Document Open Access Logo

Ogma: An Intelligent Platform for Anticipating Academic Failure and Recommending Recovery Measures (Short Paper)

Author Ricardo Queirós

PDF

File

Thumbnail PDF
PDF
OASIcs.ICPEC.2026.4.pdf
  • Filesize: 1.29 MB
  • 6 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Applied computing → Education
  • Applied computing → E-learning
  • Human-centered computing → Interactive systems and tools
  • Computing methodologies → Artificial intelligence
  • Human-centered computing → HCI design and evaluation methods
  • Human-centered computing → Collaborative and social computing systems and tools
Keywords
  • Generative AI
  • Educational chatbot
  • Pedagogical agent
  • Gamification
  • Moodle
  • Retrieval-augmented generation
  • Self-regulated learning

Metrics

Abstract

The growth of class sizes, the increasing complexity of course content, and the multiplicity of digital platforms make it progressively harder for instructors to identify, in a timely manner, students who are at risk of academic failure. This short paper presents the concept of Ogma, a learning analytics application with artificial intelligence components designed to anticipate signs of academic decline and suggest personalized recovery measures. The proposal combines heterogeneous data - student-produced work, grade records, forum messages, and logs from other educational applications - to build a dynamic risk profile. Based on this profile, the system provides instructors with a dashboard containing graded alerts, explanations of risk factors, and pedagogical recommendations generated by a pipeline that combines machine learning, retrieval-augmented generation, and large language models. The paper presents the motivation for the problem, synthesizes related work, and describes the design of a reference architecture for Ogma.

Cite As Get BibTex

Ricardo Queirós. Ogma: An Intelligent Platform for Anticipating Academic Failure and Recommending Recovery Measures (Short Paper). In 7th International Computer Programming Education Conference (ICPEC 2026). Open Access Series in Informatics (OASIcs), Volume 145, pp. 4:1-4:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.ICPEC.2026.4

Author Details

Ricardo Queirós
  • CRACS, INESC TEC & ESMAD, Polytechnic of Porto, Portugal

Funding

  • Queirós, Ricardo: This work is funded by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the support UID/50014/2025 (https://doi.org/10.54499/UID/50014/2025).

References

  1. Kimberly E. Arnold and Matthew D. Pistilli. Course signals at purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pages 267-270, 2012. URL: https://doi.org/10.1145/2330601.2330666.
  2. Rebecca Ferguson. Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6):304-317, 2012. URL: https://doi.org/10.1504/IJTEL.2012.051816.
  3. S. M. Jayaprakash, E. W. Moody, E. J. M. Lauría, J. R. Regan, and J. D. Baron. Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1):6-47, 2014. URL: https://doi.org/10.18608/jla.2014.11.3.
  4. Patrick Lewis, Ethan Perez, Aleksandras Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive NLP tasks. In Advances in Neural Information Processing Systems 33, pages 9459-9474, 2020. Google Scholar
  5. Phil Long and George Siemens. Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5):31-40, 2011. Google Scholar
  6. Jairo López-Zambrano, Juan A. Lara, and Cristóbal Romero. Towards portability of models for predicting students at risk of failing and dropping out in university. Applied Sciences, 11(23):11363, 2021. URL: https://doi.org/10.3390/app112311363.
  7. Abdullah Namoun and Abdullah Alshanqiti. Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1):237, 2021. URL: https://doi.org/10.3390/app11010237.
  8. Martin Paulsen and Kasper Hornbæk. Learning analytics dashboards for teachers: A systematic review of design, data, and actionability. Computers and Education Open, 6:100176, 2024. URL: https://doi.org/10.1016/j.caeo.2024.100176.
  9. A. Pilicita Garrido and E. Barra. Sentiment analysis with transformers applied to education: Systematic review. International Journal of Interactive Multimedia and Artificial Intelligence, 9(2):72-83, 2025. URL: https://doi.org/10.9781/ijimai.2025.02.008.
  10. Beat A. Schwendimann, María Jesús Rodríguez-Triana, Andrii Vozniuk, Luis P. Prieto, Mina S. Boroujeni, Adrian Holzer, Denis Gillet, and Pierre Dillenbourg. Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1):30-41, 2017. URL: https://doi.org/10.1109/TLT.2016.2599522.
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact