DATA ANALYTICS OF STUDENTS CONTINUOUS ASSESSMENT ACTIVITY DATA

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DATA ANALYTICS OF STUDENTS CONTINUOUS ASSESSMENT ACTIVITY DATA

K. Calderón, M. Serrano, N. Serrano, C. Blanco (2021).  DATA ANALYTICS OF STUDENTS CONTINUOUS ASSESSMENT ACTIVITY DATA. 10.
2021 The 17th CDIO International Conference, RMUTT and Chulalongkorn University, Thailand
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Codex is a web-based tool for the online edition of theoretical and practical teaching content, and for assessment of STEM subjects. It has been developed at TECNUN, the Engineering School of the University of Navarra. The Codex application helps teachers to promote active learning (Standard 8) and continuous assessment (Standard 11) without increasing the teacher's workload. Codex is being implemented in the classrooms, which opens another door for the improvement of the learning experience. Codex stores a significant amount of data from each student, which can be used both by the teacher to adapt his or her teaching method and by the student to see what his or her strengths and weaknesses are and be counseled personally. All of this is supported by up-to-date data. The aim of this project is to apply different Data Analytics and Machine Learning methods to the obtained data in the application from a subject called Digital Technology, in order to obtain a prediction of students’ grades and performance at each moment of the course, based on his/her behavior and that of previous years' students. This allows the teachers to know information related to performance of their class, and the students, to see towards what result they are heading.

Authors (New): 
Kevin Calderón
María Serrano
Nicolás Serrano
Carmen Blanco
Pages: 
10
Affiliations: 
University of Navarra, Spain
Keywords: 
Learning analytics
continuous assessment
Automated assessment
CDIO Standard 8
CDIO Standard 11
Year: 
2021
Reference: 
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http://doi.org/10.1002/widm.1230
Jordan, S. (2009). Assessment for learning: pushing the boundaries of computer-based assessment. Cumbria (Vol. 3).: 
Larruson, J., & White, B. A. (2014). Learning analytics: From research to practice (1st ed.). Berlin, Germany: Springer.: 
Raschka, S., & Mirjalili, V. (2017). Python Machine Learning (2nd ed.). Packt.: 
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), 1–21.: 
http://doi.org/10.1002/widm.1355
Serrano, N., Blanco, C., Calderón, K., Gutiérrez, I., & Serrano, M. (2021). Continuous assessment with flipped learning and automated assessment. In Proceedings of 17th International CDIO Conference. Bangkok, Tailand.: 
Serrano, N., Blanco, C., Carias, F., & Reina, E. (2018). Information from Automated Evaluation in an Engineering School.: 
http://doi.org/10.4995/head18.2018.8132
on February 10, 2022 - 09:12   Maria Siiskonen
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