ESTRELLA

Expand Latinx Learners’ Authentic Experiences in Computer Programming

The scientific careers of the future increasingly require advanced understandings and applications of computer programming and mathematics. This project is designed to broaden participation in these career pathways and careers by iteratively developing and testing bilingual computer programming curricula in middle school mathematics classes attended primarily by multilingual Latinx students. Aligned with standards in computer programming and mathematics for middle schools, these Spanish and English curricula will guide educators and students through authentic programming tasks, such as those practiced by programmers in Artificial Intelligence fields.

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About

This project will use a mixed methods research design to study whether and how computer programming supports the teaching and learning of mathematical content; whether and how the use of the bilingual curricula support teachers’ interactions with Latinx students; whether and how the curricula support Latinx and emergent bilingual students’ mathematics learning; and whether changes occurred in middle school students’ attitudes and learning, among other outcomes.

The project will employ qualitative analyses of transcripts from interviews with middle school teachers, undergraduate facilitators, and middle school student facilitators; teaching documents such as lesson plans; video-recordings of classroom observations and professional development sessions; and student work. It will also employ quantitative analysis of pre- and post-measures of attitudes and learning in mathematics.

In addition to sharing the curricula on a public website, the project will result in video tutorials that will support online and offline class delivery for middle school mathematics teachers with examples of teacher-adapted materials. Empirical research and implications for practitioners will be disseminated widely in peer-reviewed journals and professional conferences.

Publications

Website

Web App

Book Chapters

  1. LópezLeiva, C., Celedón-Pattichis, S., & Pattichis, M. S. (2020). Participation in the Advancing Out-of-School Learning in Mathematics and Engineering (AOLME) project: Supporting middle school Latinas’ bilingual and STEM identities. In B. Polnick & B. Irby (Eds.), Women of color in STEM: Navigating the waters of public schools. Charlotte, NC: Information Age Publishing.

Published Conference Proceedings

Bilingualism & Computing Education

  1. E. Cantú, S. Celedón-Pattichis, M. S. Pattichis, A. R. Johnson, H. H. Lee, I. Tovar, and C. A. LópezLeiva, "¿Qué Funciona? Translanguaging, epistemic agency, and storytelling for the authoring of ideas in computer science," 2023, doi: 10.3102/2011070.
  2. J. A. Lecea-Yanguas, M. S. Pattichis, and S. Celedón-Pattichis, "Bilingual students leading bilingual computational thinking collaborative practices: A rigorous systemic functional linguistics approach," 2023, doi: 10.3102/2009935.
  3. H. H. Lee, S. Celedón-Pattichis, M. S. Pattichis, A. R. Johnson, E. Cantú, I. Tovar, and C. A. LópezLeiva, "Knowing and enjoying: Expanding Latinx students' experiences with an integrated computer science and mathematics curriculum," 2023, doi: 10.3102/2012330.
  4. C. LópezLeiva, S. Celedón-Pattichis, and M. S. Pattichis, "Entre hilos, colmillos, y monstruos: Newcomer Latinx bilingual students learning computer programming through translanguaging," 2022, doi: 10.3102/IP.22.1892928.
  5. C. A. LópezLeiva, S. Celedón-Pattichis, I. Demir, J. A. Lecea-Yanguas, and M. S. Pattichis, "Attitude Scale Results of Student Confidence Over Time: Participation in an Integrated Mathematics/Computer Programming Curriculum," in Proc. IV International Colloquium on Languages, Cultures, Identity in School and Society, F. Ramos, Ed., 2018, pp. 158–165.

Mathmatics & Computer Science Education

  1. H. H. Lee, S. Celedón-Pattichis, C. LópezLeiva, M. S. Pattichis, and Y. Song, "Emergent interests to well-developed interests in STEM," 2024, doi: 10.22318/icls2024.111198.
  2. A. Johnson, S. Celedón-Pattichis, M. S. Pattichis, E. Cantú Jr., H. H. Lee, I. Tovar, and C. A. LópezLeiva, "Can I be a mathematician and computer programmer? Identity-building through moments of playful talk (Poster)," 2023, doi: 10.3102/2009419.

AI, Computer Vision & Learning Analytics

  1. S. Janampa and M. S. Pattichis, "DETRPose: Real-Time End-to-End Multi-Person Pose Estimation via Modified Transformer Decoder and Novel Denoising Keypoints," arXiv preprint arXiv:2506.13027, 2025. [Online] available: arxiv.2506.13027.
  2. S. Janampa and M. S. Pattichis, "LINEA: Fast and Accurate Line Detection Using Scalable Transformers," arXiv preprint arXiv:2505.16264, 2025. [Online] available: arxiv.2505.16264.
  3. S. Janampa and M. S. Pattichis, “DT-LSD: Deformable Transformer-based Line Segment Detection,” arXiv preprint arXiv:2411.13005, 2024. [Online] available: arXiv.2411.13005.
  4. S. Janampa and M. S. Pattichis, “SOFI: Multi-Scale Deformable Transformer for Camera Calibration with Enhanced Line Queries,” arXiv preprint arXiv:2409.15553, 2024. [Online] available: arXiv.2409.15553.
  5. S. Janampa, P. Tran, E. Guaderrama, S. Celedón-Pattichis, and M. S. Pattichis, "Dynamic 3D scene reconstruction from classroom videos," 2024, doi: 10.1109/IEEECONF60004.2024.10943077.
  6. M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Digital video representations for teaching mathematics and coding to middle school students," in Proc. 24th Int. Conf. Digital Signal Processing (DSP), 2023, doi: 10.1109/DSP58604.2023.10167969.
  7. V. Jatla, S. Teeparthi, M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Long-term human video activity quantification of student participation," in Proc. 55th Asilomar Conf. Signals, Systems, and Computers, Pacific Grove, CA, USA, 2021, [Online]. Available: https://ivpcl.unm.edu/bibtex_php/Conferences_Pdfs/SVj_Asilomar_2021-submitted_compressed_PDFA.pdf.
  8. W. Shi, M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Person detection in collaborative group learning environments using multiple representations," in Proc. 55th Asilomar Conf. Signals, Systems, and Computers, Pacific Grove, CA, USA, 2021, [Online]. available: https://arxiv.org/pdf/2112.12217.
  9. W. Shi, M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Talking detection in collaborative learning environments," in Computer Analysis of Images and Patterns (CAIP), Lecture Notes in Computer Science, vol. 13053, 2021, doi: 10.1007/978-3-030-89131-2_22.
  10. L. Tapia, A. Gomez, M. Esparza, V. Jatla, M. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Bilingual speech recognition by estimating speaker geometry from video data," in Computer Analysis of Images and Patterns (CAIP), Lecture Notes in Computer Science, vol. 13052, 2021, doi: 10.1007/978-3-030-89128-2_8.
  11. S. Teeparthi, V. Jatla, M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Fast hand detection in collaborative learning environments," in Computer Analysis of Images and Patterns (CAIP), Lecture Notes in Computer Science, vol. 13052. Switzerland: Springer, 2021, doi: 10.1007/978-3-030-89128-2_43.
  12. P. Tran, M. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Facial recognition in collaborative learning videos," in Computer Analysis of Images and Patterns (CAIP), Lecture Notes in Computer Science, vol. 13053. Cham, Switzerland: Springer, 2021, doi: 10.1007/978-3-030-89131-2_23.
  13. L. A. Sanchez Tapia, M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "The Importance of the Instantaneous Phase for Face Detection Using Simple Convolutional Neural Networks," in Proc. IEEE Southwest Symp. Image Analysis and Interpretation (SSIAI), Albuquerque, NM, USA, 2020, doi: 10.1109/SSIAI49293.2020.9094589.
  14. A. Jacoby, M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Context-sensitive Human Activity Classification in Collaborative Learning Environments," in Proc. IEEE Southwest Symp. Image Analysis and Interpretation (SSIAI), Las Vegas, NV, USA, Apr. 2018, pp. 141–144, doi: 10.1109/SSIAI.2018.8470331.
  15. W. Shi, M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Dynamic Group Interactions in Collaborative Learning Videos," in Proc. 52nd Asilomar Conf. Signals, Systems, and Computers, Pacific Grove, CA, USA, 2018, doi: 10.1109/ACSSC.2018.8645132.
  16. W. Shi, M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, "Robust Head Detection in Collaborative Learning Environments Using AM-FM Representations," in Proc. IEEE Southwest Symp. Image Analysis and Interpretation (SSIAI), Las Vegas, NV, USA, Apr. 2018, pp. 65–68, doi: 10.1109/SSIAI.2018.8470355.

Refereed Journal Article

Bilingualism & Computing Education

  1. E. Cantú, S. Celedón-Pattichis, J. A. Lecea-Yanguas, H. H. Lee, and M. S. Pattichis, "Translanguaging analysis of multilingual middle school students' authentic mathematics-based computing experiences," Bilingual Research Journal, 2025, doi: 10.1080/15235882.2025.2501802.
  2. A. Ynostroza, S. Celedón-Pattichis, M. Pattichis, I. Tovar, and M. Ibarra, "Translanguaging in computer programming: '¿Qué no es un cereal?'," International Journal of Multicultural Education, vol. 27, 2025, doi: 10.18251/IJME.V27I1.4847.
  3. S. Celedón-Pattichis, G. Kussainova, C. A. LópezLeiva, and M. S. Pattichis, "Fake it until you make it: Participation and positioning of a bilingual Latina student in mathematics and computing," Teachers College Record, vol. 124, 2022, doi: 10.1177/01614681221104106.
  4. S. Celedón-Pattichis, C. A. LópezLeiva, M. S. Pattichis, and M. Civil, "Teaching and learning mathematics and computing in multilingual contexts," Teachers College Record, vol. 124, 2022, doi: 10.1177/01614681221103929.
  5. C. A. LópezLeiva, G. Noriega, S. Celedón-Pattichis, and M. S. Pattichis, "From students to cofacilitators: Latinx students' experiences in mathematics and computer programming," Teachers College Record, vol. 124, 2022, doi: 10.1177/01614681221104104.

Mathematics & Computer Science Education

  1. M. S. Pattichis, H. H. Lee, S. Celedón-Pattichis, and C. LópezLeiva, "Teaching computer programming using mathematics: Examples from middle-school and graduate school," SN Computer Science, vol. 5, 2024, doi: 10.1007/s42979-024-03386-z.
  2. S. Celedón-Pattichis, "On fostering creative collaborations and future directions," Teachers College Record, vol. 125, 2023, doi: 10.1177/01614681241228029.

AI, Computer Vision, & Learning Analytics

  1. V. Jatla, S. Teeparthi, U. Egala, S. Celedón-Pattichis, and M. S. Pattichis, "Fast and accurate video analysis and visualization of classroom activities using multiobjective optimization of extremely low-parameter models," IEEE Access, vol. 13, 2025, doi: 10.1109/ACCESS.2025.3567437.
  2. W. Shi, P. Tran, S. Celedón-Pattichis, and M. S. Pattichis, "Long-term human participation assessment in collaborative learning environments using dynamic scene analysis," IEEE Access, vol. 12, 2024, doi: 10.1109/ACCESS.2024.3387932.
  3. A. Gomez, M. S. Pattichis, and S. Celedón-Pattichis, "Speaker diarization and identification from single channel classroom audio recordings using virtual microphones," IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3177584.

Refereed Papers/Presentations at International/National Professional Meetings

Thesis

Sravani Teeparthi, "Long Term Object Detection and Tracking in Collaborative Learning Environments" University of New Mexico, 2021

This thesis contributes robust methods for computer keyboard detection, tracking, and student hand detection. For hand detection, the thesis integrates object detection with clustering and time-projections for accurate, long-term assessment of student participation. The hand detection method was integrated into a writing detection system and can also be used for later research on recognizing student gestures.
arXiv

  • Fast Video-based Face Recognition in Collaborative Learning Environments
    Phuong Tran
    M.S. Thesis, University of New Mexico, 2021
    View Thesis

    • Phuong Tran, "Fast Video-based Face Recognition in Collaborative Learning Environments," University of New Mexico, 2021

    The thesis's goal is to develop a fast method for face recognition in digital videos that is applicable to large datasets. The thesis introduces several methods to address the problems associated with video face recognition. The thesis develops the AOLME dataset of 138 student faces (81 boys and 57 girls) of ages 10 to 14, who are predominantly Latina/o students. Compared to the baseline method, the final optimized method resulted in fast recognition times with significant improvements in face recognition accuracy. Using face prototype sampling only, the proposed method achieved an accuracy of 71.8% compared to 62.3% for the baseline system, while running 11.6 times faster.
    arXiv