ScaffoMapping: Assisting concept mapping for video learners

Shan Zhang; Xiaojun Meng; Can Liu; Shengdong Zhao; Vibhor Sehgal; Morten Fjeld

doi:10.1007/978-3-030-29384-0_20

ScaffoMapping: Assisting concept mapping for video learners

Shan Zhang^*, Xiaojun Meng, Can Liu, Shengdong Zhao, Vibhor Sehgal, Morten Fjeld

^*Corresponding author for this work

School of Creative Media

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

7 Citations (Scopus)

Abstract

Previous research has shown that having learners construct concept maps can bring better learning outcome. However, in video learning scenario, there is not sufficient support for learners to create concept maps from educational videos. Through a preliminary study, we identified two main difficulties video learners face in creating concept maps: navigation difficulty and learning difficulty. To help users to overcome such difficulties, we design scaffolds to assist learners in concept mapping. We present ScaffoMapping, a system aiming for scaffolded concept map creation on educational videos through automatic concept extraction and timestamp generation. Our study, which compares ScaffoMapping with the baseline approach, shows that (1) Learners can create higher quality concept maps with ScaffoMapping. (2) ScaffoMapping enables better learning outcomes in video learning scenario.

Original language	English
Title of host publication	Human-Computer Interaction – INTERACT 2019 - 17th IFIP TC 13 International Conference, Proceedings
Editors	David James, Fernando Loizides, Lennart Nacke, Helen Petrie, Marco Winckler, Panayiotis Zaphiris
Publisher	Springer, Cham
Pages	314-328
ISBN (Electronic)	978-3-030-29387-1
ISBN (Print)	978-3-030-29386-4
DOIs	https://doi.org/10.1007/978-3-030-29384-0_20
Publication status	Published - Sept 2019
Event	17th IFIP TC13 International Conference on Human-Computer Interaction, INTERACT 2019 - Paphos, Cyprus Duration: 2 Sept 2019 → 6 Sept 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11747 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th IFIP TC13 International Conference on Human-Computer Interaction, INTERACT 2019
Place	Cyprus
City	Paphos
Period	2/09/19 → 6/09/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Research Keywords

Concept map
Scaffolding
Video learning

Access Output

10.1007/978-3-030-29384-0_20

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Permanent Link

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Cite this

Zhang, S., Meng, X., Liu, C., Zhao, S., Sehgal, V., & Fjeld, M. (2019). ScaffoMapping: Assisting concept mapping for video learners. In D. James, F. Loizides, L. Nacke, H. Petrie, M. Winckler, & P. Zaphiris (Eds.), Human-Computer Interaction – INTERACT 2019 - 17th IFIP TC 13 International Conference, Proceedings (pp. 314-328). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11747 LNCS). Springer, Cham. https://doi.org/10.1007/978-3-030-29384-0_20

Zhang, Shan ; Meng, Xiaojun ; Liu, Can et al. / ScaffoMapping : Assisting concept mapping for video learners. Human-Computer Interaction – INTERACT 2019 - 17th IFIP TC 13 International Conference, Proceedings. editor / David James ; Fernando Loizides ; Lennart Nacke ; Helen Petrie ; Marco Winckler ; Panayiotis Zaphiris. Springer, Cham, 2019. pp. 314-328 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "ScaffoMapping: Assisting concept mapping for video learners",

abstract = "Previous research has shown that having learners construct concept maps can bring better learning outcome. However, in video learning scenario, there is not sufficient support for learners to create concept maps from educational videos. Through a preliminary study, we identified two main difficulties video learners face in creating concept maps: navigation difficulty and learning difficulty. To help users to overcome such difficulties, we design scaffolds to assist learners in concept mapping. We present ScaffoMapping, a system aiming for scaffolded concept map creation on educational videos through automatic concept extraction and timestamp generation. Our study, which compares ScaffoMapping with the baseline approach, shows that (1) Learners can create higher quality concept maps with ScaffoMapping. (2) ScaffoMapping enables better learning outcomes in video learning scenario.",

keywords = "Concept map, Scaffolding, Video learning",

author = "Shan Zhang and Xiaojun Meng and Can Liu and Shengdong Zhao and Vibhor Sehgal and Morten Fjeld",

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booktitle = "Human-Computer Interaction – INTERACT 2019 - 17th IFIP TC 13 International Conference, Proceedings",

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Zhang, S, Meng, X, Liu, C , Zhao, S, Sehgal, V & Fjeld, M 2019, ScaffoMapping: Assisting concept mapping for video learners. in D James, F Loizides, L Nacke, H Petrie, M Winckler & P Zaphiris (eds), Human-Computer Interaction – INTERACT 2019 - 17th IFIP TC 13 International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11747 LNCS, Springer, Cham, pp. 314-328, 17th IFIP TC13 International Conference on Human-Computer Interaction, INTERACT 2019, Paphos, Cyprus, 2/09/19. https://doi.org/10.1007/978-3-030-29384-0_20

ScaffoMapping: Assisting concept mapping for video learners. / Zhang, Shan; Meng, Xiaojun; Liu, Can et al.
Human-Computer Interaction – INTERACT 2019 - 17th IFIP TC 13 International Conference, Proceedings. ed. / David James; Fernando Loizides; Lennart Nacke; Helen Petrie; Marco Winckler; Panayiotis Zaphiris. Springer, Cham, 2019. p. 314-328 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11747 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - ScaffoMapping

T2 - 17th IFIP TC13 International Conference on Human-Computer Interaction, INTERACT 2019

AU - Zhang, Shan

AU - Meng, Xiaojun

AU - Liu, Can

AU - Zhao, Shengdong

AU - Sehgal, Vibhor

AU - Fjeld, Morten

PY - 2019/9

Y1 - 2019/9

N2 - Previous research has shown that having learners construct concept maps can bring better learning outcome. However, in video learning scenario, there is not sufficient support for learners to create concept maps from educational videos. Through a preliminary study, we identified two main difficulties video learners face in creating concept maps: navigation difficulty and learning difficulty. To help users to overcome such difficulties, we design scaffolds to assist learners in concept mapping. We present ScaffoMapping, a system aiming for scaffolded concept map creation on educational videos through automatic concept extraction and timestamp generation. Our study, which compares ScaffoMapping with the baseline approach, shows that (1) Learners can create higher quality concept maps with ScaffoMapping. (2) ScaffoMapping enables better learning outcomes in video learning scenario.

AB - Previous research has shown that having learners construct concept maps can bring better learning outcome. However, in video learning scenario, there is not sufficient support for learners to create concept maps from educational videos. Through a preliminary study, we identified two main difficulties video learners face in creating concept maps: navigation difficulty and learning difficulty. To help users to overcome such difficulties, we design scaffolds to assist learners in concept mapping. We present ScaffoMapping, a system aiming for scaffolded concept map creation on educational videos through automatic concept extraction and timestamp generation. Our study, which compares ScaffoMapping with the baseline approach, shows that (1) Learners can create higher quality concept maps with ScaffoMapping. (2) ScaffoMapping enables better learning outcomes in video learning scenario.

KW - Concept map

KW - Scaffolding

KW - Video learning

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85072988471&origin=recordpage

U2 - 10.1007/978-3-030-29384-0_20

DO - 10.1007/978-3-030-29384-0_20

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-030-29386-4

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 314

EP - 328

BT - Human-Computer Interaction – INTERACT 2019 - 17th IFIP TC 13 International Conference, Proceedings

A2 - James, David

A2 - Loizides, Fernando

A2 - Nacke, Lennart

A2 - Petrie, Helen

A2 - Winckler, Marco

A2 - Zaphiris, Panayiotis

PB - Springer, Cham

Y2 - 2 September 2019 through 6 September 2019

ER -

Zhang S, Meng X, Liu C , Zhao S, Sehgal V, Fjeld M. ScaffoMapping: Assisting concept mapping for video learners. In James D, Loizides F, Nacke L, Petrie H, Winckler M, Zaphiris P, editors, Human-Computer Interaction – INTERACT 2019 - 17th IFIP TC 13 International Conference, Proceedings. Springer, Cham. 2019. p. 314-328. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-29384-0_20

ScaffoMapping: Assisting concept mapping for video learners

Abstract

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UN SDGs

Research Keywords

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