IP 8: Attentional Record and Analysis

In this Intellectual Production, my task was to analyze 12 hours and record what I paid attention to. This includes multi-tasking and how much attention I paid attention to the task. I logged Friday, February 23rd, 2024. This day happened to fall on our February break; therefore, I was not at work. The Scotties Tournament of Hearts (women’s curling) was on. My wife was curling in a local bonspiel in town, and we had some distractions from the ongoing teacher strikes and sanctions in Saskatchewan. We also have a very busy baby, who you will be able to see takes up much attention.

Over the 12 hours, I logged 42 different tasks across the time frame. I will also share my cell phone screen time, looking at the notifications I have received, the types of apps I used, the top apps I use, and the number of times I unlocked my phone. I will be the first to admit that my phone is an ultimate distraction and that I will spend more time on it than I would like to admit. Therefore, it would be essential to provide additional analysis on my cellphone usage over this time period.

Through my analysis of my attention, Citton (2017) highlights attention being a matter of selection. In attention economies, others are consistently vying for our attention. “Attention is a matter of selection, the positions of power in an attention economy are defined according to their ability to filter the flows that pass through us” (Citton, 2017, p. 178). This was interesting in my analysis. The advertisements and commercials that are presented within the Scotties Tournament of Hearts, as well as the flyers I received in the mail, passed through my filter. It begs the question, what are the consequences of what I opted to pay attention to.

Further analysis of my attention could be categorized into collective attention, joint attention, and individual attention (Citton, 2017).

Collective Attention

Collective is attention at the broadest scale. Within my day, this includes the engagement that I did with my Teacher Association group chat when we heard about province-wide sanctions that were going to be imposed on my first day back to work, although I may not have messaged back in my group chat, I gave attention to a significant number of people, as well as was mindful of the situation on a provincial scale. As well as my engagement with the Scotties Tournament of Hearts as well as viewing TikTok videos on my front page, which have been viewed thousands of times before I had even viewed them.

Joint Attention

Joint Attention refers to the attention of other specific people who are close to me. In my day, it was clear that this was a large majority of the day. I paid significant attention to my baby, my wife, and to a chat I had with a friend over Snapchat. Joint attention is critical for child development, thus reading to my child, and playing with my child is a major component of our days.

Individual Attention

Citton (2017) identifies individual attention as objects on which our bodies and spirits are nourished. For me, these were the tasks that I spent time on specifically for me. On this particular day, it included planning my law lessons for work, as well as washing my car, walking to go get the mail, and listening to some music.

Attentional Category

Within my analysis of my attention. I decided it would be important to see what I was valuing with my attention though the analysis of specific instances (e.g. “playing with baby” was coded as “family”). It was also important for me to see how long I was paying attention to the attentional objects. This analysis was interesting, as you can see that I have a total of 14 hours and 50 minutes. Often throughout the day, I would have been multitasking. The duration of my logged attention would have been logged twice. For example, watching curling on TV will playing on the floor with my baby is the reason why the entertainment sections of the graph are so different. While I had the TV on for an hour, passively paying attention, I also was doing other things, making breakfast, and playing with my child.

Attentional State

The analysis of my attentional state was first analyzed through how I was feeling while completing the task. These states were then further coded into whether the state was positive, neutral, or negative. The data highlights that a vast majority of my day was positive.

Voluntary or Automatic

Citton (2017) identifies that our automatic attention is the attention like hearing our name being spoken in a party. Within my analysis, I logged 9.5% of my attention as automatic in the 42 tasks tracked. This includes hearing notifications on my cellphone as well as the feeling of hunger. Voluntary attention is then the opposite, the times where I had to specifically focus my attention on specific tasks.

Cellphone Usage

Throughout my day, you can see that I had 3 hours and 21 minutes of screen time. I know that I am often on my phone, but this was shocking to see, as it is not reflected well in my data that I have tracked. I think that this is partially due to my cellphone becoming a form of automatic attention, as compared to voluntary. Often when my phone does go off, I have to pause what I am doing and look at the notification. The notifications graph identifies the areas of which my attention is drawn. It is not surprising to see the correlation between social notifications and the amount of time spent on social apps. Often I will turn off notifications on my devices if I find them not helpful or unwanted.

Attention in Education

Within the topic of attention economies in education, we need to ask ourselves, how do we engage students? In looking at de Castell and Jenson (2004), the authors identify the cultural shift education has undertaken from teacher-centred to learner-centred. Thus, our shift from how we paid attention in school has also vastly changed our course. However, de Castell and Jenson discuss how video games and gaming, in general, can change the course of education through immersive experiences and their success in holding the user’s attention. Learning theories such as Seymour Papert’s constructionist approach, where students develop meaning through making, allow students to direct their attention in immersive opportunities with or without technology.

In addition, de Castell and Jenson (2004) identify that a reason why video games might be successful in holding student’s attention is that video games “help their players ‘learn’ quickly” (p. 396). Proper differentiation strategies that focus on individualized learning opportunities, such as the Modern Classroom Project, help teachers through blended instruction, self-paced structures, and mastery-based learning. This approach helps students maintain better attention because students can navigate at their own pace until they have mastered a topic.

References

de Castell, S., & Jenson, J. (2004). Paying attention to attention: New economies for learning. Links to an external site.Educational Theory, 54(4), 381–397.

Citton, Y. (2017). Introduction and conclusion: From attention economy to attention ecology. In Y. Citton, The ecology of attention. John Wiley & Sons. 

IP #4 – Media Convergence

Create a “mind map” (using whatever tool you please) for the concept of ‘media convergence’ that sets out, with examples, the 5 processes of convergence that Jenkins distinguishes. Then, in a <5-minute MAX video (try to go beyond the ‘talking head’ format) explain your mapping of these aspects of media convergence, and conclude with a thoughtful and well-justified account of what you think are some key educational implications of media convergence.

References

University of Minnesota Libraries Publishing. (2010). Understanding media and culture: An introduction to mass communication.

Jenkins, H. (2001). Convergence? I diverge. MIT Technology Review.

Jenkins, H. (2006). Convergence culture: Where old and new media collide. New York University Press.

IP #3: Algorithms

“At a time when state funding for public goods such as universities, schools, libraries, archives, and other important memory institutions is in decline in the US, private corporations are providing products, services and financing on their behalf. With these trade-offs comes an exercising of greater control over the information, which is deeply consequential for those already systematically oppressed…” (Noble, p. 123)

Think and respond to the following questions:

• Explain in your own words what “content prioritization” (Noble, p. 156) means (give some examples) and how (in lay terms) content prioritization algorithms work. With control over the “largest digital repository in the world” (Noble, p. 157), how have Google’s content prioritization algorithms been “consequential for those already systematically oppressed”? How do they impact your professional life? (give specific examples and briefly discuss)
• What are some ways PageRank impacts your personal life? (specific examples and briefly discuss) (How) can you impact PageRank? Explain.

References

Gössl, S. L. (2023). Chapter 2: Recommender systems and discrimination. In S. Genovesi, K. Kaesling, & S. Robbins (Eds.), Recommender Systems: Legal and Ethical Issues. Springer.

MIT Media Lab. (2019). AI blindspot: A discovery process for preventing, detecting, and mitigating bias in AI systems. Aiblindspot.media.mit.edu. https://aiblindspot.media.mit.edu/

Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press.

TEDx Talks. (2014). How biased are our algorithms? | Safiya Umoja Noble | TEDxUIUC [YouTube Video]. In YouTube. https://www.youtube.com/watch?v=UXuJ8yQf6dI

Wikipedia. (2019, September 6). Algorithmic bias. Wikipedia; Wikimedia Foundation. https://en.wikipedia.org/wiki/Algorithmic_bias

Wikipedia. (2024, January 3). ImageNet. Wikipedia. https://en.wikipedia.org/wiki/ImageNet#Bias_in_ImageNet

Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist — it’s time to make it fair. Nature, 559(7714), 324–326. https://doi.org/10.1038/d41586-018-05707-8

IP #2: Artificial Intelligence

Who were these people, and how did/does each contribute to the development of artificial intelligence? How did/does each think “intelligence” could be identified?

	My Responses	Chat-GPT
Alan Matheson Turing	A British mathematician, Turing is famous for his participation in codebreaking the German Enigma machine (Biography, 2020). Turing, known for the Turing test, contributed to artificial intelligence because it raises the question, “Can machines think?” (Turing, 1950). The Turing test determined if humans could detect computer answers from human answers.	A pioneering British mathematician and computer scientist, Turing laid the foundation for AI with his Turing Test, a measure of a machine’s ability to exhibit human-like intelligence. His work on the Enigma machine during World War II and the Turing machine concept profoundly influenced computer science.
John McCarthy	Coining the term artificial intelligence, McCarthy invented LISP, a programming language for AI (John McCarthy, 2019). McCarthy believes intelligence to be “the ability to achieve goals in the world” (Sutton, 2020, p. 66).  For example, this could include people or machines such as a thermostat, as they can achieve goals.	An American computer scientist, McCarthy coined the term “artificial intelligence” in 1955. He founded the Stanford AI Lab and developed the Lisp programming language, a key tool for AI research. McCarthy focused on creating machines capable of reasoning and problem-solving, advancing AI as a distinct field.
Herb Simon	Simon developed the general problem solver (GPS), and the logic theorist, believed in demystifying thinking. Simon believes that human intelligence and information processing have three common operations: scanning for patterns, storing patterns in memory, and applying patterns (Frantz, 2003, p. 271).	An American cognitive psychologist and computer scientist, Simon, along with Allen Newell, developed the “Logic Theorist,” the first AI program. Simon emphasized the importance of problem-solving and decision-making processes in AI. He won the Nobel Prize in Economics for his work on bounded rationality.
Marvin Minsky	Minsky, a mathematician and computer scientist, built the first neural network learning machine. Minsky, along with McCarthy, cofounded the Artificial Intelligence Project, a research centre at MIT (Dennis, 2019).  He believed “the brain is a machine whose functions could be replicated in a computer” (BBC News, 2016).	An American cognitive scientist, Minsky co-founded the MIT AI Lab and made significant contributions to AI, coining the term “perceptron.” His research focused on neural networks and machine perception, guiding the field’s early development. Minsky advocated for understanding human cognition to replicate intelligence in machines.
Timnit Gebru	Gebru, a leader in AI ethics, has done work in identifying AI bias, and the harms of AI in terms of discrimination. She has identified four main risks of large language models; environmental and financial costs, massive data, inscrutable models, research opportunity costs, and illusions of meaning (Hao, 2020).	An Ethiopian-American computer scientist and AI ethics researcher, Gebru contributed to addressing bias and ethical concerns in AI. Co-founder of the Black in AI community, she worked at Google on fairness and accountability. Gebru emphasized the need for inclusive, ethically grounded AI, urging responsible development and deployment.

How do “machine (programming) languages” differ from human (natural) ones?

Programming languages differ from human languages through a variety of means. The first revolves around the study of morphology. For example, in human languages we can often alter the meaning, and context of language using stems, root words, prefixes, and suffixes. In programming, this does not exist. The second major difference revolves around the strict rules programming languages follow. Programming languages were invented with the thought of logic, precision, and being irrefutable, on the contrary, human languages are full of slang, accents, or vernacular that is subject to variations based on context and culture. This does not exist in a programming language.  

How does “machine (artificial) intelligence” differ from the human version?

Chollet (2019) identifies two main characteristics of intelligence; one focuses on accomplishing task-specific skills, and the second focuses on the generality and the ability to adapt to various environments. Task-specific skills rely heavily on logic, routines, and developing a large knowledge base (p. 5). Chollet shares the example of AI chess; for humans to become proficient in chess, they need to use general intelligence to acquire a specific skill. However, when a machine is optimized for learning, it is built specifically to handle a narrow task and focus in on a specific task. Whereas generalization, defined as “the ability to handle situations (or tasks) that differ from previously encountered situations” (Chollet, 2019, pp. 9-10), tends to aim to mirror human cognitive abilities.  

How does “machine learning” differ from human learning? 

One of the main differences between machine learning and human learning is the ability to understand bias. Machine learning relies on being exposed to large data sets of information to judge and predict the information it receives (Heilweil, 2020). Jones (2020), identifies how Facebook used machine learning to place people into “micro-categories” and produces targeted posts to reach niche groups of people. We have seen how this can create gender and racial bias. Digital literacy skills are becoming more invaluable, to differentiate between the biases and determine how algorithms work.

Turing Test: how do YOUR answers to these questions differ from what a machine could generate?

My answers to the above questions would differ from what a machine can produce to some degree. Of course, in my writing you can see citations and my writing style (with likely the odd grammar error). However, regarding the content within the answers, in the chart’s first question, you can see very similar answers between the Chat-GPT and my responses. This could be because the answers to these questions are surface-level questions, able to be found directly by a Google search, or through some reading of some articles. I would be interested to see the remainder of the questions and how the answers are compared. I would think the answers would result in a similar response. This is because the questions do not allow for personal opinion, but rather a statement of facts that an AI program like Chat-GPT could recreate. By adding a personal or cultural context to the questions I am asking, I believe that the answers would become more differentiated and varied.

References

BBC News. (2016, January 26). AI pioneer Marvin Minsky dies aged 88. BBC News. https://www.bbc.com/news/technology-35409119

Biography. (2020, July 22). Alan Turing – education, movie & quotes. Biography. https://www.biography.com/scientists/alan-turing

Chollet, F. (2019, November 5). On the measure of intelligence.

Dennis, M. A. (2019). Marvin Minsky | American scientist. In Encyclopædia Britannica. https://www.britannica.com/biography/Marvin-Lee-Minsky

Frantz, R. (2003). Herbert Simon. Artificial intelligence as a framework for understanding intuition. Journal of Economic Psychology, 24(2), 265–277. https://doi.org/10.1016/s0167-4870(02)00207-6

Hao, K. (2020). We read the paper that forced Timnit Gebru out of Google. here’s what it says. MIT Technology Review. https://www.technologyreview.com/2020/12/04/1013294/google-ai-ethics-research-paper-forced-out-timnit-gebru

Heilweil, R.  (2020, February 18). Why algorithms can be racist and sexist. A computer can make a decision faster. That doesn’t make it fair.Links to an external site. Vox.

John McCarthy (computer scientist). (2019, October 15). Wikipedia. https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)

Jones, R. H. (2020). The rise of the Pragmatic Web: Implications for rethinking meaning and interaction. In C. Tagg & M. Evans (Eds.), Message and medium: English language practices across old and new media (pp. 17-37). De Gruyter Mouton.

Sutton, R. S. (2020). John McCarthy’s definition of intelligence. Journal of Artificial General Intelligence, 11(2), 1–100. https://doi.org/10.2478/jagi-2020-0003

Turing, A. (1950). Computing machinery and intelligence. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/lix.236.433

IP #1: Users, Uses, and Usability

Usability

Usability is part of the design process. Usability refers to the quality of interactions that demonstrate if users can efficiently utilize a system (Issa & Isaias, 2015, p. 29). Usability needs to be continuously monitored, user configurable, and iterative, even after software release. Continued attention to the user’s needs is critical to the development process of software creation to ensure that technology is functional, efficient, and meets the specific goals and needs of the user.

Educational Usability

The goal of educational technology is to increase student success. Whether this includes increased teacher efficiency in streamlining processes or increased student engagement, the common goal is for student success. The creation of software and systems from an educational usability perspective includes a microscopic view of answering the question “Who is the user, and what are the user needs?” while still addressing the need to motivate the user. In many cases, in education, users will lack motivation to learn how to use the software. Special attention needs to be paid to how learners connect with the software. This includes careful user configuration of the software to include aspects such as accessibility, cultural relevance, and motivation. Educational usability must consider including opportunities to make the systems and software configurable for diverse students from an asset-based approach.

Furthermore, the usability of the software needs to be flexible not only in how the user and the system exchange information but also to include a user configuration to focus on specific curriculum content and acknowledge a variety of perspectives and worldviews. Furthermore, educational usability needs to focus specifically on the modification and redefinition stages of the SAMR Model. Focused on the questions, “Does the technology allow for the creation of new tasks previously inconceivable?” and “Does the technology allow for significant task redesign?” (Puentedura, 2013). The inclusion of the SAMR model addresses the functionality, efficiency, and effectiveness of usability while focusing on the creation of software that is innovative for learning.

Woolgar’s Examples

Example #1: “Users don’t necessarily know best”

In this example, the direction and the vision of technology were deemed to be more important than the user’s needs and desires for technical features (Woolgar, 1990, p. 74). When users provide feedback to the technical teams, it often revolves around existing features in technology. However, Woolgar states that companies have access to understand the vision and direction of the technology more than an ordinary user. Thus, the weight of user input is not as critical in developing new features (pg. 75). This logic undermines usability because it does not consider the user’s needs and does not encourage users to be a part of the design process.

Example #2: The Manuals

In a second case configuring the user revolves around the manuals (Woolgar, 1990, p. 80). The series of documentation provided in technology configures the user as it encourages the user to utilize the technology in specific ways, guiding users towards its “correct” operation (p. 81). The use of manuals for specific prescribed purposes discourages users from utilizing the technology or apps for creative thinking. The manual provides a linear path for users to explore the technology. In addition, within the established trials, the users and the machine itself were not yet established (p. 82). Woolgar identified if novice users were not able to operate the machine correctly, it was due to “user error,” and the fault lies to the user, as compared to experienced users, the fault would lie to the machine. Further identifying “configuring the user” and having to operate within the confines of the system.

Quote Discussion

Looking at the two quotes, Issa & Isaias (2015) identify that usability is part of the design process that focuses specifically on how and why users utilize the technology. It focuses on gathering feedback from users to improve the systems. Meanwhile, Woolgar (1990) focuses specifically on the machine and how the machine itself needs to calibrate and configure the user to conform to the parameters set out by the system. Woolgar’s trials highlighted that usability is best left defined by the company itself, rather than the specific users. This is evident through the deficiencies in company knowledge about users and the prioritization of the future direction of technology from the company’s perspective.  A balance is needed between the two examples, and increased transparency and feedback are needed from all stakeholders to increase usability. 

References

Issa T., Isaias P. (2015). Usability and human computer interaction (HCI). In: Sustainable Design. Springer, London. https://doi-org.ezproxy.library.ubc.ca/10.1007/978-1-4471-6753-2_2

Puentedura, R. R. (2013, May 29). SAMR: Moving from enhancement to transformation [Web blog post]. Retrieved from http://www.hippasus.com/rrpweblog/archives/000095.html

Woolgar, S. (1990). Configuring the user: The case of usability trials. Routledge & Kegan Paul etc. https://doi-org.ezproxy.library.ubc.ca/10.1111/j.1467-954X.1990.tb03349.x