| 000 -LEADER |
|---|
| fixed length control field |
a |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20250106114215.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250106b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
24967 |
| Author |
Kaur, R. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Quantitative approach for appraising quality of online education. |
| 250 ## - EDITION STATEMENT |
|---|
| Volume, Issue number |
Vol.38(2), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Sangli |
| Name of publisher, distributor, etc. |
K.E. Society's Rajarambapu Institute of Technology |
| Year |
2024 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Pagination |
17-33p. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Conventional approaches fall short of managing the current scale of data, underscoring the significance of big data analytics in the field of data science. The study highlights the importance of big data analytics in data science, focusing on sentiment analysis and developing a machine learning framework for identifying sentiments in product reviews and assessing Massive Open Online Courses (MOOCs). Online education proved its value to the academic community during the COVID-19 pandemic, when much of the world was at a standstill. Since the coronavirus outbreak, Massive Open Online Courses (MOOCs) have taken over many public schools and undergraduate degree programs as the new norm. Nowadays, online courses are being incorporated into traditional educational programs. Therefore, it has become crucial to develop a framework that both government and nongovernment organizations could use to assess public opinion before implementing online education programs. This article offers a methodology for utilizing deep learning and natural language processing techniques to examine public sentiments toward online education and courses such as MOOCs.Public reviews from Coursera and Udemy are utilized to assess the quality of online courses. Using sentiment analysis, the proposed methodology employs the Bidirectional Encoder Representations from Transformers (BERT) model to classify public reviews into positive and negative categories. Positive and negative reviews are clustered using the k-means algorithm to evaluate the quality of MOOCs. The study's findings indicate that MOOCs with qualified professors are beneficial for learning basic concepts. But they may lack advanced knowledge, real-world experience, and enough examples. These courses are lengthy, and students request more questions after lessons to comprehend the concept. The proposed framework assists in assessing public opinion before implementing online education and may provide valuable insight to the developers of the courses. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
4642 |
| Topical term or geographic name entry element |
Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
7169 |
| Co-Author |
Sharma, R. |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Journal of engineering education transformations (JEET) |
| International Standard Serial Number |
2349-2473 |
| Place, publisher, and date of publication |
Sangli Rajarambapu Institute Of Technology |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| URL |
https://journaleet.in/articles/a-quantitative-approach-for-appraising-quality-of-online-education |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
