Application of Semantic Analysis and LSTM-GRU in Developing a Personalized Course Recommendation System

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10250694" target="_blank" >RIV/61989100:27230/22:10250694 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000880874600001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000880874600001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app122110792" target="_blank" >10.3390/app122110792</a>

Result language

angličtina

Original language name

Application of Semantic Analysis and LSTM-GRU in Developing a Personalized Course Recommendation System

Original language description

The selection of elective courses based on an individual&apos;s domain interest is a challenging and critical activity for students at the start of their curriculum. Effective and proper recommendation may result in building a strong expertise in the domain of interest, which in turn improves the outcomes of the students getting better placements, and enrolling into higher studies of their interest, etc. In this paper, an effective course recommendation system is proposed to help the students in facilitating proper course selection based on an individual&apos;s domain interest. To achieve this, the core courses in the curriculum are mapped with the predefined domain suggested by the domain experts. These core course contents mapped with the domain are trained semantically using deep learning models to classify the elective courses into domains, and the same are recommended based on the student&apos;s domain expertise. The recommendation is validated by analyzing the number of elective course credits completed and the grades scored by a student who utilized the elective course recommendation system, with the grades scored by the student who was subjected to the assessment without elective course recommendations. It was also observed that after the recommendation, the students have registered for a greater number of credits for elective courses on their domain of expertise, which in-turn enables them to have a better learning experience and improved course completion probability.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20300 - Mechanical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

2076-3417

Volume of the periodical

12

Issue of the periodical within the volume

21

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

nestrankovano

UT code for WoS article

000880874600001

EID of the result in the Scopus database

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