Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium |
| Untertitel | 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II |
| Herausgeber/-innen | Maria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 458-462 |
| Seitenumfang | 5 |
| Band | 2 |
| ISBN (Print) | 9783031116469 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 23rd International Conference on Artificial Intelligence in Education, AIED 2022 - Durham, Großbritannien / Vereinigtes Königreich Dauer: 27 Juli 2022 → 31 Juli 2022 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 13356 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Informal learning on the Web using search engines as well as more structured learning on Massive Open Online Course (MOOC) platforms have become very popular. However, the automatic assessment of this content with regard to the challenging task of predicting (potential) knowledge gain has not been addressed by previous work yet. In this paper, we investigate whether we can predict learning success after watching a specific type of MOOC video using 1) multimodal features, and 2) a wide range of text-based features describing the structure and content of the video. In a comprehensive experimental setting, we test four different classifiers and various feature subset combinations. We conduct a feature importance analysis to gain insights in which modality benefits knowledge gain prediction the most.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium : 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II. Hrsg. / Maria Mercedes Rodrigo; Noburu Matsuda; Alexandra I. Cristea; Vania Dimitrova. Band 2 Springer Science and Business Media Deutschland GmbH, 2022. S. 458-462 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13356 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Predicting Knowledge Gain for MOOC Video Consumption
AU - Otto, Christian
AU - Stamatakis, Markos
AU - Hoppe, Anett
AU - Ewerth, Ralph
PY - 2022
Y1 - 2022
N2 - Informal learning on the Web using search engines as well as more structured learning on Massive Open Online Course (MOOC) platforms have become very popular. However, the automatic assessment of this content with regard to the challenging task of predicting (potential) knowledge gain has not been addressed by previous work yet. In this paper, we investigate whether we can predict learning success after watching a specific type of MOOC video using 1) multimodal features, and 2) a wide range of text-based features describing the structure and content of the video. In a comprehensive experimental setting, we test four different classifiers and various feature subset combinations. We conduct a feature importance analysis to gain insights in which modality benefits knowledge gain prediction the most.
AB - Informal learning on the Web using search engines as well as more structured learning on Massive Open Online Course (MOOC) platforms have become very popular. However, the automatic assessment of this content with regard to the challenging task of predicting (potential) knowledge gain has not been addressed by previous work yet. In this paper, we investigate whether we can predict learning success after watching a specific type of MOOC video using 1) multimodal features, and 2) a wide range of text-based features describing the structure and content of the video. In a comprehensive experimental setting, we test four different classifiers and various feature subset combinations. We conduct a feature importance analysis to gain insights in which modality benefits knowledge gain prediction the most.
KW - Knowledge gain
KW - Resource quality
KW - Web learning
UR - http://www.scopus.com/inward/record.url?scp=85135885336&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2212.06679
DO - 10.48550/arXiv.2212.06679
M3 - Conference contribution
AN - SCOPUS:85135885336
SN - 9783031116469
VL - 2
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 458
EP - 462
BT - Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium
A2 - Rodrigo, Maria Mercedes
A2 - Matsuda, Noburu
A2 - Cristea, Alexandra I.
A2 - Dimitrova, Vania
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Artificial Intelligence in Education, AIED 2022
Y2 - 27 July 2022 through 31 July 2022
ER -