Gamified Learning Implemented Via Microservices

Gamified Learning Implemented Via Microservices

Atreyi Bhattacharyya

Dept. of Information Technology Usha Mittal Institute of Technology SNDT Womens University Santacruz(W), Mumbai

Namita Jadhav

Dept. of Information Technology Usha Mittal Institute of Technology SNDT Womens University Santacruz(W), Mumbai

Anusha Jha

Dept. of Information Technology Usha Mittal Institute of Technology, SNDT Womens University Santacruz(W), Mumbai.

Prachi Dhannawat

Dept. of Information Technology Usha Mittal Institute of Technology SNDT Womens University Santacruz(W), Mumbai

Abstract – Traditional educational methodologies often struggle to sustain student engagement, support personalized learning, and ensure effective knowledge retention. Gamification has emerged as an effective approach to address these challenges by integrating game- based elements such as scores, badges, grades, and leaderboards into the learning process. These elements enhance motivation, focus, and active participation among learners. Empirical studies indicate that gamified learning environments improve engagement and knowledge retention by enabling interactive and adaptive educational experiences. Gamification further supports a student- centric learning model through techniques such as Knowledge Tracing, Adaptive Learning, and performance- based question prediction, allowing instructional content to dynamically adjust based on individual learner progress. The effective implementation of such systems requires careful alignment with curriculum objectives, accessibility for diverse learners, and a balanced integration of competition and collaboration. Modern educational platforms increasingly adopt microservices- based architectures to achieve scalability, flexibility, and maintainability. This paper aims to design and implement a gamified educational platform using a microservices architecture, leveraging Docker for containerization and Kubernetes for orchestration, along with other supporting web technologies. The objective is to enable independent service deployment, efficient resource utilization, fault tolerance, and seamless scalability. Despite challenges such as maintaining long-term engagement and minimizing distractions, the integration of gamification with containerized microservices offers a scalable and effective

solution for enhancing contemporary digital education systems.

Keywords Gamification, Educational Technology, Microservices Architecture, Docker, Kubernetes, Adaptive Learning, Knowledge Tracing, E-learning Platforms, Containerization, Student Engagement.

1. INTRODUCTION

   The rapid advancement of digital technologies has significantly reshaped the educational landscape, transitioning learning methodologies from traditional lecture-based instruction to more interactive and student-centered approaches. Among these advancements, the integration of gamification into education has gained substantial attention due to its ability to enhance student engagement, motivation, and learning effectiveness. Gamification incorporates game design elements such as quizzes, challenges, progress tracking, and leaderboards into educational environments, fostering active participation and sustained learner interest [1]. While traditional Learning Management Systems (LMS) provide structured content delivery, they often rely on passive learning models that limit student interaction and personalization. As a result, maintaining long-term engagement and adapting content to individual learner needs remain persistent challenges. Studies indicate that gamified learning environments improve focus, motivation, and knowledge retention by offering interactive and immersive experiences [1].The proposed Gamified Learning Implemented via Microservices system addresses these limitations by combining gamification techniques with a microservices architecture. This approach enables modular development, independent scaling of system components, and improved fault tolerance. The use of containerization

   technologies allows seamless deployment and efficient resource utilization, ensuring system reliability and scalability. Additionally, adaptive learning mechanisms support personalized content delivery based on learner performance, enhancing learning outcomes. By transforming conventional LMS platforms into dynamic, interactive ecosystems, the proposed system offers a practical, scalable, and efficient solution for modern digital education.

2. LITERATURE REVIEW

   In the past, traditional instructional methods have been the foundation of the educational system, providing a framework of learning environments and curriculum delivery. However, these methods have been associated with a rigid and instructor-based approach to teaching, a lack of student involvement, and a focus on memorization. This has been shown to have a negative impact on students’ motivation and the ability to cater to different learning styles. Research has shown that passive learning environments can have a negative impact on students’ motivation and ability to retain knowledge. Passive lecture-based instruction has been shown to reduce student engagement and limit knowledge retention in traditional classroom environments [1].In addition, the traditional educational system has been associated with a lack of feedback and the use of standardized assessments, which may not be a true reflection of students’ ability and the promotion of critical thinking skills. Furthermore, the lack of flexibility in the curriculum and the instructional approach has been shown to have a negative impact on the ability of the educational system to cater to the needs of the 21st century.Recent developments in educational technology have encouraged the use of digital learning platforms in addressing the limitations of conventional teaching methods. Gamification has been proposed as a means of improving the educational system by the implementation of the principles of game design and the use of elements of games in the educational system. This has been shown to have a positive impact on students’ motivation and their ability to remain engaged in the learning process. Gamification provides immediate feedback and reward mechanisms that positively influence knowledge retention and learner motivation [6].

   Overall, the current research has shown the potential of gamification to have a positive impact on students’ motivation and ability to remain engaged in the learning process, while addressing the shortcomings of the traditional educational system. This has highlighted the need to conduct further research on the implementation of gamification in the educational system.

   1. A.Limitations of the Traditional Learning Method

      Various studies have shed light on the limitations of the traditional method of teaching and learning. According to studies, passive learning methods, such as lectures, have a negative impact on students’ motivation and interest in the subject of study, especially during the primary stages of their educational journey. This may lead to a decline in their academic performance and a higher likelihood of dropping out of school. Traditional educational environments may create an alienation gap between teachers and students, leading to reduced interaction and engagement [3].Another limitation of

      the traditional method of teaching and learning is that delayed feedback and testing methods lead to a lack of understanding of the subject of study, as students are encouraged to focus only on memorization rather than omprehension. Standardized testing and delayed feedback mechanisms frequently emphasize memorization over conceptual understanding, restricting the development of critical thinking skills [2].Observational studies have also highlighted the monotony of the traditional method of teaching and learning. Most students have expressed their preference for a flexible learning environment, especially one that utilizes technology to a greater degree. Student motivation often declines during early academic years in structured traditional learning systems, which may impact academic performance and retention rates [4].

   2. Gamification as an Engaging Alternative

   Gamification has been the focus of a number of studies and has proven to be effective in improving students’ learning experience and their academic performance. Gamification involves integrating game elements into non-game contexts to enhance engagement and learning experiences [5].Various studies have compared the traditional method of teaching and learning with the gamification method and have highlighted the positive impact of the gamification method on students’ academic performance, their level of confidence, and their satisfaction level. Gamified learning approaches have been shown to improve student motivation, confidence, and academic performance compared to traditional teaching methods [6].In addition to these positive impacts, gamification has also improved the participation rates of students and their collaborative learning behaviors. Learning management systems incorporating gamification techniques demonstrate increased participation, collaboration, and learner satisfaction [7]. Flexible digital learning environments that integrate gamification can enhance interaction, collaboration, and engagement in both physical and virtual classrooms [8]. Despite the positive impacts of gamification, there have been some limitations and challenges associated with the gamification method of teaching and learning.

   The current gamified learning platforms have limitations in that they are monolithic and lack scalability and modularity. They also lack adaptive assessment features. Conventional learning management systems have limitations in personalization and timely feedback to learners. This could affect the learning motivation and performance of students. There was also a lack of effective academic gamification for the integration of gamification features with scalable microservices architectures and adaptive quiz features. Therefore, this paper proposes a gamified learning platform based on a microservices architecture to address the limitations of conventional gamified learning platforms.

3. SURVEY ANALYSIS AND RESULTS

   This section presents the analysis of survey data collected from 200 students along with supporting empirical evidence from existing literature.

   1. Motivation in Traditional Learning

      Survey results (Fig.1) indicate the following distribution of motivational levels:

      • 52% reported feeling moderately motivated
      • 31% reported feeling slightly motivated
      • 11.5% reported no motivation
      • 5.5% reported high motivation

        Thus, 42.5% of respondents experience low to no motivation, while only a small proportion report strong motivation.These findings are consistent with prior research. A longitudinal study conducted by Scherrer and Preckel demonstrated that student motivation declines during the first academic year in higher education and is associated with increased dropout intentions and reduced academic success [4]. The study further identified perceived lecturer support and instructional quality as significant mitigating factors.The alignment between survey findings and empirical evidence suggests that traditional lecture-based systems may not sufficiently sustain student motivation.

        Fig. 1. Distribution of students motivation levels in traditional

        learning environments.

   2. Rigidity and Lack of Flexibility

      Regarding structural rigidity:

      168 out of 200 students (84%) reported that traditional learning feels monotonous and tedious.This reflects a strong perception of inflexibility in standardized curricula, fixed schedules, and uniform pacing.Watterston and Zhao argue that rigid time-based schooling models negatively impact engagement and recommend increased flexibility in learning formats and autonomy [10]. Additionally, research indicates that personalized learning approaches significantly improve engagement and retention compared to standardized models [11].The survey data and supporting literature collectively indicate that structural rigidity contributes to disengagement in traditional education systems.

      Fig. 2. Survey results on whether traditional learning is perceived as monotonous.

   3. Assessment and Feedback Mechanisms

      Traditional assessment systems rely heavily on standardized examinations and delayed feedback.Research demonstrates that timely and constructive feedback significantly enhances learning outcomes [12], [13]. Transforming traditional classrooms into feedback-rich environments using gamified response systems has shown measurable improvements in motivation and engagement [2]. Furthermore, conventional testing methods have been criticized for emphasizing memorization over critical thinking [14], while competency- based grading systems better reflect student progress and mastery [15].These findings highlight limitations in traditional assessment practices and the need for more adaptive and formative evaluation methods.

   4. Perceived Impact of Gamification

      To evaluate receptiveness toward gamification:

      79% of respondents (158 out of 200) indicated that engagement and motivation would increase if a structured reward-based system were implemented.Empirical evidence supports this perception. Challenge-based gamification has been shown to significantly improve academic achievement, motivation, confidence, and satisfaction [6]. Gamified Learning Management Systems have demonstrated enhanced engagement and active learning [7]. Additionally, interactive gamification techniques in virtual classrooms have been found to increase participation and collaborative problem-solving [8].

      These results suggest that gamification offers a viable strategy to address motivational decline and structural rigidity in traditional learning systems.

      Fig. 3. Students perception of engagement enhancement

      through rewards, challenges, and leaderboards.

   5. Summary of Findings

   The combined analysis indicates:

   1. A substantial proportion of students report moderate to low motivation in traditional learning environments.
   2. Traditional systems are widely perceived as rigid and monotonous.
   3. Conventional assessment models lack continuous and personalized feedback.
   4. A significant majority of students support reward- based engagement mechanisms.
   5. Empirical studies demonstrate measurable improvements in motivation and academic performance through gamification.

   These findings provide empirical justification for integrating gamified strategies into educational frameworks.

4. PROPOSED SYSTEM

   The proposed system brings in the concept of a Gamified Learning Platform that is designed and developed using a microservices architecture approach to increase engagement, motivation, and learning. The traditional learning space often faces issues of low interactivity and lack of personalization. The education sector is undergoing a paradigm shift that encompasses both innovative built learnig environments and significant reform of the pedagogical core , to better prepare students across all curriculum areas and learning stages to succeed in a rapidly changing and interconnected world To overcome these issues, the proposed system brings in the concept of gamification and software architecture design to create a scalable and interactive learning space.The proposed system is designed based on the fundamental components of gamification, which include quizzes, leaderboards, scores, badges, and adaptive feedback, that encourage active engagement and continuous learning.

   1. Quiz as Games

      The quiz module transforms assessment into game-based learning to increase engagement and motivation. The questions

      are designed using Blooms Taxonomy, allowing assessment from Knowledge to Evaluation. The six levels include Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. The levels provide a hierarchy for gamification, where each level builds on the previous one. In GLIM, the levels correspond to milestones such as badges, which are unlocked upon meeting certain criteria, facilitating systematic skill development. The gamification system employs points, levels, and badges to link learning outcomes to cognitive development. The quiz microservice is responsible for question delivery, automatic scoring, and feedback. In addition to the regular quiz functionality, GLIM provides a Game zone with four interactive game modules.

      The Game zone component in the GLIM includes four interactive game modules to engage students in different learning activities. Each game is a part of the Game Service microservice, with real-time tracking of scores and achievements, which in turn helps in leaderboards and badges. Each game is mapped with particular subjects to maintain alignment with the course content.

      1. Query Crafter SQL Mastery Game: This module involves students in database scenarios where they have to build proper SQL queries to answer questions. The games are designed with increasing levels of difficulty to test students ability to apply their knowledge in real-life situations, with instant feedback on the correctness of the queries.
      2. Mind Match Logic and Memory Builder: This is a memory-based matching game where students have to match related terms with definitions, concepts with examples, or questions with answers. The game module is designed to encourage students to revisit course material repeatedly, improving recall and logical reasoning skills through active learning.
      3. Crossword Concept Recall Challenge: Students are required to complete subject-based crossword puzzles where clues test their knowledge of important concepts and terms. Correct answers unlock letters to complete the puzzle, providing a fun way to reinforce vocabulary and conceptual knowledge.
      4. Coding Challenge Problem-Solving Module: This module involves students in programming problems of different levels of difficulty. Students have to write, execute, and submit their code solutions, which are then automatically evaluated and fed back to them. Successful completion of the problem earns points and helps in advancing the gamification process.

      Each of the four games is fully functional in the Game Service microservice and is integrated with the authentication, quiz, and database services in the platform. The performance of the students in these games is tracked in real time, with scores and achievements updated accordingly, thus providing a seamless and engaging gamified learning experience.

   2. Progress Tracking for Individual Students

      The progress tracking module is responsible for monitoring the performance of individual students by analyzing quiz results to determine mastered competencies and areas of improvement. Students are provided with visual progress tracking, while teachers can analyze performance trends on Blooms taxonomy levels, including challenges of application or analysis. This microservice enables adaptive learning by providing targeted

      feedback and remediation based on learning gaps.The design of the progress tracking module (Fig.4) applies quiz data through Item Response Theory (IRT) for ability estimation and Bayesian Knowledge Tracing (BKT) for skill learning. The combined analysis provides role-specific dashboards for different users:Students view their progress tracking, mastery levels, and remediation plans.Teachers view performance analysis, class trends, and areas of Blooms Taxonomy levels that are most difficult. The two output strategy delivers role- specific, educational intelligence to each user.

      Fig. 4. Architecture of Progress Tracking Module

      1. Knowledge Tracing (KT)

         Knowledge Tracing is a model of a students dynamic knowledge state over time by estimating the probability of correct answers based on previous performance. In GLIM, Bayesian Knowledge Tracing (BKT) is used to track mastery of particular skills with parameters such as prior knowledge, learning rate, probability of guessing, and probability of slip. These parameters are constantly updated after every interaction, allowing for personalized feedback and adaptive quiz suggestions on Blooms taxonomy levels.

      2. Item Response Theory (IRT)

      Item Response Theory (IRT) is used in GLIM to estimate student ability and adapt question difficulty. It examines the relationship between learner ability and question characteristics using parameters such as difficulty, discrimination, and guessing probability. This makes it possible to dynamically select suitable questions, improving assessment validity and enabling personalized learning paths.

   3. Course Content and Assessment Management

      The instructor module allows faculty to upload course-specific content and administer quizzes through the quiz microservice. This maintains alignment with course content and assessments. As a separate microservice, it facilitates modification of resources or quiz timelines without affecting other system components. Faculty can upload lecture notes, presentations, and videos categorized by topics and administer different types of quizzes (multiple choice, coding, one-timer). Automatic grading and performance analysis enable student feedback and tracking by instructors.

   4. Question Bank Integration

   The Question Bank is a repository of all assessment questions with metadata for dynamic quizzes. Each question has a unique

   ID and category, making it possible for the quiz microservice to retrieve questions based on the learners subject, module, and difficulty level.

   The Question Bank contains the following attributes:

   • question id Unique ID for each question.
   • course Name of the course the question belongs to.
   • topic Specific topic or module within the course.
   • level Difficulty of the question (Easy / Medium / Hard).
   • question type Type of question (MCQ / Coding / One-timer / Paragraph / Aptitude).
   • question text Actual content of the question.
   • options Possible answer choices for MCQs (stored in JSON or text).
   • correct answer Correct option(s) or solution.
   • created at Timestamp when the question was created.
   • created by User or instructor who created the question.
5. TECHNOLOGIES USED

   A gamified learning platform developed with contemporary web tech and a microservices architecture for scalability, flexibility, and effective functionality. Tech stack: frotend development, backend microservices, databases, containers, and secure API communication.

   • • Frontend

       React.js and Next.js for dynamic rendering, quick navigation, and responsive user experience. Modular, component-based development.

     • Backend

       Node.js microservices for authentication, quizzing, gamification, and course management. Scalability and independent deployment.

     • Database

       PostgreSQL as the main database for storing user, course, quiz, and gamification information. Reliable, structured data management with robust transactions.

     • Containerization

       Docker for containerized services. Ensures identical deployment across environments, simplicity in dependencies, scalability, and portability.

     • Authentication & Authorization

   API-based authentication and secure communication between services for securing user data and managing access.

6. ARCHITECTURE

   GLIM uses a microservices architecture with containers to provide modularity, scalability, and smooth deployment. With the advent of microservices architecture, this issue got mitigated as it was possible to scale the databases horizontally as well as vertically, simply with the help of few docker-flags.

   This helped in processing large number of server requests, balancing the load on the server, quicker deployments [9] .It also provides support for gamified learning with quizzes, leaderboards, adaptive testing, and performance analysis. Each service is deployed using Docker containers to provide the same execution environment for development, testing, and production. The microservices architecture approach provides support for independent deployment, scaling, and maintenance of services, thus reducing compatibility and deployment issues.

   Fig. 5. High-Level Docker-based System Architecture

   As shown in Fig.5, the system uses a microservices architecture that is containerized. The API Gateway is the only entry point that handles front-end requests to specific service containers named Auth, Game, Curriculum/Blooms Module, and PostgreSQL, which are all hosted in a Docker container. Core Components:

   1. Frontend Component

      The frontend component, built using React/Next.js, handles learning content access, quizzes, performance analysis, and course management for students, teachers, and administrators. Containerized deployment provides the same execution and smooth updates without affecting the backend.

   2. Backend Component

      Handles authentication, course management, quiz creation, gamification, and analytics. Built using Django/Node.js, it provides APIs for easy communication between the frontend, database, and other services. Containerized deployment provides reliable, scalable, and consistent API execution.

   3. Authentication Microervice

      The Authentication Service is a microservice that deals with user identity authentication and safe access to the platform. It is responsible for user registration, login authentication, session management, and authentication of credentials while ensuring safe communication between services. The Authentication Service is an independent and containerized service that improves security, scalability, and modularity, making it easy to integrate with other microservices such as quiz, performance, and database services.

   4. Game Microservice

      The Game Service is a microservice that deals with activities related to quizzes in the GLIM system. It is responsible for creating quizzes, fetching questions, evaluating responses, calculating scores, and storing the results of assessments in the database. The service is also connected to adaptive learning

      components such as Knowledge Tracing and Item Response Theory for evaluating student performance and facilitating personalized assessments. The service is containerized for scalability and smooth communication between services.

      The Game Microservice is responsible for managing all the interactive game components such as Query Crafter, Mind Match, Crossword, and Coding Challenge. The Game Microservice handles game logic, scoring, and real-time achievement unlocking, interacting with the database microservice for storing player progress and updating leaderboards.

   5. Database Microservice

      Runs in a separate container with PostgreSQL. It holds user profiles, learning materials, quizzes, scores, leaderboards, and analytics. Isolation improves security, maintainability, and long-term storage that is independent of applications.

   6. Curriculum Planner and Blooms Taxonomy Module

      The Curriculum Planner module integrates learning content with categories of Blooms Taxonomy, ensuring the systematic delivery of learning and the creation of strategic assessments. The module allows teachers to upload and manage their notes, PDFs, and discipline-specific syllabus materials, all of which are organized based on the cognitive levels from Knowledge to Evaluation. This ensures that learning materials and assessments are leveled in a manner that takes students through a progressive development of skills. Students can access these learning materials at any time.

   7. Inter-Service Communication

      As shown in Fig. 6, the front-end communicates with the API Gateway via secure REST APIs (JSON), which then routes requests to internal services through authenticated REST calls within the Docker network. The Authentication Service manages user credentials and sessions, the Game Service handles quiz data retrieval and scoring, and the Curriculum Planner Service processes course content all persisting data through the Database Service. This containerized communication model ensures service isolation, secure data transfer and modular scalability.

      Fig. 6. Inter-service communication via secure REST APIs

   8. Adaptive Learning (IRT and KT)

   Adaptive learning is one of the essential components of GLIM, which makes it possible to adapt the level of difficulty of questions to the performance of students. This is achieved

   by the combination of two different algorithms: Item Response Theory (IRT) and Knowledge Tracing (KT), which are implemented as separate microservices in the Docker environment.

   Fig. 7. Adaptive Learning Workflow in System

   Fig. 7 illustrates the adaptive learning process in System. Student responses gathered from the frontend are sent via the backend to the IRT and KT containers. The IRT module changes the difficulty level of questions according to ability levels, while the KT module updates mastery levels of concepts. The updated performance data is stored in the database, and the frontend uses this data to display personalized feedback to students.

   Adaptive assessments include:

   Item Response Theory (IRT): dynamically adjusts question difficulty based on the ability of learners for adaptive quizzes.

   P(,b)=1+e(b)1 (1)

   Algorithm 1

   Input: student_ability (), question_difficulty (b)

   Output: response_result (correct / incorrect) 1: Compute probability:

   P = 1 / (1 + e^-( – b))

   2: Generate random value r [0,1] 3: If r < P then

   4: response_result Correct

   5: Else

   6: response_result Incorrect

   7: End If

   8: Return response_result

   The above pseudocode explains the process by which the Item Response Theory (IRT) calculates the probability of a student responding to a question correctly. This is done while taking into account the student’s ability as well as the difficulty level of the question, which allows the GLIM to change the difficulty level of the question dynamically.

   Knowledge Tracing (KT): traces learners undersanding of concepts from response history to predict performance and inform learning.

   Algorithm 2

   Input: student_id, concept_id, response

   Output: updated_mastery_level, updated_success_probability

   1: If response == Correct then

   2: Increase mastery_level(concept_id)

   3: Else

   4: Decrease mastery_level(concept_id)

   5: End If

   6:Update probability_of_future_success(concept_id)

   7:Return updated_mastery_level, updated_success_probability

   The above pseudocode illustrates the Knowledge Tracing (KT) process, where the knowledge state of the learner is updated after each attempt at a question. Based on the mastery levels of concepts, GLIM is able to predict future performance and generate personalized quizzes based on the learners performance.

   These services, as separate microservices, interact with the backend and database for optimal learning paths.

   Architecture Benefits:

   • Modular and scalable architecture
   • Containerized deployment
   • Better maintainability and flexibility
   • Adaptive and learning-driven personalization
   • Efficient inter-service communication
7. CONCLUSION

Traditional learning approaches, although foundational to formal education, exhibit significant limitations in fostering student engagement, personalization, and long-term knowledge retention. The standardized and rigid instructional structure often results in passive participation, reduced motivation, and limited adaptability to diverse learning styles. The findings of this study, supported by existing literature, emphasize the necessity for more dynamic and learner-centered educational models.Gamification offers a promising solution by integrating interactive mechanisms such as points, leaderboards, adaptive feedback, challenges, and collaborative activities into the learning process. These elements transform learning from passive content consumption into active participation, thereby enhancing motivation, engagement, and conceptual understanding. Additionally, real time feedback and problem solving scenarios contribute to the development of critical thinking and collaborative skills, which are essential in modern educational and professional environments.To ensure scalability, flexibility, and maintainability of the proposed system, a containerized microservices architecture was adopted. By decomposing the platform into independent services such as authentication, quiz management, and curriculum modules each component can be developed, deployed, and scaled independently. The integration of an API gateway and container orchestration within a Docker-based environment enhances system resilience, modularity, and performance. This architectural approach not only supports the dynamic requirements of gamified learning systems but also enables future extensibility and technological integration.While challenges remain in balancing engagement with academic rigor and ensuring seamless system integration, the combined application of gamification principles and microservices-based architecture demonstrates strong potential to transform traditional education. Strategic implementation of these technologies can lead to more adaptive, personalized, and

effective learning environments, ultimately improving student motivation and learning outcomes.

ACKNOWLEDGMENT

I take great pleasure in expressing my gratitude to all those who have contributed to and motivated me during my project work. I extend my sincere gratitude to Prof. Prachi Dhannawat for their invaluable guidance, insightful feedback, and continuous support throughout this project. Their expertise has been instrumental in shaping this work.I am also grateful to the Information technology department of Usha Mittal Institute of technology for providing the necessary resources and a conducive research environment. Additionally, I appreciate the support of my fellow teammates for their constructive discussions and valuable input, which have enriched my understanding of the subject . This project would not have been possible without the contributions of those mentioned above, to whom I am truly grateful.

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