Programming Course Research Articles

The relationships between online program formatting, technology satisfaction, and course quality

This study aims to explore students’ perceived quality of graduate courses in relation to their satisfaction with the technological tools used in online master’s level courses. Invariance testing and structural equation modelling were used to demonstrate the validity of technology usage scales and effectively test links between course type, technology satisfaction, and course quality. The study participants included 364 graduate students enrolled at a large university in the Midwest who were taking blended and fully online courses. The participants responded to a scale regarding their experience with the course. Interestingly, program type (blended vs. fully online) did not significantly impact technology satisfaction or the perceived quality of the course, even after taking into account the technology satisfaction mediator on course quality. Program type is not related to course quality, although technology satisfaction does contribute to quality ratings. The implications for instructors suggest that the development of technology-centric activities is warranted to improve the quality of online education courses.

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.

Slow and Steady or Fast and Furious: An Analysis of Completion Duration in open.uom.lk

The open.uom.lk platform is an open learning platform developed by the University of Moratuwa, Sri Lanka, offering free asynchronous online IT related courses. The platform was developed as an initiative to meet the growing human capital requirement of the IT industry in Sri Lanka, one of the only sectors showing strong growth even during the country’s recent economic crisis. In the two years since the release of its flagship course Trainee Full-Stack Developer, there have been over 270,000 enrolled learners. The course consists of six subjects covering Python programming, web design, and a professional practice module that includes a capstone project. Despite the massive number of enrollments, as in many other MOOCs, completion rates remain low. For instance, for the two beginner courses in programming and web design, into which all participants are enrolled automatically, completion rates stand at 7-10%. Completion rates for subsequent subjects are higher given that enrollment is conditional on completing prerequisites. From a planning perspective, it is not just estimates of how many participants complete the programs but also the time they take to do so that are integral for determining the future growth of the IT industry. As such, this study aims to develop a model for course completion using survival analysis which has the advantage of being able to model censored data (e.g. when duration is unknown due to non-completion) and provide forecasts of completion rates at different points in time. The analysis uses student activity completion reports and demographic information of participants including employment and educational background. The findings suggest that the course interaction variables, particularly the speed of completing early activities, are more important for predicting course completion than the demographic variables, though education and employment status are also significantly correlated with completion. The survival model could then be used for purposes of predicting both the completion probability as well as the timeframe within which the completion may be expected.

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.

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.

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.

Analyzing descriptive report writing among English language learners in Japan through a genre-based approach to second language writing

Abstract This mixed-methods study evaluated the evolution of thematic development in descriptive report writing among 12 first-year English for Academic Purposes (EAP) university students in Japan over the course of a 15-week program. The program employed a genre-based approach within a Systemic Functional Linguistic framework. Four consecutive information report essays (two pre-essays, a post-essay, and a delayed test essay) were analyzed to assess the student’s writing progress. The results revealed a general progression, starting with limited Theme development in the first pre-essay, to some students exhibiting a growing familiarity with coherence and cohesion in terms of the generic structure of descriptive reports.

Examining Diversity and Equity in LIS Education: A Syllabus to Reading List Analysis

To determine how effectively the Master of Library and Information Science curriculum at Indiana University - Indianapolis is addressing the new ALA core competency related to social justice, we analyzed all the course syllabi and are now in the process of analyzing all course readings, using various text analysis methods. We found the initial review of syllabi to lack the level of granularity that would have revealed the different kinds of diversity and equity issues in the curriculum. Additionally, courses we know for certain that covered topics related to social justice, the syllabus alone indicated otherwise. For these reasons, we decided to examine the readings that comprised the entire curriculum. With 68 courses and over 3000 readings to date, gathering the data for analysis has taken several months of time. Metadata and PDFs of course readings were stored in Zotero and extracted from there for analysis. Terminology, as identified from the study that examined all ALA-accredited LIS program course descriptions, will be used to frame the textual analysis (Dill, Grote, & Hardin, 2023). We will present the topics and trends identified through bibliometric analysis, topic modeling, and visual analysis of the citation data. This analysis will shed light on areas of strength and for improvement within curriculum with the goal of preparing students to effectively work in socially just library environments.

Mediating Effects of Students’ Study Time on ICT Resource Availability, Teacher Competence and Academic Performance in Programming Languages

This study investigated the mediating role of students' study time in the relationships between the availability of ICT (information and communication technology) resources, teacher competence, and academic performance in programming language courses. The researchers used a sample of 287 undergraduate students and employed structural equation modeling (SEM) to analyze the hypothesized mediating effects. The findings showed that both ICT resource availability and teacher competence had positive and significant indirect effects on academic performance, which were partially mediated by the amount of time students spent studying. Specifically, the indirect effect of ICT resource availability on academic performance through study time was 0.159 (p < 0.001), and the indirect effect of teacher competence on academic performance through study time was 0.195 (p < 0.001). These results suggest that while ICT resources and teacher competence directly contribute to improved academic performance in programming languages, they also indirectly influence performance by promoting increased study time among students. The mediating role of study time highlights the importance of creating an environment that supports and encourages students to engage in self-directed learning activities, in addition to providing adequate technological resources and competent teaching. The findings have important implications for educational practitioners and policymakers, as they emphasize the need to adopt a holistic approach to enhancing student learning outcomes in programming courses by understanding the complex interplay between various factors and their indirect effects.

An Independent Learning System for Flutter Cross-Platform Mobile Programming with Code Modification Problems

Nowadays, with the common use of smartphones in daily lives, mobile applications have become popular around the world, which will lead to a rise in Flutter framework. Developed by Google, Flutter with Dart programming provides a cross-platform development environment to create visually appealing and responsive user interfaces across mobile, web, and desktop platforms using a single codebase. However, due to time and staff limitations, the Flutter/Dart programming course is not included in curricula, even in IT departments in universities. Therefore, independent learning environments for students are essential to meet this growing popularity. Previously, we have developed programming learning assistant system (PLAS) as a web-browser-based self-learning platform for novice students. PLAS offers various types of exercise problems designed to cultivate programming skills step-by-step through a lot of code reading and code writing practices. Among them, one particular type is the code modification problem (CMP), which asks to modify the given source code to satisfy the new specifications. CMP is expected to be solved by novices with little effort if they have knowledge of other programming languages. Thus, PLAS with CMP will be an excellent platform for independent learning. In this paper, we present PLAS with CMP for the independent learning of Flutter/Dart programming. To improve the readability of the source code by students, we provided rich comments on grammar or behaviors. Besides, the code can be downloaded so that students can check and run it on an IDE. For evaluations, we generated 38 CMP instances for basic and multimedia/storage topics in Flutter/Dart programming and assigned them to 21 master students at Okayama University, Japan, who have never studied it. The results confirm the validity of the proposal.

The Good and Bad of AI Tools in Novice Programming Education

As AI coding tools become more prevalent in programming, it is essential to understand how they influence programming education. This study, conducted in a first-semester Introduction to Programming course, aimed to determine the positive and negative effects of these tools on students’ learning experiences and their ability to develop essential programming skills. Using a mixed-methods approach, we collected data from 73 teams of engineering students over a 12-week period. Students completed surveys and reported on their AI tool usage. We analyzed this data quantitatively to identify trends in tool familiarity, usage, and student satisfaction. Additionally, qualitative analysis of student reports provided insights into the specific ways AI tools were used and their perceived benefits and drawbacks. The findings revealed a significant increase in AI tool familiarity (from 28% to 100%) and usage among students. Students’ satisfaction with AI tools improved over time. The most prevalent tasks for which novice programmers used AI tools included creating comments (91.7%), identifying and correcting bugs (80.2%), and seeking information (68.5%), while other tasks were less common. While these tools offered benefits like assisting in learning and enhancing real-world relevance, they also raised concerns about cheating, over-reliance on AI tools, and a limited understanding of core programming concepts.

Developing a Micro-Credential Curriculum in IoT for Malaysian Community Colleges: Key Topics and Projects

The Internet of Things (IoT) is a crucial course in Information Technology programs, merging electronic circuit concepts with programming skills to create interconnected devices. This study aimed to design a Malaysian Community College micro-credential curriculum for IoT by identifying relevant theoretical and practical topics and mini-projects. A need for a micro-credential module with suitable instructional materials was identified, enabling learners to apply theoretical concepts through mini-projects. Using the Fuzzy Delphi (FD) method, data from 19 experts via semi-structured interviews were analyzed to design the Fuzzy Delphi instrument. This instrument was distributed to 30 industry experts and academic practitioners, who ranked the elements on a five-point Likert scale. Fuzzy evaluation to establish consensus for inclusion requires an average expert agreement of 75%, a d-construct threshold of 0.2, and an average fuzzy number of 0.5. Consensus was reached for six out of nine theoretical topics and nine out of 13 practical topics. “Fundamental concepts of IoT” was deemed the most critical theoretical topic with 97% consensus, while "basic connectivity of IoT hardware" was the top practical topic with 100% consensus. For mini-projects, experts accepted two out of nine theoretical topics and nine out of 13 hands-on topics. The top authentic projects were “Air Quality Monitoring” and “Smart Home Security”, with 83% and 80% consensus, respectively. This study lays the groundwork for developing learning outcomes and assessment strategies for a micro-credential course on IoT at Community Collegesto equip TVET students with industry-relevant competencies, thus making them more employable.

Cybersecurity activities for education and curriculum design: A survey

Cyber threats are one of the main concerns in this growing technology epoch. To tackle this issue, highly skilled and motivated cybersecurity professionals are increasingly in demand to prevent, detect, respond to, or even mitigate the effects of such threats. However, the world faces a workforce shortage of qualified cybersecurity professionals and practitioners. To address this dilemma, several cybersecurity educational programs have been introduced, such as specialized cybersecurity courses in computer science graduate programs. With the increasing demand, different cybersecurity courses are introduced at the high school level, undergraduate computer science and information systems programs, and even at the government level. Due to the peculiar nature of cybersecurity, educational institutions face many issues when designing a curriculum or cybersecurity activities. In this paper, we study existing cybersecurity curriculum approaches and activities. We also present case studies on cybersecurity education around the globe.

Development and Implementation of a Web-Based Management Information System for Enhancing Computer Programming Training in Education

In the current era of information technology, web-based training systems are critical for enhancing the efficiency of training processes. This study focuses on the development of a Management Information System (MIS) tailored for computer programming training, utilizing a web-based training (WBT) platform. The primary objective is to introduce a framework for a web-based MIS that optimizes the management and delivery of computer programming courses. Computer programming education presents unique challenges, such as real-time participant tracking and dynamic content management, which are addressed by this MIS. The system was developed using the waterfall model, with data collected through interviews, observations, and requirements analysis. Key features include training schedule management, participant tracking, material organization, session management, evaluation tools, and reporting capabilities. Real-time feedback enhances participant engagement, while session management improves the overall learning experience. The web-based platform offers flexible access and simplifies the management of training processes, enabling users to track schedules, access materials, and monitor progress. Validation results show a high validity score (V = 0.883), exceeding the required threshold, and the materials received a validity score of 0.864, confirming their suitability for basic programming competency training at Medan State University. These results demonstrate the system’s effectiveness and potential for wider application, making it a valuable tool for improving the management and delivery of programming courses across various educational contexts.

교원양성기관의 생활지도 영역 교육과정 개선에 관한 합의적 질적연구

Purpose: This study aimed to examine the experiences and perceptions of in-service teachers and pre-service teachers regarding the Life Guidance Curriculum in teacher training institutions, in order to obtain insights for improving the related curriculum at these institutions. Methods: This study used a consensual qualitative research method targeting a total of thirty people from February to July 2024. Individual interviews were conducted with twenty-one current and prospective teachers, and focus group interviews were conducted with nine educational experts. Relevant research reports and policy documents were also referenced in this study. Results: As a result of analyzing the collected data, each area, category, and subcategory were derived with a total of four topics. The category of the research participants' experiences and perceptions was first, 'difficulties experienced in teaching life guidance ', second, 'life guidance competency that pre-service teachers need to qualify', and third, 'experience of taking lectures related to life guidance at teacher training institutions', and finally, 'proposal for improvement of lectures related to life guidance at teacher training institutions'. Conclusion: This study revealed a change in the field where the term of 'life guidance' was being transitioned by the concept of 'life education', and it was possible to examine the necessity of developing teachers' community leadership competencies, and the importance of life guidance linked to the subject. Through this, we propose the necessity of changing the guidance-related courses, currently categorized as elective courses in teacher training institutions, to required courses. We urge academia and teacher training institutions to pay close attention to ensuring that future guidance-related courses in teacher training programs are improved to adequately reflect the relevance and applicability to school settings.

Integration of AI Training in the Field of Higher Education in the Republic of Bulgaria: An Overview

The presented work provides a comprehensive evaluation of the current availability of education programs and courses related to of AI the field of Information Technologies and Computer Science in higher education institutions (HIEs) in the Republic of Bulgaria. More specifically, this study examines 163 bachelor’s and 239 master’s degree programs from 28 HEIs available during the 2023/24 academic year in four professional fields: (1) Electrical Engineering, Electronics, and Automation; (2) Communication and Computer Technologies; (3) Informatics and Computer Science; and (4) Mathematics. The conducted evaluation shows that 41.1% of evaluated BSc programs and 26.4% of MSc programs include at least one AI-dedicated course. Results indicate a significant presence of AI-focused education, particularly in degrees related to Informatics and Computer Science, where 47.8% of AI courses are concentrated. However, a notable disparity exists in the inclusion of AI subjects across other technical fields, particularly in Electrical Engineering and related degrees, which contain only 8% of the identified AI courses for BSc degree programs. The findings highlight the need for a broader and more accelerated integration of AI education to meet the evolving demands of both students and the labor market. This work underscores the importance of strategic curriculum adaptation to enhance the readiness of Bulgarian HEIs to support the development and application of AI technologies, addressing the skills gap and fostering a workforce capable of navigating the AI-driven future.

Improving 4C Skills Through the Application of Gamification-Based Interactive Evaluation Media Using Flipped Classroom Integrated Project Based Learning Model

This study sought to ascertain how well students' 4C skills were improved by utilizing gamification-based interactive evaluation materials in conjunction with a flipped classroom and project-based learning approach. Pre-experimental research with a quantitative approach and one sample class was the methodology employed in this study. Students enrolled in the Jambi University Chemistry Education Study Program's course on chemistry learning curriculum creation served as the study's samples. A questionnaire was used to collect data on the 4C competencies of the pupils. The normalcy of the variation in the students' 4C skills outcomes was examined. The data from the sample class is normally distributed and parametric tests can be performed if the significance value is higher than 0.05. In this study, one sample t-test was employed for hypothesis testing. To find out how well gamification-based interactive evaluation materials combined with a flipped classroom and project-based learning approach improve 4C abilities, hypothesis testing was done. The study's conclusion is that students' 4C skills can be effectively improved by implementing gamification-based interactive evaluation materials in conjunction with a flipped classroom and project-based learning approach.

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.

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.