For this learning activity I will be reflecting upon my use of YouTube as a learning resource and the ways that my behaviors and the education that I receive from it as a resource are affected by its design as a digital space as well as the design of the systems that manage it. I am choosing to analyze YouTube because I think that it is (or at least it has the potential to be) a valuable educational resource, and it is one that I access often, as I have discussed previously on this blog. It is also unique as a digital learning resource in that it is not intentionally designed as a learning resource; this means that it has unique issues when it comes to its design when it is used as a learning resource. 

Before launching into a full analysis of what I am able to do and what I am prevented from doing, I think that regarding this particular resource it is a good idea to analyze what the resource intends for the user to do. YouTube at its most basic is a platform for sharing videos that anyone can post, and then anyone can see and watch. However, as it is a business that is designed to be profitable, between every video and even during longer videos the platform serves the user advertisements. Since the platform wants to maximize the amount of ads served to the user, they have an algorithm that recommends videos for the user to watch next in order to keep them on the platform for longer and therefore generate more ad revenue for YouTube. Recommendations are based on a dataset that the company has collected from the user based on what they have watched previously, how long they watched any given video for, and which content other users that watched those videos also like to watch, what is popular on the platform at the time, etc. 

Due to YouTube’s user base being skewed fairly young, and how longer watchtime equates to more ads served, the platform serves mainly videos of long form that are designed expressly or exclusively with entertainment of this young user base as the goal. This means that as a new user it is very unlikely that they will see any quality educational content served to them. However, as this algorithm designed to cater to the individual user’s tastes gathers more data on the user, it can begin to direct that user into niches and subcultures that exist on the platform. It is here that YouTube can become its own truly useful educational tool, distinct from simply its video hosting capacity. 

Once a user has displayed interest in using YouTube to consume content designed to be in some way educational, whether that be in a more traditional academic sense, or the more “edutainment” style that is popular on the platform, the algorithm will naturally begin to serve the user more of what they like. The platform typically sticks to serving videos in the same general subject; watching a video meant to help a student develop a better visual intuition for how calculus works will lead the algorithm to recommend more videos on the topic of mathematics, calculus, or physics. Similarly, watching a video of media analysis about a work of art like a film will bring more film analysis recommendations. As such, communities, both of creators and consumers, form around these educational topics. 

The creation of these communities encourages people to share the content that they create and consume in the space with other members of the community. For example, Grant Sanderson, better known as 3Blue1Brown on YouTube, is a well-known creator in the mathematics educational community on the platform. Recently, he has held an event organized entirely without the help of anybody at YouTube meant to encourage people to try their hand at creating educational content relating to math, statistics, physics, computer science, logic, etc. You can read more about it here. Similarly, the teacher-student relation, or in this case the creator-watcher relation, can be as open or as one-way as the creator of the lessons wishes. On smaller videos, I have seen the creator answering dozens of questions in their comment section, and participating in discussion for weeks, months, or years after a video has been posted. Even on the larger videos, where there are simply too many comments for the original creator to keep up with, it is likely that other members of the community will help answer questions or engage in discussion in a peer-to-peer manner.

All this to say that the communities that the algorithm fosters are a force to be reckoned with on their own, even if the platform doesn’t incentivise all forms of educational content; it is this that I want to explore next. As a result of how algorithms select what to recommend users, it is often the case that very well made, innovative, creative educational videos on the platform simply never reach an audience. If the algorithm determines a topic is too advanced or niche for the relative mainstream of the particular subcommunity, or if the channel has poor search engine optimization, or if the account that posted it was created in the wrong geographic region, or etc etc etc… the platform will simply never serve that video to its users, and lessons of quality made for free by passionate people will be lost in the sea of data on the platform, with the creators left discouraged and frustrated. 

That being said, there are also cases where the algorithm will rightly keep a video away from users; as a platform that anyone can upload to, there will necessarily be a great many educational videos uploaded that just aren’t very good. There is no regulatory body fact checking things, aside from the platform itself attempting to regulate the most blatant misinformation, such as widespread global conspiracies or the recent covid denialism. The algorithm is meant to keep bad videos away from its users, and bring good ones to them, but again it doesn’t do this perfectly. What exactly “good” and “bad” mean are often highly contextual to the specific user, but the algorithm always has a rule #1: keep the user on the platform so that they can be served more ads. Most of the time, this means that when the platform is presenting educational content it values well made, engaging content that a user is unlikely to click away from. There is then what I like to call the “educational sweet spot;” a video that’s not too long, so as not to bore viewers, but not too short, so that the algorithm disincentivizes it since it cannot play midroll ads on it, and is solidly well made and engaging start to finish so as to keep viewers from clicking off. Thus, by the algorithmic nature of YouTube as a platform, it shapes the educational content held within it into 15-30 minute snappy presentations of concepts or ideas made to be accessible and digestible by the wider public. 

It is precisely the algorithmic nature of the platform that is both its greatest strength as an educational tool, and its greatest weakness. Lessons in subjects that I had never heard of or considered myself interested in have popped up in my feed on the platform for me to learn from that I have benefited greatly from. I have been able to competently hold conversations with linguistics, film, and philosophy students on campus using knowledge gained in part or entirely from these passion projects shared on the platform and brought to my attention by the algorithm. I have learned aspects of music theory, sound design, kinesiology, chemistry, English literature analysis, film analysis, physics, game design, political science, electrical engineering, data analysis, as well as less academic topics, such as sewing, woodworking, cooking, painting, and much more. But for every wonderful lesson that the platform and its algorithms brings to me, the user, there are a dozen more that are just as competent and capable of inspiring that moment of “Aha!” clarity in the learner that the algorithm has swept under a digital rug, having been deemed unsuitable to be shared with the general user base. 

Something that I have not touched on much is the ethics of YouTubes data collection, but this is due mostly to how open-and-shut this consideration is. YouTube is clear about how they use your data; to serve targeted advertisements to you, and to keep you on the platform to serve you more advertisements. This data is then sold on to others to further build the data profile of the user to sell to other advertising firms, etc. In this way, YouTube acts similarly to the EdTech companies mentioned by Regan, P. & Jesse, J. (2019) and Morris, S. & Stommel, J. (2017).

Overall, the learning experience in YouTube is a strange one; it is difficult to begin to access, it can have varying degrees of quality due to its nature as an platform open for any users to host any kind of video, and it can force creators to make their lessons around the whims of the algorithm rather than the benefit of the learner. But that is not to say that it isn’t a viable and valuable educational tool. I have learned a lot from the educational creators that I have found on YouTube, and I hope to continue to learn much more in the future.

References:

Regan, P. & Jesse, J. (2019). Ethical challenges of edtech, big data and personalized learning: Twenty-first century student sorting and tracking. Ethics and Information Technology, 21(3), 167-179. https://link.springer.com/article/10.1007/s10676-018-9492-2

Morris, S. & Stommel, J. (2017) A Guide for Resisting Edtech: the Case against Turnitin. Hybrid Pedagogy. https://hybridpedagogy.org/resisting-edtech/ 
Sanderson, G. (2021) The Summer of Math Exposition. 3blue1brown.com https://www.3blue1brown.com/blog/some1