Modelo de evaluación formativa en cursos virtuales de Matemáticas y su aplicación en analítica de aprendizaje

dc.contributorMoreno Cadavid, Julian
dc.contributorGUIAME - Grupo de Investigación en Informática Educativa
dc.creatorPineda Corcho, Andrés Felipe
dc.date.accessioned2022-02-07T15:12:35Z
dc.date.accessioned2022-09-21T17:21:58Z
dc.date.available2022-02-07T15:12:35Z
dc.date.available2022-09-21T17:21:58Z
dc.date.created2022-02-07T15:12:35Z
dc.date.issued2021
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/80892
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps:/repositorio.una.edu.co
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3400062
dc.description.abstractEl presente documento presenta un modelo para la puesta en marcha de cursos virtuales de matemáticas, el cual está basado en un proceso de evaluación formativa e integrado con analíticas de aprendizaje, este sigue las cinco fases del diseño instruccional ADDIE. Para su definición, se parte de un marco conceptual en el cual se exploran los diferentes conceptos en los que se basa este trabajo, seguidamente se realiza una revisión sistemática de la literatura con el fin de identificar los últimos avances en cuanto a metodologías, herramientas, así como otros modelos aplicados en esta área. A partir de los resultados previamente obtenidos, se plantean el problema y las preguntas de investigación con el fin de diseñar e implementar el modelo de evaluación formativa y como este se puede integrar con analítica de aprendizaje buscando así tener un mayor impacto en el rendimiento de los estudiantes. Finalmente, el modelo es instanciado en un ambiente virtual de aprendizaje con el fin de realizar una validación a través de un caso de estudio. Los estudiantes son divididos en tres grupos, cada uno sometido a una metodología diferente de enseñanza. El primero a través de una metodología virtual tradicional, el segundo contando además con herramientas de evaluación formativa, y el tercero igual que el segundo, pero además con la integración a analíticas de aprendizaje. Para la validación se realiza un análisis cualitativo y cuantitativo del rendimiento de los grupos a través de la misma interacción con la plataforma y una encuesta de percepción. Los resultados demuestran que la evaluación formativa y las analíticas de aprendizaje si tienen un impacto estadísticamente significativo en relación al rendimiento en el curso. (Texto tomado de la fuente)
dc.description.abstractThis document presents a model for the implementation of virtual mathematics courses, which is based on a formative evaluation process and integrated with learning analytics, this follows the five phases of the ADDIE instructional design. For its definition, it starts from a conceptual framework in which the different concepts on which this work is based are explored, followed by a systematic review of the literature in order to identify the latest advances in terms of methodologies, tools, as well as other models applied in this area. From the results obtained, the problem and research questions are raised in order to design and implement the formative assessment model and how it can be integrated with learning analytics to have a greater impact on student performance. Finally, the model is instantiated in a virtual learning environment in order to carry out a validation through a case study. Students are divided into three groups, each one involved into to a different teaching methodology. The first through a traditional virtual methodology, the second also having formative assessment tools, and the third the same as the second, but also with the integration of learning analytics. For the validation, a qualitative and quantitative analysis of the performance of the groups is carried out through the same interaction with the platform and a perception survey. The results show that formative assessment and learning analytics do have a statistically significant impact on the relationship to performance in the course.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherMedellín - Minas - Doctorado en Ingeniería - Sistemas
dc.publisherFacultad de Minas
dc.publisherMedellín, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Medellín
dc.relationAbdullah, W. M., Kabir, K. L., Hasan, N., & Islam, S. (2016). Development of a smart learning analytics system using Bangla word recognition and an improved document driven DSS. 1st International Conference on Computer and Information Engineering, ICCIE 2015, 75–78. https://doi.org/10.1109/CCIE.2015.7399321
dc.relationAgudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-Garcíaa, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning II. Computers in Human Behavior, 31(1), 542–550. https://doi.org/10.1016/j.chb.2013.05.031
dc.relationAli, L., Asadi, M., Gasevic, D., Jovanovic, J., & Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers and Education, 62, 130–148. https://doi.org/10.1016/j.compedu.2012.10.023
dc.relationAli, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A Qualitative Evaluation of Evolution of a Learning Analytics Tool. Comput. Educ., 58(1), 1–23. https://doi.org/10.1016/j.compedu.2011.08.030
dc.relationAlmenara, J. C., & Llorente, C. (2014). Las tipologías de MOOC: Su diseño e implicaciones educativas. Revista de Curriculum y Formación de Profesorado, 18(1).
dc.relationAlsultanny, Y. (2011). Selecting a suitable method of data mining for successful forecasting. Journal of Targeting, Measurement and Analysis for Marketing, 19(3–4), 207–225. https://doi.org/10.1057/jt.2011.21
dc.relationAltujjar, Y., Altamimi, W., Al-Turaiki, I., & Al-Razgan, M. (2016). Predicting Critical Courses Affecting Students Performance: A Case Study. Procedia Computer Science, 82(March), 65–71. https://doi.org/10.1016/j.procs.2016.04.010
dc.relationAndrews, R., & Haythornthwaite, C. (2007). Introduction to E-learning Research. The Sage Handbook of E-Learning Research, London: Sage Publication Ltd, 1–59.
dc.relationAngeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? COMPUTERS & EDUCATION, 113, 226–242. https://doi.org/10.1016/j.compedu.2017.05.021
dc.relationArchambault, L. M., & Barnett, J. H. (2010). Revisiting technological pedagogical content knowledge: Exploring the TPACK framework. Computers & Education, 55(4), 1656–1662. https://doi.org/10.1016/j.compedu.2010.07.009
dc.relationArkorful, V., & Abaidoo, N. (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Instructional Technology and Distance Learning, 12(1), 29–42. https://doi.org/10.3991/ijac.v3i2.1322
dc.relationAvella, J. T., Kebritchi, M., Nunn, S., & Kanai, T. (2016). Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. Online Learning Journal, 20(2), 13–29.
dc.relationAyub, M., Toba, H., Yong, S., & Wijanto, M. C. (2017). Modelling students’ activities in programming subjects through educational data mining. Global Journal of Engineering Education, 19(3), 249–255.
dc.relationBadr, G., Algobail, A., Almutairi, H., & Almutery, M. (2016). Predicting Students’ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department. Procedia Computer Science, 82(March), 80–89. https://doi.org/10.1016/j.procs.2016.04.012
dc.relationBaneres, D., Caballe, S., & Clariso, R. (2016). Towards a Learning Analytics Support for Intelligent Tutoring Systems on MOOC Platforms. Proceedings - 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2016, 103–110. https://doi.org/10.1109/CISIS.2016.48
dc.relationBaradwaj, B., & Pal, S. (2012). Mining educational data to analyze student’s performance. Internation Journal On Advanced Computer Science and Applications, 2(6), 63–69. https://doi.org/vol.2,No.6
dc.relationBarana, A., & Marchisio, M. (2016). Ten good reasons to adopt an automated formative assessment model for learning and teaching Mathematics and scientific disciplines. Procedia - Social and Behavioral Sciences - 2nd International Conference on Higher Education Advances, 228, 608–613. https://doi.org/10.1016/j.sbspro.2016.07.093
dc.relationBBC. (2019). Pruebas PISA: qué dice de la educación en América Latina los malos resultados obtenidos por los países de la región. Https://Www.Bbc.Com/Mundo/Noticias-America-Latina-50685470.
dc.relationBennett, S., Agostinho, S., & Lockyer, L. (2017). The process of designing for learning: understanding university teachers’ design work. Educational Technology Research and Development, 65(1), 125–145. https://doi.org/10.1007/s11423-016-9469-y
dc.relationBerland, M., Martin, T., Benton, T., Petrick Smith, C., & Davis, D. (2013). Using Learning Analytics to Understand the Learning Pathways of Novice Programmers. Journal of the Learning Sciences, 22(4), 564–599. https://doi.org/10.1080/10508406.2013.836655
dc.relationBienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington, DC: SRI International, 1–57. http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf
dc.relationBruner, J. S. (1960). The process of education. Cambridge: Harvard University Press, Behavioral. https://doi.org/doi:10.1002/bs.3830090108
dc.relationBurrow, M., Evdorides, H., Hallam, B., & Freer-hewish, R. (2005). Developing formative assessment for postgraduate students in engineering. European Journal of Engineering Education, 30(2), 255–263. https://doi.org/10.1080/03043790500087563
dc.relationCalvert, C. E. (2014). Developing a model and applications for probabilities of student success: a case study of predictive analytics. Open Learning, 29(2), 160–173. https://doi.org/10.1080/02680513.2014.931805
dc.relationCalvet Liñán, L., & Juan Pérez, Á. A. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. RUSC. Universities and Knowledge Society Journal, 12(3), 98. https://doi.org/10.7238/rusc.v12i3.2515
dc.relationCampagni, R., Merlini, D., Sprugnoli, R., & Verri, M. C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508–5521. https://doi.org/10.1016/j.eswa.2015.02.052
dc.relationCano, A. R., Fernandez-Manjon, B., & Garcia-Tejedor, A. J. (2016). Downtown, a subway adventure: Using Learning analytics to improve the development of a learning game for people with intellectual disabilities. Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016, 125–129. https://doi.org/10.1109/ICALT.2016.46
dc.relationChalaris, M., Gritzalis, S., Maragoudakis, M., Sgouropoulou, C., & Tsolakidis, A. (2014). ScienceDirect ICININFO Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques. Procedia - Social and Behavioral Sciences, 147, 390–397. https://doi.org/10.1016/j.sbspro.2014.07.117
dc.relationChatti, M. A., Dyckhoff, A. L., Schroeder, U., Thus, H., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5/6), 318–331. https://doi.org/DOI: 10.1504/IJTEL.2012.051815
dc.relationChen, I. H., Gamble, J. H., Lee, Z. H., & Fu, Q. L. (2020). Formative assessment with interactive whiteboards: A one-year longitudinal study of primary students’ mathematical performance. Computers and Education, 150(February 2019), 103833. https://doi.org/10.1016/j.compedu.2020.103833
dc.relationChen, J., Xu, J., Tang, T., & Chen, R. (2017). WebIntera-classroom: an interaction-aware virtual learning environment for augmenting learning interactions. Interactive Learning Environments, 25(6), 792–807. https://doi.org/10.1080/10494820.2016.1188829
dc.relationChu, H. C., Chen, J. M., & Tsai, C. L. (2017). Effects of an online formative peer-tutoring approach on students’ learning behaviors, performance and cognitive load in mathematics. Interactive Learning Environments, 25(2), 203–219. https://doi.org/10.1080/10494820.2016.1276085
dc.relationClow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695. https://doi.org/10.1080/13562517.2013.827653
dc.relationCOEC. (2001). The eLearning Action Plan: Designing tomorrow’s education. 1–19.
dc.relationConde, M. Á., García-peñalvo, F. J., Gómez-, D. A., & Theron, R. (2014). Visual learning analytics techniques applied in software engineering subjects. https://doi.org/10.1109/FIE.2014.7044486
dc.relationCorreia, C. F., & Harrison, C. (2020). Teachers’ beliefs about inquiry-based learning and its impact on formative assessment practice. Research in Science and Technological Education, 38(3), 355–376. https://doi.org/10.1080/02635143.2019.1634040
dc.relationCort, L., Alfredo, C., Gonz, Z., Men, H., Jos, P., & Herrera, C. (2015). El estudio de los hábitos de conexión en redes sociales virtuales, por medio de la minería de datos. 15.
dc.relationCosta, E. B., Fonseca, B., Santana, M. A., de Araujo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256. https://doi.org/10.1016/j.chb.2017.01.047
dc.relationCrippen, K. (2014). Examining TPACK Among K-12 Online Distance Educators in the United States. April.
dc.relationDarius, P. S. H., Gundabattini, E., & Solomon, D. G. (2021). A Survey on the Effectiveness of Online Teaching–Learning Methods for University and College Students. Journal of The Institution of Engineers (India): Series B. https://doi.org/10.1007/s40031-021-00581-x
dc.relationDawson, S., & Siemens, G. (2014). Analytics to Literacies: The Development of a Learning Analytics Framework for Multiliteracies Assessment. International Review of Research in Open and Distance Learning, 15(4), 284–305. https://doi.org/10.19173/irrodl.v15i4.1878
dc.relationDick, W., & Carey, L. (1996). The systematic design of instruction (4th ed.). Harper Collins College Publishers.
dc.relationDietz-Uhler, B., & Hurn, J. (2013). Using learning analytics to predict (and improve) student success: a faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26. http://www.ncolr.org/jiol/issues/pdf/12.1.2.pdf
dc.relationDodero, J. M., González-Conejero, E. J., Gutierrez-Herrera, G., Peinado, S., Tocino, J. T., & Ruiz-Rube, I. (2017). Trade-off between interoperability and data collection performance when designing an architecture for learning analytics. Future Generation Computer Systems, 68, 31–37. https://doi.org/10.1016/j.future.2016.06.040
dc.relationDuval, E. (2011). Attention please!: Learning analytics for visualization and recommendation. LAK ’11 Proceedings of the 1st International Conference on Learning Analytics and Knowledge, 9–17. https://doi.org/10.1145/2090116.2090118
dc.relationDyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., Schroeder, U., Bueltmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and implementation of a learning analytics toolkit for teachers. Educational Technology and Society, 15(3), 58–76.
dc.relationEbner, M. (2016). Engaging Learning Analytics in MOOCS : the good , the bad , and the ugly. International Conference on Education and New Developments, June, 2–7.
dc.relationEcheverria, L., Benitez, A., Buendia, S., Cobos, R., & Morales, M. (2016). Using a learning analytics manager for monitoring of the collaborative learning activities and students’ motivation into the Moodle system. 2016 IEEE 11th Colombian Computing Conference, CCC 2016 - Conference Proceedings. https://doi.org/10.1109/ColumbianCC.2016.7750772
dc.relationEckert, K. B. (2015). Análisis de Deserción-Permanencia de Estudiantes Universitarios Utilizando Técnica de Clasificación en Minería de Datos Analysis of Attrition-Retention of College Students Using Classification Technique in Data Mining. 8, 3–12. https://doi.org/10.4067/S0718-50062015000500002
dc.relationEddy, C. M., Harrell, P., & Heitz, L. (2017). An observation protocol of short-cycle formative assessment in the mathematics classroom. Investigations in Mathematics Learning, 9(3), 130–147. https://doi.org/10.1080/19477503.2017.1308699
dc.relationEfklides, A. (2006). Metacognition and affect: What can metacognitive experiences tell us about the learning process? Educational Research Review, 1, 3–14.
dc.relationFaber, J. M., & Visscher, A. J. (2018). The effects of a digital formative assessment tool on spelling achievement: Results of a randomized experiment. Computers and Education, 122(March), 1–8. https://doi.org/10.1016/j.compedu.2018.03.008
dc.relationFerguson, 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
dc.relationFerguson, Rebecca, & Shum, S. B. (2012). Social Learning Analytics: Five Approaches. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12, May, 23. https://doi.org/10.1145/2330601.2330616
dc.relationFernandez-Delgado, M., Mucientes, M., Vazquez-Barreiros, B., & Lama, M. (2015). Learning analytics for the prediction of the educational objectives achievement. Proceedings - Frontiers in Education Conference, FIE, 2015-Febru(February), 0–3. https://doi.org/10.1109/FIE.2014.7044402
dc.relationFernández-Gallego, B., Lama, M., Vidal, J. C., & Mucientes, M. (2013). Learning analytics framework for educational virtual worlds. Procedia Computer Science, 25, 443–447. https://doi.org/10.1016/j.procs.2013.11.056
dc.relationFernández-Pampillón Cesteros, A. M. (2009). Las plataformas e-learning para la enseñanza y el aprendizaje universitario en Internet. Las Plataformas de Aprendizaje. Del Mito a La Realidad. Biblioteca Nueva, 45–73.
dc.relationFidalgo-Blanco, Á., Sein-Echaluce, M. L., García-Peñalvo, F. J., & Conde, M. Á. (2015). Using Learning Analytics to improve teamwork assessment. Computers in Human Behavior, 47, 149–156. https://doi.org/10.1016/j.chb.2014.11.050
dc.relationFoorman, B. R., York, M., Santi, K. L., & Francis, D. (2008). Contextual effects on predicting risk for reading difficulties in first and second grade. Reading and Writing, 21(4), 371–394. https://doi.org/10.1007/s11145-007-9079-5
dc.relationFoster, B., Perfect, C., & Youd, A. (2012). A completely client-side approach to e-assessment and e-learning of mathematics and statistics. International Journal of E-Assessment, 2(2). https://doi.org/10.1016/j.ijproman.2014.03.004
dc.relationGameel, B. G. (2017). Learner Satisfaction with Massive Open Online Courses. American Journal of Distance Education, 31(2), 98–111. https://doi.org/10.1080/08923647.2017.1300462
dc.relationGarcía, O. A., & Secades, V. A. (2013). Big data and learning analytics: A potential way to optimize elearning technological tools. Proceedings of the International Conference E-Learning 2013, 313–317. http://www.scopus.com/inward/record.url?eid=2-s2.0-84886911408&partnerID=tZOtx3y1
dc.relationGaševi, D., Dawson, S., & Siemens, G. (2015). Let’s not forget : Learning Analytics are about Learning Course Signals : Lessons Learned. TechTrends‏, 59(1), 64–71.
dc.relationGašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. Internet and Higher Education, 28, 68–84. https://doi.org/10.1016/j.iheduc.2015.10.002
dc.relationGiannakos, M. N., Sampson, D. G., & Kidziński, Ł. (2016). Introduction to smart learning analytics: foundations and developments in video-based learning. Smart Learning Environments, 3(1), 12. https://doi.org/10.1186/s40561-016-0034-2
dc.relationGikandi, J. W., Morrow, D., & Davis, N. E. (2011). Online formative assessment in higher education: A review of the literature. Computers and Education, 57(4), 2333–2351. https://doi.org/10.1016/j.compedu.2011.06.004
dc.relationGovindarajan, K., Kumar, V. S., & Kinshuk. (2017). Dynamic Learning Path Prediction - A Learning Analytics Solution. Proceedings - IEEE 8th International Conference on Technology for Education, T4E 2016, 188–193. https://doi.org/10.1109/T4E.2016.047
dc.relationGraham, C. R. (2011). Theoretical considerations for understanding technological pedagogical content knowledge (TPACK). Computers & Education, 57(3), 1953–1960. https://doi.org/10.1016/j.compedu.2011.04.010
dc.relationGreller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. EDUCATIONAL TECHNOLOGY & SOCIETY, 15(3), 42–57. https://doi.org/http://hdl.handle.net/1820/4506
dc.relationGunness, S., & Singh, U. (2016). Integrating Learning Analytics for higher-order thinking e-Assessments. 2015 International Conference on Computing, Communication and Security, ICCCS 2015. https://doi.org/10.1109/CCCS.2015.7374143
dc.relationHaj-Yahya, A., & Olsher, S. (2020). Preservice teachers’ experiences with digital formative assessment in mathematics. International Journal of Mathematical Education in Science and Technology. https://doi.org/10.1080/0020739X.2020.1842527
dc.relationHansen, G. (2020). Formative assessment as a collaborative act. Teachers` intention and students` experience: Two sides of the same coin, or? Studies in Educational Evaluation, 66(September 2019), 100904. https://doi.org/10.1016/j.stueduc.2020.100904
dc.relationHeinich, R., Michael, M., & James, R. (1985). Instructional media and the new technologies of instruction / Robert Heinich, Michael Molenda, James D. Russell. In Instructional media and the new technologies of instruction (2nd ed.). Wiley.
dc.relationHeritage, M., & Niemi, D. (2006). Toward a framework for using student mathematical representations as formative assessments. Educational Assessment, 11(3–4), 265–282. https://doi.org/10.1080/10627197.2006.9652992
dc.relationHernandez-Garcia, A., Gonzalez-Gonzalez, I., Jimenez-Zarco, A. I., & Chaparro-Pelaez, J. (2015). Applying social learning analytics to message boards in online distance learning: A case study. Computers in Human Behavior, 47, 68–80. https://doi.org/10.1016/j.chb.2014.10.038
dc.relationHernández, D., López, C., & Mejía, M. (2008). Utilización de una plataforma tecnológica para el desarrollo de los contenidos de la asignatura de relaciones públicas de la Facultad de Ciencias Económicas de la Universidad Francisco Gavidia, para su aplicación en la enseñanza moderna. Universidad Francisco Gavidia. http://hdl.handle.net/11592/7259
dc.relationHošková-Mayerová, Š., & Rosická, Z. (2015). E-Learning Pros and Cons: Active Learning Culture? Procedia - Social and Behavioral Sciences, 191, 958–962. https://doi.org/10.1016/j.sbspro.2015.04.702
dc.relationHughes, G., & Dobbins, C. (2015). The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs). Research and Practice in Technology Enhanced Learning, 10(1), 10. https://doi.org/10.1186/s41039-015-0007-z
dc.relationHung, J., Hsu, Y.-C., & Rice, K. (2012). Integrating Data Mining in Program Evaluation of K-12 Online Education. Educational Technology & Society, 15(3), 27–41. https://doi.org/10.1207/s15327752jpa8502
dc.relationHwang, G.-J., Sung, H.-Y., Hung, C.-M., Yang, L.-H., & Huang, I. (2013). A knowledge engineering approach to developing educational computer games for improving students’ differentiating knowledge. British Journal of Educational Technology, 44(2), 183–196. https://doi.org/10.1111/j.1467-8535.2012.01285.x
dc.relationIfenthaler, D. (2017). Designing Effective Digital Learning Environments: Toward Learning Analytics Design. Technology, Knowledge and Learning, 22(3), 401–404. https://doi.org/10.1007/s10758-017-9333-0
dc.relationIhantola, P., Butler, M., Edwards, S. H., Tech, V., Korhonen, A., Petersen, A., Rivers, K., Rubio, M. Á., Sheard, J., Spacco, J., Szabo, C., & Toll, D. (2015). Educational Data Mining and Learning Analytics in Programming : Literature Review and Case Studies. ITiCSE. https://doi.org/10.1145/2858796.2858798
dc.relationISO. (2016). SC36 / WG8 N80 Learning Analytics Interoperability - Reference model. International Organization for Standardization, 2016.
dc.relationKappe, R., & Van Der Flier, H. (2012). Predicting academic success in higher education: What’s more important than being smart? European Journal of Psychology of Education, 27(4), 605–619. https://doi.org/10.1007/s10212-011-0099-9
dc.relationKaur, P., Singh, M., & Josan, G. S. (2015). Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector. Procedia Computer Science, 57, 500–508. https://doi.org/10.1016/j.procs.2015.07.372
dc.relationKawazoe, M., & Arts, L. (n.d.). CASE STUDY E-learning / e-assessment systems based on webMathematica for university mathematics education. 15(2), 17–24.
dc.relationKleftodimos, A., & Evangelidis, G. (2016). Using open source technologies and open internet resources for building an interactive video based learning environment that supports learning analytics. Smart Learning Environments, 3(1), 9. https://doi.org/10.1186/s40561-016-0032-4
dc.relationKurt, S. (2017). ADDIE Model: Instructional Design. Educational Technology. https://educationaltechnology.net/the-addie-model-instructional-design/
dc.relationKurvinen, E., Lokkila, E., Lindén, R., Kaila, E., & Laakso, M. (2016). Automatic Assessment and Immediate Feedback in Third Grade Mathematics. Proceedings of Ireland International Conference on Education. https://doi.org/10.1145/2674683.2674685
dc.relationKyaruzi, F., Strijbos, J. W., Ufer, S., & Brown, G. T. L. (2019). Students’ formative assessment perceptions, feedback use and mathematics performance in secondary schools in Tanzania. Assessment in Education: Principles, Policy and Practice, 26(3), 278–302. https://doi.org/10.1080/0969594X.2019.1593103
dc.relationLaveti, R. N., Kuppili, S., Ch, J., Pal, S. N., & Babu, N. S. C. (2017). Implementation of learning analytics framework for MOOCs using state-of-the-art in-memory computing. 2017 5th National Conference on E-Learning & E-Learning Technologies (ELELTECH), 1, 1–6. https://doi.org/10.1109/ELELTECH.2017.8074997
dc.relationLebron, D., & Shahriar, H. (2015). Comparing MOOC ­ Based Platforms : Reflection on Pedagogical Support , Framework and Learning Analytics. International Conference on Collaboration Technologies and Systems (CTS) 2015, 167–174. https://doi.org/10.1109/CTS.2015.7210417
dc.relationLee, D. (2021). The process of designing for online learning. Educational Technology Research and Development, 69(1), 289–293. https://doi.org/10.1007/s11423-020-09915-w
dc.relationLiu, Q., & Fan, G. (2014). Using learning analytics technologies to find learning structures from online examination system. Proceedings - 2014 International Conference of Educational Innovation Through Technology, EITT 2014, 192–196. https://doi.org/10.1109/EITT.2014.38
dc.relationLonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90–97. https://doi.org/10.1016/j.chb.2014.07.013
dc.relationLu, O., Huang, J., Huang, A., & Yang, S. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220–234. https://doi.org/10.1080/10494820.2016.1278391
dc.relationMa, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. The Internet and Higher Education, 24, 26–34. https://doi.org/10.1016/j.iheduc.2014.09.005
dc.relationMaldonado, J. J., Palta, R., Vázquez, J., Bermeo, J. L., Pérez-sanagustín, M., & Munoz-gama, J. (2016). MOOCs Based on Self-Regulated Learning and Learning Styles.
dc.relationMansor, M. S. A., & Ismail, A. (2012). Learning Styles and Perception of Engineering Students Towards Online Learning. Procedia - Social and Behavioral Sciences, 69, 669–674. https://doi.org/10.1016/j.sbspro.2012.11.459
dc.relationMardikyan, S., & Badur, B. (2011). Analyzing teaching performance of instructors using data mining techniques. Informatics in Education, 10(2), 245–257.
dc.relationMartín-Monje, E., Castrillo, M. D., & Mañana-Rodríguez, J. (2017). Understanding online interaction in language MOOCs through learning analytics. Computer Assisted Language Learning, 8221(November), 1–22. https://doi.org/10.1080/09588221.2017.1378237
dc.relationMartinez, M. E., & Lipson, J. I. (1989). Assessment for Learning Formative Assessment. OECD/CERI International Conference “Learning in the 21st Century: Research, Innovation and Policy,” April, 73. https://doi.org/10.5959/eimj.3.2.2011.e1
dc.relationMasoumi, D. (2012). Quality in e-learning : a framework for promoting and assuring quality in virtual institutions. Journal of Computer Assited Learning, 28, 27–41. https://doi.org/10.1111/j.1365-2729.2011.00440.x
dc.relationMeira, R., Llamas-Nistal, M., & Iglesias, M. J. F. (2017). Enhancing learners’ experience in e-learning based scenarios using Intelligent tutoring systems and learning analytics: First results from a perception survey. 2017 12th Iberian Conference on Information Systems and Technologies (CISTI), 1–4. https://doi.org/10.23919/CISTI.2017.7975976
dc.relationMerceron, A., & Yacef, K. (2005). Educational data mining: A case study. Artificial Intelligence in Education: Supporting Learning through Intelligent and Sociall Informed Technology, 467–474. https://doi.org/10.1504/IJKESDP.2009.022718
dc.relationMohamadi, Z. (2018). Comparative effect of online summative and formative assessment on EFL student writing ability. Studies in Educational Evaluation, 59(February), 29–40. https://doi.org/10.1016/j.stueduc.2018.02.003
dc.relationMorris, S. P., Seymour, K., & Limmer, H. (2019). Research protocol: Evaluating the impact of Eedi formative assessment online platform (formerly Diagnostic Questions or DQ) on attainment in mathematics at GCSE and teacher workload. International Journal of Educational Research, 93(July 2018), 188–196. https://doi.org/10.1016/j.ijer.2018.11.007
dc.relationMorrison, G., Ross, R., & Kemp, J. (2004). Designing effective instruction. John Wiley & Sons.
dc.relationNassif, A. B., Azzeh, M., Banitaan, S., & Neagu, D. (2016). Guest editorial: special issue on predictive analytics using machine learning. Neural Computing and Applications, 27(8), 2153–2155. https://doi.org/10.1007/s00521-016-2327-3
dc.relationNí Fhloinn, E., & Carr, M. (2017). Formative assessment in mathematics for engineering students. European Journal of Engineering Education, 42(4), 458–470. https://doi.org/10.1080/03043797.2017.1289500
dc.relationNjeru, A. M., & Paracha, S. (2017). Learning analytics: Supporting at-risk student through eye-Tracking and a robust intelligent tutoring system. Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017, 1002–1005. https://doi.org/10.1109/ICASI.2017.7988616
dc.relationNopiah, Z. M., Farhana, N., Fuaad, A., Rosli, N. S., Othman, H., & Omar, Z. (2013). Predicting the Performance of the Diploma Engineering Students Using the Pre-test method. Procedia - Social and Behavioral Sciences, 102(Ifee 2012), 153–157. https://doi.org/10.1016/j.sbspro.2013.10.727
dc.relationNúñez-Peña, M. I., Bono, R., & Suárez-Pellicioni, M. (2015). Feedback on students’ performance: A possible way of reducing the negative effect of math anxiety in higher education. International Journal of Educational Research, 70, 80–87. https://doi.org/10.1016/j.ijer.2015.02.005
dc.relationOliveiar, L., & Figueira, A. (2017). Visualization of sentiment spread on social networked content: Learning analytics for integrated learning environments. IEEE Global Engineering Education Conference, EDUCON, April, 1290–1298. https://doi.org/10.1109/EDUCON.2017.7943014
dc.relationPacheco-Venegas, N. D., López, G., & Andrade-Aréchiga, M. (2015). Conceptualization, development and implementation of a web-based system for automatic evaluation of mathematical expressions. Computers and Education, 88, 15–28. https://doi.org/10.1016/j.compedu.2015.03.021
dc.relationPapamitsiou, Z., & Economides, A. A. (2015). Temporal learning analytics visualizations for increasing awareness during assessment. RUSC. Universities and Knowledge Society Journal, 12(3), 129. https://doi.org/10.7238/rusc.v12i3.2519
dc.relationPark, Y., & Jo, I. H. (2017). Using log variables in a learning management system to evaluate learning activity using the lens of activity theory. Assessment and Evaluation in Higher Education, 42(4), 531–547. https://doi.org/10.1080/02602938.2016.1158236
dc.relationPauna, M. (2017). Calculus Courses’ Assessment Data. Journal of Learning Analytics, 4(2), 12–21. https://doi.org/10.18608/jla.2017.42.3
dc.relationPereira, H. A., De Souza, A. F., & De Menezes, C. S. (2016). A computational architecture for learning analytics in game-based learning. Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016, 191–193. https://doi.org/10.1109/ICALT.2016.3
dc.relationPezzino, M. (2018). Online assessment, adaptive feedback and the importance of visual learning for students. The advantages, with a few caveats, of using MapleTA. International Review of Economics Education, 28(March), 11–28. https://doi.org/10.1016/j.iree.2018.03.002
dc.relationPhelan, J., Choi, K., Vendlinski, T., Baker, E., & Herman, J. (2011). Differential improvement in student understanding of mathematical principles following formative assessment intervention. Journal of Educational Research, 104(5), 330–339. https://doi.org/10.1080/00220671.2010.484030
dc.relationPhilip Chen, C. L., Tao, D., & You, X. (2016). Big learning in social media analytics. Neurocomputing, 204, 1–2. https://doi.org/10.1016/j.neucom.2016.02.069
dc.relationPicciano, A. G. (2012). The Evolution of Big Data and Learning Analytics in American Higher Education. Journal of Asynchronous Learning Networks, 16(3), 9–20. http://files.eric.ed.gov/fulltext/EJ982669.pdf
dc.relationPinger, P., Rakoczy, K., Besser, M., & Klieme, E. (2018). Implementation of formative assessment–effects of quality of programme delivery on students’ mathematics achievement and interest. Assessment in Education: Principles, Policy and Practice, 25(2), 160–182. https://doi.org/10.1080/0969594X.2016.1170665
dc.relationPuddey, I. B., & Mercer, A. (2014). Predicting academic outcomes in an Australian graduate entry medical programme. BMC Medical Education, 14(1), 31. https://doi.org/10.1186/1472-6920-14-31
dc.relationQuinn, D., Albrecht, A., Webby, B., & White, K. (2015). Learning from experience: the realities of developing mathematics courses for an online engineering programme. International Journal of Mathematical Education in Science and Technology, 46(7), 991–1003. https://doi.org/10.1080/0020739X.2015.1076895
dc.relationRabelo, T., Lama, M., Amorim, R. R., & Vidal, J. C. (2015). SmartLAK: A Big Data Architecture for Supporting Learning Analytics Services. Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE, 1–5. https://doi.org/10.1109/FIE.2015.7344147
dc.relationRadmehr, F., & Drake, M. (2018). An assessment-based model for exploring the solving of mathematical problems: Utilizing revised bloom’s taxonomy and facets of metacognition. Studies in Educational Evaluation, 59, 41–51. https://doi.org/10.1016/j.stueduc.2018.02.004
dc.relationRakoczy, K., Pinger, P., Hochweber, J., Klieme, E., Schütze, B., & Besser, M. (2018). Formative assessment in mathematics: Mediated by feedback’s perceived usefulness and students’ self-efficacy. Learning and Instruction. https://doi.org/10.1016/j.learninstruc.2018.01.004
dc.relationRakoczy, K., Pinger, P., Hochweber, J., Klieme, E., Schütze, B., & Besser, M. (2019). Formative assessment in mathematics: Mediated by feedback’s perceived usefulness and students’ self-efficacy. Learning and Instruction, 60(January 2018), 154–165. https://doi.org/10.1016/j.learninstruc.2018.01.004
dc.relationReigeluth, C. M. (1983). Meaningfulness and instruction: Relating what is being learned to what a student knows. Instructional Science, 12(3), 197–218. https://doi.org/10.1007/BF00051745
dc.relationRetalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S., & Kargidis, T. (2006). Towards Networked Learning Analytics – A concept and a tool. 1–8.
dc.relationRojas, I. G., & García, R. M. C. (2012). Towards efficient provision of feedback supported by learning analytics. 12th IEEE International Conference on Advanced Learning Technologies, ICALT, 599–603.
dc.relationRoldan, J. F., & Atehortúa, R. (2016). La Educación Virtual un Campo para el Análisis: Ventajas y Desventajas.
dc.relationRoman, D. (2011). Impacto del e-learning en el proceso de aprendizaje de estudiantes de educación a distancia Propuesta de diseño de metodología para evaluar el impacto del e-learning en el proceso de aprendizaje de estudiantes de educación a distancia. Universidad Nacional de Colombia.
dc.relationRomero, C., López, M. I., Luna, J. M., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers and Education, 68, 458–472. https://doi.org/10.1016/j.compedu.2013.06.009
dc.relationRomero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers and Education, 51(1), 368–384. https://doi.org/10.1016/j.compedu.2007.05.016
dc.relationRubio, F., Thomas, J. M., & Li, Q. (2017). The role of teaching presence and student participation in Spanish blended courses. Computer Assisted Language Learning, 8221(November), 1–25. https://doi.org/10.1080/09588221.2017.1372481
dc.relationRuipérez-Valiente, J. A., Muñoz-Merino, P. J., Leony, D., & Delgado Kloos, C. (2015). ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform. Computers in Human Behavior, 47, 139–148. https://doi.org/10.1016/j.chb.2014.07.002
dc.relationSchildkamp, K., van der Kleij, F. M., Heitink, M. C., Kippers, W. B., & Veldkamp, B. P. (2020). Formative assessment: A systematic review of critical teacher prerequisites for classroom practice. International Journal of Educational Research, 103(May), 101602. https://doi.org/10.1016/j.ijer.2020.101602
dc.relationSerrano-Laguna, Á., Torrente, J., Moreno-Ger, P., & Manjón, B. F. (2012). Tracing a little for big improvements: Application of learning analytics and videogames for student assessment. Procedia Computer Science, 15, 203–209. https://doi.org/10.1016/j.procs.2012.10.072
dc.relationShen, C., & Kuo, C.-J. (2015). Learning in massive open online courses: Evidence from social media mining. Computers in Human Behavior, 51, 568–577. https://doi.org/10.1016/j.chb.2015.02.066
dc.relationShimada, A., Mouri, K., & Ogata, H. (2017). Real-Time Learning Analytics of e-Book Operation Logs for On-site Lecture Support. Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017, 274–275. https://doi.org/10.1109/ICALT.2017.74
dc.relationSiemens, G., & Latour, —bruno. (2015). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
dc.relationSin, K., & Muthu, L. (2015). Application of big data in education data mining and learning analytics-A literature review. Ictact Journal on Soft Computing: Special Issue on Soft Computing Models for Big Data, 5(4), 1035–1049.
dc.relationSingleton, A. D. (2009). Data mining course choice sets and behaviours for target marketing of higher education. Journal of Targeting, Measurement and Analysis for Marketing, 17(3), 157–170. https://doi.org/10.1057/jt.2009.13
dc.relationSladek, R. M., Bond, M. J., Frost, L. K., Prior, K. N., Prideaux, D., Roberts, C., Eva, K., Centeno, A., McCrorie, P., McManus, C., Mercer, A., Puddey, I., Ferguson, E., James, D., Madeley, L., Patterson, F., Knight, A., Dowell, J., Nicholson, S., … Rosenthal, J. (2016). Predicting success in medical school: a longitudinal study of common Australian student selection tools. BMC Medical Education, 16(1), 187. https://doi.org/10.1186/s12909-016-0692-3
dc.relationSnodgrass Rangel, V., Bell, E. R., Monroy, C., & Whitaker, J. R. (2015). Toward a New Approach to the Evaluation of a Digital Curriculum Using Learning Analytics. Journal of Research on Technology in Education, 47(2), 89–104. https://doi.org/10.1080/15391523.2015.999639
dc.relationSrilekshmi, M., Sindhumol, S., Chatterjee, S., & Bijlani, K. (2017). Learning Analytics to Identify Students At-risk in MOOCs. Proceedings - IEEE 8th International Conference on Technology for Education, T4E 2016, 194–199. https://doi.org/10.1109/T4E.2016.048
dc.relationTempelaar, D., Rienties, B., Mittelmeier, J., & Nguyen, Q. (2018). Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior, 78, 408–420. https://doi.org/10.1016/j.chb.2017.08.010
dc.relationTempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167. https://doi.org/10.1016/j.chb.2014.05.038
dc.relationTeresinha, M., & Steiner, A. (2016). Proposed methodology for the creation of a classification label : a school performance case study. 177–191.
dc.relationThai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., & Schmidt-Thieme, L. (2010). Recommender system for predicting student performance. Procedia Computer Science, 1(2), 2811–2819. https://doi.org/10.1016/j.procs.2010.08.006
dc.relationTlili, A., Essalmi, F., Ayed, L. J. Ben, Jemni, M., & Kinshuk. (2017). A Smart Educational Game to Model Personality Using Learning Analytics. 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), 131–135. https://doi.org/10.1109/ICALT.2017.65
dc.relationTlili, A., Essalmi, F., Jemni, M., & Kinshuk. (2015). An educational game for teaching computer architecture: Evaluation using learning analytics. 2015 5th International Conference on Information & Communication Technology and Accessibility (ICTA), 1–6. https://doi.org/10.1109/ICTA.2015.7426881
dc.relationTormey, R., Hardebolle, C., Pinto, F., & Jermann, P. (2020). Designing for impact: a conceptual framework for learning analytics as self-assessment tools. Assessment and Evaluation in Higher Education, 45(6), 901–911. https://doi.org/10.1080/02602938.2019.1680952
dc.relationTseng, S.-F., Tsao, Y.-W., Yu, L.-C., Chan, C.-L., & Lai, K. R. (2016). Who will pass? Analyzing learner behaviors in MOOCs. Research & Practice in Technology Enhanced Learning, 11(1), 1–11. https://doi.org/10.1186/s41039-016-0033-5
dc.relationvan den Herik, H. J., Plaat, A., Levy, D. N. L., & Dimov, D. (2014). Plagiarism in game programming competitions. Entertainment Computing, 5(3), 173–187. https://doi.org/10.1016/j.entcom.2014.02.002
dc.relationVan der Kleij, F. M. (2019). Comparison of teacher and student perceptions of formative assessment feedback practices and association with individual student characteristics. Teaching and Teacher Education, 85, 175–189. https://doi.org/10.1016/j.tate.2019.06.010
dc.relationVan Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2014). Supporting teachers in guiding collaborating students: Effects of learning analytics in CSCL. Computers and Education, 79, 28–39. https://doi.org/10.1016/j.compedu.2014.07.007
dc.relationVan Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2015). Teacher regulation of cognitive activities during student collaboration: Effects of learning analytics. Computers and Education, 90, 80–94. https://doi.org/10.1016/j.compedu.2015.09.006
dc.relationvan Leeuwen, A., van Wermeskerken, M., Erkens, G., & Rummel, N. (2017). Measuring teacher sense making strategies of learning analytics: a case study. Learning: Research and Practice, 3(1), 42–58. https://doi.org/10.1080/23735082.2017.1284252
dc.relationVirvou, M., Alepis, E., & Sidiropoulos, S. C. (2016). A learning analytics tool for supporting teacher decision. IISA 2015 - 6th International Conference on Information, Intelligence, Systems and Applications, 8–10. https://doi.org/10.1109/IISA.2015.7388012
dc.relationWang, S. M. (2014). A module-based learning analytics system for facebook supported collaborative creativity learning. Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014, 495–496. https://doi.org/10.1109/ICALT.2014.146
dc.relationXing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181. https://doi.org/10.1016/j.chb.2014.09.034
dc.relationYasmin. (2013). Application of the classification tree model in predicting learner dropout behaviour in open and distance learning. Distance Education, 34(2), 218–231. https://doi.org/10.1080/01587919.2013.793642
dc.relationYassine, S., Kadry, S., & Sicilia, M. (2016). A Framework for Learning Analytics in Moodle for Assessing Course Outcomes A Framework for Learning Analytics in Moodle for Assessing Course Outcomes. April, 261–266. https://doi.org/10.1109/EDUCON.2016.7474563
dc.relationYeh, Y. C. (2009). Integrating e-learning into the direct-instruction model to enhance the effectiveness of critical-thinking instruction. Instructional Science, 37(2), 185–203. https://doi.org/10.1007/s11251-007-9048-z
dc.relationYi, B., Wang, Y., Zhang, D., Liu, H., Shu, J., Zhang, Z., & Lv, Y. (2017). Learning Analytics-Based Evaluation Mode for Blended Learning and Its Applications. 2017 International Symposium on Educational Technology (ISET), 147–149. https://doi.org/10.1109/ISET.2017.42
dc.relationZakharov, A., & Carnoy, M. (2015). Are teachers accurate in predicting their students’ performance on high stakes’ exams? The case of Russia. International Journal of Educational Development, 43, 1–11. https://doi.org/10.1016/j.ijedudev.2015.04.007
dc.relationZengin, K., Esgi, N., Erginer, E., & Aksoy, M. E. (2011). A sample study on applying data mining research techniques in educational science: Developing a more meaning of data. Procedia - Social and Behavioral Sciences, 15, 4028–4032. https://doi.org/10.1016/j.sbspro.2011.04.408
dc.relationZhai, X., Li, M., & Guo, Y. (2018). Teachers’ use of learning progression-based formative assessment to inform teachers’ instructional adjustment: a case study of two physics teachers’ instruction. International Journal of Science Education, 40(15), 1832–1856. https://doi.org/10.1080/09500693.2018.1512772
dc.relationZhang, X., Meng, Y., Ordóñez de Pablos, P., & Sun, Y. (2017). Learning analytics in collaborative learning supported by Slack: From the perspective of engagement. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2017.08.012
dc.relationZhou, Q., Han, X., Yang, J., & Cheng, J. (2014). Design and implementation of learning analytics system for teachers and learners based on the specified LMS. Proceedings - 2014 International Conference of Educational Innovation Through Technology, EITT 2014, 79–82. https://doi.org/10.1109/EITT.2014.21
dc.relationZhou, X., Wu, B., & Jin, Q. (2015). Open Learning Platform Based on Personal and Social Analytics for Individualized Learning Support. 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 1741–1745. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.316
dc.relationZhu, M., Liu, O. L., & Lee, H. S. (2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers and Education, 143(September 2018), 103668. https://doi.org/10.1016/j.compedu.2019.103668
dc.rightsReconocimiento 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleModelo de evaluación formativa en cursos virtuales de Matemáticas y su aplicación en analítica de aprendizaje
dc.typeTesis


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