Pilar: An Intuitive, Free, and Analytical Platform for Recording, Managing, and Analyzing Grades
DOI:
https://doi.org/10.37843/rted.v19i1.800
Article Sidebar
Main Article Content
Abstract
Contemporary educational practice is permeated by processes of digital transformation that offer opportunities to optimize teaching, evaluation, and pedagogical decision-making. The study aims to design, develop, and implement a free, lightweight, and intuitive digital tool, called Pillar, aimed at automating the management of evaluation processes through visual and immediate analytics. The investigation unfolded under a technological paradigm, with the design science method, quantitative-descriptive approach in the validation phase, technological design, applied type, and cross-section. The sample is made up of 31 volunteer university teachers from areas such as agricultural sciences, engineering, and social sciences, with teaching experience of between 5 and 25 years. Document review, software engineering, functional tests, and validation testing were used as techniques; As instruments, they will use a matrix of requirements, functionality tests, and a questionnaire of 20 items, of which 16 were closed-ended with a Likert scale. The results showed that Pilar integrated modules for the academic record, the configuration of rubrics, the automated calculation of measurements, the visualization of data, and the generation of reports. The functional tests show operational stability in most of the functions, while the teaching validation reflects a high value of utility, ease of use, and perceived efficiency. It was concluded that Pilar constitutes a viable technological alternative to optimize evaluative management and support data-based pedagogical decision making.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Those authors who have publications in our journal accept the following terms:
- When a work is accepted for publication, the author retains rights of reproduction, distribution of his/her article for exploitation in all countries of the world in the format provided by our magazine and any other magnetic medium, optical, and digital.
- Authors will retain their copyright and guarantee the journal the right first to publish their work, which will be simultaneously subject to the Creative Commons Acknowledgment License (Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)). That allows third parties to copy and redistribute the material in any medium or format, under the following conditions: Acknowledgment - You must properly acknowledge authorship, provide a link to the license, and indicate if any changes have been made. You may do so in any reasonable way, but not in a way that suggests you have the licensor's endorsement or receive it for your use. NonCommercial - You may not use the material for a commercial purpose. NoDerivatives - If you remix, transform, or build from the material, you cannot broadcast the modified material. There are no additional restrictions - You cannot apply legal terms or technological measures that legally restrict you from doing what the license allows.
- Authors may adopt other non-exclusive license agreements to distribute the published version of the work (e.g., deposit it in an institutional archive or publish it in a monographic volume) provided that the initial publication in this journal is indicated.
- Authors are allowed and recommended to disseminate their work through the Internet (e.g., in institutional telematic archives, repositories, libraries, or their website), producing exciting exchanges and increasing the published work's citations.
- Request of withdrawal an article has to be done in writing by the author to the Editor, becoming effective after a written response from the Editor. For this purpose, the author or authors will send correspondence via e-mail: [email protected].
- The author will not receive financial compensation for the publication of his work.
- All Docentes 2.0 Journal publications are under the Open Journal System (OJS) platform at: https://ojs.docentes20.com/.
Citaciones del Artículo
References
Amarasinghe, I., Michos, K., Crespi, F., & Hernández-Leo, D. (2024). Learning analytics support to teachers' design and orchestrating tasks. Journal of Computer Assisted Learning, 40(6), 2416–2431. https://doi.org/10.1111/jcal.12711
Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022). A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educational Research Review, 37, Artículo 100489. https://doi.org/10.1016/j.edurev.2022.100489
Barradas-Arenas, U. D., Cocón-Juárez, J. F., Pérez-Cruz, D., & Vázquez-Aragón, M. R. (2023). El impacto de los simuladores en el aprendizaje de los sistemas digitales. Revista Tecnológica-Educativa Docentes 2.0, 16(1), 67-76. https://doi.org/10.37843/rted.v16i1.350
Basogain-Urrutia, J. X. (2021). Evaluación en línea: Herramientas, limitaciones y alternativas en un contexto de pandemia. Revista Tecnológica-Educativa Docentes 2.0, 10(2), 30-41. https://doi.org/10.37843/rted.v10i2.243
Cohenour, C., & Hilterbran, A. (2016). Automated grading of Excel® workbooks using Matlab®. En ASEE's 123rd Annual Conference & Exposition. New Orleans, LA.
Dada, I. D., Akinwale, A. T., & Tunde-Adeleke, T.-J. (2025). A Structured Dataset for Automated Grading: From Raw Data to Processed Dataset. Data, 10(6), 87. https://doi.org/10.3390/data10060087
Doak, H. (2023). Front-end for a semi-automated grading tool. Wellington Faculty of Engineering Symposium. https://ojs.victoria.ac.nz/wfes/article/view/8396
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304-317. https://doi.org/10.1504/ijtel.2012.051816
Gomathy, C. K., Neethi, A. T., & Krishna, S. S. (2025). Automating student performance analysis using machine learning. Singaporean Journal of Scientific Research, 17(1), 62-70. https://doi.org/10.54216/JISIoT.170231
Hernández-Sampieri, R., & Mendoza Torres, C. P. (2020). Metodología de la investigación: las rutas cuantitativa, cualitativa y mixta. McGraw-Hill Education.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research. MIS Quarterly, 28, 75-105. https://doi.org/10.2307/25148625
Langove, S. A., & Khan, A. (2024). Automated grading and feedback systems: Reducing teacher workload and improving student performance. Journal of Advanced Data Science, 13(4). https://doi.org/10.62345/jads.2024.13.4.16
Liow, H. J. K., Yau, P. C. Y., Tang, L. M., Seow, C. K., & Cao, Q. (2024). Peer-assessed (evaluated) automated grading system: A comprehensive exploration with emphasis on batch processing data visualization and rigorous peer evaluation. En Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher. Association for Computing Machinery. https://doi.org/10.1145/3702386.3702404
Parojenog, R. C. (2023). Modified automated grading tool for senior high school advisers. International Journal of Scientific Engineering and Science, 7(11), 49-52. https://go.docentes20.com/bezc
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45-77. https://doi.org/10.2753/MIS0742-1222240302
Rivas-Huaman, R. G., Zarate-Custodio, D. M., Calderón-Gutiérrez, J. P., & Chávez-Farro, R. R. (2025). Análisis de la docencia universitaria a nivel mundial. Revista Tecnológica-Educativa Docentes 2.0, 18(2), 165-175. https://doi.org/10.37843/rted.v18i2.682
Ruth, B., & Hott, J. R. (2025). Auto-grading in Computing Education: Perceptions and Use. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE TS 2025) (pp. 276–282). Association for Computing Machinery. https://doi.org/10.1145/3641554.3701900
Selwyn, N. (2016). Is technology good for education?. Polity Press.
Suniaga, A. (2019). Metodologías Activas: Herramientas para el empoderamiento docente. Revista Docentes 2.0, 7(1), 65–80. https://doi.org/10.37843/rted.v7i1.27
Tejaswi, D., Sravanthi, B. L., Devi, S. N., Sri, M. N. S., & Jahnavi, M. (2025). AI-based automated grading system and personalized feedback in higher education. International Advanced Research Journal in Science, Engineering and Technology, 12(4). https://doi.org/10.17148/IARJSET.2025.12460
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027
Weegar, R., & Idestam-Almquist, P. (2023). Reducing workload in short answer grading using machine learning. International Journal of Artificial Intelligence in Education, 34, 247–273. https://doi.org/10.1007/s40593-022-00322-1
Wieringa, R. J. (2014). Design science methodology for information systems and software engineering. Springer. https://doi.org/10.1007/978-3-662-43839-8
Wong, B. T. M., Li, K. C., & Liu, M. (2025). The role of learning analytics in evaluating course effectiveness. Sustainability, 17(2), 559. https://doi.org/10.3390/su17020559