REVIEW ON PROGRAMMING LANGUAGE LEARNING MODELS AND INSTRUCTIONAL APPROACHES: CURRENT TRENDS AND FUTURE DIRECTIONS

Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4437 REVIEW ON PROGRAMMING LANGUAGE LEARNING MODELS AND INSTRUCTIONAL APPROACHES: CURRENT TRENDS AND FUTURE DIRECTIONS SANAL KUMAR T S1 AND R THANDEESWARAN1* 1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India. *Corresponding author. E-mail: [email protected]; Contributing author: [email protected] ABSTRACT Programming has become a fundamental subject in the academic curriculum, but the learning process presents unique challenges. It requires not only systematic study and dedication but also the application of logical thinking and problem-solving skills in practical contexts. This complexity makes programming particularly difficult for beginners, who often face challenges in understanding foundational concepts like sequencing, decision-making, and looping. As a result, various pedagogical methods have evolved to address the difficulty in programming learning. To better understand the recent developments in programming educational approaches, we aim to provide a detailed review of learning models and instructional approaches in programming learning. Following this, we explore the cognitive factors influencing the learner and the essential aspects of the learner’s learning style and preferences in programming education. Finally, we conclude the review by discussing how these techniques can be combined to formulate future pedagogical approaches in programming instruction. Consequently, this review proposes integrating the learning style model with adaptive e-learning environments (ALE) in a blended learning approach as a better solution to address the hurdles of programming learning difficulty. Given this, the review paper provides a comprehensive overview of the programming learning environments, strategies, instructional approaches, cognitive factors, and learning styles leveraged in programming education, which future researchers can utilize. Keywords: Programming Education; Difficulty In Programming; Instructional Methods; Learning Style Models; Adaptive Learning Environments. 1. INTRODUCTION Programming has become an essential part of primary and higher education, extending beyond traditional computer science fields to basic sciences and management. Despite its importance, learning to program poses unique challenges, as it demands not only theoretical understanding but also practical application of concepts through logical thinking and problemsolving. Core programming constructs like sequencing, decision-making, and looping are particularly challenging for beginners, who often struggle to grasp these foundational elements. This difficulty has led to high dropout and failure rates in introductory programming courses, which has, in turn, sparked significant research into better instructional methods for programming education [1]. In response to these challenges, researchers have proposed various teaching and learning methods to make programming more accessible, incorporating diverse learning models and instructional strategies. Although some methods have shown promising results, there is still a lack of consensus on the most effective approaches [2]. This review investigates current trends in programming education, exploring the impact of different learning models, instructional approaches, and cognitive factors that shape how students learn programming. By analyzing these elements, the review aims to clarify which Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4438 approaches have the greatest potential to improve learning outcomes and student retention in programming courses. This review differs from previous works in both motivation and approach. While prior studies have focused on evaluating isolated techniques or proposing specific frameworks, this work aims to provide a comprehensive evaluation of existing learning models and instructional strategies in programming education. Unlike earlier reviews that primarily catalog methodologies, this study emphasizes the individual preferences and cognitive barriers faced by learners and highlights how these factors influence learning outcomes. Furthermore, it explores the integration of adaptive learning technologies with traditional methods to better address the diverse needs of learners. The primary objective of this study is to examine existing models and methods used to teach programming and assess their effectiveness within current educational trends. Additionally, this review aims to identify the key challenges in programming instruction, such as cognitive and motivational barriers, and propose directions for future research. This includes exploring integrative instructional strategies that may address the limitations of existing models and meet the diverse needs of learners in a rapidly evolving digital environment. In doing so, the review provides insights into how programming education can be improved to support students more effectively, potentially reducing dropout rates and fostering a deeper understanding of programming principles. 1.1. Programming Learning Difficulty Difficulty in learning programming is a universal scenario. Researchers reported several causes for difficulty in programming learning. The most critical issue is the lack of problem-solving abilities, which most students lack. Learning and writing computer programs can be particularly difficult for students who have not been properly introduced to the fundamental concepts and skills required. Without a strong foundation, students may struggle with understanding the syntax and logic of programming languages, debugging errors, and applying theoretical knowledge to practical problems. The lack of skilled instructors to teach programming is an issue, as well as a lack of scientific methods or techniques to learn the art of programming [3]. Instructors who teach programming language subjects have less pedagogical support to prepare and practice computer programming, especially when the teacher needs to teach it as an introductory course. Researchers propose different methods to address these problems, which may vary depending on the learner’s learning capability. Learning programming can be challenging for several reasons. First, programming requires a strong foundation in logic and problem-solving skills, which can take time for beginners to develop. The abstract nature of programming concepts, such as algorithms, data structures, and syntax, often leads to confusion and frustration. Additionally, debugging and troubleshooting code demand patience and a systematic approach, skills that take time to cultivate. A learning curve focuses on programming learning, which estimates a learner’s performance over time. The steep learning curve is further compounded by the need to understand the intricacies of different programming languages and tools. Finally, the lack of immediate feedback and personalized guidance in traditional learning environments can hinder progress, making it hard for students to identify and correct their mistakes promptly. These factors combined make programming a demanding subject for mastery [3]. 1.2. Objective of The Study a. To analyze and understand diverse learning models, instructional strategies, and cognitive elements that influence programming education, focusing on approaches that support novice learners and enhance programming comprehension and retention. b. To identify and assess key gaps in the literature related to existing instructional methods, learner engagement strategies, and adaptive learning models in programming, particularly those that fail to account for individual learning preferences and the unique challenges posed by programming for beginners. c. To propose future directions and improvements by integrating insights from recent technological advancements, such as adaptive and blended Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4439 learning approaches, that address the limitations found in current programming instruction and better support diverse learner needs and cognitive differences. 1.3. Outline This paper is organized as follows: Section I covers the significance and challenges of introductory programming courses. Section II provides an in-depth review of the literature on learning environments, strategies, instructional approaches, and cognitive factors relevant to programming education, while also highlighting traditional learning style models. Section III identifies gaps within the existing research, and Section IV outlines the research methodology used in this study. Section V presents current trends and suggests future directions. Section VI discusses several critical challenges remain unaddressed in programming education, followed by limitation in the study, and finally, Section VI offers the conclusion. The schematic arrangement is depicted in Fig.1. 2. LITERATURE REVIEW In this study, a selective review of the existing literature has been conducted in alignment with the research framework’s objectives. This Figure 1: The schematic arrangement of this paper approach aids in identifying unresolved gaps within the selected research area and offers suggestions for future research directions. 2.1. Programming Learning Environments Researchers and developers build various learning tools and interfaces to address the difficulties in learning programming. The aim is Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4440 to ease the learning process and increase student success rate. Text-based, Block-based, gamebased, and visual programming are programming environments designed to teach and facilitate coding skills, particularly for beginners or young learners [4]. Both text-based and block-based programming environments are educational tools that cater to different learning stages and preferences. Block-based programming is often used in introductory programming courses for children and beginners, as it visually represents the flow of logic without requiring detailed knowledge of syntax. Text-based programming, on the other hand, is used for more advanced programming education and professional development, requiring learners to understand and use the precise syntax of a programming language [2]. Game-based programming involves learning to code through interactive and engaging game scenarios. These environments are designed to teach programming concepts by integrating them into game design and gameplay [5]. This method is highly motivational for learners, as it combines the fun of gaming with the educational aspects of coding. It helps learners understand programming logic and problem-solving skills within a gamified context. On the other hand, visual programming environments allow users to create programs by manipulating elements graphically rather than writing code textually. These environments are designed to simplify the coding process by enabling users to drag and drop visual elements that represent code structures and logic. Moreover, visual programming benefits beginners, young learners, and those who benefit from a more intuitive and visual approach to understanding programming concepts with ease [6]. In the following sub-sections, we summarized different studies related to Text-based, Blockbased, game-based, and visual programming environments. 2.1.1. Text-based programming In the early days, only text-based programming languages were used to teach programming. Programming learning with High-level programming languages started with text-based procedure-oriented languages such as Fortran, Cobol, and Algol [2]. Text-based programming environments can be either text editors or IDEs (Integrated development environments). Because of its unfriendly nature, it was difficult for the average student to learn and write programs legitimately. The lack of pedagogical methods made the process very cumbersome. Novice learners are not expected to learn programming with text-based programming environments since their first experience confirms it. Simpler programming languages and environments are advisable because they are more approachable. Nowadays, programming starts in the early stages of education. The complicated and sophisticated programming constructs confuse the novice programmer and negatively affect the learning process.[2]. 2.1.2. Block-based programming Traditional programming instruction often relies on text-based programming environments. However, the syntactic and semantic challenges associated with text-based coding have driven the development of block-based and game-based programming approaches. For novice learners, block-based programming languages are more convenient and easy to learn, moreover, they help them to reduce the programming learning barrier [7]. The first modern block-based programming language was AgentSheets [4], designed for kids to learn programming hassle-free. It is a gamebased learning platform that uses a block-based programming method. AgentSheet was designed by Dr. Alexander Repenning as a research product to enhance computational thinking among children [3]. StarLogo is a visual and 3D environment that uses block coding to help novice learners model simple games and learn programming quickly. Scratch is another blockbased visual programming environment that allows students to develop interactive media-rich projects. Scratch is getting much attention among online coding platforms for kids and young students. It is designed as a single-window, multipane interface that makes it much more userfriendly to novice programmers and ensures that all major components are easily accessible. All commands are readily accessible from the command palette, which includes different categories like Motion, Looks, Sound, control, etc. [3]. Scratch is also used in Dynamic Programming (DP) to solve large problems that Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4441 computer science students worldwide find hard to understand. The problem with pure block-based programming learners is that writing text-based programs requires additional effort and time, which are significantly different from the blockbased environment. Block-C is another blockbased language based on C programming language that empowers novice students to learn programming by focusing only on logic rather than complicated syntax. The advantage of Block C is that the transition facility from textual C to Block C and Block C to textual C is straightforward. Most educators suggest Block C because all other introductory Block-based languages like Scratch and Star Logo are treated as toy-like structures without real connection with C, Java, or Python [7]. Due to its ease of use and reduced complexity, instructors often choose block-based programming environments as a starting point for novice learners. 2.1.3. Game-based programming Game-based programming involves learning to code by immersing students in interactive and engaging game scenarios. This method leverages the mechanics and elements of games to teach programming concepts in a way that is both enjoyable and educational. These studies [5, 8– 14] collectively explore the integration and impact of game-based learning (GBL) in programming education across various levels. In this paper [8], the’ PROBSOL’ application was introduced to enhance problem-solving skills in novice programmers, and survey results show positive feedback from both genders, emphasizing the applications’ user-friendliness and efficiency. Another study [9] focuses on GBL’s potential to improve student engagement and programming skills, with findings supporting the effectiveness of educational games in fostering enjoyment and learning. In this work [10], they aim to enhance a Python programming course by combining innovative game modules with traditional teaching methods, emphasizing motivation and competency improvement. Another study [11] explores the integration of game-based learning and game design as a novel teaching method for programming in primary and secondary schools. The study aims to fill the gap in information regarding game design’s specific role in teaching programming, focusing on novice learners. In this study [5], they developed ZTECH, a proprietary game-based learning tool designed to enhance students’ understanding of object-oriented programming. This role-playing game offers an interactive environment with quests and challenges, fostering self-motivation and an engaging learning experience for objectoriented programming concepts.’ Programmer Adventure Land,’ a game-based learning courseware [12], employs a problem-based strategy to enhance college students’ understanding of computer programming. The results indicate that the problem-based learning approach improves satisfaction, enjoyment, motivation, and user interface in learning computer programming. Additionally, initiatives like’ CODING4GIRLS’ [13] use game development for programming skill cultivation, showing positive acceptance among students in lower secondary education. Lastly, a study [14] examines the intentions of younger adolescents toward game-based programming learning, identifying key factors and moderating effects of gender and grade level on students’ attitudes. Overall, these studies underscore the valuable role of GBL in diverse programming education contexts. However, game-based programming can be challenging because it requires good game design. At the same time, it can distract from core programming concepts and may not work for all learning styles. 2.1.4. Visual programming Visual programming environments (VPEs) allow users to create programs by manipulating graphical elements instead of writing text-based code. These environments provide a user-friendly interface where visual icons, blocks, or diagrams represent programming concepts. NetsBlox is a visual programming environment designed for learning distributed programming principles, offering accessible abstractions like message passing and Remote Procedure Calls. The study [15] examined the effectiveness of a Visual Programming Language (VPL) intervention, finding that it significantly improved students’ grasp of fundamental programming concepts, particularly benefiting those without a computer science background. This paper [16] conducts a meta-analysis of 29 studies, revealing the positive impact of block-based visual programming tools Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4442 on students’ academic achievement, with influencing factors identified. This article [17] investigates challenges in programming education, focusing on object-oriented programming, and suggests the need for a comprehensive learning environment. In this work [18], they emphasize the importance of addressing algorithmic thinking and problemsolving in programming education, recommending Scratch as a suitable visual programming language for beginners. This research [6] focuses on teaching programming to primary school children through online interactive environments, demonstrating positive attitudes and improved problem-solving skills. The study [19] explores the impact of teachers’ pedagogical perspectives on integrating visual programming in early formal education, emphasizing the potential promotion of constructivist pedagogy. Lastly, the study [20] investigates the correlation between students’ satisfaction levels with visual learning environments (Greenfoot and Alice) and their academic performance in programming courses, revealing a significant positive correlation and highlighting the impactful role of these environments in improving students’ outcomes compared to traditional methods. While VPEs simplify complex concepts by using graphics and visual blocks instead of traditional text-based syntax, they may not cater to all learning styles effectively. VPEs are particularly beneficial for visual learners who process information best when it is presented in a graphical format, as well as for kinesthetic learners who benefit from hands-on, interactive tasks. Therefore, while VPEs can be valuable for introducing programming concepts, they might need to be supplemented with other instructional methods to accommodate the diverse needs of all learners. 2.2. Programming Learning Strategies and Instructional Approaches Researchers have developed a variety of innovative instructional approaches to teach programming effectively. This section examines several instructional methods for programming education (Fig. 2), organized into distinct categories as outlined below. Figure 2: Learning strategies and Instructional approaches 2.2.1. Peer programming (Peer assisted learning) strategy Peer-assisted learning [21] is a concept to help students each other in the learning process. The learning group consists of two students who are ”coach” and ”learner” with instructor support at any time. In this study [22], mobile, agile peer assisted learning was used to overcome the difficulties of programming learning. They designed a platform to support programming learning using C++. Here [23], students learned an introductory course on Arduino, which uses a peer-based learning technique. A peer learning agent [21] acts as an aid to learning programming and analyzes the learning levels of the student. They used the Bayesian network and programming pedagogical methods to simulate this system. [24] used the C programming language PAL (Peer-assisted learning environment) to learn programming and tested it with experimental and control groups. All the above work systematically claims that peerassisted learning improves the interest and learning capability of the students. The main drawback of this technique is the inability of the peer learning agent to understand learner specific preferences and learning characteristics of the learner. Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4443 2.2.2. Group-based or Collaborative learning Collaborative learning, or group-based learning, is a student-centric approach that empowers students to participate actively in the course material and with each other [25]. It involves students working in teams or groups, promoting interaction, discussion, and joint problemsolving. Through this active engagement, students enhance their understanding of content and develop crucial interpersonal and teamwork skills, taking ownership of their learning journey [26]. This paper [27] discusses redesigning an introductory programming course using the community of inquiry learning framework and design patterns from online communities and team-based learning. An experimental evaluation involving 562 students indicates that the experimental groups outperformed the control group, with the CoL (community of inquiry learning framework) and TBL (team-based learning) methodology leading to higher levels of understanding due to increased participation rates. A real-time collaboration version of MIT App Inventor (MAI) was proposed [28] to facilitate cross-region and multi-user collaborative software development. An empirical study compared self-efficacy and collaborative behavior of learners using MAI with and without real-time collaboration, finding that engagement in joint behavior increased with realtime collaboration, particularly among CS-major groups. In this study [29], they aim to improve computer programming skills through collaborative learning with a problem-based practice strategy. Results from testing with two classes show that the proposed strategy enhances students’ programming skills in a collaborative learning environment. In this paper [30], they introduce a novel computer-supported collaborative learning group designed for problem-based collaborative learning in computer programming education. The system enables learners and tutors from different locations to collaboratively address programming challenges using synchronous and asynchronous tools within a shared workspace. The study outlines the groupware’s functionalities and conducts an experimental evaluation, applying the unified technology acceptance theory, to assess learners’ Behavioral Intention (BI) within the context of Algerian higher education. The primary limitation of this collaborative learning is its inflexibility to recognize and adapt to personalize learner demands and requirements. 2.2.3. Pair programming Pair programming is a teamwork approach where two individuals share a computer while working together to develop software. While it has been employed in industry, its popularity has grown significantly in educational contexts [31]. In introductory programming courses, pair programming is commonly utilized because there is substantial evidence that it enhances students’ learning of programming concepts. Studies [31, 32] indicate that pair programming offers advantages such as improved success rates in introductory courses, higher retention within majors, enhanced software quality, increased student confidence in solutions, and better learning outcomes. Additionally, evidence suggests that women, in particular, benefit from pair programming. Furthermore, the transition from paired to solo programming appears to be straightforward for students, although scheduling and partner compatibility remain significant challenges. 2.2.4. Mind mapping teaching A mind map is a diagram that organizes information into a hierarchy, revealing relationships among various elements. It can be used to assist novice learners in teaching programming. Mind maps are helpful for creativity, understanding programming challenges, and designing solutions. The integration of mind mapping into programming education has garnered substantial attention, yielding valuable insights across diverse contexts [33]. An experimental study explored the effectiveness of using Mind Maps as a brainstorming and conceptualization tool for programming. The results were compelling: employing mind maps within textand blockbased programming environments significantly improved student’s learning outcomes. Moreover, researchers explicitly used Scratch to investigate the impact on computational thinking (CT) skills when integrating mind mapping into programming language instruction [34]. Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4444 Comparing two mind mapping approachesconstruct-on-scaffold (COS-MM) and constructby-self (CBSMM)-the study [35] revealed that COS-MM was more effective in enhancing student’s CT skills. Furthermore, in the context of primary school instruction, mind mapping facilitated programming comprehension and promoted creativity among students engaged in the Scratch course. Beyond primary education, a study in Malaysia explored the effects of mind mapping combined with cooperative learning (MMCL) versus traditional cooperative learning (CL). The striking results: MMCL significantly improved programming performance and enhanced students’ metacognitive knowledge [36]. Lastly, an experimental study [37] focused on modern programming languages, concluding that incorporating mind-mapping tools enhances undergraduate students’ critical thinking and problem-solving skills. In summary, mind mapping is a versatile pedagogical approach fostering creativity, cognition, and collaboration in the dynamic landscape of programming education. A limitation of mind mapping is that it doesn’t account for basic learning habits or individual preferences, making it difficult for learners to select learning materials that align with their understanding styles. 2.2.5. Tangible user interface Tangible User Interfaces (TUIs) represent an innovative interface paradigm that effectively addresses the limitations of traditional Graphical User Interfaces (GUIs). Unlike GUIs, which rely on mouse and keyboard input, TUIs allow users to interact with systems by manipulating real-life physical objects. Research indicates that the tangibility and hands-on manipulation of physical elements enhance performance, learning, decision-making, and user retention, leading to a more substantial learning gain. Furthermore, using tangible interfaces injects playfulness into problem-solving, making the learning experience more engaging [38]. This section explores the potential of tangible user interfaces (TUI) to enhance the learning of abstract programming concepts, focusing on a study [38] that investigated the usability of a TUI system designed for teaching basic Java programming. The researchers developed a prototype using Processing and ReacTI Vision and assessed its usability with the System Usability Scale. While the system showed acceptable usability, identified limitations highlight areas for further improvement in TUI-based programming education. Addressing the growing demand for programming skills starting at a young age, this study [39] examines the impact of tangible programming interfaces compared to traditional visual methods, particularly for children around six. The study showcases the” Follow Your Objective” (FYO) platform, a cost-effective tangible programming solution featuring an intuitive programming board, puzzle-based tangible blocks, and a mobile robot. Preliminary experiments with FYO demonstrated enhanced programming skills among children, indicating the effectiveness of tangible puzzle-based platforms for early programming education. This work [40] discusses strategies to facilitate the learning of recursion, a complex programming technique, especially in functional languages. A proposed solution involves an interactive interface based on a tangible block world with augmented reality and software feedback, using stack blocks as analogies for list data structures. The goal is to enable students to intuitively grasp recursive concepts and transition to writing recursive programs in sequential Erlang, promoting effective recursion learning within programming education. TUIs are mainly designed to support visual and kinesthetic learners, providing interactive, hands-on learning experiences. However, they often overlook the preferences and learning styles of traditional, more text-based learners. 2.2.6. Flipped classroom-based collaborative learning A Flipped Classroom (FC) reverses the traditional learning model by having students review instructional materials at home and engage in interactive, hands-on activities in class. The efficacy of the FC teaching method in computer programming education has been the subject of several research studies. Here is an overview of these studies and their key findings: Research studies have extensively examined the efficacy of the FC teaching method in computer programming education. One study combined FC with Problem-based Learning (PBL), finding significant improvements in students’ Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4445 understanding of programming concepts, particularly benefiting weaker students and making learning enjoyable for mature students [41]. Another mixed-method study revealed that students in a flipped classroom exhibited higher academic achievements and more positive attitudes toward programming. However, challenges such as technological requirements and low attendance were noted [42]. Additional research demonstrated that an adapted FC approach positively impacted programming success and self-efficacy, although it did not significantly affect attitudes toward programming [43]. A study focusing on self-regulation found varying levels of self-regulated learning skills among students, emphasizing the potential of problem-based activities in flipped learning [44]. Another investigation showed a positive change in students’ acceptance of programming after implementing the FC model, with students agreeing that it improved their learning experience [45]. Furthermore, a comparative study revealed that students in a flipped classroom achieved higher test scores and had positive perceptions of in-class activities [46]. Lastly, research exploring students’ perspectives on enriching programming and algorithm teaching with the FC approach found that most students had positive views, highlighting its potential effectiveness in programming courses [47]. These studies [41-47], collectively suggest that the flipped classroom approach holds promise for improving programming education outcomes, enhancing student engagement, and fostering positive attitudes toward programming. However, problems such as technology integration and attendance must be solved to fully realize this teaching technique’s benefits. 2.2.7. Programming using e-learning Programming education is crucial for students in various disciplines, necessitating effective learning strategies and motivation. Several studies have explored innovative approaches and e-learning systems to enhance motivation and learning outcomes in programming courses. Here’s an overview of these studies: The study [48] investigates motivating factors influencing undergraduate students’ learning in computer programming courses. The study highlights the Programming Assignment aSsessment System (PASS), an e-learning infrastructure to support programming education. Key motivating factors include individual attitude, clear direction, and reward/recognition. The study suggests that wellfacilitated e-learning environments can enhance motivation and self-efficacy. This study [49] addresses challenges in distance and e-learning for programming subjects by proposing virtual pair programming (VPP). The research focuses on asynchronous VPP and assesses its effectiveness in teaching object-oriented programming at Open University Malaysia (OUM). Positive feedback from learners suggests the potential of asynchronous VPP, with suggestions for further enhancements. This study [50] highlights the need for improving conceptual learning in basic computer programming using personalized elearning environments. The study incorporates personalized information like learning problems, styles, and performance levels to tailor the learning experience. Results indicate that students using the personalized e-learning environment demonstrated improved understanding and positive attitudes toward programming. The work [51] develops a PROBSOL application to enhance novice programmers’ problem-solving skills in introductory programming courses. The study compares web-based and mobile app versions of PROBSOL and assesses their impact on student engagement and learning outcomes. Results show improved cognitive gains, logic capabilities, and reduced attrition rates using PROBSOL. The study [52] proposes an elearning model for programming instruction to secondary school students to enhance motivation and understanding. The model emphasizes collaborative learning to address the challenges of learning ICT subjects. The study aims to meet the demand for informatics competencies in secondary school curricula. This study [53] introduces interactive multimedia e-learning to help undergraduate students grasp the fundamental concepts of logic and algorithms in programming. The study addresses the limitations of traditional e-learning systems and focuses on enhancing independent learning through multimedia materials and interactive exercises. This study [54] explores the impact of interactive instructions in e-learning on the effectiveness of a programming course, particularly during the COVID-19 pandemic. The study assesses Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4452 blended learning surpasses traditional instructional methods in effectiveness and can significantly enrich students’ educational experiences [92]. Despite these benefits, a notable gap exists in understanding and implementing blended learning within introductory programming courses considering learners’ learning preferences. To address this, integrating Adaptive e-learning with blended teaching is a better pedagogical technique that can be adopted in programming learning. It provides flexibility for learners at different paces when programming is introduced. Adaptive e-learning involves tailoring instructional content to match learners’ styles or preferences. These systems create models based on each learner’s needs. This adaptive approach is widely recognized for enhancing learners’ performance and the overall quality of the learning process [93]. Typically, an adaptive e-learning environment includes three key models: a) the Content model, which outlines the structure of instructional material and learning outcomes; b) the Instructional model, which identifies and delivers personalized instructional content; and c) the Learner model; which tracks learner characteristics and responses to refine the system [77]. continuously. 5.2. Learning style enabled instructional video ALEs Learning style is essential while designing and delivering learning materials to learners. The diverse nature of human beings is reflected in their adoption of different learning preferences and receptive perspectives. Learning style refers [74] to how each person prefers to learn and process information. It’s about understanding the best way for an individual to acquire knowledge and retain it. In e-learning, understanding your learning style can impact how you process and apply information effectively. Figure 3: A General Model Of Learning Style Enabled Instructional Video Ales In conventional adaptive learning environments, the Instructional model delivers personalized learning objects (LOs) that more or less match the learner’s preference. LOs are usually presentation slides, lecture notes, or textbook materials for ease of construction. After the COVID-19 pandemic, pedagogical approaches shifted, and conventional LOs gave way to online and pre-recorded instructional videos. According to the cognitive paradigm of multimodal learning, visual and auditory engagement plays a significant role in assimilating educational information. Metaanalyses have demonstrated that integrating complementary information through auditory and visual channels significantly enhances learning outcomes compared to relying on a single channel [94]. As a result, dual-channel processing allows learners to absorb and retain information more effectively, as the brain can handle verbal and visual information simultaneously without overloading one cognitive channel. Therefore, many e-learning environments, particularly those focused on programming education, leverage video content to deliver instructional material [95]. Videos are Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4453 preferred over traditional text and audio-based approaches because they combine visual elements like code demonstrations and graphical explanations with auditory instructions, creating a richer, more engaging learning experience. This multimodal approach helps learners understand complex programming concepts more thoroughly and retain information longer. Existing literature [80-90] overlooked a crucial learner attribute: each student’s learning style and preference. These studies assumed that all students shared a uniform learning preference while watching and learning from instructional videos. This assumption neglects the diverse ways in which students absorb and process information. Different learners may prefer visual aids, textual explanations, auditory guidance, or a combination of these methods. By not considering these varied preferences, the studies fail to address students’ individual needs, potentially limiting the effectiveness of instructional videos for a significant portion of the learning audience. Integrating individual learning preference instructional videos in an ALE presents a highly effective approach to personalized education [96, 97]. This method acknowledges the diverse ways students process and absorb information, offering a customized educational experience that adjusts to each student’s unique preferences (Fig.3). For instance, some students could gain more from visual aids like diagrams and animations. In contrast, others might find textual explanations or auditory instructions more understandable within the video learning mode. By incorporating these varying instructional styles into an adaptive elearning platform, the system can dynamically adjust the content delivery to match the specific learning preferences of each student. These integrations enhance learners’ engagement and retention rates by providing a more intuitive and accessible learning experience. Instead of a one-size-fits-all approach, students receive content in the format that best suits their cognitive and perceptual strengths, leading to more effective learning outcomes. This personalized approach can also increase motivation and reduce frustration as students interact with instructional materials that resonate with their preferred learning style. Overall, implementing individual learning preference instructional videos within an adaptive e-learning environment can significantly improve the quality of education and student success rates. 6. OPEN RESEARCH ISSUES Despite significant advancements in programming education, several critical challenges remain unaddressed, presenting numerous open research opportunities. As learners come from diverse backgrounds with varying cognitive abilities, preferences, and motivations, existing instructional methods often fail to provide personalized and effective learning experiences. Key areas requiring further exploration include the integration of adaptive learning technologies, the development of interdisciplinary curricula, the creation of personalized learning materials, and the improvement of collaborative and peer-assisted learning methods. Addressing these open issues is essential to overcome current limitations and improve the overall effectiveness of programming education. 6.1. Adaptive and personalized learning models AI and machine learning have the potential to revolutionize education by creating truly adaptive learning systems that personalize the learning experience based on the needs, preferences, and progress of individual students. These systems can dynamically adjust content, pace, and feedback to match learners’ cognitive abilities and learning styles. Machine learning algorithms can analyze large datasets of student interactions to predict difficulties, recommend appropriate resources, and tailor assessments. Effective frameworks for integrating adaptive systems with traditional teaching should combine human expertise with AIdriven insights. This ensures that educators maintain control over the instructional process. At the same time, they can benefit from real-time analytics and personalized interventions provided by AI. Hybrid models, where AI supports teachers by automating routine tasks such as grading and tracking progress, allow educators to focus on more complex aspects of teaching, such as fostering critical thinking and creativity. In addition, the frameworks should emphasize transparency and interpretability, allowing teachers and students to understand how adaptive recommendations are made, thus fostering trust in the system. Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4454 6.2. Human-AI collaboration in programming learning The optimal balance between automated systems and human educators lies in leveraging the strengths of both to create a more effective and engaging learning environment. Automated systems excel at providing personalized content, providing instant feedback, and analyzing large amounts of student data to identify learning patterns and potential difficulties. They can handle routine tasks such as grade and progress tracking, allowing educators to focus on higher-order skills such as critical thinking, creativity, and emotional support. However, human educators play a crucial role in motivating learners, fostering a sense of community, and adapting to nuanced social and emotional cues that machines cannot fully interpret. Achieving this balance involves integrating AI as a supportive tool rather than a replacement, ensuring that automation enhances, rather than diminishes, the educator's role. Collaborative frameworks should also provide educators with intuitive interfaces to interpret AI-driven insights and allow them to make informed decisions, maintaining a human-centered approach to learning. Learning Classifier Systems (LCS) can be utilized to generate dynamic rules to achieve the optimal balance, human-centered informed decisions between automated systems and human educators. 6.3. Learner specific programming learning video creation Creating learner-specific programming learning videos tailored to individual preferences poses significant challenges for educators, making it an open research question. Personalizing video content requires adapting factors such as teaching style, personalized examples, and difficulty level to align with each learner's cognitive style, prior knowledge, and learning speed. Producing such customized videos manually from scratch is timeconsuming and resource-intensive. This highlights the need for automated or semi-automated systems that dynamically generate or adapt video content based on learner profiles. Future research could focus on developing AI-driven frameworks that use natural language processing, video synthesis, and machine learning to create personalized instructional videos. Such systems could enable educators to input high-level content, which the AI would transform into tailored videos. Finally, addressing the learners' specific needs while significantly reducing the workload on educators. 7. LIMITATIONS This section presents a few limitations that need to be considered for contextualizing its findings. First, the scope of the review is limited to programming learning, which narrows its applicability to other fields of education. While programming education has its unique set of challenges and instructional needs, the findings and proposed approaches may not directly translate to other disciplines, such as mathematics or language learning. Future research should explore how the proposed methods might be adapted or expanded for broader educational contexts. Second, despite being generally acknowledged in the field of education, learning styles could not always precisely represent the actual preferences or aptitudes of certain students. Learning styles alone are insufficient for predicting how a student learns best, as they can oversimplify complex cognitive processes. As a result, while this study identifies learning styles as one of the potential path for improving programming education, their usefulness in developing personalised learning experiences may be limited if they are used without accompanying cognitive or behavioural data. Third, the availability of research that integrates learning styles specifically with programming education is relatively limited. This lack of extensive prior work constrains the review’s ability to draw robust conclusions and propose well-supported frameworks. The study, therefore, mainly compiles existing, isolated works rather than synthesizing well-established models. Further research is needed to build a comprehensive understanding of how learning styles can be effectively leveraged in programming pedagogy. Lastly, the nature of this review is primarily a plain literature survey, aimed at consolidating existing research rather than providing a detailed critical analysis. While this approach helps to map the current landscape of programming education and identify research gaps, it does not delve deeply into evaluating the strengths and weaknesses of specific methods. A more critical evaluation, which could assess the comparative effectiveness of different approaches and offer in-depth critiques, would provide greater value for guiding future research efforts. Addressing these limitations in future Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4455 studies could enhance the overall impact of research on programming education by offering more comprehensive insights and practical solutions. 8. CONCLUSION This review examines recent advancements in programming learning models and instructional approaches, providing an in-depth analysis of programming learning environments, including text-based, block-based, game-based, and visual programming formats. A thorough evaluation of instructional methods and learning style models has also been conducted, highlighting the importance of integrating various approaches to enhance learner engagement, retention, and overall learning experiences. Our findings suggest that combining adaptive video learning environments with individualized learning styles can significantly improve online and e-learning formats for programming education. Future work could extend this review to explore specific techniques, such as the learning style model for programming and personalized instructional video models, which promise to improve programming instruction and learner outcomes. REFERENCES [1]. Mutanga MB. The Effect of Cognitive Factors in Determining students’ Success in Computer Programming. Journal of Theoretical and Applied Information Technology. 2020;98(17):3606–3618. [2]. Weintrop D, Wilensky U. How block-based, text-based, and hybrid block/text modalities shape novice programming practices. International Journal of Child-Computer Interaction. 2018 Sep;17:83–92. https://doi.org/10. 1016/j.ijcci.2018.04.005. [3]. Repenning A. Moving Beyond Syntax: Lessons from 20 Years of Blocks Programing in AgentSheets. Journal of Visual Languages and Sentient Systems. 2017 Jul;3(1):68–91. https://doi.org/10.18293/vlss2017-010. [4]. Repenning A, Sumner T. Agentsheets: a medium for creating domain-oriented visual languages. Computer. 1995 Mar;28(3):17– 25. https://doi.org/10.1109/2.366152. [5]. Wong YS, Hayati MYM, Tan WH. A propriety game-based learning game as learning tool to learn object-oriented programming paradigm. In: Serious Games: Second Joint International Conference, JCSG 2016, Brisbane, QLD, Australia, September 26-27, 2016, Proceedings 2. Springer; 2016. p. 42–54. [6]. Asad K, Tibi M, Raiyn J. Primary School Pupils’ Attitudes toward Learning Programming through Visual Interactive Environments. World Journal of Education. 2016 Oct;6(5). https://doi.org/10.5430/wje. v6n5p20. [7]. Weintrop D. Block-based programming in computer science education. Communications of the ACM. 2019 Jul;62(8):22–25. https://doi.org/10.1145/3341221. [8]. Malik SI, Al-Emran M, Mathew R, Tawafak RM, Alfarsi G. Comparison of E-Learning, M-Learning and Game-based Learning in Programming Education – A Gendered Analysis. International Journal of Emerging Technologies in Learning (iJET). 2020 Aug;15(15):133. https://doi.org/10.3991/ ijet.v15i15.14503. [9]. Mathrani A, Christian S, Ponder-Sutton A. PlayIT: Game based learning approach for teaching programming concepts. Journal of Educational Technology & Society. 2016;19(2):5–17. [10]. M P, N NM, Dakshina R, S S, R BS. Learning Analytics: Game-based Learning for Programming Course in Higher Education. Procedia Computer Science. 2020;172:468–472. https://doi.org/10.1016/j.procs.2020.05.143. [11]. Rugelj J, Lapina M. Game design based learning of programming. In: Proceedings of SLET-2019–International Scientific Conference Innovative Approaches to the Application of Digital Technologies in Education and Research; 2019. p. 20–23. [12]. Chang CS, Chung CH, Chang JA. Influence of problem-based learning games on effective computer programming learning in higher education. Educational Technology Research and Development. 2020 Jun;68(5):2615–2634. https://doi.org/10. 1007/s11423-020-09784-3. [13]. Holenko Dlab M, Hoic-Bozic N. Effectiveness of game development-based learning for acquiring programming skills in lower secondary education in Croatia. Education and Information Technologies. 2021 Mar;26(4):4433–4456. https://doi.org/10. 1007/s10639-021-10471w. Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4456 [14]. Hu Y, Su CY, Fu A. Factors influencing younger adolescents’ intention to use gamebased programming learning: A multigroup analysis. Education and Information Technologies. 2022 Mar;27(6):8203–8233. https: //doi.org/10.1007/s10639-022-10973-1. [15]. Tsai CY. Improving students’ understanding of basic programming concepts through visual programming language: The role of self-efficacy. Computers in Human Behavior. 2019 Jun;95:224–232. https://doi.org/10. 1016/j.chb.2018.11.038. [16]. Hu Y, Chen CH, Su CY. Exploring the Effectiveness and Moderators of BlockBased Visual Programming on Student Learning: A Meta-Analysis. Journal of Educational Computing Research. 2020 Jul;58(8):1467–1493. https://doi.org/10.1177/0735633120945935. [17]. Batiha Q, Sahari N, Aini N, Mohd N. Adoption of Visual Programming Environments in Programming Learning. International Journal on Advanced Science, Engineering and Information Technology. 2022 Sep;12(5):1921. https://doi.org/10.18517/ ijaseit.12.5.15500. [18]. Iskrenovic-Momcilovic O. Choice of visual programming language for learning programming. International Journal of Computers. 2017;2. [19]. Kesler A, Shamir-Inbal T, Blau I. Active Learning by Visual Programming: Pedagogical Perspectives of Instructivist and Constructivist Code Teachers and Their Implications on Actual Teaching Strategies and Students’ Programming Artifacts. Journal of Educational Computing Research. 2021 Jun;60(1):28–55. https://doi.org/10.1177/ 07356331211017793. [20]. Dina NZ, Wuryanto E, Marjianto RS. Evaluation on the Effectiveness of Visual Learning Environment on Programming Course From Students’ Perspectives. IIUM Engineering Journal. 2019 Jun;20(1):100– 107. https:// doi.org/10.31436/iiumej.v20i1.993. [21]. Han KW, Lee E, Lee Y. The Impact of a PeerLearning Agent Based on Pair Programming in a Programming Course. IEEE Transactions on Education. 2010 May;53(2):318–327. https://doi.org/10.1109/te.2009.2019121. [22]. Ngatirin NR, Zainol Z, Fang WC. Mobile Agile Peer Assisted Learning. In: 2016 3rd International Conference on Computer and Information Sciences (ICCOINS). IEEE; 2016. Available from: http://dx.doi.org/10. 1109/iccoins.2016.7783191. [23]. Mart´ın-Ramos P, Lopes MJ, Lima da Silva MM, Gomes PEB, Pereira da Silva PS, Domingues JPP, et al. First exposure to Arduino through peer-coaching: Impact on students’ attitudes towards programming. Computers in Human Behavior. 2017 Nov;76:51–58. https://doi.org/10.1016/j.chb. 2017.07.007. [24]. Altintas T, Gunes A, Sayan H. A peerassisted learning experience in computer programming language learning and developing computer programming skills. Innovations in Education and Teaching International. 2014 Dec;53(3):329–337. https://doi.org/10.1080/ 14703297.2014.993418. [25]. Webb NM, Ender P, Lewis S. ProblemSolving Strategies and Group Processes in Small Groups Learning Computer Programming. American Educational Research Journal. 1986;23(2):243. https://doi.org/10.2307/ 1162957. [26]. Wang XM, Hwang GJ. A problem posingbased practicing strategy for facilitating students’ computer programming skills in the team-based learning mode. Educational Technology Research and Development. 2017 Nov;65(6):1655–1671. https://doi.org/10. 1007/s11423-017-9551-0. [27]. Cabrera I, Villalon J, Chavez J. Blending Communities and Team-Based Learning in a Programming Course. IEEE Transactions on Education. 2017 Nov;60(4):288–295. https: //doi.org/10.1109/te.2017.2698467. [28]. Hsu TC, Abelson H, Patton E, Chen SC, Chang HN. Self-efficacy and behavior patterns of learners using a real-time collaboration system developed for group programming. International Journal of Computer-Supported Collaborative Learning. 2021 Dec;16(4):559–582. https://doi. org/10.1007/s11412-021-093573. [29]. Irwan I, Susanti W, Desnelita Y, Gustientiedina G, Wongso F, Fudholi A. Problem-based Collaborative Learning Strategy in Computer Programming. SAR Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4457 Journal - Science and Research. 2022 Mar;p. 40–45. https://doi. org/10.18421/sar51-05. [30]. Chorfi A, Hedjazi D, Aouag S, Boubiche D. Problem-based collaborative learning groupware to improve computer programming skills. Behaviour & Information Technology. 2020 Jul;41(1):139–158. https: //doi.org/10.1080/0144929x.2020.1795263. [31]. Bowman NA, Jarratt L, Culver KC, Segre AM. How Prior Programming Experience Affects Students’ Pair Programming Experiences and Outcomes. In: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education. ITiCSE ’19. ACM; 2019. Available from: http://dx.doi.org/10.1145/3304221.3319781. [32]. Wei X, Lin L, Meng N, Tan W, Kong SC, Kinshuk. The effectiveness of partial pair programming on elementary school students’ Computational Thinking skills and selfefficacy. Computers & Education. 2021 Jan;160:104023. https://doi.org/10. 1016/j.compedu.2020.104023. [33]. Liu Y, Tong Y, Yang Y. The application of mind mapping into college computer programming teaching. Procedia Computer Science. 2018;129:66–70. [34]. Zhao L, Liu X, Wang C, Su YS. Effect of different mind mapping approaches on primary school students’ computational thinking skills during visual programming learning. Computers & Education. 2022 May;181:104445. https://doi.org/10.1016/j. compedu.2022.104445. [35]. Su YS, Shao M, Zhao L. Effect of Mind Mapping on Creative Thinking of Children in Scratch Visual Programming Education. Journal of Educational Computing Research. 2021 Dec;60(4):906–929. https://doi.org/10. 1177/07356331211053383. [36]. Ismail MN, Ngah NA, Umar IN. The Effects of Mind Mapping with Cooperative Learning on Programming Performance, Problem Solving Skill and Metacognitive Knowledge among Computer Science Students. Journal of Educational Computing Research. 2010 Jan;42(1):35–61. https://doi.org/10.2190/ec. 42.1.b. [37]. Gul S, Asif M, Nawaz Z, Haris Aziz M, Khurram S, Qaiser Saleem M, et al. Sustainable Learning of Computer Programming Languages Using Mind Mapping. Intelligent Automation & Soft Computing. 2023;36(2):1687–1697. https://doi.org/10. 32604/iasc.2023.032494. [38]. Nathoo A, Gangabissoon T, Bekaroo G. Exploring the Use of Tangible User Interfaces for Teaching Basic Java Programming Concepts: A Usability Study. In: 2019 Conference on Next Generation Computing Applications (NextComp). IEEE; 2019. Available from: http://dx.doi.org/10.1109/ nextcomp.2019.8883563. [39]. Caceres PC, Venero RP, Cordova FC. Tangible programming mechatronic interface for basic induction in programming. In: 2018 IEEE Global Engineering Education Conference (EDUCON). IEEE; 2018. Available from: http://dx.doi.org/10.1109/ educon.2018.8363226. [40]. Vidarte JDT, Rinderknecht C, Kim JI, Kim H. A Tangible Interface for Learning Recursion and Functional Programming. In: 2010 International Symposium on Ubiquitous Virtual Reality. IEEE; 2010. Available from: http://dx.doi.org/10.1109/isuvr.2010.18. [41]. Chis AE, Moldovan AN, Murphy L, Pathak P, Muntean CH. Investigating flipped classroom and problem-based learning in a programming module for computing conversion course. Journal of Educational Technology & Society. 2018;21(4):232–247. [42]. Ta¸spolat A, Ozdamli F, Soykan E. Pro- ¨ gramming Language Training With the Flipped Classroom Model. SAGE Open. 2021 Apr;11(2):215824402110214. https:// doi.org/10.1177/21582440211021403. [43]. Ozyurt H, ¨ Ozyurt ¨ O. Analyzing the effects of ¨ adapted flipped classroom approach on computer programming success, attitude toward programming, and programming selfefficacy. Computer Applications in Engineering Education. 2018;26(6):2036– 2046. [44]. C¸ akıro˘glu U, ¨ Ozt¨urk M. Flipped class- ¨ room with problem based activities: Exploring self-regulated learning in a programming language course. JSTOR; 2017. . [45]. Yan OS, Cheng G. Exploring the impact of flipped classroom on students’ acceptance of programming in secondary education. In: 2017 IEEE 6th International Conference on Teaching, Assessment, and Learning for Engineering (TALE). IEEE; 2017. Available Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4458 from: http://dx.doi.org/10.1109/ tale.2017.8252341. [46]. Ozyurt ¨ O, ¨ Ozyurt H. A qualitative study ¨ about enriching programming and algorithm teaching with flipped classroom approach. Pegem Egitim ve Ogretim Dergisi. 2017;7(2). [47]. Pattanaphanchai J. An Investigation of Students’ Learning Achievement and Perception using Flipped Classroom in an Introductory Programming course: A Case Study of Thailand Higher Education. Journal of University Teaching and Learning Practice. 2019 Dec;16(5):36–53. https://doi.org/10.53761/ 1.16.5.4. [48]. Law KMY, Lee VCS, Yu YT. Learning motivation in e-learning facilitated computer programming courses. Computers & Education. 2010 Aug;55(1):218–228. https: //doi.org/10.1016/j.compedu.2010.01.007. [49]. Zın AM, Idrıs S, Subramanıam NK. Improving learning of programming through elearning by using asynchronous virtual pair programming. Turkish Online Journal of Distance Education. 2006;7(3):162–173. [50]. Chookaew S, Panjaburee P, Wanichsan D, Laosinchai P. A Personalized E-Learning Environment to Promote Student’s Conceptual Learning on Basic Computer Programming. Procedia - Social and Behavioral Sciences. 2014 Feb;116:815– 819. https://doi. org/10.1016/j.sbspro.2014.01.303. [51]. Malik SI, Mathew R, Al-Nuaimi R, AlSideiri A, Coldwell-Neilson J. Learning problem solving skills: Comparison of Elearning and M-learning in an introductory programming course. Education and Information Technologies. 2019 Mar;24(5):2779–2796. https: //doi.org/10.1007/s10639-019-09896-1. [52]. Tundjungsari V. E-learning model for teaching programming language for secondary school students in Indonesia. In: 2016 13th International Conference on Remote Engineering and Virtual Instrumentation (REV). IEEE; 2016. Available from: http://dx.doi. org/10.1109/rev.2016.7444477. [53]. Mutiawani V, Juwita. Developing e-learning application specifically designed for learning introductory programming. In: 2014 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE; 2014. Available from: http://dx.doi.org/10. 1109/icitsi.2014.7048250. [54]. Nariman D. In: Impact of the Interactive eLearning Instructions on Effectiveness of a Programming Course. Springer International Publishing; 2020. p. 588–597. Available from: http://dx.doi.org/10.1007/ 978-3-03050454-0 61. [55]. Troussas C, Krouska A, Sgouropoulou C. A Novel Teaching Strategy Through Adaptive Learning Activities for Computer Programming. IEEE Transactions on Education. 2021 May;64(2):103–109. https://doi.org/10. 1109/te.2020.3012744. [56]. Gavrilovi´c N, Arsi´c A, Domazet D, Mishra A. Algorithm for adaptive learning process and improving learners’ skills in Java programming language. Computer Applications in Engineering Education. 2018 Aug;26(5):1362–1382. https://doi.org/10. 1002/cae.22043. [57]. Chrysafiadi K, Virvou M. Fuzzy Logic for Adaptive Instruction in an E-learning Environment for Computer Programming. IEEE Transactions on Fuzzy Systems. 2015 Feb;23(1):164–177. https://doi.org/10.1109/ tfuzz.2014.2310242. [58]. Chrysafiadi K, Virvou M, Sakkopoulos E. In: Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment. Springer International Publishing; 2019. p. 205–223. Available from: http://dx.doi.org/ 10.1007/978-3-03013743-4 11. [59]. Chrysafiadi K, Virvou M, Tsihrintzis GA, Hatzilygeroudis I. An Adaptive Learning Environment for Programming Based on Fuzzy Logic and Machine Learning. International Journal on Artificial Intelligence Tools. 2023 Aug;32(05). https://doi.org/10.1142/ s0218213023600114. [60]. Luo J, Wang T. Analyzing Students’ Behavior in Blended Learning Environment for Programming Education. In: Proceedings of the 2020 The 2nd World Symposium on Software Engineering. WSSE 2020. ACM; 2020. Available from: http://dx.doi.org/10. 1145/3425329.3425346. [61]. Tadlaoui MA, Chekou M. A blended learning approach for teaching python programming language: towards a post pandemic pedagogy. International Journal of Advanced Computer Research. 2021 Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4459 Jan;11(52):13–22. https:// doi.org/10.19101/ijacr.2020.1048120. [62]. Mabni Z, Shamsudin N, Aliman S, Abdul Latif R. Factors Influencing Students’ Performance in the First Computer Programming Course Taught Using Blended Learning Approach. Environment-Behaviour Proceedings Journal. 2020 Dec;5(SI3):181– 186. https://doi.org/10.21834/ebpj.v5isi3.2559. [63]. Srivatanakul T. Emerging from the pandemic: instructor reflections and students’ perceptions on an introductory programming course in blended learning. Education and Information Technologies. 2022 Nov;28(5):5673–5695. https://doi.org/10. 1007/s10639-022-11328-6. [64]. Murai Y, Muramatsu H. Application of creative learning principles within blended teacher professional development on integration of computer programming education into elementary and middle school classrooms. Information and Learning Sciences. 2020 Jun;121(7/8):665–675. https://doi.org/ 10.1108/ils-04-2020-0122. [65]. F¨orster A, Dede J, Udugama A, F¨orster A, Helms D, Kniefs L, et al. A blended learning approach for an introductory computer science course. Education Sciences. 2021;11(8):372. [66]. Ya˘gci M. A Web-based Blended Learning Environment for Programming Languages: Students’ Opinions. Journal of Education and Training Studies. 2017 Feb;5(3):211. https://doi.org/10.11114/jets.v5i3.2118. [67]. Hwang GJ, Sung HY, Chang SC, Huang XC. A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Computers and Education: Artificial Intelligence. 2020;1:100003. https: //doi.org/10.1016/j.caeai.2020.100003. [68]. Msane J, Mutanga B, Chani T. Students’ Perception of the Effect of Cognitive Factors in Determining Success in Computer Programming: A Case Study. International Journal of Advanced Computer Science and Applications. 2020;11(7). https://doi.org/10. 14569/ijacsa.2020.0110724. [69]. Dirzyte A, Vijaikis A, Perminas A, Rimasiute-Knabikiene R, Kaminskis L, Zebrauskas G. Computer Programming ELearners’ Personality Traits, Self-Reported Cognitive Abilities, and Learning Motivating Factors. Brain Sciences. 2021 Sep;11(9):1205. https://doi.org/10.3390/brainsci11091205. [70]. Talsma K, Sch¨uz B, Norris K. Miscalibration of self-efficacy and academic performance: Self-efficacy = self-fulfilling prophecy. Learning and Individual Differences. 2019 Jan;69:182–195. https://doi.org/10.1016/j. lindif.2018.11.002. [71]. Kanaparan G, Cullen R, Mason D. Effect of Self-efficacy and Emotional Engagement on Introductory Programming Students. Australasian Journal of Information Systems. 2019 Jul;23. https://doi.org/10.3127/ajis. v23i0.1825. [72]. Ismayilova K, Klassen RM. Research and teaching self-efficacy of university faculty: Relations with job satisfaction. International Journal of Educational Research. 2019;98:55–66. https://doi.org/10.1016/j. ijer.2019.08.012. [73]. Aivaloglou E, Hermans F. Early Programming Education and Career Orientation: The Effects of Gender, SelfEfficacy, Motivation and Stereotypes. In: Proceedings of the 50th ACM Technical Symposium on Computer Science Education. SIGCSE ’19. ACM; 2019. Available from: http://dx.doi.org/10. 1145/3287324.3287358. [74]. Honey P, Mumford A, et al. The manual of learning styles. vol. 3. Peter Honey Maidenhead; 1992. [75]. Felder RM, Silverman LK, et al. Learning and teaching styles in engineering education. Engineering education. 1988;78(7):674–681. [76]. Chen SY, Wang JH. Individual differences and personalized learning: a review and appraisal. Universal Access in the Information Society. 2021;20(4):833–849. [77]. Ali N, Eassa F, Hamed E. Personalized learning style for adaptive e-learning system. International Journal of Advanced Trends in Computer Science and Engineering. 2019;8(1):223–230. https://doi.org/10. 30534/ijatcse/2019/4181.12019. [78]. Fleming N, Mills C. VARK: A guide to learning styles. 2001;. [79]. El Bachari E, Abdelwahed E, El Adnani M. Design of an adaptive e-learning model based on learner’s personality. Ubiquitous Computing and Communication Journal. 2010;5(3):1–8. [80]. Noetel M, Griffith S, Delaney O, Sanders T, Parker P, del Pozo Cruz B, et al. Video improves learning in higher education: A Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4460 systematic review. Review of educational research. 2021;91(2):204–236. https://doi. org/10.31234/osf.io/kynez. [81]. Sabli´c M, Mirosavljevi´c A, Skugor A. Videoˇ based learning (VBL)—past, present and future: An overview of the research published from 2008 to 2019. Technology, Knowledge and Learning. 2021;26(4):1061– 1077. https: //doi.org/10.1007/s10758-02009455-5. [82]. Hsu FH, Lin IH, Yeh HC, Chen NS. Effect of Socratic Reflection Prompts via videobased learning system on elementary school students’ critical thinking skills. Computers & Education. 2022;183:104497. https://doi. org/10.1016/j.compedu.2022.104497. [83]. Richter E, Hußner I, Huang Y, Richter D, Lazarides R. Video-based reflection in teacher education: Comparing virtual reality and real classroom videos. Computers & Education. 2022;190:104601. https://doi.org/10. 1016/j.compedu.2022.104601. [84]. Kilbury M, B¨ohnke A, Thiel F. Producing Staged Videos for Teacher Education: Development and Content Validation of Video Scripts on the Topic of Handling Classroom Disruptions. Education Sciences. 2023;13(1):56. https://doi.org/10. 3390/educsci13010056. [85]. Lin YT, Yeh MKC, Tan SR. Teaching programming by revealing thinking process: Watching experts’ live coding videos with reflection annotations. IEEE Transactions on Education. 2022;65(4):617–627. https://doi. org/10.1109/te.2022.3155884. [86]. Breslyn W, Green AE. Learning science with YouTube videos and the impacts of Covid19. Disciplinary and interdisciplinary science education research. 2022;4(1):1–20. https:// doi.org/10.1186/s43031-022-00051-4. [87]. Scagnoli NI, Choo J, Tian J. Students’ insights on the use of video lectures in online classes. British Journal of Educational Technology. 2019;50(1):399–414. [88]. Liao CW, Chen CH, Shih SJ. The interactivity of video and collaboration for learning achievement, intrinsic motivation, cognitive load, and behavior patterns in a digital gamebased learning environment. Computers & Education. 2019;133:43–55. https://doi.org/ 10.1016/j.compedu.2019.01.013. [89]. Wachtler J, Hubmann M, Z¨ohrer H, Ebner M. An analysis of the use and effect of questions in interactive learning-videos. Smart Learning Environments. 2016;3(1):1– 16. https:// doi.org/10.1186/s40561-0160033-3. [90]. Kleftodimos A, Evangelidis G. Using open source technologies and open internet resources for building an interactive video based learning environment that supports learning analytics. Smart Learning Environments. 2016;3:1–23. https://doi.org/10.1186/ s40561-016-0032-4. [91]. Demaidi MN, Qamhieh M, Afeefi A. Applying blended learning in programming courses. IEEE Access. 2019;7:156824– 156833. https: //doi.org/10.1109/access.2019.2949927. [92]. Alammary A. Blended learning models for introductory programming courses: A systematic review. PloS one. 2019;14(9):e0221765. https: //doi.org/10.1371/journal.pone.0221765. [93]. Kolekar SV, Pai RM, MM MP. Prediction of Learner’s Profile Based on Learning Styles in Adaptive E-learning System. International Journal of Emerging Technologies in Learning. 2017;12(6). https://doi.org/10.3991/ijet. v12i06.6579. [94]. Mayer RE. Applying the science of learning: evidence-based principles for the design of multimedia instruction. American psychologist. 2008;63(8):760. https://doi.org/10. 1037/0003-066x.63.8.760. [95]. Sanal Kumar TS, Thandeeswaran R. A general model for an instructional videobased personalized programming learning environment and its practical implications. In: 2023 12th International Conference on Advanced Computing (ICoAC); 2023. p. 1– 6. [96]. T S SK, Thandeeswaran R. Adapting videobased programming instruction: An empirical study using a decision tree learning model. Education and Information Technologies. 2024 Jan;https://doi.org/10.1007/ s10639-02312390-4. [97] Sanal Kumar TS, Thandeeswaran R. An improved adaptive personalization model for instructional video-based e-learning environments. Journal of Computers in Education. 2024 Jan;https://doi.org/10.1007/ s40692-02300310-x. Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4461 ABBREVIATIONS The following abbreviations are shown in Table.1 are used in this manuscript. Table 1: Abbreviations Abbreviations Meaning BI Behavioral Intention CBS-MM Construct By Self Mind Mapping CL Cooperative Learning CoL Community of inquiry learning framework COS-MM Construct-On Scaffold Mind Mapping CS Computer Science CT Computational Thinking DP Dynamic Programming FC Flipped Classroom FSLSM Felder Silverman Learning Style Model FYO Follow Your Objective GBL Game-Based Learning GUI Graphical User Interface IDE Integrated Development Environment ITS Intelligent Tutoring System LO Learning Object MAI MIT App Inventor MBTI Myers-Briggs Type Indicator MIT Massachusetts Institute of Technology MMCL Mind Mapping Combined with Cooperative Learning PAL Peer Assisted Learning PASS Programming Assignment aSsessment System PBL Problem-Based Learning RBT Revised Bloom Taxonomy TBL Team-Based Learning TUI Tangible User Interface VARK Visual, Auditory, Read/Write, and Kinesthetic VPL Visual Programming Language VPP Virtual Pair Programming