Innovación Educativa: Un Ecosistema de Evaluación Adaptativa
Educational Innovation: An Ecosystem of Adaptive Assessment
DOI:
https://doi.org/10.56712/latam.v6i3.4040Palabras clave:
educación personalizada, exámenes adaptativos, competencias genéricas, objetos de aprendizaje, sistemas de aprendizaje adaptativosResumen
Se aborda la necesidad de modernizar los sistemas de evaluación educativa a los requerimientos del siglo XXI. Para alcanzar este objetivo, se propone un ecosistema de evaluación con exámenes adaptativos, los cuales, integran diversos universos de generalización, para una evaluación exhaustiva y personalizada del aprendizaje estudiantil. El ecosistema incluye objetos de conocimiento: hechos, conceptos, principios y procedimientos, asegurando cubrir el conocimiento disciplinar. Además, incorpora el universo actitudinal, evaluando competencias esenciales como ética, responsabilidad y colaboración, incluyendo competencias genéricas cruciales para el desarrollo integral del estudiante. Este sistema de evaluación adaptativa ofrece la generación de perfiles de ejecución individuales, para adaptar las estrategias de enseñanza y aprendizaje a las necesidades de cada estudiante, promoviendo una educación más personalizada. La incorporación de este ecosistema de evaluación adaptativa en el proceso educativo, moderniza la evaluación y prepara a los estudiantes con las habilidades y conocimientos necesarios para enfrentar los desafíos futuros.
Descargas
Citas
Abuaiadah, D., Burrell, C., Bosu, M. F., Joyce, S., & Hajmoosaei, A. (2019). Assessing Learning Outcomes of Course Descriptors Containing Object Oriented Programming Concepts. New Zealand Journal of Educational Studies, 54(1), 345 - 356. https://doi.org/10.1007/s40841-019-00139-y
Akavova, A., Temirkhanova, Z., & Lorsanova, Z. (2023). Adaptive learning and artificial intelligence in the educational space. E3S Web of Conferences. https://doi.org/10.1051/e3sconf/202345106011
Alshumaimeri, Y. (2023). Understanding context: An essential factor for educational change success. Contemporary Educational Researches Journal. https://doi.org/10.18844/cerj.v13i1.8457
Alyasin, A., Nasser, R. N., El Hajj, M., & Harb, H. (2023). Assessing Learning Outcomes in Higher Education: From Practice to Systematization. TEM Journal. https://doi.org/10.18421/tem123-41
Apoki, U. C., Al-Chalabi, H. K. M., & Crişan, G. C. (2019). From Digital Learning Resources to Adaptive Learning Objects: An Overview.
Barrit, C., Lewis, D., & Wiesler, W. (1999). Reusable Learning object strategy: Definition creation process and guidellines for building, version 3.1 Cisco Systems. https://www.mindmeister.com/generic_files/get_file/519411?filetype=attachment_file
Bloom, B. S. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals: Handbook I, Cognitive Domain. Longmans.
Breckler, S. J., & Wiggins, E. C. (1989). Affect versus evaluation in the structure of attitudes. Journal of Experimental Social Psychology, 25, 253-271. https://doi.org/10.1016/0022-1031(89)90022-X
Ciolacu, M. I., Tehrani, A. F., Binder, L., & Svasta, P. (2018). Education 4.0 - Artificial Intelligence Assisted Higher Education: Early recognition System with Machine Learning to support Students' Success. 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME), 23-30. https://doi.org/10.1109/SIITME.2018.8599203
Conner, M. T., & Norman, P. (2020). Predicting long-term healthy eating behaviour: understanding the role of cognitive and affective attitudes. Psychology & Health, 36, 1165 - 1181. https://doi.org/10.1080/08870446.2020.1832675
Delors, J. (1996). La Educación encierra un tesoro, informe a la UNESCO de la Comisión Internacional sobre la Educación para el Siglo XXI (compendio).
Dmitriy, K. (2020). Use of dikw methodology for educational proposals in the framework of innovative learning implementation. Science Education, 2020, 48-53. https://doi.org/10.24195/2414-4665-2020-4-6
Er-Radi, H., Aammou, S., & Jdidou, A. (2023). Personalized learning through adaptive content modification. Conhecimento & Diversidade. https://doi.org/10.18316/rcd.v15i39.11153
Fernandes, M. S. G., & González, M. O. A. (2019). CREATION: creativity techniques to generate ideas of new products. Product Management & Development. https://doi.org/10.4322/pmd.2019.012
Ghafournia, N. (2015). Standard Assessments: Merits and Demerits and the Alternative Assessments. Asian Social Science, 11, 166. https://doi.org/10.5539/ASS.V11N13P166
Graf, A. (2023). Exploring the Role of Personalization in Adaptive Learning Environments. International Journal Software Engineering and Computer Science (IJSECS). https://doi.org/10.35870/ijsecs.v3i2.1200
Hedlund, A. (2021). Beliefs and Attitudes that Influence Learning. GiLE Journal of Skills Development. https://doi.org/10.52398/gjsd.2021.v1.i2.pp44-57
Howard, S. J., Woodcock, S., Ehrich, J., & Bokosmaty, S. (2017). What are standardized literacy and numeracy tests testing? Evidence of the domain‐general contributions to students’ standardized educational test performance. British Journal of Educational Psychology, 87, 108–122. https://doi.org/10.1111/bjep.12138
Husain, F. N. (2024). Education Technology Professional Development Trainers (EDTPD) for Blooms Digital Assessment Taxonomy (BDT) Assessment Model. International Journal of Management Technology, 11(1), 68-90.
Hwang, G.-j., Sung, H.-Y., Chang, S.-C., & Huang, X.-C. (2020). A fuzzy expert system-based adaptive learning approach to improving students' learning performances by considering affective and cognitive factors. Comput. Educ. Artif. Intell., 1, 100003. https://doi.org/10.1016/j.caeai.2020.100003
Ilevbare, F. M., & Idemudia, E. S. (2017). Knowledge and Compliance of Lactating Mothers on Exclusive Breastfeeding in Village of Vhembe District, South Africa. Gender and behaviour, 15, 10502-10510. https://doi.org/10.4314/GAB.V15I4
Jian, M. J. K. O. (2023). Personalized learning through AI. Advances in Engineering Innovation. https://doi.org/10.54254/2977-3903/5/2023039
Komleva, N. V., & Vilyavin, D. A. (2020). Digital Platform for Creating Personalized Adaptive Online Courses. Open Education. https://doi.org/10.21686/1818-4243-2020-2-65-72
Krstikj, A., Sosa Godina, J., García Bañuelos, L., González Peña, O. I., Quintero Milián, H. N., Urbina Coronado, P. D., & Vanoye García, A. Y. (2022). Analysis of Competency Assessment of Educational Innovation in Upper Secondary School and Higher Education: A Mapping Review. Sustainability. https://doi.org/10.3390/su14138089
Kumpas-Lenk, K., Eisenschmidt, E., & Veispak, A. (2018). Does the design of learning outcomes matter from students’ perspective? Studies in Educational Evaluation. https://doi.org/10.1016/J.STUEDUC.2018.07.008
Liu, F., Dai, Q., Zhao, L., & Shi, X. (2020). A new Teaching-Objective Achievement based Adaptive Teaching Continuous Improvement Method. 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 857-862. https://doi.org/10.1109/TALE48869.2020.9368409
Malygin, A. A. (2023). Adaptive assessment in certification procedures for students and graduates. Alma mater. Vestnik Vysshey Shkoly. https://doi.org/10.20339/am.08-23.039
McTighe, J. (2020). Standards Are Not Curriculum. Science and Children, 58.
Morozov, A., Ganicheva, I., & Savotina, N. (2021). Development of Professional Competencies of Students in the Process of Practical Training at the University. Proceedings of the 1st International Scientific Forum on Sustainable Development of Socio-economic Systems. https://doi.org/10.5220/0010669600003223
Muangprathub, J., Boonjing, V., & Chamnongthai, K. (2020). Learning recommendation with formal concept analysis for intelligent tutoring system. Heliyon, 6. https://doi.org/10.1016/j.heliyon.2020.e05227
Mujtaba, D. F., & Mahapatra, N. R. (2020). Artificial Intelligence in Computerized Adaptive Testing. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 649-654. https://doi.org/10.1109/CSCI51800.2020.00116
Munzenmaier, C. (2013). Perspectivas Bloom´s Taxonomy: What’s Old Is New Again. The eLearning Guild
Murphy, D. H., & Castel, A. D. (2021). Responsible remembering and forgetting as contributors to memory for important information. Memory & Cognition, 49, 895 - 911. https://doi.org/10.3758/s13421-021-01139-4
Ogundeji, O. M., Madu, B. C., & Onuya, C. C. (2019). Scientific Explanation of Phenomena and Concept Formation as Correlates of Students’ Understanding of Physics Concepts. European Journal of Physics Education, 10, 10-19. https://doi.org/10.20308/EJPE.V10I3.240
Onorato, P., Gratton, L., Oss, S., & Malgieri, M. (2019). From the dicey world to the physical laws: dice toy models for bridging microscopic and macroscopic understanding of physical phenomena. Journal of Physics: Conference Series, 1287. https://doi.org/10.1088/1742-6596/1287/1/012026
Petersen, A. K., & Gundersen, P. (2019). Challenges in Designing Personalised Learning Paths in SPOCs. Designs for Learning. https://doi.org/10.16993/DFL.112
Pominov, D. A. (2021). Adaptive trainer for preparing students for math exams. Neurocomputers. https://doi.org/10.18127/j19998554-202102-04
Prasad, G. (2021). Evaluating student performance based on bloom’s taxonomy levels. Journal of Physics: Conference Series, 1797. https://doi.org/10.5539/ELT.V10N9P245
Rahman, S. A., & Manaf, N. F. A. (2017). A critical analysis of Bloom’s taxonomy in teaching creative and critical thinking skills in Malaysia through English literature. English Language Teaching, 10, 245-256. https://doi.org/10.5539/ELT.V10N9P245
Rao, N. J., Spady, W. G., & Spady, W. G. (2020). Outcome-based Education: An Outline. Higher Education for the Future, 7, 21 - 25. https://doi.org/10.1177/2347631119886418
Rimfeld, K., Malanchini, M., Hannigan, L. J., Dale, P. S., Allen, R., Hart, S. A., & Plomin, R. (2019). Teacher assessments during compulsory education are as reliable, stable and heritable as standardized test scores. Journal of child psychology and psychiatry, and allied disciplines. https://doi.org/10.1111/jcpp.13070
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2023). Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education. ArXiv, abs/2309.10892. https://doi.org/10.48550/arXiv.2309.10892
Sireci, S. G. (2020). Standardization and UNDERSTAND ardization in Educational Assessment. Educational Measurement: Issues and Practice. https://doi.org/10.1111/emip.12377
Stainbank, L. J. (2022). Addressing the learning outcomes for professional skills using an integrated teaching strategy. Cogent Education, 9. https://doi.org/10.1080/2331186X.2022.2109798
Sweet, L. (2019). Using a learning and skill acquisition plan to develop a learner's knowledge, skills, and professional practice attitudes. Ultrasound in Medicine & Biology. https://doi.org/10.1016/j.ultrasmedbio.2019.07.511
Szabo, C., & Sheard, J. (2022). Learning Theories Use and Relationships in Computing Education Research. ACM Transactions on Computing Education, 23, 1 - 34. https://doi.org/10.1007/978-3-319-64792-0_1
Tackett, S. A., Raymond, M. R., Desai, R., Haist, S. A., Morales, A., Gaglani, S. M., & Clyman, S. G. (2018). Crowdsourcing for assessment items to support adaptive learning. Medical Teacher, 40, 838 - 841. https://doi.org/10.1080/0142159X.2018.1490704
Tan, G.-X., & Liu, Y. (2021). Application of Data Mining Algorithms in Data Analysis of Information Education Evaluation. Journal of Physics: Conference Series, 2074. https://doi.org/10.1088/1742-6596/2074/1/012089
Tapalova, O., Zhiyenbayeva, N., & Gura, D. (2022). Artificial Intelligence in Education: AIEd for Personalised Learning Pathways. Electronic Journal of e-Learning.
Tetzlaff, L., Schmiedek, F., & Brod, G. (2020). Developing Personalized Education: A Dynamic Framework. Educational Psychology Review, 33, 863 - 882. https://doi.org/10.1007/s10648-020-09570-w
Vardakosta, E., Priniotakis, G., Papoutsidakis, M., Sigala, M., Tsikritsis, A., & Nikolopoulos, D. (2023). Design Thinking as a Co-Creation Methodology in Higher Education. A Perspective on the Development of Teamwork and Skill Cultivation. European Journal of Educational Research. https://doi.org/10.12973/eu-jer.12.2.1029
Vastaranta, M., Saarinen, N., Yrttimaa, T., & Tokola, T. (2020). Fundamental laws and principles in geoinformation science.
Verhavert, S., Furlong, A., & Bouwer, R. (2022). The Accuracy and Efficiency of a Reference-Based Adaptive Selection Algorithm for Comparative Judgment. Frontiers in Education,
Vosniadou, S. (2019). The Development of Students' Understanding of Science. Frontiers in Education.
Wang, N., Wang, D., & Zhang, Y. (2020). Design of an adaptive examination system based on artificial intelligence recognition model. Mechanical Systems and Signal Processing, 142, 106656. https://doi.org/10.1016/j.ymssp.2020.106656
Wolf, L. J., Haddock, G., & Maio, G. R. (2020). Attitudes. Oxford Research Encyclopedia of Psychology. https://doi.org/10.1093/acrefore/9780190236557.013.247
Wong, J., Baars, M., Koning, B. B. d., Zee, T. V. d., Davis, D., Khalil, M., Houben, G.-J., & Paas, F. (2019). Educational Theories and Learning Analytics: From Data to Knowledge. Utilizing Learning Analytics to Support Study Success. https://doi.org/10.1007/978-3-319-64792-0_1
Yan, Y., & Wen, Y.-Y. Y. (2023). An Evaluation Method for Curriculum Learning Outcomes Achievement Based on Cloud Model under the OBE Concept. Advances in Education, Humanities and Social Science Research. https://doi.org/10.56028/aehssr.6.1.176.2023
Zhu, X., Shui, H., & Chen, B. (2023). The Synthesis Lab: Empowering Collaborative Learning in Higher Education through Knowledge Synthesis. Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing. https://doi.org/10.1145/3584931.3606996
Zhuang, Y., Liu, Q., Huang, Z., Li, Z., Jin, B., Bi, H., Chen, E., & Wang, S. (2022). A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3477495.3531928
Zmigrod, L., Eisenberg, I. W., Bissett, P. G., Robbins, T. W., & Poldrack, R. A. (2021). The cognitive and perceptual correlates of ideological attitudes: a data-driven approach. Philosophical Transactions of the Royal Society B: Biological Sciences, 376. https://doi.org/10.1098/rstb.2020.0424
Publicado
Cómo citar
Número
Sección
