AI-POWERED STUDENT ASSESSMENT: A CNN-DRIVEN APPROACH TO ACADEMIC MONITORING AND PARENT ENGAGEMENT

Authors

  • Kapil Kumar Computer Science & Engineering, Echelon Institute of Technology, Faridabad
  • Himanshu Computer Science & Engineering, Echelon Institute of Technology, Faridabad
  • Piyush Sharma Computer Science & Engineering, Echelon Institute of Technology, Faridabad
  • Shefali Madan Computer Science & Engineering, Echelon Institute of Technology, Faridabad

DOI:

https://doi.org/10.29121/granthaalayah.v12.i2.2024.6106

Keywords:

Academic, Parent, Engagement, Assessment, Ai, Effective Student Monitoring, Parental Engagement

Abstract [English]

In the modern educational landscape, effective student monitoring and parental engagement are crucial for academic success. However, traditional approaches such as periodic parent-teacher meetings and paper-based reports often fail to provide timely and actionable insights. These limitations hinder parents from identifying their child’s learning gaps early and make it difficult for teachers to maintain consistent communication across large student populations.
To address these challenges, the Student Assessment & Performance (SAP) Tracker leverages Convolutional Neural Networks (CNNs) to analyze and interpret student handwriting, scanned assignments, and exam sheets for automated performance evaluation. By integrating CNN-based image recognition with academic data, the system offers deeper insights into student behavior, comprehension patterns, and learning progress over time. This AI-enhanced assessment enables the early detection of academic struggles and fosters proactive intervention.
Built using a client-server architecture, the SAP Tracker features a Flutter-based frontend and a secure backend, seamlessly integrated with a cloud-based database for real-time data synchronization. This infrastructure ensures scalability, low latency, and accessibility across platforms. The SAP Tracker empowers parents with immediate access to grades, attendance, assignment analytics, and school notifications, strengthening the home-school connection. Ultimately, the system enhances student outcomes through timely support, increased parental involvement, and data-driven educational insights.

Downloads

Download data is not yet available.

References

Armbrust, M., et al. (2010). A View of Cloud Computing. Communications of the ACM, 53 (4), 50–58. https://doi.org/10.1145/1721654.1721672 DOI: https://doi.org/10.1145/1721654.1721672

Baker, R. S., & Inventado, P. S. (2014). Educational Data Mining and Learning Analytics. Learning Analytics, 1 , 61–75. https://doi.org/10.1007/978-1-4614-3305-7_4 DOI: https://doi.org/10.1007/978-1-4614-3305-7_4

Barger, M. M., & Byrd, S. P. (2011). Teacher-Parent Communication: Why and How. Educational Horizons, 89 (3), 58–65.

Bull, S., & Kay, J. (2016). SMILI: A Framework for Interfaces to Learning Data in Open Learner Models. International Journal of Artificial Intelligence in Education, 26 (1), 293–331. https://doi.org/10.1007/s40593-015-0090-8 DOI: https://doi.org/10.1007/s40593-015-0090-8

Cummings, C. (2020). Data Privacy in Education: A Review of Privacy Policies in Popular Edtech Apps. EdTech Journal .

Duan, L., & Zhou, Y. (2021). Automated Diagram Analysis using Deep Convolutional Networks. IEEE Access, 9 , 1120–1132.

Dutta, A., & Sengupta, S. (2020). CNN-Based Automatic Essay Scoring System. International Journal of Artificial Intelligence and Applications, 11 (1), 21–29.

Epstein, J. L. (2011). School, Family, and Community Partnerships: Preparing Educators and Improving Schools . Routledge.

Fan, X., & Chen, M. (2001). Parental Involvement and students' Academic Achievement: A Meta-Analysis. Educational Psychology Review, 13 (1), 1–22. https://doi.org/10.1023/A:1009048817385 DOI: https://doi.org/10.1023/A:1009048817385

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning . Mit Press.

Heffernan, N. T., & Heffernan, C. L. (2014). The Assistments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching. International Journal of Artificial Intelligence in Education, 24 (4), 470–497. https://doi.org/10.1007/s40593-014-0024-x DOI: https://doi.org/10.1007/s40593-014-0024-x

Hill, N. E., & Tyson, D. F. (2009). Parental Involvement in Middle School: A Meta-Analytic Assessment of the Strategies that Promote Achievement. Developmental Psychology, 45 (3), 740–763. https://doi.org/10.1037/a0015362 DOI: https://doi.org/10.1037/a0015362

Kraft, M. A., & Dougherty, S. M. (2013). The Effect of Teacher-Family Communication on Student Engagement. Journal of Research on Educational Effectiveness, 6 (3), 199–222. https://doi.org/10.1080/19345747.2012.743636 DOI: https://doi.org/10.1080/19345747.2012.743636

Kumar, R., & Banerjee, P. (2020). Low-Latency Cloud Architectures for Real-Time Educational Systems. International Journal of Cloud Computing, 9 (2), 189–207.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521 (7553), 436–444. https://doi.org/10.1038/nature14539 DOI: https://doi.org/10.1038/nature14539

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86 (11), 2278–2324. https://doi.org/10.1109/5.726791 DOI: https://doi.org/10.1109/5.726791

Macfadyen, L. P., & Dawson, S. (2012). Numbers are Not Enough: Why E-Learning Analytics Failed to Inform an Institutional Strategic Plan. Educational Technology & Society, 15 (3), 149–163.

Memon, M., & Ahmed, R. (2019). Intelligent Grading of Handwritten Math Answers using Deep Learning. Procedia Computer Science, 163 , 507–514.

Patel, A., & Shah, D. (2022). Automated Grading using CNN for Handwritten Assignments. Journal of Educational Technology, 18 (3), 145–153.

Schwendimann, B. A., et al. (2017). Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research. IEEE Transactions on Learning Technologies, 10 (1), 30–41. https://doi.org/10.1109/TLT.2016.2599522 DOI: https://doi.org/10.1109/TLT.2016.2599522

Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. Arxiv Preprint Arxiv:1409.1556 .

Singh, V., & Rao, M. (2021). Impact of Digital Parent Engagement Tools on Student Outcomes. Educational Technology Research and Development, 69 (6), 3085–3101.

Thomas, J., & George, S. (2023). Comparative Study of Student Information Systems in K-12 Education. Computers & Education Open, 4 , 100093.

Verbert, K., et al. (2014). Learning Dashboards: An Overview and Future Research Opportunities. Personal and Ubiquitous Computing, 18 (6), 1499–1514.

Voigt, P., & Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR). Springer . https://doi.org/10.1007/978-3-319-57959-7 DOI: https://doi.org/10.1007/978-3-319-57959-7

Warschauer, M. (2004). Technology and Social Inclusion: Rethinking the Digital Divide. MIT Press . https://doi.org/10.7551/mitpress/6699.001.0001 DOI: https://doi.org/10.7551/mitpress/6699.001.0001

Zhang, Y., et al. (2021). Deep Learning Approaches for Optical Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (4), 1032–1045.

Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory Iinto Practice, 41 (2), 64–70. https://doi.org/10.1207/s15430421tip4102_2 DOI: https://doi.org/10.1207/s15430421tip4102_2

Downloads

Published

2024-02-29

How to Cite

Kumar, K., Himanshu, Sharma, P., & Madan, S. (2024). AI-POWERED STUDENT ASSESSMENT: A CNN-DRIVEN APPROACH TO ACADEMIC MONITORING AND PARENT ENGAGEMENT. International Journal of Research -GRANTHAALAYAH, 12(2), 136–149. https://doi.org/10.29121/granthaalayah.v12.i2.2024.6106