Details
| Original language | English |
|---|---|
| Title of host publication | Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings |
| Editors | Carlos Alario-Hoyos, María Jesús Rodríguez-Triana, Maren Scheffel, Inmaculada Arnedillo-Sánchez, Sebastian Maximilian Dennerlein |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 455-460 |
| Number of pages | 6 |
| ISBN (print) | 9783030577162 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 15th European Conference on Technology Enhanced Learning, EC-TEL 2020 - Heidelberg, Germany Duration: 14 Sept 2020 → 18 Sept 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12315 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
OERs have high-potential to satisfy learners in many different circumstances, as they are available in a wide range of contexts. However, the low-quality of OER metadata, in general, is one of the main reasons behind the lack of personalised, OER based services such as search and recommendation. As a result, the applicability of OERs remains limited. Nevertheless, OER metadata about covered topics (subjects) is essentially required by learners to build effective learning pathways towards their individual learning objectives. Therefore, in this paper, we report on a work in progress project proposing an OER topic extraction approach, applying text mining techniques, to generate high-quality OER metadata about topic distribution. This is done by: 1) collecting 27 lectures from Coursera and Khan Academy in the area of an important skill in the area of Data Science (i.e. Text Mining as our first focus), 2) applying Latent Dirichlet Allocation (LDA) on the collected resources in order to extract existing topics related to the skill, and 3) defining topic distributions covered by a particular OER. To evaluate our model, we used the data-set of educational resources from Youtube, and compared our topic distribution results with their manually defined target topics with the help of 3 experts in the area of data science. As a result, our model extracted topics with 76% of F1-score.
Keywords
- Machine learning, OER, Open Educational Resource, Text mining, Topic extraction
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings. ed. / Carlos Alario-Hoyos; María Jesús Rodríguez-Triana; Maren Scheffel; Inmaculada Arnedillo-Sánchez; Sebastian Maximilian Dennerlein. Springer Science and Business Media Deutschland GmbH, 2020. p. 455-460 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12315 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Extracting topics from open educational resources
AU - Molavi, Mohammadreza
AU - Tavakoli, Mohammadreza
AU - Kismihók, Gábor
PY - 2020
Y1 - 2020
N2 - OERs have high-potential to satisfy learners in many different circumstances, as they are available in a wide range of contexts. However, the low-quality of OER metadata, in general, is one of the main reasons behind the lack of personalised, OER based services such as search and recommendation. As a result, the applicability of OERs remains limited. Nevertheless, OER metadata about covered topics (subjects) is essentially required by learners to build effective learning pathways towards their individual learning objectives. Therefore, in this paper, we report on a work in progress project proposing an OER topic extraction approach, applying text mining techniques, to generate high-quality OER metadata about topic distribution. This is done by: 1) collecting 27 lectures from Coursera and Khan Academy in the area of an important skill in the area of Data Science (i.e. Text Mining as our first focus), 2) applying Latent Dirichlet Allocation (LDA) on the collected resources in order to extract existing topics related to the skill, and 3) defining topic distributions covered by a particular OER. To evaluate our model, we used the data-set of educational resources from Youtube, and compared our topic distribution results with their manually defined target topics with the help of 3 experts in the area of data science. As a result, our model extracted topics with 76% of F1-score.
AB - OERs have high-potential to satisfy learners in many different circumstances, as they are available in a wide range of contexts. However, the low-quality of OER metadata, in general, is one of the main reasons behind the lack of personalised, OER based services such as search and recommendation. As a result, the applicability of OERs remains limited. Nevertheless, OER metadata about covered topics (subjects) is essentially required by learners to build effective learning pathways towards their individual learning objectives. Therefore, in this paper, we report on a work in progress project proposing an OER topic extraction approach, applying text mining techniques, to generate high-quality OER metadata about topic distribution. This is done by: 1) collecting 27 lectures from Coursera and Khan Academy in the area of an important skill in the area of Data Science (i.e. Text Mining as our first focus), 2) applying Latent Dirichlet Allocation (LDA) on the collected resources in order to extract existing topics related to the skill, and 3) defining topic distributions covered by a particular OER. To evaluate our model, we used the data-set of educational resources from Youtube, and compared our topic distribution results with their manually defined target topics with the help of 3 experts in the area of data science. As a result, our model extracted topics with 76% of F1-score.
KW - Machine learning
KW - OER
KW - Open Educational Resource
KW - Text mining
KW - Topic extraction
UR - http://www.scopus.com/inward/record.url?scp=85091181893&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2006.11109
DO - 10.48550/arXiv.2006.11109
M3 - Conference contribution
AN - SCOPUS:85091181893
SN - 9783030577162
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 455
EP - 460
BT - Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Proceedings
A2 - Alario-Hoyos, Carlos
A2 - Rodríguez-Triana, María Jesús
A2 - Scheffel, Maren
A2 - Arnedillo-Sánchez, Inmaculada
A2 - Dennerlein, Sebastian Maximilian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020
Y2 - 14 September 2020 through 18 September 2020
ER -