DEEP LEARNING-BASED EDUCATION DECISION SUPPORT SYSTEM FOR STUDENT E-LEARNING PERFORMANCE PREDICTION

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020673" target="_blank" >RIV/62690094:18450/23:50020673 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scpe.org/index.php/scpe/article/view/2188" target="_blank" >https://www.scpe.org/index.php/scpe/article/view/2188</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.12694/scpe.v24i3.2188" target="_blank" >10.12694/scpe.v24i3.2188</a>

Jazyk výsledku

angličtina

Název v původním jazyce

DEEP LEARNING-BASED EDUCATION DECISION SUPPORT SYSTEM FOR STUDENT E-LEARNING PERFORMANCE PREDICTION

Popis výsledku v původním jazyce

Information Technology (IT) and its advancements change the education environment. Conventional classroom education has been transformed into a modernized form. Education field decision-makers are always searching for new technologies that provide fast solutions to support Education Decision Support Systems (EDSS). There is a significant need for an effective decision support system to utilize student data which helps the university in making the right decisions. The Electronic learning system (e-learning) provides a live forum for faculties and students to connect with learning portals and virtually execute educational activities. Even though these modern approaches support the education system, active student participation still needs to be improved. Moreover, accurately measuring student performance using collected attributes remains difficult for parents and teachers. Therefore, this paper seeks to understand and predict student performance using effective data processing and a deep learning-based decision model. The implementation of EDSS starts with data preprocessing, Extraction-Transformation-Load (ETL), a data mart area to store the extracted data with Online Analytical Processing (OLAP) processing, and decision-making using Deep Graph Convolutional Neural Network (DGCNN). The statistical evaluation is based on the student dataset from the Kaggle repository. The analyzed results depict that the proposed EDSS model on an independent data mart with efficient decision support and OLAP provides a better platform to make academic decisions and help educators to make necessary decisions notified to the students. © 2023 SCPE.

Název v anglickém jazyce

DEEP LEARNING-BASED EDUCATION DECISION SUPPORT SYSTEM FOR STUDENT E-LEARNING PERFORMANCE PREDICTION

Popis výsledku anglicky

Information Technology (IT) and its advancements change the education environment. Conventional classroom education has been transformed into a modernized form. Education field decision-makers are always searching for new technologies that provide fast solutions to support Education Decision Support Systems (EDSS). There is a significant need for an effective decision support system to utilize student data which helps the university in making the right decisions. The Electronic learning system (e-learning) provides a live forum for faculties and students to connect with learning portals and virtually execute educational activities. Even though these modern approaches support the education system, active student participation still needs to be improved. Moreover, accurately measuring student performance using collected attributes remains difficult for parents and teachers. Therefore, this paper seeks to understand and predict student performance using effective data processing and a deep learning-based decision model. The implementation of EDSS starts with data preprocessing, Extraction-Transformation-Load (ETL), a data mart area to store the extracted data with Online Analytical Processing (OLAP) processing, and decision-making using Deep Graph Convolutional Neural Network (DGCNN). The statistical evaluation is based on the student dataset from the Kaggle repository. The analyzed results depict that the proposed EDSS model on an independent data mart with efficient decision support and OLAP provides a better platform to make academic decisions and help educators to make necessary decisions notified to the students. © 2023 SCPE.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF19_073%2F0016949" target="_blank" >EF19_073/0016949: Rozvoj interní grantové agentury Univerzity Hradec Králové</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Scalable Computing

ISSN

1895-1767

e-ISSN

1895-1767

Svazek periodika

24

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

RO - Rumunsko

Počet stran výsledku

12

Strana od-do

327-338

Kód UT WoS článku

001077849000009

EID výsledku v databázi Scopus

2-s2.0-85171265435