This document outlines David Nußbaumer's master's thesis presentation on empirically analyzing automated editing of raw learning video footage. The presentation covers the introduction, methodology, and results. The introduction discusses video editing, quality measures, and characteristics of good learning videos. The methodology section explains that reference videos will be edited manually and automatically while tracking time consumption. Videos will also be rated in a survey to measure quality differences. The goal is to evaluate if automated editing can save time while preserving video quality for learning.
https://imatge.upc.edu/web/publications/keyframe-based-video-summarization-designer
This Final Degree Work extends two previous projects and consists in carrying out an improvement of the video keyframe extraction module from one of them called Designer Master, by integrating the algorithms that were developed in the other, Object Maps.
Firstly the proposed solution is explained, which consists in a shot detection method, where the input video is sampled uniformly and afterwards, cumulative pixel-to-pixel difference is applied and a classifier decides which frames are keyframes or not.
Last, to validate our approach we conducted a user study in which both applications were compared. Users were asked to complete a survey regarding to different summaries created by means of the original application and with the one developed in this project. The results obtained were analyzed and they showed that the improvement done in the keyframes extraction module improves slightly the application performance and the quality of the generated summaries.
https://imatge.upc.edu/web/publications/keyframe-based-video-summarization-designer
This Final Degree Work extends two previous projects and consists in carrying out an improvement of the video keyframe extraction module from one of them called Designer Master, by integrating the algorithms that were developed in the other, Object Maps.
Firstly the proposed solution is explained, which consists in a shot detection method, where the input video is sampled uniformly and afterwards, cumulative pixel-to-pixel difference is applied and a classifier decides which frames are keyframes or not.
Last, to validate our approach we conducted a user study in which both applications were compared. Users were asked to complete a survey regarding to different summaries created by means of the original application and with the one developed in this project. The results obtained were analyzed and they showed that the improvement done in the keyframes extraction module improves slightly the application performance and the quality of the generated summaries.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
5. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
5
Introduction (1)
A. Core of the Thesis
Evaluation Manual Editing vs. Automated Editing
Can time be saved and quality preserved?
6. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
6
Introduction (1)
A. Core of the Thesis
Evaluation Manual Editing vs. Automated Editing
Can time be saved and quality preserved?
Survey and workflow time tracking
9. www.tugraz.at ■
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
9
Introduction (2)
B. Learning Videos
Specific Type “Frontal Lecture / Studio Recording”
Recording with teleprompter (screen) text
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
10
Introduction (2)
B. Learning Videos
Specific Type “Frontal Lecture / Studio Recording”
Recording with teleprompter (screen) text
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
11
Introduction (2)
B. Learning Videos
Specific Type “Frontal Lecture / Studio Recording”
Recording with teleprompter (screen) text
… Die Schülerinnen und Schüler können
diese also nicht nur im Gegenstand
Informatik beziehungsweise
Digitale Grundbildung erarbeiten, sondern
auch in den Fächern Bewegung und Sport,
Bildnerische Erziehung …
Screen Text:
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
14
Introduction (3)
C. Video Editing
Part of Postproduction
Take the best (parts) and leave the rest
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
15
Introduction (3)
C. Video Editing
Part of Postproduction
Take the best (parts) and leave the rest
Concatenate parts in a way that viewers are not
distracted
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
16
Introduction (4)
C. Video Editing
… Die Schülerinnen und Schüler können
diese also nicht nur im Gegenstand
Informatik beziehungsweise
Digitale Grundbildung erarbeiten, sondern
auch in den Fächern Bewegung und Sport,
Bildnerische Erziehung …
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28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
17
Introduction (4)
C. Video Editing
… Die Schülerinnen und Schüler können
diese also nicht nur im Gegenstand
Informatik beziehungsweise
Digitale Grundbildung erarbeiten, sondern
auch in den Fächern Bewegung und Sport,
Bildnerische Erziehung …
18. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
18
Introduction (4)
C. Video Editing
… Die Schülerinnen und Schüler können
diese also nicht nur im Gegenstand
Informatik beziehungsweise
Digitale Grundbildung erarbeiten, sondern
auch in den Fächern Bewegung und Sport,
Bildnerische Erziehung …
19. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
19
Introduction (4)
C. Video Editing
… Die Schülerinnen und Schüler können
diese also nicht nur im Gegenstand
Informatik beziehungsweise
Digitale Grundbildung erarbeiten, sondern
auch in den Fächern Bewegung und Sport,
Bildnerische Erziehung …
20. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
20
Introduction (4)
C. Video Editing
… Die Schülerinnen und Schüler können
diese also nicht nur im Gegenstand
Informatik beziehungsweise
Digitale Grundbildung erarbeiten, sondern
auch in den Fächern Bewegung und Sport,
Bildnerische Erziehung …
21. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
21
Introduction (4)
C. Video Editing
… Die Schülerinnen und Schüler können
diese also nicht nur im Gegenstand
Informatik beziehungsweise
Digitale Grundbildung erarbeiten, sondern
auch in den Fächern Bewegung und Sport,
Bildnerische Erziehung …
22. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
22
Introduction (4)
C. Video Editing
… Die Schülerinnen und Schüler können
diese also nicht nur im Gegenstand
Informatik beziehungsweise
Digitale Grundbildung erarbeiten, sondern
auch in den Fächern Bewegung und Sport,
Bildnerische Erziehung …
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
28
Introduction (5)
D. Video Quality
QoS: Quality of Service
QoE: Quality of Experience
QoP: Quality of Perception
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
31
Introduction (6)
E. „Good” Learning Videos should
Visualize content (Mayer, 2002).
Avoid unnecessary audio noise (Richardson, 1998)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
32
Introduction (6)
E. „Good” Learning Videos should
Visualize content (Mayer, 2002).
Avoid unnecessary audio noise (Richardson, 1998)
Maintain a consistent (sound) volume (Robinson et al., 2003)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
33
Introduction (6)
E. „Good” Learning Videos should
Visualize content (Mayer, 2002).
Avoid unnecessary audio noise (Richardson, 1998)
Maintain a consistent (sound) volume (Robinson et al., 2003)
Implement as discreet cuts as possible (Lima et al., 2012)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
36
Methodology (1)
A. Reference Videos
Ten Raw Recordings with belonging Screen Text
Length between 50s and 4min
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
37
Methodology (1)
A. Reference Videos
Ten Raw Recordings with belonging Screen Text
Length between 50s and 4min
One to Five Takes
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
38
Methodology (1)
A. Reference Videos
Ten Raw Recordings with belonging Screen Text
Length between 50s and 4min
One to Five Takes
German / English and Male / Female Split
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Measure Time
Manual Workflow performed by Video Editors
tracked with Stopwatch
41
Methodology (2)
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28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Measure Time
Manual Workflow performed by Video Editors
tracked with Stopwatch
Corresponding Steps tracked with Process Time in
Automated Workflow
42
Methodology (2)
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28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Measure Time
Manual Workflow performed by Video Editors
tracked with Stopwatch
Corresponding Steps tracked with Process Time in
Automated Workflow
Direct Time Consumption Comparison
43
Methodology (2)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
Two videos and two versions
Embedded in an Online Survey (LimeSurvey)
48
Methodology (3)
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28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
Two videos and two versions
Embedded in an Online Survey (LimeSurvey)
Rating Questions about Quality (QoP and QoE)
49
Methodology (3)
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28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
Two videos and two versions
Embedded in an Online Survey (LimeSurvey)
Rating Questions about Quality (QoP and QoE)
Open Question
50
Methodology (3)
51. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
Two videos and two versions
Embedded in an Online Survey (LimeSurvey)
Rating Questions about Quality (QoP and QoE)
Open Question
t-Test for Significance
51
Methodology (3)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
53
Methodology (3)
Video 2 Video 3
manually | automatically manually | automatically
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
54
Methodology (3)
Video 2 Video 3
manually | automatically manually | automatically
Group A Evaluation
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
55
Methodology (3)
Video 2 Video 3
manually | automatically manually | automatically
Group B Evaluation
Group A Evaluation
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
1. The words were pronounced clearly and distinctly.
2. I can learn well with this learning video.
3. Generally I like this video.
4. The content of the video matches the subtitles.
5. Image Quality is good in my opinion.
6. Sound Quality is good in my opinion.
56
Methodology (3)
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28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
1. The words were pronounced clearly and distinctly.
2. I can learn well with this learning video.
3. Generally I like this video.
4. The content of the video matches the subtitles.
5. Image Quality is good in my opinion.
6. Sound Quality is good in my opinion.
57
Methodology (3)
QoP
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
1. The words were pronounced clearly and distinctly.
2. I can learn well with this learning video.
3. Generally I like this video.
4. The content of the video matches the subtitles.
5. Image Quality is good in my opinion.
6. Sound Quality is good in my opinion.
58
Methodology (3)
QoP
QoE
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
C. Measure Quality
1. The words were pronounced clearly and distinctly.
2. I can learn well with this learning video.
3. Generally I like this video.
4. The content of the video matches the subtitles.
5. Image Quality is good in my opinion.
6. Sound Quality is good in my opinion.
59
Methodology (3)
QoP
QoE
Open Question: What was particularly good or bad?
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
A. Time Consumption
61
Results (1)
Video
Time: Manual
Workflow
Time: Automated
Workflow
Time Saved in %
1
2
3
4
5
6
7
8
9
10
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
A. Time Consumption
62
Results (1)
Video
Time: Manual
Workflow
Time: Automated
Workflow
Time Saved in %
1 193s
2 250s
3 141s
4 101s
5 91s
6 80s
7 60s
8 261s
9 171s
10 273s
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
A. Time Consumption
63
Results (1)
Video
Time: Manual
Workflow
Time: Automated
Workflow
Time Saved in %
1 193s 44s
2 250s 23s
3 141s 35s
4 101s 19s
5 91s 23s
6 80s 22s
7 60s 19s
8 261s 73s
9 171s 40s
10 273s 48s
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
A. Time Consumption
64
Results (1)
Video
Time: Manual
Workflow
Time: Automated
Workflow
Time Saved in %
1 193s 44s 77 %
2 250s 23s 90 %
3 141s 35s 75 %
4 101s 19s 81 %
5 91s 23s 74 %
6 80s 22s 72 %
7 60s 19s 68 %
8 261s 73s 72 %
9 171s 40s 76 %
10 273s 48s 82 %
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
A. Time Consumption
65
Results (1)
Video
Time: Manual
Workflow
Time: Automated
Workflow
Time Saved in %
1 193s 44s 77 %
2 250s 23s 90 %
3 141s 35s 75 %
4 101s 19s 81 %
5 91s 23s 74 %
6 80s 22s 72 %
7 60s 19s 68 %
8 261s 73s 72 %
9 171s 40s 76 %
10 273s 48s 82 %
Automated Workflow on average 76% faster (SD 6%)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
129 Participants in Online Survey
Group A: 74 Participants
Group B: 55 Participants
68
Results (2)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
129 Participants in Online Survey
Group A: 74 Participants (V2 M - V3 A)
Group B: 55 Participants (V2 A - V3 M)
69
Results (2)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
129 Participants in Online Survey
Group A: 74 Participants (V2 M - V3 A)
Group B: 55 Participants (V2 A - V3 M)
85 men and 44 women
70
Results (2)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
129 Participants in Online Survey
Group A: 74 Participants (V2 M - V3 A)
Group B: 55 Participants (V2 A - V3 M)
85 men and 44 women
Content watched mostly on Smartphones with
built-in Speakers (44.2% / 30.2%)
71
Results (2)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
129 Participants in Online Survey
Group A: 74 Participants (V2 M - V3 A)
Group B: 55 Participants (V2 A - V3 M)
85 men and 44 women
Content watched mostly on Smartphones with
built-in Speakers (44.2% / 30.2%)
Participants learn with videos at least a few times
a month or more often(61.3%)
72
Results (2)
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
73
Results (3)
Question
Video 2 Video 3
Manual Automated t-Test Manual Automated t-Test
The words were pronounced
clearly and distinctly.
I can learn well with
this learning video.
Generally I like this
video.
The content of the
video matches the subtitles.
Image quality is good
in my opinion.
Sound quality is good
in my opinion.
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
74
Results (3)
Question
Video 2 Video 3
Manual Automated t-Test Manual Automated t-Test
The words were pronounced
clearly and distinctly. 3.6 3.9 0.35
I can learn well with
this learning video. 3.1 2.6 0.03
Generally I like this
video. 3.2 3.0 0.47
The content of the
video matches the subtitles. 3.7 4.0 0.22
Image quality is good
in my opinion. 4.0 4.0 0.58
Sound quality is good
in my opinion. 3.8 4.0 0.34
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
75
Results (3)
Question
Video 2 Video 3
Manual Automated t-Test Manual Automated t-Test
The words were pronounced
clearly and distinctly. 3.6 3.9 0.35
I can learn well with
this learning video. 3.1 2.6 0.03
Generally I like this
video. 3.2 3.0 0.47
The content of the
video matches the subtitles. 3.7 4.0 0.22
Image quality is good
in my opinion. 4.0 4.0 0.58
Sound quality is good
in my opinion. 3.8 4.0 0.34
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
76
Results (3)
Question
Video 2 Video 3
Manual Automated t-Test Manual Automated t-Test
The words were pronounced
clearly and distinctly. 3.6 3.9 0.35 4.2 4.1 0.58
I can learn well with
this learning video. 3.1 2.6 0.03 3.1 3.3 0.53
Generally I like this
video. 3.2 3.0 0.47 3.6 3.6 0.98
The content of the
video matches the subtitles. 3.7 4.0 0.22 4.0 3.9 0.63
Image quality is good
in my opinion. 4.0 4.0 0.58 4.2 4.1 0.64
Sound quality is good
in my opinion. 3.8 4.0 0.34 4.2 4.0 0.43
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Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
79
Results (4) What did you find particularly bad about the learning
Video?
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David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
80
Results (4)
Video 2
Manually Edited
10 %
12 %
45 %
32 %
Greenscreen is distracting
Missing visualization of the content
Audio Quality Lacking
Other (General Comments)
What did you find particularly bad about the learning
Video?
81. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
81
Results (4)
Video 2
Manually Edited
10 %
12 %
45 %
32 %
Greenscreen is distracting
Missing visualization of the content
Audio Quality Lacking
Other (General Comments)
Video 2
Automatically
Edited
7 %
65 %
28 %
What did you find particularly bad about the learning
Video?
82. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
Only one quality question has a statistically
significant difference (automated rated worse than
manually)
82
Results (5)
83. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
B. Preserving Quality
Only one quality question has a statistically
significant difference (automated rated worse than
manually)
Other quality factors are not influenced
83
Results (5)
87. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
87
Conclusion
1. Time can be saved drastically
2. Quality can almost be preserved
3. Not following principles of multimedia content creation can
affect quality
88. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
88
Conclusion
1. Time can be saved drastically
2. Quality can almost be preserved
3. Not following principles of multimedia content creation can
affect quality
4. Indiscreet Cuts can distract viewers from content
89. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
89
Conclusion
1. Time can be saved drastically
2. Quality can almost be preserved
3. Not following principles of multimedia content creation can
affect quality
4. Indiscreet Cuts can distract viewers from content
5. Bad Audio Quality affects the viewers concentration
90. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
90
Conclusion
1. Time can be saved drastically
2. Quality can almost be preserved
3. Not following principles of multimedia content creation can
affect quality
4. Indiscreet Cuts can distract viewers from content
5. Bad Audio Quality affects the viewers concentration
6. Greenscreen should be avoided
92. www.tugraz.at ■
28.04.2022
David Nußbaumer
Empirical Analysis of Automated Editing of
Raw Learning Video Footage
Cho, Sunghyun, Jue Wang, and Seungyong Lee (Aug. 2012). Video deblurring
for hand-held cameras using patch-based synthesis". In: ACM Transactions
on Graphics 31.4, pp. 1{9. doi: 10.1145/2185520.2185560.
Lima, Edirlei Soares de et al. (July 2012). Automatic Video Editing for Video-
Based Interactive Storytelling". In: 2012 IEEE International Conference on
Multimedia and Expo. IEEE. doi: 10.1109/icme.2012.83.
Mayer, Richard E. (2002). Multimedia Learning". In: The Annual Report of Educational
Psychology in Japan. Vol. 41, pp. 27-29.
Richardson, Craig H. (1998). Improving Audio Quality in Distance Learning Applications."
In: Distance Learning '98. Proceedings of the AnnualConference
on Distance Teaching and Learning (14th, Madison,WI, August 5-7, 1998).
Robinson, Charles Q., Steve R. Lyman, and Je rey Riedmiller (2003). Intelligent
Program Loudness Measurement and Control: What Satis
fi
es Listeners?"
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References