Hacia el desarrollo de un sistema de evaluación adaptativa computarizada basada en reglas de asociación secuenciales

Palabras clave: Exámenes Adaptativos Computarizados, Minería de datos, Reglas de Asociación Secuenciales, Educación

Resumen

En este trabajo se muestran los resultados de un análisis de 38 artículos de investigación de las principales editoriales informáticas, para comparar los métodos de minería de patrones secuenciales que utilizaron, además se propone un sistema de evaluación adaptativa computarizada (CAT) basada en reglas de asociación secuenciales. Anteriormente, se presentó un sistema CAT que integra reglas de asociación como un método de selección de ítems. En este caso, el sistema suministrará a los estudiantes ítems que correspondan a su nivel de conocimiento a través de las repuestas que ellos ingresen en la prueba, además de las respuestas correctas de otros estudiantes en la misma prueba basadas en reglas de asociación secuenciales con mayor soporte. Las tecnologías propuestas para el desarrollo del sistema son Java, y el entorno de desarrollo NetBeans, además del marco de trabajo JavaServer Faces, el sistema gestor de bases de datos MySQL, apegado a la metodología de ingeniería web basada en UML (UWE por sus siglas en inglés).

Descargas

La descarga de datos todavía no está disponible.

Citas

Aktas, D. E., & Aktas, M. S. (2021). Sequential Rule Mining on the Student Behavior Data of an E-Learning Platform in the Field of Financial Sciences: Case Study. 3rd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2021, June, 12– 13. https://doi.org/10.1109/ICECCE52056.2021.9514230

Al-Twijri, M. I., Luna, J. M., Herrera, F., & Ventura, S. (2022). Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns. Cognitive Computation, 14(4), 1474–1495. https://doi.org/10.1007/s12559-022-10015-5

Anwar, T., & Uma, V. (2022). CD-SPM: Cross-domain book recommendation using sequential pattern mining and rule mining. Journal of King Saud University - Computer and Information Sciences, 34(3), 793–800. https://doi.org/10.1016/j.jksuci.2019.01.012

Bermudez, R. S., Sison, A. M., & Medina, R. P. (2020). Extended PrefixSpan for Efficient Sequential Pattern Mining in a Game-based Learning Environment. ACM International Conference Proceeding Series, 118–122. https://doi.org/10.1145/3379310.3381044

Chen, C. M., & Wang, W. F. (2020). Mining Effective Learning Behaviors in a Web-Based Inquiry Science Environment. Journal of Science Education and Technology, 29(4), 519–535. https://doi.org/10.1007/s10956-020-09833-9

Cheng, S. C., Cheng, Y. P., & Huang, Y. M. (2021). To Implement Computerized Adaptive Testing by Automatically Adjusting Item Difficulty Index on Adaptive English Learning Platform. Journal of Internet Technology, 22(7), 1599–1607. https://doi.org/10.53106/160792642021122207013

Cheng Tan, K., Zantedeschi, D., Kumar, A., & Gaspar, A. (2020). Genetic Algorithm Cleaning in Sequential Data Mining: Analyzing Solutions to Parsons’ Puzzles. 2330– 2333. https://doi.org/10.1145/3520304

Czibula, G., Mihai, A., & Crivei, L. M. (2019). S PRAR: A novel relational association rule mining classification model applied for academic performance prediction. Procedia Computer Science, 159, 20–29. https://doi.org/10.1016/j.procs.2019.09.156

Deeva, G., & De Weerdt, J. (2019). Understanding Automated Feedback in Learning Processes by Mining Local Patterns. Lecture Notes in Business Information Processing, 342, 56–68. https://doi.org/10.1007/978-3- 030-11641-5_5

Doko, E., Bexheti, L. A., Hamiti, M., & Etemi, B. P. (2018). Sequential pattern mining model to identify the most important or difficult learning topics via mobile technologies. International Journal of Interactive Mobile Technologies, 12(4), 109–122. https://doi.org/10.3991/ijim.v12i4.9223

Fatahi, S., Shabanali-Fami, F., & Moradi, H. (2018). An empirical study of using sequential behavior pattern mining approach to predict learning styles. Education and Information Technologies, 23(4), 1427–1445. https://doi.org/10.1007/s10639-017-9667-1

Gómez Fuentes, M. del C., & Cervantes Ojeda, J. (2017). Introducción a la Programación Web con Java: JSP y Servlets, JavaServer Faces Obra ganadora del Tercer Concurso para la publicación de libros de texto. www.cua.uam.mx

González Mendoza, G. (2015). Herramienta de Desarrollo Netbeans. Universidad Del Norte, 1–5.

He, Q., Borgonovi, F., & Paccagnella, M. (2021). Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks. Computers and Education, 166. https://doi.org/10.1016/j.compedu.2021.104170

Istiyono, E., Dwandaru, W. S. B., Setiawan, R., & Megawati, I. (2020). Developing of computerized adaptive testing to measure physics higher order thinking skills of senior high school students and its feasibility of use. European Journal of Educational Research, 9(1), 91–101. https://doi.org/10.12973/eu-jer.9.1.91

Klašnja-Milićević, A., Vesin, B., & Ivanović, M. (2018). Social tagging strategy for enhancing e-learning experience. Computers and Education, 118, 166–181. https://doi.org/10.1016/j.compedu.2017.12.002

Kong, M., & Pollock, L. (2020, November 19). SemiAutomatically Mining Students’ Common ScratchProgramming Behaviors. ACM International Conference Proceeding Series. https://doi.org/10.1145/3428029.3428034

Latypova, V. (2022). Work with Free Response Implementation Process Analysis Based on Sequential Pattern Mining in Engineering Education. 2022 6th International Conference on Information Technologies in Engineering Education, Inforino 2022 - Proceedings. https://doi.org/10.1109/Inforino53888.2022.9782969

Liu, Y., Yi, X., Chen, R., Zhai, Z., & Gu, J. (2018). Feature extraction based on information gain and sequential pattern for English question classification. IET Software, 12(6), 520–526. https://doi.org/10.1049/ietsen.2018.0006

López Herrera, P. (2016). Comparación del desempeño de los Sistemas Gestores de Bases de Datos MySQL y PostgreSQL. [Universidad Autónoma del Estado deMéxico]. In Comparación del desempeño de los Sistemas Gestores de Bases de Datos MySQL y PostgreSQL. http://ri.uaemex.mx/handle/20.500.11799/62548%0Ahttp://hdl.handle.net/20.500.11799/62548%0Ahttp://ri.uaemex.mx/bitstream/handle/20.500.11799/62548/TesisPatriciaLopezHerrera.pdf?sequence=3

Malekian, D., Bailey, J., & Kennedy, G. (2020). Prediction of students’ assessment readiness in online learning environments: The sequence matters. ACM International Conference Proceeding Series, 382–391. https://doi.org/10.1145/3375462.3375468

Mudrick, N. V., Azevedo, R., & Taub, M. (2018). Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning. Computers in Human Behavior, 96, 223–234. https://doi.org/10.1016/j.chb.2018.06.028

Niemeijer, K., Feskens, R., Krempl, G., Koops, J., & Brinkhuis, M. J. S. (2020). Constructing and predicting school advice for academic achievement: A comparison of item response theory and machine learning techniques. ACM International Conference Proceeding Series, 462–471. https://doi.org/10.1145/3375462.3375486

Norm Lien, Y. C., Wu, W. J., & Lu, Y. L. (2020). How Well Do Teachers Predict Students’ Actions in Solving an IllDefined Problem in STEM Education: A Solution Using Sequential Pattern Mining. IEEE Access, 8, 134976– 134986. https://doi.org/10.1109/ACCESS.2020.3010168

Pacheco-Ortiz, J., Rodríguez-Mazahua, L., Mejía-Miranda, J., Machorro-Cano, I., & Juárez-Martínez, U. (2021). Towards Association Rule-Based Item Selection Strategy in Computerized Adaptive Testing. Studies in Computational Intelligence, 966(2), 27–54. https://doi.org/10.1007/978-3-030-71115-3_2

Pogorskiy, E., & Beckmann, J. F. (2022). Learners’ web navigation behaviour beyond learning management systems: A way into addressing procrastination in online learning? Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100094

Qu, S., Li, K., Wu, B., Zhang, S., & Wang, Y. (2019). Predicting student achievement based on temporal learning behavior in MOOCs. Applied Sciences (Switzerland), 9(24). https://doi.org/10.3390/app9245539

Real, E. M. H., Pimentel, E. P., & Braga, J. C. (2021). Analysis of Learning Behavior in a Programming Course using Process Mining and Sequential Pattern Mining. Proceedings - Frontiers in Education Conference, FIE, 2021-Octob. https://doi.org/10.1109/FIE49875.2021.9637146

Salmerón, L. (2011). ¿ Por qué realizar un examen mejora nuestro aprendizaje ? Lecciones científicas y educativas del efecto del test. Ciencia Cognitiva. Revista Electrónica de Divulgación, 5(1), 19–21.

Shih, W. C. (2018). Mining Sequential Patterns to Explore Users’ Learning Behavior in a Visual Programming App. Proceedings - International Computer Software and Applications Conference, 2,126–129. https://doi.org/10.1109/COMPSAC.2018.10216

Song, W., & Ye, W. (2021). Mining Unexpected Sequential Patterns from MOOC Data. Proceedings - 12th IEEE International Conference on Big Knowledge, ICBK 2021, 434–439. https://doi.org/10.1109/ICKG52313.2021.00064

Song, W., Ye, W., & Fournier-Viger, P. (2022). Mining sequential patterns with flexible constraints from MOOC data. Applied Intelligence, 52(14), 16458–16474. https://doi.org/10.1007/s10489-021-03122-7

Tarus, J. K., Niu, Z., & Kalui, D. (2018). A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft Computing, 22(8), 2449–2461. https://doi.org/10.1007/s00500-017-2720-6

Taub, M., & Azevedo, R. (2018a). Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning.

Taub, M., & Azevedo, R. (2018b). How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System? International Journal of Artificial Intelligence in Education, 29(1), 1–28. https://doi.org/10.1007/s40593- 018-0165-4

Taub, M., Azevedo, R., Bradbury, A. E., Millar, G. C., & Lester, J. (2018). Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment. Learning and Instruction, 54, 93–103. https://doi.org/10.1016/j.learninstruc.2017.08.005

UWE - UML-based web engineering. (n.d.). Retrieved March 2, 2023, from https://uwe.pst.ifi.lmu.de/index.html

Wan, S., & Niu, Z. (2020). A hybrid e-learning recommendation approach based on learners’ influence propagation. IEEE Transactions on Knowledge and Data Engineering, 32(5), 827–840. https://doi.org/10.1109/TKDE.2019.2895033

Wang, R., & Zaïane, O. R. (2018). Sequence-Based Approaches to Course Recommender Systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11029 LNCS, 35–50. https://doi.org/10.1007/978-3-319-98809-2_3

Weiss, D. J., & Kingsbury, G. G. (2016). Application of Computerized Adaptive Testing to Educational Problems Author ( s ): David J . Weiss and G . Gage Kingsbury Source : Journal of Educational Measurement , Vol . 21 , No . 4 , [ Application of Computers to Published by : National Council on Me. 21(4), 361–375.

Weka 3 - Data Mining with Open Source Machine Learning Software in Java. (n.d.). Retrieved March 2, 2023, from https://www.cs.waikato.ac.nz/ml/weka/index.html

Wong, J., Khalil, M., Baars, M., de Koning, B. B., & Paas, F. (2019). Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course. Computers and Education, 140. https://doi.org/10.1016/j.compedu.2019.103595

Yang, J. (2021). Effective Learning Behavior of Students’ Internet Based on Data Mining. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021, 847–850. https://doi.org/10.1109/ICBAIE52039.2021.9390049

Yildirim, D., & Usluel, Y. (2022). Interrelated analysis of interaction, sequential patterns and academic achievement in online learning. In Australasian Journal of Educational Technology (Vol. 2022, Issue 2).

Zhang, N., Biswas, G., & Hutchins, N. (2022). Measuring and Analyzing Students’ Strategic Learning Behaviors in Open-Ended Learning Environments. International Journal of Artificial Intelligence in Education, 32(4), 931–970. https://doi.org/10.1007/s40593-021-00275-x

Zheng, J., Xing, W., & Zhu, G. (2019). Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment. Computers and Education, 136, 34–48. https://doi.org/10.1016/j.compedu.2019.03.005

Zhu, G., Xing, W., & Popov, V. (2019). Uncovering the sequential patterns in transformative and nontransformative discourse during collaborative inquiry learning. Internet and Higher Education, 41, 51–61. https://doi.org/10.1016/j.iheduc.2019.02.001

Publicado
2023-09-11
Cómo citar
Reyes-García, A. U., Rodríguez-Mazahua, L., Pacheco-Ortiz, J., Abud-Figueroa, M. A., & Juárez-Martínez, U. (2023). Hacia el desarrollo de un sistema de evaluación adaptativa computarizada basada en reglas de asociación secuenciales. Pädi Boletín Científico De Ciencias Básicas E Ingenierías Del ICBI, 11(Especial2), 61-69. https://doi.org/10.29057/icbi.v11iEspecial2.10705