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Empirical Analysis of Automated Editing of Raw Learning Video Footage

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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.

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www.tugraz.at ■ SCIENCE PASSION 
 TECHNOLOGY MASTER’S THESIS PRESENTATION 1 Empirical Analysis of Automated Editing of Raw Learning Video Footage David Nußbaumer 28.04.2022
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 2 Structure 1. Introduction 2. Methodology 3. Results 4. Conclusion
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 3 Introduction (1) A. Core of the Thesis
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 4 Introduction (1) A. Core of the Thesis Evaluation Manual Editing vs. Automated Editing
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?
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
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 7 Introduction (2) B. Learning Videos
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 8 Introduction (2) B. Learning Videos Specific Type “Frontal Lecture / Studio Recording”
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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:
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 12 Introduction (3) C. Video Editing
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 13 Introduction (3) C. Video Editing Part of Postproduction
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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 …
www.tugraz.at ■ 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 …
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 …
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 …
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 …
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 …
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 …
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 23 Introduction (4) C. Video Editing Two Segments / One Cut
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 24 Introduction (4) C. Video Editing Two Segments / One Cut
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 25 Introduction (5) D. Video Quality
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 26 Introduction (5) D. Video Quality QoS: Quality of Service
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 27 Introduction (5) D. Video Quality QoS: Quality of Service QoE: Quality of Experience
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 29 Introduction (6) E. „Good” Learning Videos should
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 30 Introduction (6) E. „Good” Learning Videos should Visualize content (Mayer, 2002).
www.tugraz.at ■ 28.04.2022 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)
www.tugraz.at ■ 28.04.2022 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)
www.tugraz.at ■ 28.04.2022 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)
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 34 Methodology (1) A. Reference Videos
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 35 Methodology (1) A. Reference Videos Ten Raw Recordings with belonging Screen Text
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage A. Reference Videos 39 Methodology (1)
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Measure Time 40 Methodology (2)
www.tugraz.at ■ 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 41 Methodology (2)
www.tugraz.at ■ 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)
www.tugraz.at ■ 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)
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Measure Time 44 Methodology (2) Manual Workflow
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Measure Time 45 Methodology (2) Automated 
 Workflow
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage C. Measure Quality 46 Methodology (3)
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 47 Methodology (3)
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) 48 Methodology (3)
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) 49 Methodology (3)
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 50 Methodology (3)
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)
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage C. Measure Quality 52 Methodology (3) Video 2 Video 3
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 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. 56 Methodology (3)
www.tugraz.at ■ 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
www.tugraz.at ■ 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. 58 Methodology (3) QoP QoE
www.tugraz.at ■ 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. 59 Methodology (3) QoP QoE Open Question: What was particularly good or bad?
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage A. Time Consumption 60 Results (1)
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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 %
www.tugraz.at ■ 28.04.2022 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%)
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Preserving Quality 66 Results (2)
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Preserving Quality 129 Participants in Online Survey 67 Results (2)
www.tugraz.at ■ 28.04.2022 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)
www.tugraz.at ■ 28.04.2022 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)
www.tugraz.at ■ 28.04.2022 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)
www.tugraz.at ■ 28.04.2022 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)
www.tugraz.at ■ 28.04.2022 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)
www.tugraz.at ■ 28.04.2022 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.
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 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
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Preserving Quality 77 Results (3)
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Preserving Quality 78 Results (3)
www.tugraz.at ■ 28.04.2022 David Nußbaumer 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?
www.tugraz.at ■ 28.04.2022 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?
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?
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)
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)
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 84 Conclusion
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 85 Conclusion 1. Time can be saved drastically
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 86 Conclusion 1. Time can be saved drastically 2. Quality can almost be preserved
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
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
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
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
www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage Thank you for your time and attention. 91 Fin
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?"
 
 92 References

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Empirical Analysis of Automated Editing of Raw Learning Video Footage

  • 1. www.tugraz.at ■ SCIENCE PASSION 
 TECHNOLOGY MASTER’S THESIS PRESENTATION 1 Empirical Analysis of Automated Editing of Raw Learning Video Footage David Nußbaumer 28.04.2022
  • 2. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 2 Structure 1. Introduction 2. Methodology 3. Results 4. Conclusion
  • 3. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 3 Introduction (1) A. Core of the Thesis
  • 4. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 4 Introduction (1) A. Core of the Thesis Evaluation Manual Editing vs. Automated Editing
  • 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
  • 7. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 7 Introduction (2) B. Learning Videos
  • 8. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 8 Introduction (2) B. Learning Videos Specific Type “Frontal Lecture / Studio Recording”
  • 9. www.tugraz.at ■ 28.04.2022 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
  • 10. www.tugraz.at ■ 28.04.2022 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
  • 11. www.tugraz.at ■ 28.04.2022 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:
  • 12. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 12 Introduction (3) C. Video Editing
  • 13. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 13 Introduction (3) C. Video Editing Part of Postproduction
  • 14. www.tugraz.at ■ 28.04.2022 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
  • 15. www.tugraz.at ■ 28.04.2022 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
  • 16. www.tugraz.at ■ 28.04.2022 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 …
  • 17. www.tugraz.at ■ 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 …
  • 23. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 23 Introduction (4) C. Video Editing Two Segments / One Cut
  • 24. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 24 Introduction (4) C. Video Editing Two Segments / One Cut
  • 25. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 25 Introduction (5) D. Video Quality
  • 26. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 26 Introduction (5) D. Video Quality QoS: Quality of Service
  • 27. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 27 Introduction (5) D. Video Quality QoS: Quality of Service QoE: Quality of Experience
  • 28. www.tugraz.at ■ 28.04.2022 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
  • 29. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 29 Introduction (6) E. „Good” Learning Videos should
  • 30. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 30 Introduction (6) E. „Good” Learning Videos should Visualize content (Mayer, 2002).
  • 31. www.tugraz.at ■ 28.04.2022 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)
  • 32. www.tugraz.at ■ 28.04.2022 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)
  • 33. www.tugraz.at ■ 28.04.2022 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)
  • 34. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 34 Methodology (1) A. Reference Videos
  • 35. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 35 Methodology (1) A. Reference Videos Ten Raw Recordings with belonging Screen Text
  • 36. www.tugraz.at ■ 28.04.2022 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
  • 37. www.tugraz.at ■ 28.04.2022 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
  • 38. www.tugraz.at ■ 28.04.2022 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
  • 39. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage A. Reference Videos 39 Methodology (1)
  • 40. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Measure Time 40 Methodology (2)
  • 41. www.tugraz.at ■ 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 41 Methodology (2)
  • 42. www.tugraz.at ■ 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)
  • 43. www.tugraz.at ■ 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)
  • 44. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Measure Time 44 Methodology (2) Manual Workflow
  • 45. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Measure Time 45 Methodology (2) Automated 
 Workflow
  • 46. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage C. Measure Quality 46 Methodology (3)
  • 47. 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 47 Methodology (3)
  • 48. 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) 48 Methodology (3)
  • 49. 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) 49 Methodology (3)
  • 50. 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 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)
  • 52. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage C. Measure Quality 52 Methodology (3) Video 2 Video 3
  • 53. www.tugraz.at ■ 28.04.2022 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
  • 54. www.tugraz.at ■ 28.04.2022 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
  • 55. www.tugraz.at ■ 28.04.2022 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
  • 56. www.tugraz.at ■ 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. 56 Methodology (3)
  • 57. www.tugraz.at ■ 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
  • 58. www.tugraz.at ■ 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. 58 Methodology (3) QoP QoE
  • 59. www.tugraz.at ■ 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. 59 Methodology (3) QoP QoE Open Question: What was particularly good or bad?
  • 60. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage A. Time Consumption 60 Results (1)
  • 61. www.tugraz.at ■ 28.04.2022 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
  • 62. www.tugraz.at ■ 28.04.2022 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
  • 63. www.tugraz.at ■ 28.04.2022 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
  • 64. www.tugraz.at ■ 28.04.2022 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 %
  • 65. www.tugraz.at ■ 28.04.2022 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%)
  • 66. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Preserving Quality 66 Results (2)
  • 67. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Preserving Quality 129 Participants in Online Survey 67 Results (2)
  • 68. www.tugraz.at ■ 28.04.2022 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)
  • 69. www.tugraz.at ■ 28.04.2022 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)
  • 70. www.tugraz.at ■ 28.04.2022 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)
  • 71. www.tugraz.at ■ 28.04.2022 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)
  • 72. www.tugraz.at ■ 28.04.2022 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)
  • 73. www.tugraz.at ■ 28.04.2022 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.
  • 74. www.tugraz.at ■ 28.04.2022 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
  • 75. www.tugraz.at ■ 28.04.2022 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
  • 76. www.tugraz.at ■ 28.04.2022 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
  • 77. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Preserving Quality 77 Results (3)
  • 78. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage B. Preserving Quality 78 Results (3)
  • 79. www.tugraz.at ■ 28.04.2022 David Nußbaumer 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?
  • 80. www.tugraz.at ■ 28.04.2022 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)
  • 84. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 84 Conclusion
  • 85. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 85 Conclusion 1. Time can be saved drastically
  • 86. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage 86 Conclusion 1. Time can be saved drastically 2. Quality can almost be preserved
  • 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
  • 91. www.tugraz.at ■ 28.04.2022 David Nußbaumer Empirical Analysis of Automated Editing of 
 Raw Learning Video Footage Thank you for your time and attention. 91 Fin
  • 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?"
 
 92 References