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Exploring exam strategies of successful first year engineering students

Published: 23 March 2020 Publication History

Abstract

At present, universities collect study-related data about their students. This information can be used to support students at risk of failing their studies. At the Faculty of Mechanical Engineering (FME), Czech Technical University in Prague (CTU), the group of the first-year students is the most vulnerable. The most critical part of the first year is the winter exam period when students usually divide into those who will pass and fail. One of the most important abilities, students need to learn, is exam planning, and our research aims at the exploration of the exam strategies of successful students. These strategies can be used for improving first-year students retention. The outgoing research on the analysis of exam strategies of the first-year students in the academic year 2017/2018 is reported. From a total of 361 first-year students, successful students have been selected. The successful student is the one who finished all three mandatory exams before the end of the first exam period. From the exam sequences of 153 selected students, a "layered" Markov chain probabilistic model has been constructed. It uncovered the most common exam strategies taken by those students.

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Cited By

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  • (2024)Enhancing Student Discussion Forum Analysis Through Natural Language ProcessingDigital Transformation in Education and Artificial Intelligence Application10.1007/978-3-031-62058-4_2(14-26)Online publication date: 3-Jul-2024
  • (2022)Mining Sequential Learning Trajectories With Hidden Markov Models For Early Prediction of At-Risk Students in E-Learning EnvironmentsIEEE Transactions on Learning Technologies10.1109/TLT.2022.319748615:6(783-797)Online publication date: 1-Dec-2022

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LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
March 2020
679 pages
ISBN:9781450377126
DOI:10.1145/3375462
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 March 2020

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Author Tags

  1. Markov chains
  2. exam strategies
  3. modelling

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  • Short-paper

Funding Sources

  • Grantová Agentura ðeské Republiky

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LAK '20

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LAK '20 Paper Acceptance Rate 80 of 261 submissions, 31%;
Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

View all
  • (2024)Enhancing Student Discussion Forum Analysis Through Natural Language ProcessingDigital Transformation in Education and Artificial Intelligence Application10.1007/978-3-031-62058-4_2(14-26)Online publication date: 3-Jul-2024
  • (2022)Mining Sequential Learning Trajectories With Hidden Markov Models For Early Prediction of At-Risk Students in E-Learning EnvironmentsIEEE Transactions on Learning Technologies10.1109/TLT.2022.319748615:6(783-797)Online publication date: 1-Dec-2022

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