Prerequisite Graph Extraction from Lectures

Kód výsledku v IS VaVaI

RIV/00216208:11320/22:BS8MU3TQ - isvavai.cz

Výsledek na webu

https://link.springer.com/chapter/10.1007/978-3-031-11647-6_128

DOI - Digital Object Identifier

10.1007/978-3-031-11647-6_128

Jazyk výsledku

angličtina

Název v původním jazyce

Prerequisite Graph Extraction from Lectures

Popis výsledku v původním jazyce

The proliferation of video-sharing platforms and MOOCs has raised new challenges in the field of education. A challenging topic that is gaining an increased popularity is the identification of prerequisite relations between concepts in video lectures. In this paper, we propose unsupervised methods for prerequisite identification and the creation of a prerequisite graph. The contribution, compared to existing approaches, is the development of methods which (i) do not rely on external knowledge, (ii) do not require extensive training, and (iii) are intended to exploit both the lecture transcript and its visual features. Results from the preliminary evaluation are encouraging, and provide insights on the extraction of prerequisite relations from video transcripts compared to textbooks.

Název v anglickém jazyce

Prerequisite Graph Extraction from Lectures

Popis výsledku anglicky

The proliferation of video-sharing platforms and MOOCs has raised new challenges in the field of education. A challenging topic that is gaining an increased popularity is the identification of prerequisite relations between concepts in video lectures. In this paper, we propose unsupervised methods for prerequisite identification and the creation of a prerequisite graph. The contribution, compared to existing approaches, is the development of methods which (i) do not rely on external knowledge, (ii) do not require extensive training, and (iii) are intended to exploit both the lecture transcript and its visual features. Results from the preliminary evaluation are encouraging, and provide insights on the extraction of prerequisite relations from video transcripts compared to textbooks.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium

ISBN

978-3-031-11647-6

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

616-619

Název nakladatele

Springer International Publishing

Místo vydání

—

Místo konání akce

Cham

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000877459600127