Activity Theory in Young Users of Digital Technology: an observation of the iGeneration

Introduction

A field study observing the digital interaction of young users was conducted on a second-generation, three-year-old boy of Afro-Indo Caribbean descent. The observation was conducted in the observer’s home.

The purpose of the study was to better understand the intuitive use of young children. How do they know to navigate and interact with features as they do? How do they learn these behaviors in unsupervised environments? How are their behaviors reinforced and applied across devices?

Many of these questions were a result of the above curiosities and a desire to better understand the cognitive processes at play as noted by Kuhlthau:

“A model representing the user’s sense-making process of information seeking ought to incorporate three realms of activity: physical, actual actions taken; affective, feelings experienced; and cognitive, thoughts concerning both process and content. A person moves from the initial state of information need to the goal state of resolution by a series of choices made through a complex interplay within these three realms (MacMullin & Taylor, 1984). The criteria for making these choices are influenced as much by environmental constraints, such as prior experience, knowledge, and interest, information available, requirements of the problem, and time allotted for resolution, as by the relevancy of the content of the information retrieved” (p. 362).

The structure of this field report was a combination of interview and observation. The purpose structure was due primarily to the subject’s age.

Disclosure

The subject of the observation is the observer’s nephew. Alluded to below, one of the many reasons why this observation was informally conducted was due to general curiosity. This initial curiosity began when noticing the subject’s use of mobile devices but, most recently, when he began sending nonsensical messages. The messages were initially thought to have been a prank by the subject’s mother but upon further inquiry, and frequent occurrences, the messages were a combination of drawn shapes or autocompleted phrases that were illogically constructed.

Screenshot of chat messages sent between the subject and the study's author.

Observations within the scope of this study included 1) how interaction changed or remained the same across an iPhone and iPad 2) interaction with specific features and implicit restrictions imposed on the user (e.g., inability to read or write).

“Motivation for doing the work”

According to “The Ethics of Fieldwork” by PERCS: The Program for Ethnographic Research and Community Studies – Elon University, listing motivations for conducting such studies better align the researcher with the outcome of the intended study and the benefits to the research field as a whole.

For many reasons, this study was not formally conducted. However, there are two reasons worth noting within the scope of this report. The first reason is due to the little experience the observer possessed in field studies containing child subjects. Leveraging practices from the readings within the study resulted in applying generalized techniques and procedures intended for adult subjects to a child subject.

Outlined later in greater detail, this posed many issues as one would expect the application of techniques and procedures reapplied in very different circumstances. However, motivation for pursuing this study prompted an attempt and a review of not only this study but also the review of unique requirements for child subjects. Hence, the second reason why the observation was not formally conducted: to better understand at what scale technology impacts early childhood development.

Possible Harms: skewed results due to the misuse of techniques and procedures.

Possible Benefits: generated interest to pursue, rectify, and advance this study.

Techniques

General tasks were assigned:

  1. Interact with a mobile feature
  2. Find and watch your favorite YouTube video
  3. Play an educational game
  4. Play a non-educational game
Subject playing the mobile app game "Grom Skate" on an iPhone.

As the subject worked through each task, some intervention and rewarding were required. Having known the subject, the tasks created were short in length–sufficient enough for possible naps or breaks–and the entire observation spanned across several hours. Snacks were rewarded for good behavior and for completing a task without interruption.

After completing the above tasks, the subject was closely observed to document any behavior which didn’t occur while completing those tasks.

Questions and notes from the observation

  • Activity Theory: in-practice
    • How would his actions change if the technology changed as Nardi claims, “Activity theory holds that the constituents of activity are not fixed but can dynamically change as conditions change” (p.38)?
  • Attention span: what does his actions say about the effect of technology on youth users’ ability to focus?
    • Never completes viewing of videos and tends to navigate to either the search bar or another video within 30 seconds to 2 minutes of viewing.
    • Viewing videos of greater interest last longer than 2 minutes but are never fully completed.
    • When a task was issued, the subject wanted to continue on longer for all tasks but the educational game. For the educational game, the user became frustrated unless there was sufficient guided intervention.
    • Voyeurism and the gaming culture: the subject’s attention was only kept when watching YouTube videos of others playing videos games or playing with toys.
  • Distributed Cognition (Nardi, p. 38): pattern recognition?
    • Participant cannot read nor sufficiently write. However, he is able to search YouTube videos he’s previously watched but is unable to search newly watched videos. To return to new videos, he taps the arrow icon to return to the video.
    • How he searches is by typing in the first few letters he remembers from the videos he’s views frequently. For retained previous search results, he reviews the list and selects which is most recognizable. He watches and then returns to the search bar if the video isn’t what he wanted. If the video is what he was looking for, he scrolls to the recommended videos to find new content and selects those items or searches content from the same channel of the video he’s currently viewing.
  • Signifiers and affordances
    • Participant understood the significance of the hamburger menu, toggles, touchscreen interface features such as swiping, device volume control and locking mechanisms, and other navigational signifiers such as the back/forward and up/down arrows.
  • Interaction
    • iPhone and iPad
      • YouTube and either a mobile app/feature.
    • No major differences in interaction other than the subject’s level of comfort and which device he preferred to use when.
      • The iPhone was generally used when sitting up.
      • The iPad was generally used when laid back.
  • Intervention
    • The study would be better conducted in a more controlled environment/location, without the mother nearby and by an individual with a balanced relationship.
    • Observer’s relationship with the subject was unbalanced. This required swapping between the mother as an instructor to guide him through exercises.
      • With the mother, the subject was at ease and felt less intimidated by the instructions and how they needed to be carried out.
      • The subject preferred guided instructions as opposed to unguided instructions. While guided instructions were more successful with the observer, they weren’t as successful as with the mother.
      • The subject enjoyed general instructions with sufficient freedom to navigate and course-correct by intuition than delegated navigational instructions.

Conclusion

The above study would do well with well-controlled environment, an unrelated observer with sufficient trust, and a well-vetted plan of tasks.

Additionally, prior to an observation containing child subjects, it would be helpful to know positive and negative triggers, learn what they like and what they dislike, review popular content for that age group and test the level of interest on the subjects, provide ideal rewards for completed tasks, and create a balance reward system.

Overall, this observation did provide an opportunity to analyze the subject’s behavior more closely and to develop a thoughtful hypothesis. Nardi explains that “activity theory recognizes that changing conditions can realign the constituents of an activity” (p. 38).

My original assumption that technology results in specific behaviors in young users has shifted to a hypothesis which accounts for the bidirectional relationship between any user and technology: technology reinforces or redistributes behaviors in young users which may predict their usage of other technologies and platforms, and related social behaviors.

References:

Kuhlthau, Carol C. “Inside the Search Process: Information Seeking from the User’s Perspective”, “Journal of the American Society for Information Science 42(5): 361–371. https://ils.unc.edu/courses/2014_fall/inls151_003/Readings/Kuhlthau_Inside_Search_Process_1991.pdf

McGrath Joseph E. “Methodology matters: doing research in the behavioral and social sciences”. https://lms.pratt.edu/pluginfile.php/876402/mod_resource/content/0/mcgrath-methodology%20matters.pdf

Nardi, Bonnie A. “Studying Context: A Comparison of Activity Theory, Situated Action Models, and Distributed Cognition”. https://lms.pratt.edu/pluginfile.php/876415/mod_resource/content/1/nardi-ch4.pdf

PERCS: The Program for Ethnographic Research & Community Studies, “The ethics of fieldwork”. Elon University. http://www.elon.edu/docs/e-web/org/ percs/EthicsModuleforWeb.pdf

Wilson, T. D. (2000). “Human information behavior.” Informing Science 3(2): 49–56. https://www.ischool.utexas.edu/~i385e/readings/Wilson.pdf

How Netflix Learns What You Like

On Thursday, February 28th, NYU Tandon School of Engineering held a live streaming event featuring a talk given by Netflix’s Director of Machine Learning, Tony Jebara. The topic covered was “Machine Learning for Personalization”, which Jebara provided company use cases and solutions for content personalization.

Netflix Director of Machine Learning, Tony Jebara

Netflix, a streaming media-service, is well regarded within the machine learning field for developing impressive machine learning models that incorporate advanced feedback mechanisms to train and improve those models.

According to the Director of Machine Learning, Tony Jebara, every Netflix user’s experience is unique across a range of personalized content. A few examples of personalized content provided by Jebara were rankings, homepage generation, promotions, image selections, searches, advertisement displays, and push notifications.

Content Personalization

Content personalization is a technique leveraged by many companies, across many industries, for the business of either creating content, distributing it or both. Content encompasses everything from online articles to advertisements. In Digital Disconnect, McChesney describes that the popular digital method “personalizes content for individuals, and the content is selected based on what is considered most likely to assist the sale” (p.157).

Entrepreneur lauded Netflix and other media companies who are successfully leveraging machine learning to develop custom experiences but notes a dichotomy which plagues user’s and their preferences. The trade-off between conveniently custom experiences or inconveniently anonymous reintroductions. On one side, users face issues surrounding privacy or unpleasant information dictation.

Opposite to their praises as personalization gurus, Fast Company highlighted some of the negative criticisms Netflix has also received. When companies curate the content users consume, there’s a risk of receiving biased information whether it be political or racial. Berkowitz opens with, “How companies advertise to you says a lot about how they see you” when referring to the racial bias in the algorithms used by not only Netflix, in this case, but many of the other companies working to deploy advanced content personalization algorithms.

“Filter Bubbles”

Regarding the politically charged dictation of content, Castells remarks, “The networks themselves reflect and create distinctive cultures. Both they and the traffic they carry are largely outside national regulation. Our dependence on the new modes of informational flow gives to those in a position to control them enormous power to control us. The main political arena is now the media, and the media are not politically answerable” (p. 34).

McChesney adds how these practices also lead to an issue he considers the “personalization bubble” or what he specifically alludes to as the “filter bubble” (p.157). Users are trapped in an experience they believe to be unique or new but is perpetuated by the same content delivery—just done differently (p. 70).

McChesney references Eli Pariser’s The Filter Bubble: How the New Web is Changing What We Read and How We Think when stating, “Pariser’s Filter Bubble documented how the Internet is quickly becoming a personalized experience wherein people get different results on Google searches for identical queries, based on their history” (p.157).

When Netflix Intervenes

In his talk, Jebara claimed that “prediction is valuable but actual intervention is what we want to understand.” Their algorithms are two-fold—ensuring that experiences are uniquely specific without providing recommendations that are too specific, which may lead to either a negative user experience and a potential unsubscribe from the service.

We’ve all experienced moments of interacting with a digital platform that, over time and with enough data aggregation, begins to recommend content or display ads across devices and sites outside the ownership of the originating platform. If frightened enough, we may have even gone as far as to deleting our browser cookies, adjusting our privacy settings or even unsubscribing from the service.

Algorithm Feedback

Jebara mentioned that a multitude of mixed-method machine learning algorithms are implemented to hone everything from predictive analytics and image curation to user-enforced restrictions and feedback mechanisms.

Jebara described their method take rate as a curatorial feedback strategy which tests different personalization experiences on several users to determine which of the content shown resulted in an actual viewing.

This strategy uniquely prefers the measurement of the number of viewers that strategy worked for over the number of viewers a specific piece of content was shown to. Jebara noted this method enables Netflix experts to learn from users by letting them show what content they prefer and in which ways they’re drawn to recommendations.

User Generated Feedback

This is a major shift from their previous user experience of providing users with the ability to ranking rank content using a star ranking system. Overtime and through observation, Netflix realized they couldn’t rely on that ranking system as a source of truth for which content users ranked highly versus which they’d prefer to watch. Jebara added users were not truthful in their telling of which content they preferred. Shifting away from user interaction to user observation has enabled a greater foundation for developing recommendation systems.

Conclusion

As content personalization algorithms advance, consumers will become a more passive actor in teaching content personalization algorithms. Every attempt at restricting interaction with such algorithms will lead only to yet another loophole identified by machine learning experts. How those companies manage those algorithms and exploit those loopholes are examples of the digital power dynamic which exists between the content generators and the content consumers.

References:

Berkowitz, Joe. “Is Netflix racially personalizing artwork for its titles?One writer’s experience with Netflix’s title art has us wondering whether the company is quietly using race in its algorithm for visually recommending films”. Fast Company (2018). https://www.fastcompany.com/90253578/is-netflix-racially-personalizing-artwork-for-its-titles

Castells, Manuel. The Information Age: Economy, Society, and Culture. Wiley-Blackwell (2010). https://lms.pratt.edu/pluginfile.php/876462/mod_resource/content/1/manuel_castells_the_rise_of_the_network_societybookfi-org.pdf

Chmielewski, Dawn C. “Netflix’s Use of Artwork Personalization Attracts Online Criticism”. Deadline (2018).  https://deadline.com/2018/10/netflixs-artwork-personalization-attracts-online-criticism-1202487598/

McChesney, Robert W. Digital Disconnect. The New Press (2013): 63-171.

Wirth, Karl. “Netflix Has Adopted Machine Learning to Personalize Its Marketing Game at Scale: Here’s how you can humanize marketing strategies. Entrepreneur (2018). https://www.entrepreneur.com/article/311931