Eligibility Checker App for Education Guidance System with Integrated Exam and College Navigator

Eligibility Checker App for Education Guidance System with Integrated Exam and College Navigator

Abstract – This study introduces an Eligibility Checker app called Eligi Hub, an intelligent, cross-platform application designed to assist students and aspirants in navigating higher education opportunities and government exam eligibility. Developed using the Flutter framework for enhanced cross-platform compatibility, the application offers a user-friendly interface where users can input their qualifications and age. By using dynamic algorithms and a comprehensive database, the system identifies and accurates a personalized list of government exams for which users meet the eligibility criteria. In addition to eligibility determination, the application provides a robust educational guidance system for students exploring higher education options after completing school. The platform categorizes available courses, highlights top- ranked colleges across India, and provides direct access to official college websites. It further integrates entrance exam details, offering seamless navigation to official resources for aspirants. Eligi Hub aims to simplify the decision- making process for students by delivering tailored, reliable information, thereby empowering them to make informed academic and professional choices. The scalable and adaptable architecture of the app makes it an ideal tool for educational planning, enhancing accessibility and streamlining pathways to academic and career success.

Keywords – Government Exam Eligibility, Higher Education Guidance, Cross- Platform Application, Personalized Recommendations, Educational Planning, Entrance Exams, College Ranking, Intelligent Algorithms, Student Empowerment

1. INTRODUCTION

   The increasing complexity of academic and professional pathways has underscored the need for intelligent systems that assist students and aspirants in making informed decisions regarding their future endeavors. Selecting the right higher education opportunities and government exams requires access to reliable, personalized, and user-friendly tools. The proposed

   application, Eligi Hub, addresses this need by providing a comprehensive platform for students to explore government exam eligibility and higher education options based on their qualifications and age [1],[4]. Research indicates that personalized recommendations based on qualifications and age are key to helping students make informed educational choices, highlighting the importance of systems like Eligi Hub [2].

   Several existing systems offer fragmented solutions for educational planning, either focusing on eligibility criteria for specific exams or providing general information about colleges and courses. However, there is a lack of an integrated solution that combines these features with intelligent algorithms for personalized recommendations. By using the cross- platform Flutter framework, Eligi Hub integrates advanced algorithms and a structured database to bridge this gap effectively [2],[13]. Previous studies have shown that combining such algorithms with detailed, real-time data provides users with a more streamlined and accurate decision-making process [12].

   Eligi Hub employs a dynamic algorithm that matches user- provided inputs such as age and qualifications with detailed eligibility criteria from a comprehensive database of government exams. The system curates a personalized list of relevant exams, providing aspirants with focused options tailored to their academic and professional aspirations. Furthermore, the app categorizes higher education courses available for students after completing school. It highlights top- ranked colleges across India, provides access to their official websites, and presents details about entrance exams required for each course [11]. Such a personalized approach to college and exam recommendations has proven to increase engagement and user satisfaction [9].

   To enhance accessibility and user engagement, the platform offers seamless navigation between related resources, including exam details, college websites, and entrance exam links. These features empower students with accurate and actionable insights, simplifying their decision-making processes while reducing the challenges associated with gathering information from disparate sources [8]. Ensuring accessibility and ease of use is critical, as recent research suggests that user engagement is significantly impacted by how easily users can navigate educational platforms [7].

   Eligi Hub offers a scalable, adaptable, and user-centric platform for aspirants, enhancing their ability to plan effectively for academic and professional growth [1]. The scalability of this solution positions it as a valuable tool for educational planning in the coming years [6].The figure 1 shows the logo of the eligibility checker app Eligi Hub.

   Figure: 1 Logo-Eligi Hub

2. METHODOLOGY

   The proposed application begins with user input, where the application collects essential details such as age, qualifications, and preferred educational streams through an intuitive interface as shown in the figure 2. These inputs are processed using dynamic algorithms that match user profiles with a comprehensive database containing eligibility criteria for various government exams and information on higher education options. The following figure shows the basic inputs that are given by the user to get the appropriate exams that they are eligible for.

   Figure: 2 A user interface of a "Railway Form" within the application

   For government exam eligibility, the application cross- references user inputs with a structured database containing details about each exam, such as qualifications, age limits, and prerequisites. The system dynamically filters the data and generates a personalized list of exams the user is eligible for, providing detailed information such as deadlines, patterns, and links to official resources.

   Similarly, the application provides a detailed exploration of higher education opportunities by categorizing courses based on streams like Science, Commerce, and Arts. It highlights top-ranked colleges across India with links to their official websites, enabling users to access accurate and relevant information.

   Figure: 3 A user interface screen titled "Higher Studies" within an application. It represents a category of academic fields

   The system also identifies entrance exams associated with specific courses and provides navigation to relevant official portals for exam registration and preparation. The application organizes resources hierarchically, ensuring seamless navigation between related option, such as courses, colleges, and exams. The algorithm processes these datasets dynamically, ensuring users receive tailored and relevant recommendations.

3. PREPROCESSING AND DATA HANDLING

   Preprocessing user input and managing data efficiently are fundamental steps in the development of Eligi Hub, ensuring accurate eligibility determination and higher education recommendations. The process begins with collecting user inputs,such as age, qualifications, and preferences, through the apps interactive interface. These inputs are validated and normalized to ensure consistency, reducing the risk of errors in subsequent processing.

   The first preprocessing step involves refining the input data to handle variations in user-provided information, such as inconsistent formats for age or qualifications. This is followed by structuring the data into a standardized format that can be seamlessly processed by the systems algorithms.

   The application then connects to its comprehensive database of government exams, college information, and course details. The database is indexed and optimized for quick data retrieval, ensuring real-time responsiveness. Data from the database is preprocessed by filtering irrelevant or outdated information, keeping only the most relevant entries based on the users input criteria by using the state management techniques and built-in Dart functions.

   To enhance the performance of recommendation algorithms, advanced preprocessing techniques such as data segmentation are applied. For instance, eligibility criteria are broken down into smaller components, such as age range, educational background, and subject preferences. These segments are then matched dynamically against user profiles, significantly improving the accuracy of the recommendations.

   Data Visualization plays a key role in helping users interact with the system effectively. The curated lists of government exams and higher education options are presented using an intuitive and categorized layout, ensuring clarity and ease of navigation. By preprocessing and organizing data into structured , accessible formats, Eligi Hub ensures a seamless user experience while maintaining the robustness and accuracy of its recommendation engine.

   These preprocessing methodsinput validation, data standardization, database optimization, and dynamic segmentation form the backbone of Eligi Hub. They ensure the app operates efficiently and accurately, providing users with personalized and reliable information to guide their academic and professional decisions.

4. PROCESS FLOW

   The process flow of the Eligi Hub application is designed to deliver a seamless and efficient user experience, guiding users through eligibility assessment for government exams and exploration of higher education options.

   The flow begins with the User Input stage, where individuals provide their personal information. In the Data Acquisition phase, the application interacts with a robust database containing comprehensive information on government exams, eligibility criteria, courses, colleges, and entrance exams. This data is dynamically fetched based on user inputs, ensuring real-time responses and personalized recommendations. The database is regularly updated to maintain the accuracy and relevance of the information. The Preprocessing stage prepares the acquired data for analysis. This includes filtering to exclude irrelevant or outdated entries, segmenting data into structured categories. In the Eligibility Determination stage, the application employs intelligent algorithms to match user profiles with the eligibility criteria for various government exams. The below figure is the user interface that displays exams in which the user is eligible for, in the railway sector.

   Figure: 5 Filtered details of the Railway service exams for which the user is eligible.

   Simultaneously, in the Higher Education Recommendation phase, the application categorizes available courses and displays top-ranked colleges based on user preferences. Users can view course-specific details, explore entrance exams required for admission, and access direct links to official college websites or exam portals. This ensures users have all necessary resources to make informed decisions about their educational paths.

   Figure: 4 The key stages in the development process of the app.

   Figure: 6 The interface of the app that provides details about entrance exams for architecture courses.

   The Navigation and Exploration phase allows users to delve deeper into the curated lists of exams, courses, or colleges. The application integrates hyperlinks to facilitate seamless navigation. Users can easily move between related resources, such as connecting a selected course to its associated entrance exam and the colleges offering it.

   Finally, the Output Display stage presents the processed information in an accessible and engaging format. This process flow ensures that Eligi Hub provides an intuitive and efficient platform for users to explore academic and professional opportunities, leveraging intelligent algorithms and comprehensive datasets to deliver personalized recommendations and valuable insights.

   Figure: 7 Comprehensive Flow of the Eligibility Checker App: From User Input to Personalized Results for Higher Studies and Job Opportunities.

5. PERFORMANCE ENHANCEMENT

   To enhance the performance of the Eligi Hub recommendation system, several key optimizations were implemented. These include efficient data handling, intelligent filtering mechanisms, and advanced algorithms that improve the speed and accuracy of the recommendation process. By streamlining the data flow and optimizing computational resources, the system ensures fast, real-time performance even under heavy user traffic. The use of asynchronous processing, caching mechanisms, and load balancing guarantees a seamless user experience, reducing latency and ensuring timely recommendations for users. A critical component of performance enhancement is the use of machine learning techniques to continuously improve recommendation accuracy. As the system receives more user interactions, it dynamically adapts its recommendations, offering personalized suggestions based on user preferences and historical data. This allows Eligi Hub to provide more precise and relevant exam recommendations over time, making the app more valuable to users as they progress in their academic or career journeys.

   By integrating advanced predictive models, the app also offers proactive guidance, suggesting upcoming exams, deadlines, and important updates before the user might even realize the need. This predictive functionality helps users stay ahead of crucial academic or professional milestones, further enhancing the apps utility in managing their career paths.

6. RESULT AND DISCUSSION

   The Eligi Hub recommendation system has proven to be an effective tool for helping students navigate the complex landscape of government exams and higher education opportunities. By leveraging machine learning algorithms and a robust user profile-based recommendation system, the app delivers accurate and personalized exam suggestions tailored to the individuals qualifications and career goals

   User feedback and comparative testing with traditional educational advisors confirmed the system's effectiveness in offering relevant and timely information, making it easier for students to identify suitable exams and programs. This efficiency is particularly beneficial in applications such as exam preparation, career planning, and academic advisement, where users benefit from clear and actionable guidance.

7. CONCLUSION

   Eligi Hub offers a reliable and user-friendly solution for students seeking personalized guidance on government exams and educational opportunities. By using a powerful recommendation system based on user profiles, the app provies accurate and relevant suggestions for exams and programs suited to individual qualifications. Continuous updates and performance enhancements ensure that the system remains accurate, efficient, and scalable, even as it adapts to new educational offerings and user demands. Validation tests under various traffic conditions, such as different types of qualifications, age groups, and educational aspirations, show that the app effectively supports decision-making, helping users navigate the often- complex process of selecting the right academic or professional path. The apps success in delivering personalized recommendations with minimal computational resources makes it a valuable tool for students with varying needs, and future iterations may include more advanced features, such as AI-based personalized learning paths and predictive exam-related notifications. Future improvements may include the integration of natural language processing (NLP) techniques to interpret more nuanced user inputs and to suggest more customized recommendations. Additionally, expanding the database to include more diverse exams and educational opportunities will further broaden the apps appeal.

8. ACKNOWLEDGEMENT

We express our sincere gratitude to K.L.N. Innovation and Research Park (KLN.IRP) and Department of Information Technology of K.L.N. College of Engineering for providing us the support till the completion of the above project.

REFERENCES

1. Castilla, Rodrigo, Alex Pacheco, and Jorge Franco. "Digital government: Mobile applications and their impact on access to public information." SoftwareX 22 (2023): 101382.

2. Chimuco, Francisco T., João BF Sequeiros, Carolina Galvão Lopes, Tiago MC Simões, Mário M. Freire, and Pedro RM Inácio. "Secure cloud- based mobile apps: attack taxonomy, requirements, mechanisms, tests and automation." International Journal of Information Security 22, no. 4 (2023):833-867.

3. Peeling, Rosanna W., and Samuel K. Sia. "Lessons from COVID-19 for improving diagnostic access in future pandemics." Lab on a Chip 23, no. 5 (2023): 1376-1388.

4. Davis, Jordan P., Eric R. Pedersen, Brian Borsari, Sarah Bowen, Jason Owen, Angeles Sedano, Reagan Fitzke et al. "Development of a mobile mindfulness smartphone app for post-traumatic stress disorder and alcohol use problems for veterans: Beta test results and study protocol for a pilot randomized controlled trial." Contemporary Clinical Trials 129 (2023): 107181.

5. Fernandez, Ma Roxanne, Josie Athens, Carlos Balsalobre-Fernandez, Masayoshi Kubo, and Kim Hébert-Losier. "Concurrent validity and reliability of a mobile iOS application used to assess calf raise test kinematics." Musculoskeletal Science and Practice 63 (2023): 102711.Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, Electron spectroscopy studies on magneto-optical media and plastic substrate interface, IEEE Transl. J. Magn. Japan, vol. 2, pp. 740741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].

6. LeBlanc, Matthew R., Thomas W. LeBlanc, Qing Yang, Jennifer McLaughlin, Kerry Irish, and Sophia K. Smith. "A mobile app to support self- management in patients with multiple myeloma or chronic lymphocytic leukemia: Pilot randomized controlled trial." JMIR cancer 9, no. 1 (2023): e44533.

7. Lin, Pearl MC, Wai Ching Wilson Au, and Thomas Baum. "Service quality of online food delivery mobile application: an examination of the spillover effects of mobile app satisfaction." International Journal of Contemporary Hospitality Management 36, no. 3 (2024): 906-926.

8. Samala, Agariadne Dwinggo, and Mita Amanda. "Immersive Learning Experience Design (ILXD): Augmented Reality Mobile Application for Placing and Interacting with 3D Learning Objects in Engineering Education." International Journal of Interactive Mobile Technologies 17, no. 5 (2023).

9. Dharmawan, Donny, Wenny Desty Febrian, Sugeng Karyadi, and Indra Sani. "Application of Heuristic Evaluation Method to Evaluate User Experience and User Interface of Personnel Management Information Systems to Improve Employee Performance." Jurnal Informasi Dan Teknologi (2024): 14-20.

10. [10] Woelfle, Tim, Silvan Pless, Oscar Reyes, Andrea Wiencierz, Anthony Feinstein, Pasquale Calabrese, Konstantin Gugleta, Ludwig Kappos, Johannes Lorscheider, and Yvonne Naegelin. "Reliability and acceptance of dreaMS, a software application for people with multiple sclerosis: a feasibility study." Journal of Neurology 270, no. 1 (2023):262-271.

11. Kyrylova, Oksana, Neliia Blynova, and Viktoriia Pavlenko. "The perspectives for mobile application use in media education." Interactive Learning Environments (2023): 1-8.

12. Anand, Komal, Vikas Arya, Sheena Suresh, and Anshuman Sharma. "Quality dimensions of augmented reality-based mobile apps for smart- tourism and its impact on customer satisfaction & reuse intention." Tourism Planning & Development 20, no. 2 (2023): 236-259.

13. Nadeem, Muhammad, Henry Shen, Lincoln Choy, and Julien Moussa

    H. Barakat. "Smart diet diary: real-Time mobile application for food recognition." Applied System Innovation 6, no. 2 (2023): 53.

14. Costales, Jefferson, Melody Gabas, Allen Paul Olguera, and Bernadette Macabebe. "Petidentityapp: A Mobile Application With Emergency Rescue And Domestic Pet Adoption For Pet Paradise Ph." In Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval, pp. 252-260. 2023.

15. Iqbal, Hena, Yasmin Alhayek, Noora Alfurais, and Nujuom Assar. "Mobile App Development Privacy and Security Checklist During Covid-19." In The Fusion of Artificial Intelligence and Soft Computing Techniques for Cybersecurity, pp. 57-76. Apple Academic Press, 2024.