Predictive modeling for student grade prediction using machine learning and visual analytics

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Predictive modeling for student grade prediction using machine learning and visual analytics

Popis výsledku v původním jazyce

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&apos; 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&apos; 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.

Název v anglickém jazyce

Predictive modeling for student grade prediction using machine learning and visual analytics

Popis výsledku anglicky

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&apos; 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&apos; 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-64368-114-6

ISSN

0922-6389

e-ISSN

—

Počet stran výsledku

11

Strana od-do

32-42

Název nakladatele

IOS Press BV

Místo vydání

Amsterdam

Místo konání akce

Japonsko

Datum konání akce

22. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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