Proposed Solution for Programmer’s Knowledge

Proposed Solution for Programmers Knowledge

Vinit Chaudhari

Computer Engineering Pimpri Chinchwad College o Engineering

Pune, India

Varad Deshmukh

Computer Engineering Pimpri Chinchwad College o

Engineering Pune, India

Archana Kadam

Assistant Professor

Pimpri Chinchwad College of Engineering

Pune, India

Lubhavan Burghate

Computer Engineering Pimpri Chinchwad College of

Engineering Pune, India

Tejas Bhame

mputer Engineering

mpri Chinchwad College of Engineering

Pune, India

Dipesh Walte

dependent Scholar Pune, India

Abstract In todays fast-moving technological world, it becomes very difficult to keep track of various programming concepts used by the programmer in particular project out of many done throughout in career.

The development of Proctored Knowledge Maps aims to create structured and comprehensive visual representations of the expertise of the programmer. This system serves as a tool to track, assess, and validate the proficiency of a programmer in various programming languages and technologies. Not only does it provide code analysis, tracking, and visualization, but it also integrates proctoring mechanisms for integrity verification. By implementing gamification, Multilanguage support, and detailed analytics, this system transforms traditional coding assessments into an engaging and insightful process. This paper explores the methodologies behind the development of proctored knowledge maps and their impact on skill evaluation and continuous learning.

Index TermsKnowledge Maps, Proctoring, Code Analysis, Code Visualization, Performance Tracking, Programming, Gamification, Proctoring , Education , Knowledge assessment

1. INTRODUCTION

   In software development, it becomes a problem to monitor the different programming concepts utilized in a project over a period of time. To date, there exists no specific tool that systematically tracks different coding elements within a project or offers insights into the particular concepts utilized by a coder. When organizations or developers want to examine a projects conceptual framework, remembering and classifying these pieces manually proves to be a cumbersome and lengthy task.

   Proctored Knowledge Maps present a novel solution to overcoming this problem by providing a systematic analysis of

   code. They represent a programmers level of competence in various programming notions, languages, and technologies graphically. They systematically organize numerous phases of a programmers worklike data structures, algorithms, object- oriented programming, database interactions, and software design patternsto offer important insights into coding ability. One of the major advantages of Proctored Knowledge Maps is that they can segregate and categorize code snippets into core programming areas, allowing for accurate determination of a developers strengths and weaknesses. This organized representation serves both learners and employers by making it possible to focus on specific learning and competency assessment.

   To guarantee the authenticity of these tests, proctoring systems are incorporated, observing programmers as they code and run code. This reduces cheating behaviour and ensures that the produced knowledge maps truly represent a persons actual capabilities. Sophisticated code analysis methods, both static and dynamic, are used to derive meaningful insights, enabling comprehensive assessment of programming abilities.

   By offering a consistent and mechanized means of mapping and monitoring coding topics, Proctored Knowledge Maps improve the learning experience, facilitate the development of skills, and enhance the assessment of programming ability.

2. RELATED WORK

   Several approaches have been introduced in recent years to evaluate programmers skills. Traditional coding assessments rely on online platforms and competitive programming contests, which assess problem-solving abilities but do not

   provide in-depth skill analysis. Recent advancements in AI- driven code evaluation and block chain-based certification have improved assessment credibility. Our work builds on these methodologies by integrating real-time proctoring and structured knowledge visualization.

3. LITERATURE REVIEW

   Knowledge mapping has emerged as a powerful tool for improving communication, collaboration, and knowledge management in various domains. It provides a structured way of visualizing, categorizing, and analysing information, helping organizations and individuals make informed decisions, bridge knowledge gaps, and foster innovation.

   1. Benefits of Knowledge Mapping

      Knowledge maps offer a common visual language for knowledge sharing within organizations, enhancing communication and collaboration between teams and departments [1]. By systematically identifying areas where knowledge is lacking, knowledge maps inform training and development programs, ensuring employees acquire the necessary skills to perform their tasks effectively [2]. Additionally, they facilitate innovation by revealing connections between different concepts, fostering new ideas and insights [3].

      In the realm of business intelligence (BI), knowledge maps contribute to improved decision-making by providing a holistic view of an organizations knowledge assets. This assists in identifying new opportunities, assessing risks, and developing strategic initiatives [4]. They also enhance problem-solving by uncovering the root causes of challenges and proposing effective solutions [5].

   2. Challenges in Implementing Knowledge Maps

      Despite their benefits, the successful implementation of knowledge maps faces several challenges. Engaging employees in the process requires proper training and incentives to ensure active participation. Without sufficient involvement, knowledge maps may fail to achieve their intended purpose [6]. Moreover, maintaining up-to-date knowledge maps can be difficult, particularly in organizations with rapidly evolving knowledge bases [7].

   3. Open Knowledge Maps (OKMs) and Their Applications Krigolson (2022) introduced Open Knowledge Maps (OKMs) as a novel approach to creating and utilizing knowledge maps. These maps leverage open datasets and open source software to visually represent scientific knowledge. They extract data from scholarly articles, patents, and grant proposals, structuring them into interconnected nodes representing various research topics [6].

      OKMs offer multiple applications, such as identifying emerging research areas, finding related research, and visualizing complex concepts [8]. By simplifying the discovery

      process, OKMs enable interdisciplinary collaboration and accelerate scientific advancements [9]. Krigolson argues that OKMs could revolutionize knowledge management by creating more accessible and user-friendly scientific knowledge bases [6].

   4. Knowledge Mapping in Business Intelligence

      Chen et al. (2021) reviewed the role of knowledge maps in BI, highlighting their potential to enhance decision-making and problem-solving. By integrating business knowledge into a structured format, knowledge maps enable organizations to develop effective strategies and respond efficiently to market dynamics [4]. Furthermore, they provide a foundation for predictive analytics by identifying patterns and trends that may otherwise remain hidden [5].

   5. Knowledge Mapping in Educational Technology

      Liu, Wang, and Li explored the use of knowledge maps in educational technology, emphasizing their role in improving teaching and learning experiences. By visually organizing educational content, knowledge maps make complex topics more comprehensible, promoting active learning and knowledge retention [5]. They also assist educators in designing structured instructional materials, leading to better engagement and comprehension among students [6].

   6. Applications in Decision-Making and Collaboration

      Knowledge maps serve as valuable tools for knowledge discovery and exploration. They help users identify patterns in large datasets, facilitating trend analysis and informed decision-making [10]. Additionally, knowledge maps promote collaboration by establishing a shared understanding of business knowledge across teams, thereby streamlining workflows and improving organizational efficiency [11].

   7. Adaptive Question Generation from Knowledge Bases

      Wang et al. (2021) proposed an NLP-based approach for generating assessment questions from structured knowledge bases. Using models like BERT and T5, the system formulates questions aligned with learner context and curriculum goals. This method supports adaptive learning by tailoring difficulty and content relevance, making it suitable for automated assessment tools within Proctored Knowledge Maps [12].

   8. Multimodal Analytics for Student Assessment

      Huang, Xu, and Liu (2022) introduced a framework integrating video, audio, and text data to assess student engagement. Deep learning models extract features such as facial expressions, voice modulation, and textual responses to provide real-time evaluation. Their multimodal approach enhances the reliability of proctoring systems by capturing diverse behavioural cues, aligning with the holistic assessment goals of your project [13].

   9. Stylometric Anomaly Detection in Student Responses

      Nguyen and Tran (2021) developed a stylometric analysis technique using AI to detect inconsistencies in student writing. By modelling writing style through lexical and syntactic features, their system flags anomalous submissions that may indicate plagiarism or impersonation. This technique can strengthen content authenticity checks in Proctored Knowledge Maps, especially for open-ended coding explanations [14].

   10. Augmented Reality for Proctoring Using Smart Glasses

   Akbulut and Uur (2023) presented an AR-based proctoring solution using smart glasses for 360° monitoring. The system uses AI for real-time behaviour analysis and overlays visual alerts to reduce cheating. This immersive and non-intrusive method complements the proctoring layer in knowledge mapping platforms by enhancing situational awareness and exam integrity [15].

   k. Conclusion

   Knowledge maps play a vital role in enhancing knowledge management, decision-making, and innovation across various fields. Despite challenges related to user engagement and data maintenance, their applications in BI, education, and research make them indispensable tools. Future advancements in knowledge mapping, particularly in integrating AI and automated knowledge extraction, could further refine their utility and accessibility.

4. PROPOSED SOLUTION

   To address the challenges in evaluating programming competency while ensuring security and authenticity, we propose a Proctored Knowledge Mapping System with Gamification. The system integrates proctoring mechanisms, programming activity tracking, and gamification elements to create a secure and engaging assessment framework.

   The proposed model consists of the following key components: Fig .Architecture Diagram Modules:

   • Proctoring Rules: A set of predefined rules governing the

     proctoring mechanisms to ensure the integrity of programming assessments.

   • Web Extension Proctoring: A browser-based monitoring system that tracks the users web activity to detect unauthorized reference materials during programming tasks.

   • Key Logging Proctoring: A keystroke tracking mechanism that differentiates between manually typed code and copied content, helping to analyse the originality of the programmers work.

     • Camera-Based Proctoring: A webcam-based monitoring system that captures images at strategic intervals to verify the

       presence and identity of the programmer, preventing

       impersonation.

   • Face Identification: An additional security layer that ensures that the person writing the code matches the registered user through real-time facial recognition.

   • Programming Trail Creator: A module that logs the complete programming process by recording each line of code typed. This allows for a reconstruct able trail that provides insights into the thought process and debugging approach of the programmer.

   • Gamification Rule Base: A structured set of gamification rules designed to enhance learning and engagement. These rules dictate how points, badges, and milestones are awarded based on coding behaviour and performance.

   • Gamified Points Generator: A reward mechanism that allocates points based on factors such as problem-solving efficiency, code quality, complexity, and best practices in programming.

   • Gamified Notification Generator: A real-time feedback system that provides encouraging messages and insights, such as progress updates and milestone achievements, to keep the programmer motivated.

   • Gamified Milestone and Badge Generator: A proficiency- based system that awards badges at various levels of expertise, fostering engagement and continuous improvement.

   • Gamified Proctored Knowledge Map: A consolidated visualization of the programmers learning journey, integrating insights from proctoring, programming activity, and gamification modules. This map serves as an analytical tool for tracking progress, identifying strengths, and improving weaknesses.

   The proposed solution ensures that programmers are evaluated in a secure and fair environment while also making the learning process more engaging through gamification. By combining multiple monitoring techniques with a structured reward system, this model aims to provide an accurate and comprehensive assessment of programming knowledge and skills.

5. ARCHITECTURE DIAGRAM

   Fig. Architecture Diagram

6. CONCLUSION

   Proctored Knowledge Maps transform the method of programmers and organizations monitoring, analysing, and improving coding competence. With code visualization and performance monitoring, this platform provides rich insight into programming ability and supports ongoing improvement. More than a coding test, it is a complete knowledge database, supporting developers to maintain, re-fine, and optimize their coding skills. With customization, multi-language, and proctoring functionality, it provides a well-structured, transparent, and rewarding learning experience.

   Proctored Knowledge Maps fill the gap between skill evaluation and professional development, empowering programmers to succeed in a rapidly changing tech world. The reach of proctored knowledge maps goes beyond individual assessment; they can play a critical role in schools, boot camps, and hiring processes by offering an unbiased, data-driven way to measure programming skill. Moreover, adding block chain security to the system strengthens data integrity and ensures tamper-proofing, making the evaluation process more credible and transparent.

7. REFERENCS

1. R. Burkhard, M. Meier, M. Smis, J. Allemang, and L. Honisch, Beyond Excel and PowerPoint: Knowledge maps for the transfer and creation of knowledge in organizations, in Proc. 9th Int. Conf. Information Visualization (IV05), London, UK, 2005, pp. 7681.

2. R. Oberhauser, C-TRAIL: A Program Comprehension Approach for Leveraging Learning Models in Automated Code Trail Generation, in Proc. 11th Int. Conf. Software Engineering and Applications (ICSOFTEA 2016), SciTePress, 2016.

3. ViSiTR, 3D Visualization for Code Visitation Trail Recommendations.

4. M. Chen, X. Chen, and Y. Zhang, Knowledge Mapping for Business Intelligence: A Review of the Literature, IEEE Trans. Syst., Man, Cybern.: Syst., vol. 51, no. 1, pp. 1-13, 2021.

5. T. Liu, X. Wang, and Y. Li, Knowledge Mapping for Educational Technology: A Review of the Literature, IEEE Trans. Learn. Technol., vol. 13, no. 4, pp. 825-839, 2020.

6. R. Krigolson, Open Knowledge Maps: Creating a Visual Interface to the Worlds Scientific Knowledge, IEEE Trans. Knowl. Data Eng., vol. 34, no. 12, pp. 4359-4372, 2022.

7. A. Nurpeisova et al., The Study of Mathematical Models and Algorithms for Face Recognition in Images Using Python in a Proctoring System, Computation, vol. 10, no. 8, p. 136, 2022.

8. Y. K. Chou, Actionable Gamification: Beyond Points, Badges, and Leaderboards. Packt Publishing Ltd., 2019.

9. Y. K. Chou, Gamification and Knowledge Management: Enhancing Employee Engagement Through Interactive Learning, J. Bus. Strategy, vol. 34, no. 3, pp. 22-29, 2020.

10. P. Brown, Knowledge Mapping in Strategic Management, J. Strat. Manag., vol. 45, no. 4, pp. 219-238, 2020.

11. S. White, Enhancing Corporate Knowledge Sharing Through Knowledge Maps, J. Bus. Knowl. Manage., vol. 56, no. 1, pp. 78-95,

    2019.

12. Z. Wang, Y. Cao, Z. Liu, and T. Zhao, Question Generation from Knowledge Base Using NLP Models, in Proc. 2021 Conf. Empirical Methods in Natural Language Processing (EMNLP), ACL, 2021, pp. 42224234.

13. K. Huang, Y. Xu, and M. Liu, Multimodal Learning Analytics: Integrating Video, Audio, and Text for Student Assessment, in Proc. 2022 IEEE Conf. Artificial Intelligence in Education (AIEd), IEEE, 2022, pp. 5562.

14. P. T. Nguyen and D. T. Tran, Detecting Anomalous Student Answers Using AI-Powered Stylometric Analysis, in Proc. 2021 Int. Conf. Educational Data Mining (EDM), IEEE, 2021, pp. 231238.

15. N. Akbulut and H. Uur, Augmented Reality for Proctoring: A Smart Glasses Approach, in Proc. 2023 Int. Conf. Human-Computer Interaction (HCII), Springer, 2023, pp. 512523.