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PROPOSING YOUTUBE ED FOR ONLINE LEARNING

Analysing Virtual Social Behaviour on YouTube

Project Context

This project was undertaken as part of our Understanding Virtual Social Behavior module wherein we studied the virtual behaviour which is exhibited on YouTube with specific regard to the learning channels. 

 

It also looked into the behaviours exhibited by different stakeholders, the drivers responsible and other occurring phenomenons.

 

The methodology adopted helped us map out the gaps to eventually tap into insights and hypothesis those were further validated via experiments and simulations. Based on these, a new subchannel called YouTube Ed was proposed which curbed the behaviour that caused trouble while enhancing the constructive ones making the platform more relevant for learning, engaging and inclusive. 

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Process

1

Secondary Research

2

Netnography

To understand the context, in specific about the platform, YouTube and the scope of online learning in India

To understand the existing educational channels on YouTube.

 

To map and analyse the behaviour exhibited and the driver behind them.

To map user archetypes.

3

Auto- netnography

4

Word and Picture Association

To understand the online platform and use self- reflection to record feelings, emotions and personal experience. 

To get a better understanding of the users as a part of the qualitative research and to tap into their subconscious perception. 

5

Survey

To grasp the challenges faced by the users. 

6

Data Synthesis and analysis

7

Insights and Hypothesis

8

Simulation, Validation and Solution

Understanding the platform

To understand YouTube as a platform, current education/ learning channels on it and the scope of online education in India.


 

TOOL USED: SWOT Analysis

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STRENGTH

WEAKNESS

OPPORTUNITY

THREAT

Anytime- anywhere usage

In synch with the ‘digital age’  

Geographic liberation

Equity and equality

Abundance of content

Cost-effective (free)

Boundless 

2nd largest platform in the world

Device/ gadget-friendly 

Enables sharing

Flexibility and ease of choosing the time

Lack of timely and effective feedback

Lack of collaboration and peer learning

Isolated and incompatible for group work

Lack of authenticity and credibility

No assessment of learning

Prone to bullying

Intrusive and distracting ads

Low moderation

Lack of drive to learn

Lacks transparency

Traps/ click baits

Conformity bias/ discrimination

Problem of plenty

Low retention

High internet penetration and Infrastructure

Policies in favour 

Future of education

Cost-effective

No limitations

No limit of no. of students

Spread education beyond borders

Discrimination free

Large community

Schools without classroom

Existing ecosystem of online learning

Consistency 

Uniqueness

Students tend to get laid back

Procrastination

Alternative learning platforms

Classroom education

Distance learning programme

NETNOGRAPHY

Comment Section Analysis (Feedback)

Deviations in the comment section

Discrimination
 

Irrelevant

Comparison
 

Inadequate/ lack of 

feedback
 

Stalemate
 

One way feedback

Gender

Appearance

Ethnicity

Inappropriate

Open to everyone

The only way to give feedback

Competitive

Bias

Negative Space

Laziness

Not engaging

Lack of interaction

No alternative feedback 

No platform for interaction

The comment section provides limited feedback that is mostly irrelevant, inappropriate and highly deviant. 

Behavioural Archetype

These 9 archetypes are based on the various type of viewers/ users we came across on the YouTube learning channels. Specially via the live chat and the comments section.

Behaviour Plotting

(Real vs Virtual)

Human behaviour from the real world often manifests in the virtual one. A comparative plotting of certain reoccurring and common social behaviours was done between these two.

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REAL

VIRTUAL

Inhibiting behaviour like fear, shame, etc seemed to be more in the real world whereas, behaviour like aggression, discrimination, bullying etc amplified in the virtual.

 

Strong Online Disinhibition Effect was observed. 

Mapping Behaviour

Frequently exhibited behaviours were mapped.

Aggression

Due to disinhibition the moral values and the line of social ethics also blurs. When people’s beliefs are challenged, it triggers them and amplifies their anger leading to aggressive comments.

Bullying

Cyberbullying and trolling are highly common occurring in the comment section. Multiple reasons can be attributed to the same, some being the anonymity, no defined consequences, unaccountability, social conformation and normalcy, they feel this is acceptable because everyone is doing it.

Youtube is available for everyone without any restrictions. People can keep their accounts private/ unknown and share their feelings without any inhibition of being judged. Moral values and the line of social ethics also blurs. When people’s beliefs are challenged, it triggers them and amplifies their anger leading to aggressive comments.

Discrimination

It happens against the peer who comments as well as the ones who are uploading the video. The most common one is that based on gender and then ethnicity. Women educators receive more irrelevant and inappropriate comments compared to men.    

Transactional

Like, share, subscribe! Probably the first three words that come to anyone’s mind on hearing, ‘YouTube’. The aim becomes to gain likes and views and monetize on the content. The intent of teaching and educating dilutes and gets overshadowed. 

Polarization

One of the key reason for this is confirmation bias. People likely seek out and agree with views that align with their pre-existing beliefs. Curating the content we want to see potentially makes it easier for us to listen to speakers/ educators who validate our worldview

Exploration

With 300 hours worth of video being uploaded on YouTube, there is an absolute abundance of data one can access, due to youtube’s anyone- everyone approach. With the recommendation algorithm of YouTube is extremely engaging and results in amplification of usage. 

Mapping Drivers

Mapping Drivers

Drivers behind the behaviours exhibited.

Positive

Negative

It encourages bullying, aggression, nasty and inappropriate behaviour and comments.

Anonymity

It allows the viewers to express without fear and gives a feeling of security.

Conformity

It provides a sense of self surety and confidence.

It makes the viewer irrational, blindsided and often leads to mob behaviour like bullying and also harassment.

Group thinking

There is a sense of belonging and being a part of the group. 

It makes one highly susceptible to being influenced, promotes herd mentality, loss of authenticity and suppresses new ideas and opinions.     

Gives people a chance to control or shape how others see them.

This leads to manipulation, inauthentic content, deceitfulness. The uploaders trick the viewers into believing what they desire and mostly mislead. 

Self- presentation

One does not feel restrained while communicating.

This also turns toxic and leads to bullying. This enables viewers to say mean and inappropriate things, having no consequences to face outside the internet.

Online Disinhibition

effect

One does not feel restrained while communicating.

Trust

It increases authenticity as people express themselves better and put honest opinions. They engage better when they trust the uploader too.

It can be misleading, people try to assume maximum information with minimum information. It makes the viewers baised and polarised. 

Core Drives

Humans have about 5 core drives that heavily influence our behavior and decision making. 

Drive to acquire likes, views, fame, recognition, influence and eventually the money is the key driver. Their purpose of educating or teaching is highly transactional. This is followed by the drive to feel. This deals with the feeling of acceptance, thrill and emotional experiences.

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UPLOADER

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The most common drive that was observed drive to bond. Viewers mostly want their opinion to be valued. People incline towards individuals with similar worldviews and demographics. This follows the drive to defend, which triggers them to become active when they feel threatened by another opinion or a person.

VIEWER

Drive to learn, which essentially should have been the key driver gets overshadowed and takes a back seat.

Auto-netnography

Auto-netnography roots back to Auto-ethnography. It is a key tool to understand how people interact, feel and behave with the online community.  My teammate Priyank and I created new profiles for YouTube, watched multiple learning videos and recorded our feelings, emotions and personal experiences over a certain period of time. 

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Tavleen Chohan

"Search results are confusing"

"The videos with negative comments mostly turned out to be useful. Hence, the comments were misleading."

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Priyank Jaiswal

"I was trying to look for keywords to understand the overview of the video."

"Used no. of views, likes and dislikes to choose the video to view."

“Use comments to troll the haters.” 

"I found myself getting provoked and angry during the live chat after reading the insensitive comments."

"Ads and recommendations distract and divert attention away from learning."

"Since no overview, fast forwarded most of the video to understand the content."

"Comment section environment was negative therefore, did not indulge in feedback."

Survey: Key Findings

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Majority of the participants use YouTube for learning or education.
 

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Most common reasons for NOT giving feedback on YouTube

“Use comments to troll the haters.” 

“ Does not feel like the right place to give feedback.”

“Don’t see the point.”

“Sometimes I don’t want to comment but, still appreciate.”

“Can’t relate to the previous comments.”

“Time waste!”

Around 72% ended up spending more time than planned. 

Key Observations

The feedback on YouTube is delayed and weak.

The current comment section discourages people from participating and is also misused.

FEEDBACK

Ratio of the no. of views to comments is extremely skewed. 

There is a ripple effect (One negative comment/ video leads to more)

The viewers are unable to navigate themselves through the ample amount of data hence, confusion.

PLATFORM

Recommendations are distracting.

The relationship on YouTube is highly transactional (likes, shares, etc)

There is discrimination based on gender, caste, religion, etc. 

BEHAVIOUR

The uploaders gain users trust by tapping onto their greed.

Drive to learn takes a backseat on learning channels.

Insights

YouTube’s recommendation algorithm polarises and amplifies behaviour to extremes. (Mild to wild)

Disinhibition leads to a more irresponsible behaviour
(Perceived lack of accountability)

The anyone- everyone approach is diluting the authenticity. 

Lack of element of play leads to less of engagement/ retention.

Abundance of data, leads to confusion.

Hypothesis

Simulation Result

(Validation)

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RECOMMENDATION

ALGORITHM

It leads to polarization and focuses on quantity of time spent rather than quality.

Result 1: It helped us to establish that the existing algorithm pushes viewers towards engaging and controversial videos.

Result 2: It helped us to establish that the existing recommendation algorithm polarises people and pushes them to extremes.

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FEEDBACK

Comment section biases viewers opinions and currently is irrelevant and delayed.

The simulation helped us to establish that comment section create biases amongst the viewers and sometimes result in a stalemate condition where the uploaders efforts and intent get countered by the negative, irrelevant and discouraging comments.

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RETENTION

Retention of a learner is lesser on YouTube learning channels.

The average score of participant group 2 was more than that of 1 as they retained more information. The delayed feedback also affects the learning. 

DESIGN SOLUTIONS

The crucial, primary and the broader recommendation is to create a subcategory of YouTube, making a more relevant platform for learning, suggestively calling it YouTube ed.

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Solution no.2 and no.3 are feedback framework and recommendation algorithm respectively. These two are essentially a part of the broader suggestion, the new proposed platform.

Design Solution 1

(YouTube Ed)

TRAITS:

Conscientiousness (Reliable, prompt, organised  thorough)
 

Participative


Rewarding
 

Protective


Ethical

Screen 1 (Sign up/ in)

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WHAT:
 

  • YouTube ed would require a separate sign up.
     

  • The learner would have to provide their email ID and Phone number to which they would receive an OTP to verify the user. 

 

WHY:
 

  • This would help address anyone- everyone approach.
     

  • Bring learning for the forefront.
     

  • More relevant and learning-oriented content (for both the uploader and the viewer)
     

  • Keeping the anonymity intact, the verification process via OTP would make the viewer feel more credible, accountable and less invincible. 

Screen 2 (Subject Preferences)

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WHAT:
 

  • Giving viewers an option to choose their preferred subjects that they want to learn. 



 

WHY:
 

  • Planned and focused learning.
     

  • Viewers in authority to choose what content is shown or he/she want to see.
     

  • Aid them to learn WHAT THEY WANT to learn.
     

  • Negate confusion in the abundance.
     

  • Relevant and customised content.

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FILTERS: Skill, level and language.

Focussed and relevant results, easier navigation through excessive content and curbs confusion.
 

CHALLENGE: Challenge is a section where viewers could go and participate in quizzes to test their learning. They could also challenge their friends or random opponents. Based on this they will have level-ups and special badges as they move up the leaderboard. They would get the reward for their feedback and also get feedback about their learnings. 

NOTEBOOK: Learners can save notes that have been made while watching a video. This will aid in better retention. 

COMMUNITY: A shared learning experience.
 

  • Video sharing and simultaneous viewing with peers

  • Live  video chat 

  • Sharing notes for simultaneous writing and note-making.

PROFILE: The viewer can see his/ her progress, feedback, evaluation scores, quiz rankings, etc. 

SEARCH: “What would you like to learn today?” Reminding the purpose of the platform and the viewer. 

Screen 3 (Main Screen)

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Screen 4 (Video Screen)

I. Reminder: Revision reminders can be set

YouTube note: This can be accessed while watching a video/ tutorial/ lesson. 

II. Delete: The notes can be deleted.

III. Share: These could be shared via other social media platforms.

IV. Add to notebook: These notes can be added to the notebook collection, Where all the notes can be stored. 

IV. Last edited: Shows the notes and when they were last edited.

Discussion: This would be a replacement for the “comment” section. The word comment adds an element of casualness whereas, the discussion seems more formal and serious. This would curb unnecessary commenting. It would also open a separate window of discussion and won’t be visible right under the video to reduce biases and preconceived notions. This will also help improve the environment of feedback. 

Appreciate/ Like ONLY: The dislike button would be removed and the only like button would be available. This would help reduce the negativity and discouragement that uploader faces

Alternative feedback:  A non-lingual form of feedback would be provided to express the viewer's feelings. This could help express dissatisfaction without the usage of harsh words. 

Design Solution 2 

(Feedback)

Feedback is an extremely crucial part of communication. It is a window to the future. Yet, the feedback on Educational channels of YouTube is currently delayed, skewed, deviant, unstructured, unengaging, monotonous and extremely irrelevant. Also, it creates a negative comment/ discussion atmosphere due to elevated discrimination, aggression and other sentiments. 

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ENGAGING: The current feedback section has no engaging factor and there is no perceived gain or value for their time they contribute to giving feedback. Based on the social exchange theory they weigh their loss of time higher over the return they get for the same hence, there is a lack of feedback. 

RELEVANT: The feedback should be designed such that it should minimise deviations.

PROMPT: The feedback on YouTube is delayed. This causes the negative sentiment to sore further. Rapid replies can help counter this and therefore response time matters. 

(Basic Framework for feedback on YouTube Ed)

INCLUSIVE: Most people do not feel involved in the feedback/ comment section. It is a one-way communication as the uploaders don’t involve in the comment section.

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DISCUSSION (Replacement of comment section):
 

WHAT:

  • Replacing the comment section with the discussion forum as a method of feedback. 

  • Adding a quick and alternative way of giving feedback via emoticons.

  • Removing the dislike options while only keeping the like or the appreciation button.

  • Gamifying the feedback to add the element of play and introducing incentives

  • Via gamified levels and badges to show the competency of the commenter in the discussion forum. 

 

WHY:

  • The comment is perceived to be more casual whereas, the discussion forum is perceived to be more crucial. This will remove unnecessary and random comments, making it more relevant. 

  • Viewers can provide instant feedback.

  • Removing lingual barriers hence, inclusive and prompt.

  • Makes the viewing environment less toxic.

  • Engage people.

  • Through gamification, feedback to the viewer about their learning progress.

  • The credibility of the commenter

Design Solution 3

(Algorithm)

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EXISTING ALGORITHM

  • Focuses on watch time rather than the quality and content of the video.
     

  • Mild to wild effect.
     

  • It pushes viewers to the extreme.
     

  • It creates polarity.
     

  • Amplifies the behaviour.
     

  • Becomes a trap to engage people and amplify their sensation

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DESIRED ALGORITHM

  • Create a learning environment through recommendation.
     

  • Giving multiple perspectives on the subject of holistic learning.
     

  • Help us handle information overload.
     

  • Guides us for better navigation.
     

  • Help the viewer to retain the information

Proposed Recommendation Algorithm

This shows the parameters on which the proposed YouTube’s video suggestion algorithm will work.

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Video quality and presentation

Through our research, we found that audio and video quality of the content affects the level of trust.  

1

2

3

4

5

Contextual - Demography and language

Setting the right context by understanding the basic and preliminary requirements of the user

Users history and Peers viewing list

Creating an environment where users history is used for self analysis and retention. Peers view list will further make the learning more contextual

Trending

Using technology such as IoT and AI for trend forecasting. This will aid the user to be updated with the latest innovations

User's skill level and requirements

User skill level will segregate the content according to the users current understanding hence making is relevant

This pyramid framework would help to narrow down to relevant video suggestions that would focus on learning and not on engagement time.

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“Would you like to rearrange your suggestions?"

This dialogue box could appear once in a week for if the user is not happy with the suggestions and would get a chance to rearrange. This could also be done at any given time using the profile section. Giving viewers control over what they see.  

STRIP 1: “ LEARN MORE IN ”

This bar of recommendations would show the videos from the categories the user chooses as preference while signing up.

STRIP 2: “YOU CAN TRY”

This bar of recommendations would show the videos similar concepts from other non-selected categories that are relevant to the search.

STRIP 3: “MOST POPULAR”

This bar of recommendations would show popular videos from different fields. Topics and suggestions. 

(based on the likes/ appreciation and no. views)

Learnings

This project helped me understand and capture the subconscious happenings of the user and how our behaviour from the real world manifests in the virtual one. I learnt how to adapt and use different design research tools to suit the context and draw efficient insights. Apart from that understand how the original intention of the online platform gets lost and the service is misused due to deviant behaviour.

exhibited. This also exposed me to new tools and methods like simulation design, behaviour mapping and driver analysis that play a key role in understanding the virtual world which is essentially where the future is headed. 

Complete Project

Project Team

 

Tavleen Chohan | Priyank Jaiswal 

National Institute of Design

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