Predictive modeling for student grade prediction using machine learning and visual analytics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017204" target="_blank" >RIV/62690094:18450/20:50017204 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.3233/FAIA200550" target="_blank" >http://dx.doi.org/10.3233/FAIA200550</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3233/FAIA200550" target="_blank" >10.3233/FAIA200550</a>
Alternative languages
Result language
angličtina
Original language name
Predictive modeling for student grade prediction using machine learning and visual analytics
Original language description
Data-driven plays an important role in determining the quality of services in institutions of higher learning (HEIs). Increasingly data in education is encouraging institutions to find ways to improve student academic performance. By using machine learning with visual analytics, data can be predicted based on valuable information and presented with interactive visualizations for institutions to improve decision making. Therefore, predicting students' academic performance is critical to identifying students at risk of failing a course. In this paper, we propose two approaches, such as (i) a prediction model for predicting students' final grade based on machine learning that interacts with computational models; (ii) visual analytics to visualize predictive models and insightful data for educators. The data were tested using student achievement records collected from one of the Malaysian Polytechnic databases. The data set used in this study involved 489 first semester students in Computer System Architecture (CSA) course from 2016 to 2019. The decision tree algorithms (J48), Random Tree (RT), Random Forest (RF), and REPTree) was used on the student data set to produce the best predictions of the model. Experimental results show that J48 returns the highest accuracy with 99.8 %, among other algorithms. The findings of this study can help educators predict student success or failure for a particular course at the end of the semester and help educators make informed decisions to improve student academic performance at Polytechnic Malaysia. © 2020 The authors and IOS Press. All rights reserved.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Frontiers in Artificial Intelligence and Applications
ISBN
978-1-64368-114-6
ISSN
0922-6389
e-ISSN
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Number of pages
11
Pages from-to
32-42
Publisher name
IOS Press BV
Place of publication
Amsterdam
Event location
Japonsko
Event date
Oct 22, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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