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Using mobile technologies and cloud computing with an approach of gamification stimulates and enriches the learning management system (LMS) for a unique creative learning environment that fosters boundless opportunities for learners. Today’s learners seek self-paced and motivating experiences. This study shows the importance of integrating attractive features within e-learning platforms to improve students’ achievement and engagement. This study aims to develop a Gamified Mobile Cloud-based Learning Management System (GMCLMS) and assess how it affects academic achievement and student engagement in the Object-Oriented Programming (OOP) course. Moreover, the study aims to outline and compare the proposed GMCLMS with conventional learning Management Systems (LMS). The study employs mixed research methods, using the development approach for GMCLMS and a quasi-experimental design for conducting the research. The findings reveal that the experimental group performed significantly better than the control group. Such results show that GMCLMS might be able to strategically involve students in their learning activities. The proposed system reinforcing the students’ engagement with a core OOP concepts instruction which was coupled with large effect sizes and a high Black gain ratio, underscores balanced student engagement development. These findings align with the broader literature on gamification in learning, confirming its potential to reinforce learning outcomes. This paper offers three practical suggestions: creating a foundation for the development and implementation of (GMCLMS), maximizing the impact of (GMCLMS), and enhancing the cost-effectiveness of education. The findings imply that integrating cloud-based and gamified systems in educational frameworks can improve student engagement as well as learning achievements, thus expanding the body of evidence for modern educational technologies. The findings offer actionable insights for educators and educational institutions from a factual rather than theoretical perspective.
Introduction
The COVID-19 pandemic accelerated the move towards online learning and shifted education systems globally (Huang & Zhang, 2024). While the adoption of online learning models into the teaching process has been a matter of argument for a long time, it is now a substantial option for ensuring teaching and learning continuity among unexpected disruptions (Al-Aloul, 2021; Ertan & Kocadere, 2022). After the COVID-19 pandemic, the education system shifted significantly after many even higher learning institutes adopted technological methods of instruction and blended learning alongside it (Aguilos & Fuchs, 2022; Hassan et al., 2019). To minimize costs and optimize advantages, blended learning—a mix of online and F2F instruction—has become an increasing trend in higher education (Aguilos & Fuchs, 2022; Zhang & Zhong, 2018). In blended learning, the adoption of Learning Management Systems (LMS) has become critical for adapting to the post-COVID-19 teaching and learning landscape (Gamede et al., 2022; Turnbull et al., 2022). This shift has led several higher education institutions to implement LMS platforms to supplement conventional classrooms (Al-Mamary, 2022; Zhang & Zhong, 2018). With the use newest distance-learning technology, LMS platforms enable instructors and students to create online materials, monitor student–teacher interactions, and manage the educational process (Melnychenko & Zheliaskova, 2021). Even while LMS platforms are the foundation of blended learning, they have many limitations, such as low student motivation, engagement, focus, interest, inefficient classroom activities, and feelings of isolation because of the lack of face-to-face interactions. Low levels of achievement and inadequate instruction are frequently the results of these challenges (Ertan & Kocadere, 2022; Handayani et al., 2021; Hassan et al., 2019; Mobo, 2020; Ortiz-Rojas et al., 2025; Rahayu et al., 2022). Moreover, many students do not use LMS platforms effectively, preferring to engage in activities outside these systems. Increasing student motivation and participation in the learning process thus requires further attention and research, which is the focus of this study.
In this context, gamification has emerged as a promising approach. Gamification involves integrating game elements such as points, badges, and leaderboards, as well as game dynamics like competition and collaboration, into non-gaming environments (Denden et al., 2021). Gamification is the intentional integration of different gaming elements and experiences throughout learning processes in education to promote student motivation and participation, which in turn improves learning efficiency and instructional outcomes (Aguilos & Fuchs, 2022; Denden et al., 2021; Ghoulam et al., 2024; Limantara et al., 2023). Gamification serves as an innovative educational approach that can increase student engagement, productivity, and motivation, particularly within digital learning environments (Hassan et al., 2019; Jurgelaitis et al., 2018; Pastor-Pina et al., 2015).
The two main issues in learning that are solved by gamification are the lack of student motivation and student disengagement. This is evident as gamification enhances the learning experience of the student by boosting their intrinsic motivation within the game, thus increasing engagement (Ghoulam & Bouikhalene, 2024; Naseri et al., 2023). Similarly, learning objectives can be achieved through the purposeful implementation of games, which fall under gamification (Rosa-Castillo et al., 2022; Subhash & Cudney, 2018). Muntean (2011) identified gamification as a tool for increasing participation in e-learning platforms. Schöbel et al. (2016) found that gamification can motivate students to use LMS more consistently. To gamify an LMS, game layers are added to it instead of creating a specific game that combines play and learning together. Handayani et al. (2021) and Schöbel et al. (2016) demonstrated that a gamified LMS promotes increased student interaction and engagement compared to a traditional LMS. Integrating gaming concepts into LMS enhances usability and encourages healthy competition among students, fostering interest in daily learning activities (Azmi & Singh, 2015). The combination of gamification and mobile learning is noteworthy because of the adaptive nature of mobile learning and gamification’s effect on boosting student motivation (Pambudi et al., 2018).
Cloud computing has revolutionized mobile learning (Wang et al., 2014). The infrastructure that stores and processes data outside of a mobile device is known as mobile cloud computing. With mobile cloud apps, data storage and processing power are transferred to the cloud, extending access to a broader range of mobile subscribers than just individuals with smartphones (Afolabi, 2014). Cloud-based mobile learning offers extensive resources, flexibility, and ease of use. It enables personalized and active learning, providing integrated resources that can be accessed anytime and anywhere, fostering innovative environments and creative performance (Chang et al., 2016; Haggag, 2024).
According to previous background, the authors aimed to develop the gamified mobile cloud learning management system called “GMCLMS” integrating gamification elements to enhance learning outcomes, stimulate student engagement in educational activities, and inspire them to enroll in new courses. To investigate the impact of GMCLMS, the authors conducted a learning experiment. The proposed system provides a wide range of educational courses and administers the learning environment. As a result, it enables achieving the learning goals and enhances learning outcomes.
This research is a contribution to the existing literature on the topic. Many studies regarding the application of gamification within an educational context tend to be mostly narrative, and only limited empirical research has implemented gamification within learning environments. Thus, the gap in the literature and contribution based on the existing literature is described as follows: While the current work demonstrates a dynamic platform that can be adapted to teach and learn multi-disciplinary topics, studies that are experimentally relevant developed static platforms that manage learning in a certain discipline. In this work, a cloud-based mobile learning platform integrating gamification design components has been developed for learning management. While other studies mostly focused on basic LMSs or gamification in conventional educational environments. This is why the authors decided to carry out the present study. This research effort is interesting for having an integrated approach to resolving blended learning challenges with the development of a gamified cloud mobile learning system “GMCLMS” which integrates gamification features to improve learning outcomes and students’ engagement. The development of a gamified cloud mobile learning management system (GMCLMS) comes with several challenges. These challenges must first be recognized and approached, and as such, this paper is structured accordingly. The literature review is presented right after the overview of GMCLMS architecture and main modules. After that, the methodology and procedures are outlined, followed by data collection, results, discussions, conclusions, and reflections on emerging trends.
Literature review
This section provides a summary of the literature on mobile learning, cloud-based learning, gamified learning, and gamification within e-learning systems.
Gamified learning
Gamified learning is a growing field of interest in educational research and practice. Instructors hope to improve motivation, engagement, and learning results by introducing gaming mechanics into their classrooms. This literature review digs deeper into the theoretical foundations, empirical innovations, and efforts surrounding gamified learning (Alahmari et al., 2023; Jayanti et al., 2024; Koivisto & Hamari, 2019).
Gamified learning is becoming an increasingly popular approach used by instructors to enhance the learning process and improve learning and training outcomes. Additionally, gamification is an emerging phenomenon in education, significantly impacting student learning (Alfaqiri et al., 2022; Khaldi et al., 2023). Accordingly, designing effective gamified learning experiences can be challenging for instructors. It requires understanding both the educational content and game mechanics (Kapp, 2016).
Gamified learning enriches learning experiences by including game design elements like leaderboards, levels, challenges, badges, and points. These elements are grounded in Self-Determination Theory (SDT), Flow Theory, and Goal-Setting Theory, which explain how gamified elements fulfill psychological needs, enhancing motivation and engagement (Deci & Ryan, 2000; Van Gaalen et al., 2021; Zainuddin et al., 2020). These conclusions remain supported by the latest studies. For instance, Naseri et al. (2023) concluded that the integration of gamification elements in the education sector fosters student engagement within the learning activities, ultimately enhancing their motivation and leading to better academic performance. This aligns with Balalle’s (2024) findings, which also revealed that incorporating game elements into instruction enhances student motivation, engagement, and academic performance. Kalogiannakis et al. (2021) found that gamified learning enhanced the performance, engagement, and learning outcomes of school students in science education, in addition, Kashive and Mohite (2022) noted that gamified instructional designs improved student motivation and enjoyment in higher education. Furthermore, Vang (2023) explored how gamified learning impacted students’ learning and found that game elements helped bridge knowledge gaps in middle STEM education.
Both gamification and game-based learning have demonstrated an impressive increase in academic achievement, student engagement, and motivation in vocational education settings (Dahalan et al., 2024). According to their findings, further research is required to determine which strategies for gamification are most appropriate for vocational education and learning. Hanus and Fox (2015) emphasized, that ineffective designed gamification strategies can lead to decreases in motivation, especially if students focus only on extrinsic rewards. However, Latorre-Cosculluela et al. (2025) demonstrated that the use of gamification in a university classroom with a cooperative learning model has been observed to enhance the cognitive, and personal development of students. Additionally, the emphasizes the potential of gamification to update university learning and renew the conventional approaches in a collaborative learning environment.
Therefore, using gamified learning to reinforce student motivation and engagement is a meaningful approach. Regardless of the advantages, educators should carefully consider any potential negative effects to make sure gamified experiences stimulate learning opportunities. Future studies need to be focused on the ongoing impacts of gamification on learning objectives and strategies continuing to provide substantial benefits in mobile apps for effectively executing them in various learning environments (Oliveira et al., 2024).
Gamification in e-learning systems
E-learning has always been determined as one of the emerging trends recently. However, factors such as technological improvements and enhanced internet accessibility have made e-learning popular among students (Pribilová & Beňo, 2024).
Gamification has become a popular learning strategy that improves academic performance, learning outcomes, motivation, engagement, and retention rates in both theoretical and applied course settings (Lampropoulos & Sidiropoulos, 2024). Gamification has been used more and more in e-learning systems to increase student engagement and enhance the outcomes of learning. Gamification elements in LMS platforms, such as leaderboards, badges, points, and progress-tracking systems, encourage learning activities and provide students with a sense of achievement and progress (Romsi et al., 2024; Slamet & Mukminatien, 2024). The integration of game mechanics in digital environments offers potential benefits, but findings on its effectiveness have been mixed (Khaldi et al., 2023; Looyestyn et al., 2017; Simsek & Karakus Yilmaz, 2025).
Using gamified learning management systems (LMS) has been shown to improve students’ engagement in online classes (Subiyantoro et al., 2024). It concluded that developing these gamified systems represents an effective strategy is an effective strategy to enhance student engagement in online learning and positively impact academic performance. However, the implementation of such systems is hindered by challenges like technical limitations and inadequate institutional support. In this context, another study has examined the effects and challenges of gamification in an e-learning environment (Alzahrani & Alhalafawy, 2023; Jarnac de Freitas & Mira da Silva, 2023; Kumaran et al., 2023). The most recent study by Khaldi et al. (2023) primarily focused on assessing the effectiveness of gamification in student outcomes in e-learning. Gejandran and Abdullah (2024) explored the influence of gamification on learning effectiveness within e-learning systems, finding that gamification significantly boosted student motivation and engagement. Similarly, Abou-Khalil et al. (2021) outline efficient ways to improve online learning participation from students. They ultimately reached their conclusion that three elements—student–student, student–teacher, and student-learning content interaction—are critical to the effectiveness of using an LMS.
Additionally, Hakak et al. (2019) conducted a study on e-learning systems and noted significant gains in engagement and participation when gamification was thoughtfully designed and implemented. Mora et al. (2018) found that adding game elements to an e-learning environment improved cognitive outcomes in a course on Computer Network Design. Similarly, López-Pernas et al. (2022) and Udeozor et al. (2022) found that incorporating game elements into an e-learning platform for engineering students increased the completion rate of tasks and exercises. Cajilla and Bug-os (2022) reported that their gamified LMS improved both academic performance and student satisfaction, While Hanus and Fox (2015) warned that learners in a gamified course showed drops in motivation and satisfaction over time. Smiderle et al. (2020) highlighted how students are affected by a gamified environment system as per their characteristics. Specifically, to determine how gamification affects learning behavior and engagement of university learners concerning their personality traits.
Consequently, the main contribution is providing and addressing the gaps already identified in the previous literature paving the work for future research on E-learning systems with highly incorporation of gaming elements.
Mobile and cloud-based learning
Since both cloud-based and mobile learning enable students to access learning from anywhere at any time, the need for the classroom is called into question. How educators may maximize mobile and cloud-based platforms to provide, preserve, and improve the level of learning experiences will undoubtedly be called into question by such technological developments. The idea of technology-supported learning is not new, but the increasing use of these platforms has increased the variety of learning experiences that may be provided (Kaur et al., 2022; Pedraja-Rejas et al., 2024; Surameery & Shakor, 2021).
Mobile and cloud-based learning platforms have introduced flexibility into educational environments, making learning more accessible. These platforms, when integrated with gamification elements, have been shown to improve student engagement and performance (Lampropoulos & Sidiropoulos, 2024).
Pechenkina et al. (2017) explored the effectiveness of mobile learning platforms integrated with gamified elements and found that they increased both engagement and learning outcomes in undergraduate courses. Pambudi et al. (2018) introduced the educational Android app “MEGIE” for web programming instruction, which improved learning outcomes. Similarly, Achar (2021) found that cloud-based systems offered enhanced collaboration and communication, further boosting academic performance. Kao et al. (2023) demonstrated that mobile-based gamified learning tools in accounting education significantly enhanced knowledge retention and practical skills among students.
Hou’s study (2023) focused on developing a framework for teaching science through cloud gamification, aimed at enhancing collaborative problem-solving skills in remote learning environments. By integrating cloud tools and employing three-dimensional scaffolding—cognitive, peer, and metacognitive—it designs interactive learning activities. The findings reveal that this framework significantly improves student interaction, increases motivation, enhances the quality of scientific discussions, and fosters problem-solving skills. Kherazi and Bourray (2024) explored the impact of AI-assisted gamification on user engagement in mobile language learning applications, using Duolingo as a case study. It concludes that gamification plays a significant role in boosting user engagement and motivation for language learning. Additionally, the research identified key motivating elements of gamification, including points, badges, and feedback. These conclusions are supported by recent research by Oliveira et al. (2024), which shows that gamified mobile learning platforms improve student satisfaction and academic achievement in online instructional settings. Furthermore, future research should focus on integrating innovative technologies, personalized factors, and data-driven techniques to guarantee that gamification in mobile apps continues to contribute considerable value.
Gamification and student engagement
It is believed that student academic engagement is becoming more and more significant in learning and academic achievement activities. A potential reason for the increasing emphasis on undergraduate students’ learning engagement is the understanding of the several social and academic benefits of engaged students (Bowden et al., 2019; Korhonen et al., 2024). Previous research showed that student participation is essential to addressing academic issues such as low academic achievement, social isolation, and dropout rates (Bedi, 2023). Participation from students is essential to their learning, particularly in an online setting where they could feel distanced (Dixson, 2015).
Student engagement is a critical predictor of academic success, and gamification has proven to be an effective strategy for increasing engagement. Studies have shown that gamified learning environments lead to improved engagement, information retention, and academic performance (Kao et al., 2023; Ortiz-Rojas et al., 2025; Rebaque-Rivas et al., 2023).
Gamification has been shown to have a major impact on student engagement in recent research. Slamet and Basthomi (2024) studied the effects of incorporating gamification features into Learning Management Systems (LMS) focusing on self-directed learning (SDL) for English as a Foreign Language (EFL) learners. The findings showed that while self-engagement and motivation levels among students increased, sustaining self-management and self-control posed challenges. Student performance and participation were improved after implementing gamified strategies in STEM courses as reported by Rebaque-Rivas et al. (2023). According to Vang (2023), younger learners were more actively engaged and retained information more effectively during gamified STEM activities compared to traditional Learning Environments. Kao et al. (2023) illustrated within the same context that the integration of a gamified accounting game into the TronClass platform improved students’ academic achievement as well as their motivation and engagement levels. Similarly, Sailer et al. (2021) found that well-designed gamified systems positively influenced students’ emotional and cognitive engagement in e-learning platforms. However, as Alsadoon et al. (2022) pointed out, gamification techniques that lack design properly might lead to a decrease in student engagement and an inconsistency with learning objectives. Kashive and Mohite (2022) suggested that the use of gamification strategies that align with the preferences of learners can provide the most promise for maintaining sustained engagement to enhance the e-learning experience. Succeeding learners in the online environment actively and effectively use technological resources, are motivative driven, willing to learn, and know how to leverage their prior experiences appropriately. In addition, they have a balanced ability in cooperative and self-directed learning and have remarkable communication skills (El-Sabagh, 2021; Inder, 2022; Kaed et al., 2023; Nkomo & Nat, 2021).
Overview of GMCLMS development
GMCLMS architecture
In this paper, GMCLMS is developed as an integrated platform based on gamification to manage the learning process. Figure 1 shows GMCLMS architecture. The system has two principal components: a server-side and a client side. The server’s (“backendless” which contains users’ databases and services) primary role is to facilitate the interaction between the mobile application and the cloud database. The client in this case is the Android mobile application, which users operate to access and manage the data on the servers.
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Fig. 1
GMCLMS system architecture
Backendless
Backendless is a cloud-based serverless platform that significantly streamlines and accelerates the process of development of web and mobile applications (Available at: https://backendless.com/). The mobile application related to the Backendless server in two basic steps:
Step 1: Client-side Setup:
The Software Development Kit (SDK) is added to the developed mobile application as follows:
Gradle Setup: The configuration that follows is added to create a gradle file:
Android Manifest: Backendless APIs require the Android. Permission.INTERNET permission to be added to the manifest file because they communicate with a server. The line that follows the <manifest> element is thus added:
Step 2: Mobile Application initialization:
Because the proposed mobile application makes use of the Backendless SDK, the server must provide the values for the application-ID and API-keys arguments in the application initialization call so that the server can identify the application queries. The approach described below is used by the client application to initialize the SDK.
Mobile application
The essential component of the GMCLMS system is a mobile application, it acts as the central unit that connects all components of the system. The mobile/client application consists of three layers: UI, cloud database, and Controller. For students, instructors, and administrators, the mobile application offers distinct user interfaces that facilitate access to the many services related to the mobile learning environment. A cloud database to store and retrieve all information and data needed by the system. The database contains many tables. Figure 2 shows the entity relationship schema of GMCLMS. The controller is the key coordinator among the UI, and SQL database, where the controller responds to the user’s request to get the data to view on UI.
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Fig. 2
The system’s entity relationship schema
Main modules in GMCLMS
GMCLMS system was designed and implemented using the Android Studio environment, where the interfaces were designed in XML language and programmed in JAVA language. The application was linked to a cloud computing service (online database) called Backendless. SQL was used to create some server queries. GMCLMS offers a collection of features that are divided into two classes: modules and resources. The resources are digital instructional materials usually downloaded by the lecturer in different formats including PDF, word documents, videos, images, audio files, URLs, etc. The modules allow interaction between lecturers and students. They include chats, discussions, assignments, activities, surveys, and exams. In addition, GMCLMS provides gamified quizzes, which contain game elements such as experience points (XP), levels, badges, and leaderboards. The following section details these modules.
Chat module
As a learning community, the Chat module enables participants to share ideas and thoughts in real-time synchronous discussions. This module encourages students to share and discuss their knowledge, which can lead to mutual learning and support. Moreover, the Chat module provides teachers with valuable insights into the course’s level and the parts in which students are experiencing the most difficulties. This information can help teachers adjust their teaching strategies and identify parts where additional support is necessary.
Activity module
The activity module enables lecturers to create activities for students enrolled in a specific course, academic department, and study level. On the other side, students can submit, and upload required assignments and activities in various formats (Word document, PowerPoint, video, etc.). After that, the lecturer can review these assignments, and the feedback is appended to the assignment page for each student.
Survey module
The survey module enables the lecturer to create a survey for students (see Fig. 3), to learn about their opinions and attitudes at the end of their study of a particular subject and know the extent of their absorption of the contents provided. This feature will help the lecturer design lessons and activities that suit each category of students. This contributes to a better learning experience for the students. Moreover, this module enables the lecturer to view all previously created surveys. An example of a course evaluation survey and its results are displayed in Fig. 4.
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Fig. 3
Creating a new survey
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Fig. 4
OOP cours evaluation survey
Exam module
This module is designed to create exams that assess student learning outcomes across various courses. The lecturer creates an exam for a certain lesson or subject in a specific course and identifies the information and details of the exam: academic department, academic study level, subject name, and type of exam (multiple choice, true or false). The lecturer can also determine the number of questions and the exam date, incorporating a time pressure element by setting the exam duration, either by allocating a specific time for the exam as a whole or for each question individually, as shown in Fig. 5, once students complete their answers, results are displayed immediately to enhance motivation and knowledge retention. Additionally, the system provides a detailed report to the lecturer regarding student performance and exam results. This report includes information such as the department name, study level, course name, test date and time, number of successful students, success rate, failure rate, and the names and grades of the students. Furthermore, this module allows lecturers to view all previously created exams.
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Fig. 5
Create a new exam
Gamified quizzes module
This module enables the creation of quizzes based on a gamification strategy. The functions and components of the gamified quiz items in GMCLMS are discussed in detail below.
Assign player
Initially, this module allows the lecturer to design gamified quizzes for specific lessons or subjects, specifying the information and details of the quiz. Students can either join existing gamified quizzes created by the lecturer or create their own by selecting the difficulty level of the quiz questions (easy, medium, difficult) and specifying the number of questions. The system then randomly adds questions from the question bank based on the criteria set by the students. Additionally, students can choose to play individually or in teams of two to four. After completing the gamified quiz, the winners are displayed in ranked order.
Experience points (XP)
GMCLMS provides students with two ways to gain Experience Points XP: First, points are recorded and collected depending on student contributions and engagements, including going through different learning resources such as lectures, questionnaires, and other learning activities. In addition, when students complete any actions, such as logging into the system or participating in discussions, students gain Experience Points. The number of XP given for each action depends on the amount of effort exerted. For instance, students who take part in discussions gain more XP than merely logging in. The more players participate, the easier points will be won, which reflects better lecture attendance, content understanding, and taking part in different activities. In the second way points are awarded automatically when players take a quiz, where they can gain an XP for each question they answer correctly, by giving feedback. No points are awarded for incorrect answers. In this scenario, the players are challenged by the necessity to collect a predefined number of points to reach a certain level. If a player earns 100 points, he reaches the first level, and whenever he gains 100 points more, he rises to the higher level and so on until he achieves the tenth level. This motivates students to learn and participate more to accumulate XP. Figure 6 illustrates the total points earned and the level achieved by each player.
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Fig. 6
XP given for students in the OOP course
Badges
To properly integrate badges, it is advised to utilize them only for significant achievements required for some work or after reaching a particular degree of knowledge and understanding (Denden et al., 2021). Therefore, students will have many quizzes varying in difficulty levels (easy, medium, and hard), to assess their knowledge of all the information and skills acquired about the course they have studied to get a badge, provided that achievement of a certain level in these quizzes. If the student achieves a performance level of 90% as a minimum in the easy-level test, he will receive a badge titled “Activity”. And if the student achieves the same performance level (90%) in the medium-level tests, he will obtain a badge titled “Persistence”. If the student performs at the same level (90%) in the difficult-level tests, he will obtain a badge titled “Champion”. Furthermore, if the student receives all 3 badges, the system automatically will grant him the “Graduate” advanced badge, which means that he has now graduated from that course, as shown in Fig. 7.
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Fig. 7
The badges were awarded to students in the OOP course
Leaderboards
Leaderboards are one of the most common sources of motivation since they increase competition among learners and stimulate their engagement in the learning process (Rahayu et al., 2022). The leaderboard displays each student’s ranking according to the points that has earned in quizzes compared to other peers. The leaderboard updates in real-time reflecting the changes in student rankings as additional points are earned by students. In this way, students can track their progress on the leaderboard, which motivates them to compete for better ranks. Figure 8 shows the leaderboard for the OOP course, showcasing the avatars of the top three students, their levels, and the points collected by all participants in order of their performance in the gamified quiz.
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Fig. 8
The leaderboard of the OOP course
Users’ roles in GMCLMS
In GMCLMS, three users have different kinds of privileges: lecturers, students, and administrators. The system equips lecturers with comprehensive tools for course management, lesson planning, and virtual classroom management. Moreover, only registered students are permitted to enter the digital classroom and access their respective learning resources, activities, and modules. The administrator manages all services within the system and, hence takes a pivotal role. The users can perform a log-in, authenticate, and verify session operations, and in return, a certain verification is executed as depicted in the use case diagram Fig. 9.
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Fig. 9
Use case diagram for the student’s roles in the GMCLMS system
Evaluation of GMCLMS performance
To evaluate the GMCLMS performance, the authors developed an instrument which was later presented in its primary version to a panel of expert reviewers in educational technology and computing for a validity check. For some phrases that were modified, all their feedback was accepted. For some phrases, the feedback was ignored. In its last form, the instrument was able to incorporate some of the changes without having to discard others. This is how the reviewers were able to validate the instrument utilizing the arguments that were presented in the feedback.
The instrument consists of 13 items in two main sections: “technical feasibility”, consisting of 6 items, and “didactic efficiency”, consisting of 7 items. The reliability of the instrument was measured using Cronbach’s alpha coefficient (α 0.971). Subsequently, the instrument was submitted to a panel of reviewers to get their feedback on the proposed environment. Their responses were recorded on a five-point scale (Strongly Disagree = 1, Disagree = 2, Undecided = 3, Agree = 4, Strongly Agree = 5). Figures 10 and 11 illustrate the responses from the reviewers regarding the questionnaire items utilized to assess the proposed system.
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Fig. 10
GMCLMS technical feasibility results
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Fig. 11
Results for didactic efficiency of GMCLMS
Research methodology
A detailed description of the research design, sample, research tools, data collection methods, and procedures is described in this section.
Research purpose and questions
This research aims to explore the impact of Gamified, Mobile, Cloud-Based Learning Management Systems (GMCLMS) on the enhancement of Student Engagement.
The conceptual framework for the research is depicted in Fig. 12. The main research question is “What is the impact of gamified mobile cloud learning management system (GMCLMS) on development students’ engagement?” Accordingly, three sub-research questions have been formulated: (1) What are the steps to develop a gamified mobile cloud learning management system (GMCLMS)? (2) What is the impact of a gamified mobile cloud learning management system (GMCLMS) on the development of the basic concepts of OOP (inheritance, polymorphism, and encapsulation) in comparison with conventional LMS “Moodle”. (3) What is the effect of a gamified mobile cloud learning management system (GMCLMS) on the development of student engagement (skills, participation, performance, emotional) in comparison to a conventional LMS like Moodle?
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Fig. 12
The conceptual framework (model) of the research questions
Research hypotheses
The goal of this research is to validate the following hypotheses:
H1:
There is no statistically significant difference between the mean scores of the experimental group exposed to (GMCLMS) and the control group exposed to the conventional e-learning system in the pre-application of the achievement test and the student engagement scale.
H2:
There is a statistically significant difference at the level of (0.05) between the mean scores of the experimental group (GMCLMS) and the control group (conventional LMS) in the post-application of the achievement test, favoring the experimental group.
H3:
There is a statistically significant difference at the level of (0.05) between the mean scores of the experimental group (GMCLMS) and the control group (conventional LMS) in the post-application of the student engagement factors, favoring the experimental group.
Research design
The design of this study utilized a pretest–posttest approach within a quasi-experimental framework. As depicted in Fig. 13 below, both independent and dependent variables of the study were described.
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Fig. 13
Research design
Both groups were given explanations regarding the progress of the learning activities. To achieve the learning objectives, the experimental group was guided to use GMCLMS, while the control group utilized the Conventional LMS (Moodle).
Research participants
The study’s participants were a sample of second-year students consisting of sixty students who were enrolled in the “Object Oriented Programming” course from the Department of Computer Teacher, Education, Faculty of Specific Education at Mansoura University. Participants, aged between 19 and 20 years old, represented the study population.
Every participant was selected from the first semester of the 2022–2023 academic year and received instruction from the same instructors. Two classes totaling sixty students were chosen randomly from the Department of Computer Teachers to comprise the research sample. The first group was designated as the control group (N = 30, consisting of 13 males and 17 females), while the second group was assigned as the experimental group (N = 30, also comprising 13 males and 17 females). Table 1 provides the demographic distribution of the student sample.
Table 1. Students’ demographic data
Age | Gender | Total | |
|---|---|---|---|
M | F | ||
Experimental group | 13 | 17 | 30 |
Control group | 13 | 17 | 30 |
Research instruments
Achievement test
The achievement test was designed and prepared to measure the study sample’s achievement of the cognitive aspects related to the unit titled “OOP Concepts: Inheritance, Polymorphism, and Encapsulation” of the OOP course. The test was designed around two major questions: 20 true-and-false statements and 20 multiple-choice questions with 4 options each. The achievement test was submitted to a group of specialists to verify its apparent validity using reviewers’ validation. The proposed changes were made to get the test in its final form. Moreover, the convergent validity of the test questions was calculated using Pearson’s corrected correlation coefficient. It was found that all the values of the corrected correlation coefficient ranged from 0.302 to 0.737 for the questions of the first concept (inheritance), from 0.324 to 0.689 for the questions of the second concept (Polymorphism), and from 0.310 to 0.621 for the questions of the third concept (encapsulation). They were all significant at the level of 0.05. The convergent validity for the three concepts was also calculated. Table 2 shows the values of the corrected correlation coefficient for each concept with the total score of the test.
Table 2. Values of the correlation coefficient for each concept with the total score of the test
Concepts | Correlation coefficient | Significance level |
|---|---|---|
Inheritance | 0.945 | 0.05 |
Polymorphism | 0.936 | 0.05 |
Encapsulation | 0.780 | 0.05 |
As demonstrated in Table 2, all values of the corrective correlation coefficients of concepts are significant at 0.05 level which, confirms the validity of the test. Subsequently, the test was applied to a pilot sample of 30 students, both male and female, to determine the most appropriate time limit for the test, as well as to compute the ease, difficulty, discrimination coefficients, and reliability coefficients of the test items. The pilot study indicated that the appropriate duration was approximately 40 min while the ease coefficients ranged from 0.37 to 0.80, difficulty coefficients from 0.20 to 0.63 (both acceptable for the test), and discrimination coefficients from 0.40 to 0.50 (also acceptable for the test). Test reliability was calculated with Cronbach’s alpha coefficient. Table 3 shows the values of Cronbach’s alpha for each concept and the overall test.
Table 3. Values of Cronbach’s alpha for each concept and the test as a whole
Concepts | Number of items | Cronbach’s reliability coefficient (α) |
|---|---|---|
Inheritance | 15 | 0.759 |
Polymorphism | 15 | 0.774 |
Encapsulation | 10 | 0.880 |
Total score | 40 | 0.846 |
According to Table 3, all alpha reliability values of the test are between 0.759 and 0.880. This implies that all values are above 0.7 which indicates adequacy of reliability and, hence appropriate for use.
Student engagement scale
Based on the review of student engagement research, the engagement scale was developed. The engagement of students was evaluated through the Dixson scale (Dixson, 2015). El-Sabagh (2021) adapted the “Dixson scale” into Arabic, which the authors employed by the authors. The engagement scale involved the following four dimensions: Skills, Participation/Interaction, Performance, and Emotions. Therefore, the objective of the scale is to assess the fundamental engagement factors pre and post-instruction with GMCLMS and the Conventional LMS (Moodle).
Reliability and validity of the engagement scale
The alpha coefficient for scale factor scores demonstrated a high degree of internal consistency ranging from 0.80 to 0.86, suggesting an outstanding level of reliability. In assessing the instruments for this research, the overall reliability was determined to be 0.81 with Cronbach’s alpha; therefore, the measures achieved reasonable reliability. Tools within this research were found to have exceptional validity and reliability, as they provided a precise measurement of students’ learning engagement.
The instrument was tested on a separate pilot group of 30 students who were not part of the experimental sample. Furthermore, the instrument recorded a correlation coefficient within the range of 0.73 and 0.81, signifying a sufficiency of validity for its application. Correlation coefficients along with Cronbach’s alpha from the interaction scale are displayed in Table 4.
Table 4. Correlation coefficient and Cronbach’s alpha of the engagement scale
Students’ engagement factors | Validity Pearson correlation | Reliability Alpha Cronbach |
|---|---|---|
Skills | 0.70 | 0.80 |
Participation/interaction | 0.81 | 0.84 |
Performance | 0.78 | 0.86 |
Emotion | 0.73 | 0.83 |
Full scale | 0.79 | 0.81 |
To ensure content validity, the scale was reviewed by specialists who commented on the clarity of the language’s formulation concerning the assessment of student engagement. They also made suggestions where appropriate.
Research procedures
To determine whether the two groups were homogeneous and equivalent, the first hypothesis—that is, “There is no statistically significant difference between the mean scores of the experimental group exposed to (GMCLMS) and the mean scores of the control group exposed to the conventional e-learning system in the pre-application of the achievement test and student engagement scale”—was examined. The authors confirmed the equivalency of the experimental and control groups by evaluating the pre-application scores after giving the achievement test and engagement scale to both groups before the experiment.
To verify the homogeneity of the two classes before the experiment, the t-test of independent samples was used for the engagement scale and accomplishment test. Before the experiment, the two groups were homogeneous, as indicated by the t-values not being significant at the 0.05 level. The results are shown in Tables 5 and 6.
Table 5. T-value of the difference between control vs. experimental groups in the achievement pre-test
Achievement pre-test | Group | No | Arith mean | Std. d | T “value” |
|---|---|---|---|---|---|
Experimental | 30 | 10.47 | 2.92 | 1.69 | |
Control | 30 | 11.87 | 3.44 |
Table 6. Entry levels pre-test for “students engagement” (control vs. experimental) groups
Engagement factor | Group | No | Arith mean | Std. d | T “value” |
|---|---|---|---|---|---|
Skills | Experiment | 30 | 16.76 | 1.94 | 0.354 |
Control | 30 | 19.21 | 1.43 | ||
Participation/interaction | Experiment | 30 | 10.34 | 1.02 | 0.464 |
Control | 30 | 08.96 | 1.13 | ||
Performance | Experiment | 30 | 09.65 | 0.98 | 0.291 |
Control | 30 | 10.21 | 1.02 | ||
Emotional | Experiment | 30 | 08.59 | 1.03 | 0.309 |
Control | 30 | 09.12 | 1.09 | ||
Whole engagement scale | Experiment | 30 | 21.02 | 1.45 | 0.594 |
Control | 30 | 19.81 | 1.39 |
Table 5 indicates that there were no statistically significant differences between the research groups in the pre-application of the pre-test, with a t-value of 1.69, which is not statistically significant. The mean score for the control group in the pre-measurement was 10.47, while the mean score for the experimental group was 11.87, demonstrating the equivalence of the two groups. Thus, any differences observed between the control and experimental groups following the exposure of the experimental group to the proposed system (GMCLMS) can be attributed to the effectiveness of the system in enhancing the cognitive achievement of Object-Oriented Programming (OOP) concepts.
Table 6 showed that there was no significant difference in the mean scores of both groups (p > 0.05), the results in Table 6 further confirm that there were no significant differences between the experimental and control groups regarding engagement, and each student engagement factor separately. These findings suggest that the two classes were similar before the start of the research experiment.
Learner content path in GMCLMS
The experimental group students were asked to bring their smartphones, and each received a unique account after downloading the GMCLMS APP. They received guidance and training on how to adequately navigate within the application with the prefixed elements of learning objects that were generated dynamically. At the same time, the control group completed the same course using blended instructional materials. Registered students can be granted access to the digital classroom where they can view the learning sessions, activities, and modules contained in the classroom. Through the leaderboard, students can track their progress on it, motivating them to strive for higher positions. The administrator of the system is the overall supervisor who manages all the services in the system. All users are allowed to log in, and after authentication, the operations of verification and identity confirmation are performed seamlessly.
GMCLMS experimental results
GMCLMS was adapted as an integrated platform to host an OOP course dedicated to teaching and learning OOP concepts: encapsulation, inheritance, and polymorphism (overriding and overloading) using a group of digital educational resources and materials (several educational videos). This endeavor aimed to explain the concepts theoretically and apply them practically in the VB.NET programming language, making full use of the functionalities offered by the system.
Findings
The results were analyzed and interpreted according to the research questions as follows: To answer Q1, the GMCLMS system was developed, with the steps and stages of its construction and development detailed in “Overview of GMCLMS development” section. For Question 2, the validity of the second hypothesis was examined, which stated: “There is a statistically significant difference at the level (0.05) between the mean scores of the experimental group (GMCLMS) and the control group (conventional LMS) in the post-application of the achievement test, favoring the experimental group.” An independent samples t-test was calculated for the achievement test, and the results of this hypothesis are presented in Table 7.
Table 7. T-value in post-test application and total score
Concepts achievement | Group | No | Arith mean | Std. d | T “value” |
|---|---|---|---|---|---|
Inheritance | Experimental | 30 | 11.38 | 2.672 | 22.289 (P-value: 0.002*) |
Control | 30 | 4.54 | 1.581 | ||
Polymorphism | Experimental | 30 | 11.62 | 2.381 | 28.309 (P-value: 0.002*) |
Control | 30 | 3.82 | 1.935 | ||
Encapsulation | Experimental | 30 | 7.82 | 1.366 | 21.336 (P-value: 0.002*) |
Control | 30 | 3.02 | 1.505 | ||
Total score | Experimental | 30 | 30.82 | 5.759 | 32.212 (P-value: 0.003*) |
Control | 30 | 11.38 | 3.943 |
* p < 0.05, significant at 0.05 level
According to Table 7, the following conclusions can be detected: For the concept of “inheritance,” there are statistically significant differences between the mean scores of the control and experimental groups in the post-measurement in favor of the experimental group, with a t-value of 22.289, significant at the 0.05 level. The mean score of the experimental group was 11.38, which is higher compared to the control group’s mean of 4.45. The difference between the mean scores (6.93) indicates that the differences are significant and not a result of sample size. Regarding the concept of “polymorphism,” statistically significant differences were found between the mean scores of the control and experimental groups in the post-measurement, with a t-value of 28.309, significant at the 0.05 level. The mean score of the experimental group was 11.62, which is higher than the control group’s mean of 3.82. The difference between the mean scores (7.8) indicates that the differences are significant and not a result of sample size.
Concerning the concept of “encapsulation,” statistically significant differences were also found between the mean scores of the control and experimental groups in the post-measurement in favor of the experimental group, with a t-value of 21.336, significant at the 0.05 level. The mean score of the experimental group was 7.82, which is higher compared to the control group’s mean of 3.02. The difference between the mean scores (4.8) indicates that the differences are significant and not a result of sample size.
As for the total score of the test, significant differences were found between the mean scores of the control and experimental groups in the post-measurement in favor of the experimental group, with a t-value of 32.212, significant at the 0.05 level. The mean score of the experimental group was 30.82, which is higher compared to the control group’s mean of 11.38. The difference between the mean scores (19.440) indicates that the differences are significant and not a result of sample size. These results are illustrated in Fig. 14.
[See PDF for image]
Fig. 14
Differences between mean pre-and post-test scores of the experimental group in the academic achievement
The validity of the third hypothesis, stated, “There is a statistically significant difference at the 0.05 level between the mean scores of the experimental group (GMCLMS) and the control group (conventional LMS) in the post-application of student engagement factors, favoring the experimental group,” was examined to respond to Question 2. Table 8 presents the results of this hypothesis, which was tested using an independent sample t-test.
Table 8. T-value in post-test application (engagement factors)
Engagement factors | Group | No | Arith mean | Std. d | T “value” |
|---|---|---|---|---|---|
Skills | Experimental | 30 | 28.48 | 1.14 | 3.097 (P-value: 0.001*) |
Control | 30 | 17.34 | 1.58 | ||
Participation/interaction | Experimental | 30 | 17.62 | 1.02 | 2.035 (P-value: 0.001*) |
Control | 30 | 10.94 | 1.31 | ||
Performance | Experimental | 30 | 21.12 | 1.21 | 1.972 (P-value: 0.001*) |
Control | 30 | 13.07 | 1.34 | ||
Emotional | Experimental | 30 | 20.08 | 1.62 | 2.603 (P-value: 0.001*) |
Control | 30 | 12.63 | 1.97 | ||
Total score | Experimental | 30 | 33.64 | 1.75 | 4.121 (P-value: 0.001*) |
Control | 30 | 13.12 | 2.03 |
* p < 0.05, significant at 0.05 level
Table 8 shows that students in the experimental group achieved a significantly higher mean score of engagement post-test (engagement factors items) scores than students in the control group (p < 0.05).
The experimental research was performed to evaluate the impact of the proposed GMCLMS. Independent sample t-tests were utilized to assess the prior behavioral engagement of both groups concerning the research topic. The findings indicated that students in the experimental group demonstrated higher learning achievement than those taught using the conventional LMS approach.
Effectiveness of the proposed system
To verify the effectiveness of the proposed system in developing OOP concepts, two main methods were used:
Effect size
In this study, η2 was used to calculate the effect size of the independent variable (the proposed system) on the dependent variable (academic achievement), as shown in Table 9.
Table 9. Effect size of the differences between the pre-and post-test of concepts and the total score
Concept | Effect size η2 | Effect level |
|---|---|---|
Inheritance | 0.949 (94.9%) | High |
Polymorphism | 0.960 (96.0%) | |
Encapsulation | 0.971 (97.1%) | |
Total score | 0.967 (96.7%) |
Based on Table 9, the effect size of the differences between the pre and post-test of the concepts of inheritance (0.949), polymorphism (0.960), encapsulation (0.971), and the overall test score (0.967) were all large or greater than 0.9. This means that the system proposed highly affects the development of OOP concepts for students who participate in the system.
Modified Black’s gain ratio
Modified Black’s gain ratio was calculated to identify the effectiveness of the proposed system. Modified Black’s Gain Ratio = (Y − X)(D − X) + (Y − X)/D (ALWahaibi et al., 2020; Arman, 2013). Where: Y = grade of post-test, X = grade of pre-test, D = test maximum grade (40). Table 10 shows the Black’s Gain Ratio.
Table 10. Black’s gain ratio
Concept | Raw gain | Expected gain | Modified gain |
|---|---|---|---|
Inheritance | 6.84 | 10.46 | 1.11 |
Polymorphism | 7.80 | 11.18 | 1.22 |
Encapsulation | 4.80 | 6.98 | 1.17 |
Total score | 19.44 | 18.62 | 1.69 |
According to Table 10, the Black’s Gain Ratio of the concepts is close to the reference value (1.2) and the total score is 1.69, which is greater than the reference value (1.2), indicating the effectiveness of the proposed system based on the gain equation. The findings confirm the positive impact of the proposed system in developing OOP concepts on the participating students. The researcher may attribute these results to the motivating properties of GMCLMS as a game-based learning management tool, which improved attendance at lectures and understanding of content and increased student engagement in the learning process and their participation in all educational activities. Moreover, the use of game elements and techniques leads to more motivated learning and, hence, to more effective learning outcomes. Additionally, an 8-item questionnaire was applied to measure the satisfaction of students enrolled in the OOP course about the use of the GMCLMS in learning OOP courses. The reliability of the questionnaire was measured using Cronbach’s alpha coefficient (α = 0.789), this indicates the reliability of the questionnaire. The students’ responses were measured on a five-point scale (Strongly Disagree = 1, Disagree = 2, Undecided = 3, Agree = 4, Strongly Agree = 5). Figure 15 Shows students’ responses to the questionnaire items.
[See PDF for image]
Fig. 15
Results of the student satisfaction questionnaire
As shown in Fig. 15, the average total score for all questions is 10 (SD = 19.6), which is considered very high and indicates that the students had a high level of satisfaction and positive perception towards using the proposed GMCLMS in the teaching and learning of the OOP course, which was included under the group of basic courses.
Discussions
The findings of this study provide clear evidence of GMCLMS’s usefulness in increasing student engagement and academic performance. It analyzes the effects of gamification and contributes to the literature concerning the potential impact of mobile learning on educational advancements. The findings are discussed below in conjunction with an evaluation of the existing literature, to highlight where there are agreements and disagreements.
Academic achievement
The significant improvement in students’ understanding of OOP concepts, as demonstrated by effect sizes ranging from 0.949 to 0.971 and a Black gain ratio of 1.69, aligns with several studies that emphasize gamification’s positive impact on cognitive and academic outcomes. For example: López-Pernas et al. (2022) and Mora et al. (2018) reported that gamified e-learning systems significantly enhance students’ cognitive processing and task completion rates, particularly in technical disciplines. Vang (2023) highlighted that gamification fosters conceptual clarity in STEM education, especially in STEM Education.
The findings exceed benchmarks established in these studies, showcasing the GMCLMS as a potentially superior tool for enhancing academic performance. Unlike static platforms discussed in earlier studies, the adaptive nature of GMCLMS likely contributed to these superior outcomes by tailoring gamified content to individual learner needs.
While studies similar to Hanus and Fox (2015) reported declines in academic performance due to over-reliance on extrinsic rewards, the current findings suggest that the GMCLMS’s balanced use of intrinsic and extrinsic motivators mitigates these risks. This balance allowed students to remain motivated without becoming overly dependent on tangible rewards.
Engagement and motivation
One notable finding of this study was the level of student engagement as shown by participation and feedback measurements. The integration of XP, badges, leaderboards, and other gamified elements aligns with previous research emphasizing the role of gamification in fostering motivation: Kalogiannakis et al. (2021) demonstrated that digital technologies, as gamified systems, improve both motivation and engagement in science education, providing students with a perception of progress and accomplishment. In addition, Hakak et al. (2019), Kao et al. (2023), and Ortiz-Rojas et al. (2025) similarly observed that gamified cloud-based platforms improve collaboration and emotional engagement in learning environments.
The results contribute to this area of research by illustrating the use of gamification in increasing emotional engagement in technically challenging subjects like programming. The leaderboard, in this case, fostered positive competition while also encouraging student collaboration and assistance.
A key difference in this study compared to Prior Research is the authors’ emphasis on the ongoing maintenance of sustained engagement and its components. As opposed to Hanus and Fox (2015) who described the diminishing returns on extrinsic motivators, the adaptive mechanics of GMCLMS prevented such decline. This aligns with Kashive and Mohite (2022) finding that the gamification approach sustains motivation and satisfaction over time. Additionally, increasing the desire to use online learning platforms.
Long-term engagement
One of the critical contributions of this study is its emphasis on sustained engagement through adaptive gamification strategies. The GMCLMS’s ability to adjust gamified elements dynamically, based on user progress, proved essential in maintaining long-term student interest and enthusiasm. Kashive and Mohite (2022) argued that gamification, which evolves in response to learner behavior, is crucial for overcoming the disengagement often seen in static systems. This study validates their claim by demonstrating how tailored rewards and challenges can sustain high levels of participation. Subiyantoro et al. (2024) emphasized the importance of dynamic gamification in sustaining engagement in LMS platforms, a finding that aligns strongly with the outcomes observed in this research.
The current research differs from static models, which rely only on fixed extrinsic rewards and often fail to address the evolving needs of learners. By adapting game mechanics in real-time, the GMCLMS was able to create a more engaging and personalized experience, addressing criticisms noted by Ortiz-Rojas et al. (2025) and Smiderle et al. (2020) about traditional gamification’s limitations.
The current research differs from static models, which rely only on fixed extrinsic rewards and often fail to address the evolving needs of learners. By adapting game mechanics in real-time, the GMCLMS was able to create a more engaging and personalized experience, addressing criticisms noted by Ortiz-Rojas et al. (2025) and Smiderle et al. (2020) about traditional gamification’s limitations.
Blended learning and gamification
The GMCLMS proved effective in addressing problems that pertain to blended learning such as disengagement and lack of access. The framework’s mobility as a cloud-based platform means that students can access learning materials at any time, which is essential for hybrid models of learning. According to Pechenkina et al. (2017), mobile learning platforms that included gamified aspects showed enhanced student engagement and improved learning outcomes, which were noted as benefits by the authors. Ortiz-Rojas et al. (2025) highlighted that gamification in mobile learning environments improves knowledge retention and practical skills, especially in technical and science education. The findings extend these observations to STEM disciplines, specifically in programming education.
On the other hand, while previous research concentrated on the foundational aspects of gamification in e-learning, the current study demonstrates the distinct benefits of incorporating gamification into cloud and mobile learning technologies. The GMCLMS’s unique blend of those components overcame problems relating to accessibility and engagement, making it distinctly more holistic than other solutions for blended learning environments.
Practical implications
The outcome of the study impacts instructors, instructional designers, and developers significantly. The GMCLMS helps in structuring the processes of integrating games into education systematically. Some of the principal factors are: (1) Balanced Motivation Strategies: The system offers a blended strategy of motivating participants through intrinsic and extrinsic motivators which helps to sustain learners’ interest and participation. (2) Scalability and Adaptability: Because of the cloud-based design of the system combined with its adaptive gamification features, it can be considered a scalable solution for a wide variety of learning environments, including blended and fully remote settings. (3) Application in difficult subjects: The study highlights gamification’s potential to simplify and enhance the learning of complex technical subjects, particularly in STEM education. As stated by previous research, gamified systems are especially effective in bridging knowledge gaps, making them invaluable tools in technical and STEM-focused education. Further research should investigate the impact of these systems across different educational contexts and their potential impact on academic achievement.
Conclusion
The study confirmed the efficacy of Gamified Mobile Cloud Learning Management System (GMCLMS) in enhancing academic achievement as well as the engagement of learners in the context of Object-Oriented Programming (OOP) education. The GMCLMS resolves important educational issues such as lack of motivation and withdrawal of learners as well as the technical aspects of most subjects by incorporating cloud-based mobile platforms with gamification. The students’ understanding of foundational OOP concepts has significantly improved as evidenced by large effect sizes along with high Black gain ratios which endorse the System’s ability to facilitate cognitive development and knowledge retention. These findings reinforce the existing literature on the use of gamification in education, particularly its use in STEM learning, where there is an increasing emphasis on improving outcomes.
As opposed to static systems which tend to lead to diminishing returns, the GMCLMS used dynamic system-specific gamified components based on the individual progress of the learner. This approach not only sustained student attention but also fostered collaborative and competitive participation which improved the level of engagement in the activities. The study also pointed out the skillful integration of intrinsic and extrinsic motivational aspects, which emphasized the need to balance enjoyment and challenge alongside academic work in a rigorously structured environment.
The benefits provided by the GMCLMS in blended learning contexts demonstrate its functional value. The mobile cloud-based platform enables online and offline both synchronously and asynchronously, allowing students to be active learners while minimizing passive learning filler content. GMCLMS can be a scalable solution in various educational settings ranging from higher education institutions to corporate training programs. Moreover, the system’s integration of gamification with cloud and mobile technologies presents a model for addressing the evolving needs of twenty-first-century learners, particularly in hybrid and remote learning settings.
In addition to its practical implications, this study contributes to the theoretical understanding of gamification in education. It demonstrates how adaptive mechanics and personalized experiences resolve issues identified in previous works, such as over-reliance on external incentives and rigid gamification frameworks. These findings provide a foundation for future studies to explore the integration of emerging technologies, such as artificial intelligence, blockchain, and augmented reality, to further enhance the capabilities of gamified learning systems.
Although this study provides useful insights, it has important limitations that future research should address. The sample pertained to one area of technology and was taught in one specific educational setting, which may restrict the generalizability of the results. Future research could investigate the implementation of such systems in different fields, ages, and cultures to assess the extent to which they can be applied beyond the initial context for which they were designed. Furthermore, there is a need for longitudinal research investigating the enduring impacts of gamification on learning processes and students’ engagement levels.
In conclusion, The GMCLMS system is an exceptional integration of technology and instructional tools which came as a result of the incorporation of the gamification benefits, mobile learning, and cloud computing. The GMCLMS is a notable contribution and a promising tool in instructional technology. It solves significant problems of blended learning and offers a promising approach to be adapted to different educational settings. This specific system’s innovations guide frameworks and benchmarks on how future innovations in gamified learning can be perfected. As educators and educational developers pursue methods to improve student engagement and experiences further, the findings of this study provide a meaningful blueprint for constructing viable and transformative solutions in gamified learning.
Author contributions
H. M. Ahmed and D. M. Elbourhamy created the conception of the research. H. A. El-Sabagh developed the research conceptual framework. H. M. Ahmed, H. A. El-Sabagh, and D. M. Elbourhamy investigated the research literature; H. M. Ahmed & D. M. Elbourhamy performed the research experiments/simulations. H. A. El-Sabagh developed the research model. H. M.M. Ahmed and D. M. Elbourhamy designed the App. H. M. Ahmed & H. A. El-Sabagh prepared the research tools. H. A. El-Sabagh & D. M. Elbourhamy analyzed the data. H. M. Ahmed & H. A. El-Sabagh discussed the research results. H. A. El-Sabagh supervised the project procedures. All authors prepared the original draft. H. A. El-Sabagh reviewed and edited the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
Not applicable.
Availability of data and materials
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
Ethical approval was received from the institutional ethics review committee.
Competing interests
The authors declare that they have no competing interests.
Abbreviations
Gamified, Mobile, Cloud-Based Learning Management System
Learning management system
Object-Oriented Programming
Software Development Kit
Application Programming Interface
Experience Points
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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