Please listen to the presentation, read detailed slides and return to first post to make your comments below the corresponding paper author's post. Session Chair: Shun Okuhara Session Theme: Education and Support Session Number: 5 Paper No: 8 Session and Talk No: TS5-7 Type: Full Co-authors: Tessai Hayama and Shuma Sato Title: Supporting Concept-Map Creation in Video-Based Learning based on Concept-Map Components Provision

1. Supporting Concept-Map Creation in
Video-Based Learning based on
Concept-Map Components Provision
Tessai HAYAMA and Shuma SATO
Nagaoka University of Technology, Japan
KICSS 2020
25-26, November, 2020

2. Supporting Concept-Map Creation in Video-Based Learning
based on Concept-Map Components Provision
Comparison with the
previous methods
Conventional video-based
learning system makes a
learner passive learning.
The proposed system
encourages a learner to
learn in active and step-
by-step thinking way.
Our Approach
Creating concept-map-components( “concept-label”, “link-label”, and “a pair
of concept-labels”) is difficult for many learners.
Supporting the process of creating an appropriate concept-map of the
learning content by providing concept-map-components generated
from lecture speech text.
Findings
Video-based learning
with concept-map
creation is useful:
• To create a large size
of concept-map of the
learning content
• To get satisfaction to
the own learning
The beginning of video e-learning The middle of video e-learning
The learner create a concept-map of learning content with mouse operation.
Encouraging the learner to perform active and reflective
learning while watching a video.
Area of concept-map-
component provision

3. Our developed system
Interactively creating a concept-map of the
learning content only by mouse operation.
Encouraging a learner to perform active and reflective learning.
Area of concept-map
creation
Area of
concept-map-
component
provision

4. Our developed system
Interactively creating a concept-map of the
learning content only by mouse operation.
Encouraging a learner to perform active and reflective learning.
Area of concept-map
creation
Area of
concept-map-
component
provision

5. Our developed system
Interactively creating a concept-map of the
learning content only by mouse operation.
Encouraging a learner to perform active and reflective learning.
Area of
concept-map-
component
provision
Area of concept-map
creation

6. Our developed system
Interactively creating a concept-map of the
learning content only by mouse operation.
Encouraging a learner to perform active and reflective learning.
Area of
concept-map-
component
provision
Area of concept-map
creation

7. Pre-processing: Concept-map component generation
Transcribing from speech
within lecture video via SR
Decomposing the speech text
into morphemes via
morphological analyzer.
Extracting noun terms from
morphemes and adding TF-IDF
weight to them
Adding more weight into the
technical terms by using the
technical term dictionary.
Judging pairs of the terms within one sentence
in the speech recognition result
Extracting verb, noun, adverb, and adjective
being included in the same sentence as the
related concept label pair.
Concept-label-pair candidates
Link-label candidates
Concept-label: more than 80% are nouns
Link-label︓more than 90% are verb, noun,
adverb, or adjective
A pair of concept-labels: more than 92%
are within a sentence
Human-made concept-map [Villaron’10]
Procedure
Based on the analysis of human-made concept-map
Concept-label
Concept-label
Link-label

8. Pre-processing: Concept-map component generation
Transcribing from speech
within lecture video via SR
Decomposing the speech text
into morphemes via
morphological analyzer.
Extracting noun terms from
morphemes and adding TF-IDF
weight to them
Adding more weight into the
technical terms by using the
technical term dictionary.
Judging pairs of the terms within one sentence
in the speech recognition result
Extracting verb, noun, adverb, and adjective
being included in the same sentence as the
related concept label pair.
Concept-label-pair candidates
Link-label candidates
Concept-label: more than 80% are nouns
Link-label︓more than 90% are verb, noun,
adverb, or adjective
A pair of concept-labels: more than 92%
are within a sentence
Human-made concept-map [Villaron’10]
Procedure
Based on the analysis of human-made concept-map
Concept-label
Concept-label
Link-label
Examples of the Generated Concept-map Components:
concept-label link-label concept-label
• [financial condition] -> [summary] -> [loan balance sheet]
• [business performance] -> [summary] -> [profit/loss statement]
• [loan] -> [good] -> [assets]
• [loan] -> [post-pay] -> [debt]
• [net assets] -> [-] -> [capital]
• [profit] -> [increase] -> [extraneous income]
• [cost] -> [decrease] -> [advertisement]
• [debt] -> [-] -> [extraneous income]
Lecture content: “book-keeping and accounting”

9. Motivation
Issue: The high dropout rate from the online courses
Causes: [Tominaga’14]
• Learner’s procrastination
→ Online tutoring [Huang’12]
→ Predicting learners having drop-out tendency from the system logs[Mduma’19]
• Incompatible learning style to e-learning[Combs’14]
→ Designing e-learning content with functions to match the learning style
of the e-learning[Kizilcec’14][Gro’14][Mayer’05]
(Many recent e-learning system still adopt a simple form)
→ Required for a learner to perform learning with meta-cognitive skills and
the step-by-step thinking way[Dalmolin’18][Morita’14]
(learners who have drop-out tendencies cannot understand the learning content from the
beginning of the course. [Simpton’13])
Complete rate of the course
Open University: 20%[Alraimi’15][Khalil ‘143]
JMOOC: 5% [Simpton’10][Simptom’13]

10. Research purpose
Developing E-learning system with concept-map creation
while watching a video.
Useful to understand the learning
content [Novak’90]
Video-based learning
Supporting video-based learning with meta-cognitive skills and the
step-by-step thinking way
Creating concept-map of the
learning content

11. Usage Example of the proposed system
• 15min research presentation video including 15 slides

12. Usage Example of the proposed system
• 15min research presentation video including 15 slides

13. Conclusion
• In e-learning, learners are required to be introspective, deliberate, and
logical, which is incompatible with many learners with low completion rates.
• Developing e-learning system that allows learners to deepen their
understanding of creating a concept-map while watching a video.
• Support the presentation of candidate concept-map-components from
the lecture speech texts
• Confirmed that it is useful to create concept-map while watching e-learning
video.
• Future works
• Improving the support function of creating the concept-map
• Analyzing the relationships between the learning performance and
concept-map created by the learners