mean residency time was 3 years (SD 0.14). In terms of education, 14% had health sciences, and 86% had other educational backgrounds. We did not find change in wellbeing over the six-month period (RR 1.0 (95% CI 0.9-1.1), and this trend was similar for both educational groups (RR 0.9 (95% CI 0.8-1.1). Integration score slightly increased after six months (RR 1.0 (95% CI 1.0-1.1). However, there was no difference by educational background (RR 1.0 (95% CI 0.9-1.0). Conclusions Changes in wellbeing and integration among participants in the compulsory introductory program with short residency time, do not seem to vary by educational background in a six-month period. Key messages • Schools and introductory programs in the host country can be used as a arena to enhance both the integration and wellbeing of newly arrived migrants. • Strengthening migrants’ educational opportunities in the new host country is also of importance.

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.

Predictive Modeling for Enhancing MOOC Completion Rates: A Case Study

In the realm of online asynchronous learning platforms, accurately tracking student performance to predictcourse completion times poses a significant challenge. Completion rates for MOOCs are typically low, with abias towards participants with higher education levels. Understanding factors such as student motivation,engagement, participation, and learning pathway design is crucial for improving student outcomes in onlinecourses. This research developed a predictive framework utilizing advanced deep learning techniques toaccurately forecast course completion times for participants enrolled in an introductory programming course("Python for Beginners" course on the Open Learning Platform of University of Moratuwa Sri Lanka). Byaccurately tracking student performance and leveraging a diverse dataset encompassing demographic andeducational variables, the research seeks to identify factors influencing course completion and predictindividual student outcomes. By utilising deep learning techniques, the prediction performance of the modelwill be improved, ultimately contributing to a more precise forecast of course completion times forparticipants. Evaluation of the model resulted in low Mean Absolute Error (MAE) of 0.0080 and low MeanSquared Error (MSE) of 0.0033 which promises the effectiveness of the developed method in accuratelypredicting course completion times for students. The findings of this study may help increase the successfulcompletion rate of such courses which are delivered in the online asynchronous mode. The study employedadvanced deep learning models optimized through Bayesian methods, highlighting the potential of thesetechniques to enhance MOOC completion rates by offering precise forecasts and actionable insights intostudent engagement. The comprehensive analysis revealed that variables such as 'Current_Lesson', 'SessionTime Category', and 'District_Score' significantly influence completion times. The robust methodologicalframework, including feature engineering, model training, and hyperparameter optimization, sets a precedentfor future research in the field. This research contributes to educational data mining and predictive analytics,offering a scalable approach to improving completion rates and educational outcomes across various onlinelearning platforms. Future research should explore incorporating real-time data and longitudinal studies toenhance model accuracy and generalizability. Additionally, addressing potential biases in the dataset, such asdemographic, prior knowledge, and resource access disparities, is essential to ensure the fair and equitableapplication of the model across diverse student populations. Expanding the research to include a wider rangeof courses and institutions will further validate the model's robustness and applicability in differenteducational contexts.

A Probability-Based Model for Course Completion Prediction in Online Asynchronous Learning

The sheer scale of Massive Open Online Courses (MOOCs), presents a significant challenge in delivering personalized learning experiences and effective student support. With vast participation numbers, it becomes difficult for instructors to track individual progress, pinpoint specific areas where students struggle, and understand the underlying reasons for failure to complete the course. This lack of individualized attention can lead to disengagement and higher dropout rates. A probability-based analysis offers a solution by generating student-specific predictions about their likelihood of completion. This empowers educators to proactively identify those at risk, tailor interventions, optimize resource allocation, and potentially improve the overall learning experience within the MOOC environment. This study focuses on developing a robust predictive framework to accurately estimate the probability of students completing an introductory programming course offered on the Open Learning Platform of the University of Moratuwa, Sri Lanka. The approach began with a classification model to determine the likelihood of course completion. Building upon this, a regression model was developed to generate a specific probability percentage representing the chance of a student successfully completing the course within a designated time frame. Initial findings suggested that predictions from the classification model achieved the highest accuracy when students have completed approximately 42.8% of the course materials. It is anticipated that further refinements to the methodology will improve the reliability of the predictions. A crucial aspect of this research involves determining the optimal percentage of course progress needed to yield reliable probability predictions. This is investigated through systematic analysis and experimentations including incremental model testing. The dataset encompasses diverse demographic and educational variables, enabling the identification of influential factors affecting course completion. This study provides insights on developing personalized learning strategies, intervention tactics and academic planning within online asynchronous education.

The ABCs of mental health at the university: a multi-level intervention design for promoting mental well-being.

There is an escalating concern for the mental health of university students being recognized as a high-risk group for psychological distress. Despite research emphasizing the need to integrate mental well-being into higher education, existing interventions primarily focus on challenges and support services, leaving a gap in practical insights for promoting mental well-being at the university as a whole. This paper aims to cover the theoretical and methodological foundations for the design and development of a complex multi-level intervention called the ABCs of mental health at the university (ABC-uni). Following the MRC framework for complex interventions, the design and development of a novel complex intervention is described (Phase I). Using the socio-ecological model and incorporating principles from health promotion charters, capacity building, organizational change models, and the principles of the ABCs of mental health a program theory for mental health promotion at the university is proposed. Following this theoretical foundation a logic model outlines the ABC-uni intervention components at structural, community, and individual levels. The components include staff training, campaign materials, a photovoice project, integration of mental well-being literacy into introductory programs, a credit-bearing course, and an online module. Preferably, most of these components are developed and carried out collaboratively with students at the university. As proposed in the logic model expected outcomes encompass heightened awareness, supportive environments, commitment, and accountability, aiming to enhance mental well-being across the entire university. The design and development of the intervention components occur at a Danish public university. This section addresses the strengths and limitations of the design of the ABC-uni intervention. Future research will cover the feasibility phase of each components of the ABC-uni intervention (phase II). The conceptual framework and program theory outlining mental health promotion at the university, along with the detailed description of the intervention components, provides valuable insights for fostering mental well-being in the university community.

Profiling the skill mastery of introductory programming students: A cognitive diagnostic modeling approach

Profiling the skill mastery of introductory programming students: A cognitive diagnostic modeling approach

Conflicts and contradictions in Sweden's temporary residence law for unaccompanied youth

ABSTRACT In November 2015, the Swedish Government claimed that the Swedish reception of refugees needed to change. The government presented among other things a time-limited law, TLUSE. An ethnographic study was conducted with a group of unaccompanied youth and the staff they encountered within a language introductory programme in a Swedish upper secondary school. The following questions have guided the investigation: (i) What are the main expressed principles for interpreting the law among the studied school staff? (ii) How has the application of these principles affected everyday life according to field notes and interview data? (iii) What contradictions and conflicts are there, given that TLUSE came into existence with rapid preparatory work and opinions from referral bodies being left without action? Through the lens of (Bernstein, B. 2000. Pedagogy, Symbolic Control and Identity: Theory, Research, Critique. London: Rowman and Littlefield) concept of pedagogic rights, a critical analysis was done. The study shows that TLUSE did not result in stronger inclusionary possibilities to unaccompanied youth.

Combining Reversibility With Program Visualization (PV) to Improve Introductory Programming Instructional Design

Combining Reversibility With Program Visualization (PV) to Improve Introductory Programming Instructional Design

Shuttle Time for Seniors: The Impact of 8-Week Structured Badminton Training on Markers of Healthy Aging and Evaluation of Lived Experiences-A Quasi-Experimental Study.

Engagement in sport offers the potential for improved physical and psychological well-being and has been shown to be beneficial for promoting healthy aging. Opportunities for older adults to (re)engage with sport are limited by a paucity of age-appropriate introductory sports intervention programs. As such, the study evaluated the efficacy of a newly designed 8-week badminton training program (Shuttle Time for Seniors) on markers of healthy aging and the lived experiences of participation. Forty-three older adults assigned to a control (N = 20) or intervention group (N = 23) completed pre-post assessment of physical and cognitive function, self-efficacy for exercise, and well-being. Focus groups were conducted for program evaluation and to understand barriers and enablers to sustained participation. Those in the intervention group increased upper body strength, aerobic fitness, coincidence anticipation time, and self-efficacy for exercise. Objectively improved physical and cognitive functions were corroborated by perceived benefits indicated in thematic analysis. Shuttle Time for Seniors was perceived as appropriate for the population, where the age-appropriate opportunity to participate with likeminded people of similar ability was a primary motivator to engagement. Despite willingness to continue playing, lack of badminton infrastructure was a primary barrier to continued engagement. Shuttle Time for Seniors offered an important opportunity for older adults to (re)engage with badminton, where the physical and psychosocial benefits of group-based badminton improved facets important to healthy aging. Significance/Implications: Age-appropriate introductory intervention programs provide opportunity for older adults to (re)engage with sport. However, important barriers to long-term engagement need to be addressed from a whole systems perspective.

Implementation of Ultrasound-Guided Cannulation Training Across Eight NHSBT Therapeutic Apheresis Units in England.

Ultrasound-guided cannulation (USGC) of a peripheral vein reduces the need for central vascular access device (CVAD) placement to perform a successful apheresis procedure. Effective training of healthcare professionals to acquire this skill is essential. Here, we report on the implementation of the USGC training across eight apheresis units in England. A 3-h introductory training program was devised with theoretical and practical elements. This was followed by supervised USGC practices on any patient ≥ 18 years old, regardless of venous status. Data on all supervised USGC attempts were recorded and analyzed. Over an 11-month period, 11 nurses were trained to USGC competency with another six nurses still in training, resulting in seven out of eight units having at least one USGC-competent nurse. In one unit, USGC training has not started yet. Three hundred sixty-one supervised USGC episodes on 168patients and donors took place; of these, 178 were done for training purposes only on patients who had visible and palpable veins, 179 USGC were done on patients with difficult venous status and four were not recorded. The period from first supervised USGC to competency was a median of 45 days (Range: 17-166 days), with a median of 15 successful (Range: 10-30) and two unsuccessful (Range: 1-15) USGC being performed per trainee. The placement of 57 CVADs and 41 multiple cannulation attempts have been avoided. USGC is a useful tool to reduce the need for CVAD. Training across multiple apheresis units is a lengthy procedure, but it can be successfully implemented.

Exploring the Utilisation of Generative AI Tools by Undergraduate First-Year Mechanical Engineering Students in Programming Assessments.

Integrating the fundamentals of computer science and programming skills into the undergraduate engineering curriculum has been a primary focus for many educational institutions worldwide. Learning the basics of programming from the beginning of undergraduate engineering education allows students to incorporate such skills into their future work easily. Therefore, an introductory programming course for first-year undergraduate students has been running since 2021 in the Mechanical Engineering Department at University College London intending to teach the fundamentals of Python programming language. However, it is well-known that generative artificial intelligence (Gen AI) tools in higher education are moving so fast that a wait-and-see approach cannot be taken. These applications have received much global attention from academics on their impact and proper use within the teaching-learning process. This paper investigates first-year undergraduate mechanical engineering students' use of Gen AI tools in their programming assessment. The results show that 60% of the cohort used tools that helped mainly to check their code, improve their English language, and understand error messages. However, 40% abstained from using any. Based on these findings, recommendations on how Gen AI tools can be utilised by undergraduate students in ways that support their learning and enhance their ability to achieve learning outcomes are made.

Why Students Cannot Easily Integrate Component Skills: An Investigation of the Composition Effect in Programming

Programming skills are increasingly important to the current digital economy, yet these skills have long been regarded as challenging to acquire. A central challenge in learning programming skills involves the simultaneous use of multiple component skills. This article investigates why students struggle with integrating component skills—a challenge called the composition effect. This effect was first studied in learning mathematics, and findings in that area have led to more effective instruction in mathematics. In the current work, we systematically investigate the nature of the composition effect in novice code tracing through a classroom study in a university-level introductory programming course. We designed a set of problems that require integrating component skills, along with matched problems that use the same component skills separately. The composition effect was defined as the performance difference between these two types of problems. We further singled out errors that were committed in integration problems but not in the matched problems and conducted qualitative coding to categorize these errors. Statistical analyses produced three main findings. First, we found a robust composition effect across problems and students, mostly with small or medium effect sizes. Second, we identified two potential sources of errors for this composition effect: conceptual misunderstandings of how to integrate component skills and process errors resulting from slips related to cognitive load challenges. In particular, additional conceptual knowledge often appears to be needed when component skills are integrated in specific ways. Moreover, the primary source appears to be misunderstandings of how skills integrate. Composition effects may not generalize across topics, which suggests that domain topics may serve as the conceptual basis for the composition effect. Third, we found stable individual differences in susceptibility to the composition effect in both overall and finer-grained levels. Our findings provide implications for building theories that may generally explain challenges in learning complex skills and may result in practical designs for skill integration enhanced curricula and instruction to foster student mastery in computing education.

Experiencing tensions of nepantla with inner-departmental change groups

This study explores the experiences of three Networked Improvement Communities (NICs) within mathematics departments as they work to critically transform their introductory mathematics programs. Drawing on the framework of dominant and critical axes of equity, we identify three key tensions experienced by the NICs: identity neutrality versus identity centrality, power over versus power with, and students as novices versus students as experts. These tensions are framed as productive enactments of working to change systems from within, highlighting the challenges and opportunities inherent in navigating the liminal space of nepantla. We argue that engaging with these tensions is crucial for fostering critical transformations and offer recommendations for leveraging the roles of identity, power, and student partnership to promote more equitable and inclusive mathematics education environments.

Questioning the why and the how: Collective transformative agency of experienced teachers co-designing a justice-oriented high school introductory computing program

We report findings from qualitatively analyzing audio recordings and teacher-generated products from a two-day in-person collaborative design (co-design) workshop where nine experienced high school CS teachers revised parts of an existing introductory high school program toward justice. In response to our research question of how teachers demonstrated agency as co-designers, our analysis revealed that they collaboratively questioned the purpose, the “why,” and the approach, the “how,” and captured ideas in lesson templates and unit descriptions to transform future practice. We discuss co-design with teachers as openings to explore nuances of teachers’ visions for equitable CS education. We highlight the foundational longstanding relationships and politicized trust, and enabling features of the co-design space for equitable participation and joint creation of boundary objects to sustain ideas.

Integrating Data Analytics Training into Curricula at Small Colleges: A Practical Framework for Enhancing Student Skills

Quantitative Data Analytics (QDA) is a critical skill for professionals across various natural science disciplines. However, college curricula, especially at small colleges, often lag behind the evolving workforce’s needs due to limited faculty resources. Despite abundant training materials, there is a noticeable gap in practical frameworks for effectively developing and implementing QDA training programs, particularly addressing existing curriculum gaps and resource constraints. This study presents a practical framework for developing introductory QDA training programs tailored for non-statistical majors at resource-constrained small colleges with various educational settings. Employing a design science strategy, we developed a modular co-curricular QDA Training Program (CQDATP), consisting of five training modules refined over three iterative phases. This flexible design integrates seamlessly with existing courses or can function as standalone workshops, bolstering students’ preparedness for advanced courses and research opportunities. Our program evaluation indicates that our approach significantly enhanced students’ QDA capabilities without necessitating extensive curriculum overhauls. By focusing on adaptability and resource efficiency, our framework holds the potential to benefit educators, curriculum developers, and administrators at small colleges, ensuring that graduates are well-prepared for data-driven industries, even when resources are limited.

Programming Fun(damentals): Using commercial video games to teach basic coding to adult learners

The acquisition of computational thinking and coding skills is of special significance in helping adult learners keep pace with a new context where those skills are required for many parts of the workforce. However, there is an agreement in the scientific literature that important challenges exist, such as keeping student engagement and the requirement of developing problem-solving skills, and not just learning the code syntax. The inclusion of digital Game-Based Learning (GBL) has shown great potential. To better understand how it can be helpful to adult learners in STEM degrees, a study was conducted on the application of two very popular commercial video games in different introductory programming courses at an online university, during the introduction of basic programming topics (CS1). The methods combined a descriptive qualitative and a quantitative approach, using a reflective journals and questionnaires, which helped students consider and express their experiences, how they interacted with the games, how the games helped them better understand the topics, and realize their personal progress and potential. Results showed that the use of these games as educational resources had a positive impact in their engagement and sense of self-efficacy, but only if some important instructional considerations are taken into account.

Measuring Undergraduates’ Motivation Levels When Learning to Program in Virtual Worlds

Teaching/learning programming is complex, and conventional classes often fail to arouse students’ motivation in this discipline. Therefore, teachers should look for alternative methods for teaching programming. Information and communication technologies (ICTs) can be a valuable alternative, especially virtual worlds. This study measures the students’ motivation level when using virtual worlds to learn introductory programming skills. Moreover, a comparison is conducted regarding their motivation levels when students learn in a traditional teaching setting. In this study, first-semester university students participated in a pedagogical experiment regarding the learning of the programming subject employing virtual worlds. A pre-test-post-test design was carried out. In the pre-test, 102 students participated, and the motivation level when a professor taught in a traditional modality was measured. Then, a post-test was applied to 60 students learning in virtual worlds. With this research, we have found that the activity conducted with virtual worlds presents higher motivation levels than traditional learning with the teacher. Moreover, regarding gender, women present higher confidence than men. We recommend that teachers try this innovation with their students based on our findings. However, teachers must design a didactic model to integrate virtual worlds into daily teaching activities.

Cracking the code: An evidence-based approach to teaching Python in an undergraduate earth science setting

Scientific programming has become increasingly essential for manipulating, visualizing, and interpreting the large volumes of data acquired in earth science research. Yet few discipline-specific instructional approaches have been documented and assessed for their effectiveness in equipping geoscience undergraduate students with coding skills. Here we report on an evidence-based redesign of an introductory Python programming course, taught fully remotely in 2020 in the School of Oceanography at the University of Washington. Key components included a flipped structure, activities infused with active learning, an individualized final research project, and a focus on creating an accessible learning environment. Cloud-based notebooks were used to teach fundamental Python syntax as well as functions from packages widely used in climate-related disciplines. By analyzing quantitative and qualitative data from surveys, online learning platforms, student work, assessments, and a focus group, we conclude that the instructional design facilitated learning and supported self-guided scientific inquiry. Students with less or no prior exposure to coding achieved similar success as peers with more experience, an outcome likely mediated by higher engagement with course resources. We believe that the constructivist approach to teaching introductory programming and data literacy that we present could be broadly applicable across the earth sciences and in other scientific domains.

Exploring the role of a microlearning instructional approach in an introductory database programming course: an exploratory case study

Exploring the role of a microlearning instructional approach in an introductory database programming course: an exploratory case study

Academic orientation and cannabis use-findings from a population-based study of Swedish adolescents in upper secondary school.

To examine the association between academic orientation and frequent cannabis use among Swedish adolescents in upper secondary school and include pupils from introductory programs (IPs), a large group of adolescents previously overlooked in research on adolescent cannabis use. We used cross-sectional data from two anonymous school surveys carried out in upper secondary school in 2021. The samples consisted of pupils from all academic orientations, and the analysis included 3151 pupils in higher education preparatory programs (HEPs), 1010 pupils in vocational programs (VPs), and 819 pupils in IPs. The association between the exposure academic orientation and the outcome frequent (21+ times) cannabis was analyzed using multi-level mixed-effects Poisson regression. Estimates from the first model showed a significant (P < 0.05) 2.45 times higher risk of frequent cannabis use among pupils in IPs compared with in HEPs [incidence rate ratio (IRR) 2.45, 95% confidence interval (CI) 1.28-4.66] and 82% higher in VPs (IRR 1.82, 95% CI 1.09-3.04) compared with in HEPs. However, the associations between academic orientation and frequent (21+ times) cannabis use were attenuated and no longer significant when socioeconomic status, truancy, school dissatisfaction, and early onset of substance use were adjusted for. There was a higher risk of frequent (21+ times) cannabis use among pupils in IPs, and this differential was explained by higher exposure to risk factors in this group. This result is important from a policy perspective as it provides knowledge of a previously neglected risk group for frequent cannabis use.