First-Year Engineering Students’ Strategies for Taking Exams
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00359051" target="_blank" >RIV/68407700:21220/23:00359051 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21260/23:00359051 RIV/68407700:21730/23:00359051
Result on the web
<a href="https://doi.org/10.1007/s40593-022-00303-4" target="_blank" >https://doi.org/10.1007/s40593-022-00303-4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s40593-022-00303-4" target="_blank" >10.1007/s40593-022-00303-4</a>
Alternative languages
Result language
angličtina
Original language name
First-Year Engineering Students’ Strategies for Taking Exams
Original language description
Student drop-out is one of the most critical issues that higher educational institutions face nowadays. The problem is significant for first-year students. These freshmen are especially at risk of failing due to the transition from different educational settings at high school. Thanks to the massive boom of Information and Communication Technologies, universities have started to collect a vast amount of study- and student-related data. Teachers can use the collected information to support students at risk of failing their studies. At the Faculty of Mechanical Engineering, Czech Technical University in Prague, the situation is no different, and first-year students are a vulnerable group similar to other institutions. The most critical part of the first year is the first exam period. One of the essential skills the student needs to develop is planning for exams. The presented research aims to explore the exam-taking patterns of first-year students. Data of 361 first-year students have been analysed and used to construct “layered” Markov chain probabilistic graphs. The graphs have revealed interesting behavioural patterns within the groups of successful and unsuccessful students.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GJ18-04150Y" target="_blank" >GJ18-04150Y: Predictive modeling of student performance using learning resources</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Artificial Intelligence in Education
ISSN
1560-4292
e-ISSN
1560-4306
Volume of the periodical
2023
Issue of the periodical within the volume
33
Country of publishing house
CH - SWITZERLAND
Number of pages
26
Pages from-to
583-608
UT code for WoS article
000836369300001
EID of the result in the Scopus database
2-s2.0-85135338361