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A Data Visualization for Helping Students Decide Which General Education Courses to Enroll: Case of Chulalongkorn University

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11937))

Abstract

Chulalongkorn University has been utilizing information systems for course administration system, named CU-CAS, to help manage the course syllabus, course contents and course satisfaction survey. While current students have been selecting courses based on information from seniors and friends, we recognize that the data from CU-CAS could be useful in selecting course, but have not been fully utilized. Therefore, this project aims to design a data dashboard to help students select courses to register, based on the data from course satisfaction survey by students from the past three years of course offerings. In this work, we developed CU-CAS data visualization using Google Data Studio. Data were analyzed and presented the overall of the evaluation result in term of dashboard. According to our pilot study, students make decisions for enrollment by comparing the evaluation result in the past three years, in the form of different indicators. We also collected and analyzed data from the student blogs that review courses that they took using word cloud and Markov chain. Both data from CU-CAS and blogs will be represented to students to help students make decision in registering courses. This project is one of the e orts to utilize data in a way that is easy to understand to students, allow Chulalongkorn University to understand students learning behavior, and bring back to plan and adjust teaching strategies.

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Acknowledgement

This research was supported by the Learning Innovation Center (LIC), Chulalongkorn University.

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Correspondence to Nagul Cooharojananone .

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Cooharojananone, N. et al. (2019). A Data Visualization for Helping Students Decide Which General Education Courses to Enroll: Case of Chulalongkorn University. In: Rønningsbakk, L., Wu, TT., Sandnes, F., Huang, YM. (eds) Innovative Technologies and Learning. ICITL 2019. Lecture Notes in Computer Science(), vol 11937. Springer, Cham. https://doi.org/10.1007/978-3-030-35343-8_66

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  • DOI: https://doi.org/10.1007/978-3-030-35343-8_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35342-1

  • Online ISBN: 978-3-030-35343-8

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