Category Archives: IPs

ETEC 511 – IP #1 – Algorithms

This is the first of five intellectual productions (IPs) for this course. IPs are completed individually and worth 30% of our final grade. I chose to complete this assignment in writing form to answer the four questions provided for us. Maybe in the future I will do something more creative with the IPs but thought I’d play it safe for the initial one. Proper APA formatting would have page numbers, a title page, the first line of the paragraphs indented, and hanging indents on the references. I did not meet those APA requirements due to the constraints of this website.
This IP asks you to:

1. Formulate a conception of usability (based on the Issa and Isaias (2015) chapter on HCI and Usability). Use what you’ve learned about usability from that chapter—but you are not summarizing or repeating their ideas. Rather, you are setting out the idea of ‘usability’ you have put together from reading that chapter. Do NOT overly rely on quotes. And remember to use proper citation practices. If you are using text that is not your own, quote and cite it, including page numbers.

2. Then, think about what is missing from this conception, from a specifically educational perspective, and on that basis try and patch together a reasonably grounded and defensible conception of educational usability.

3. Revisit Woolgar’s (challenging but rewarding!) account of “usability gone wrong,” which demonstrates several ways a usability study ended up configuring ‘users,’ thereby undermining the usefulness of usability. Identify and discuss 2 of Woolgar’s examples.

4. Finally, discuss the two excerpts quoted at the top of this IP, that have been drawn from your readings for this unit, and discuss differences you see in these 2 positions on the uses of usability.

HINT: Before you begin (and probably a few times while you are working on this) look up what “configuring” means, especially in the context of computer science. The purpose of the question is to help you to see, understand, and be able to explain how users are configured in the usability trials Woolgar observed. Elucidating and explaining user configuration in usability trials is necessary in order to understand Woolgar’s article and, therefore, in completing this task.

IP #1: Users, Uses and Usability

1. Conception of usability

Human-Computer Interaction (HCI) focuses on the interaction between humans and computers. Usability is one aspect of HCI which is about making something easy to use to meet the needs of the intended user. Good usability means a user can use the product to achieve their goals efficiently.

Usability can refer to everyday objects such as a door handle and it can be used to refer to technology products. Product developers should aim to achieve good usability and can achieve this by following seven usability principles outlined by Issa and Isaias (2015, p. 33): learnability, flexibility, robustness, efficiency, memorability, errors, and satisfaction. Since developers and users have different understandings of each other, usability works best when the product is configured to the user like Issa and Isaias (2015) suggest, not the user being configured to the product, as illustrated in the Woolgar (1990) reading.

ETEC 511 - IP1 - Usability - HCI - Human-Computer Interaction

2. What is missing from this conception of usability?

Missing from this conception of usability is examining usability from an educational perspective, namely that all learners have different abilities and learning contexts.

Usability and educational usability would mostly overlap with six of the usability principles. We would critique usability (learnability), free from crashing (robustness), if the user can achieve their goals in a timely manner (efficiency), easy to remember what you were doing (memorability), have meaningful feedback when errors are made (errors), and if the user’s needs were fulfilled when using the product (satisfaction).

The principle that would look different for educational usability would be flexibility. Since each learner has different abilities, experiences, goals, and needs, what works for one student may not work for others. For example, a screen reader works great for those with visual impairments, yet is useless for someone with a hearing impairment. Educational usability describes products designed for learning where each product has its own specific learning outcomes. For learning to take place, educational usability needs to be motivating, engaging, and inclusive to all students with various abilities.

Usability and educational usability could be juxtaposed with each other since usability is about completing a task without frustration and educational usability, in some cases, can take time and can be frustrating as the learner is challenged to extend their knowledge.

When looking at usability and educational usability, we need to consider the outcomes and the affordances of the technology. Is the user engaging with the technology to complete a task for training a skill or memorization, or is the user engaging with the technology for education to develop higher-order thinking skills such as problem-solving, evaluating, or synthesizing? In addition, learning is not always directly observable or measurable so the usability of educational products could be challenging to measure as what works for one student may not work for another.

3. Usability gone wrong

Woolgar (1990) is an example of poor usability. First, the people conducting the study thought they could configure the user by having them follow instructions and manuals to complete the tasks instead of letting them be self-directed to explore and learn. The users were given prompts, hints, comments, or redirected which undermines the usability process that would happen in the natural world. If these users were learning the machines for the first time, they would most likely not have someone guiding them through this process.

Second, the selection criteria for the usability testers was poor. They wanted to find subjects “who are most likely to act as users” (Woolgar, 1990, p. 75) for the trials, which is a common problem for user testing; however, the subjects selected probably were not “true novices” (Woolgar, 1990, p. 70) since they were already familiar with the product and it would be difficult to act as an outsider.

4. Positions on the uses of usability

The first excerpt from Issa and Isaias (2015, p. 29) places the user at the centre of usability. The product developer changes the product based on the needs of the user. Issa and Isaias (2015) advocate for a user-centred approach that would translate to a learner-centred approach if referring to educational usability.

The second excerpt from Woolgar (1990, p. 59), the product is the centre of usability. The product developer believes they can configure the user to interact with the product in a prescribed way. Woolgar (1990) believes the user should adapt to the machine.

The primary difference between the two positions on usability is the perception of the user’s role. If we return to the definition of usability, usability is about meeting the needs of the user. Issa and Isaias (2015) consider the users’ perspective when developing effective tools, contrasted with Woolgar’s (1990) perspective of developing the tools first, then having the users constrained by boundaries placed on them.

References

Issa, T., & Isaias, P. (2015). Usability and human computer interaction (HCI). In Sustainable Design (pp. 19-35). Springer.

Woolgar, S. (1990). Configuring the user: The case of usability trials. The Sociological Review, 38(1, Suppl.), S58-S99.

ETEC 511 – IP #2 – Artificial Intelligence

Disclaimer: Due to the technology limitations of this blog post, I was unable to create a chart to compare the answers to the questions and my references are not using true APA formatting, as they lack hanging indents.

1. First and in your own words: 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? (~50 words each). Next, using the new AI ChatGPT. ask it the same question, and you can edit to get it down to 50 words. Create a chart to show your work and ChatGBT side by side. Do the same with questions 2-4 (and do make sure in YOUR answers you reference the readings!).

Alan Matheson Turing (1912-1954)

My response

Alan Turning, a British mathematician named the father of computer science and AI, broke the Nazi’s ciphers during World War II to help the Allies defeat the Axis (Alan Turing, 2024). Turing viewed the concept of intelligence as emotional (Turing test, 2024). The Turing proof concluded that some mathematical questions cannot be answered (Turing’s proof, 2024).

ChatGPT – Prompt: “Who was Alan Turing and how did he view intelligence in 50 words or less”

Alan Turing (1912–1954) was a British mathematician and computer scientist. He viewed intelligence as a universal computation process, proposing the Turing Test to assess machine intelligence. Turing’s work in breaking German Enigma codes during WWII and his contributions to theoretical computer science laid the foundation for modern computing and AI.

John McCarthy (1927-2011)

My response

John McCarthy, a founding father of AI, was an American computer scientist and Stanford professor. He formally introduced the term “artificial intelligence”, created AI as a field, and developed the programming language Lisp (John McCarthy, 2023). He believed there is no difference between human and machine tasks (Crawford, 2021).

ChatGPT – Prompt: “Who was John McCarthy and how did he view intelligence in 50 words or less”

John McCarthy (1927–2011) was an American computer scientist and AI pioneer. He coined the term “artificial intelligence” and viewed intelligence as computational problem-solving. McCarthy contributed to early AI systems and the development of the programming language LISP. His work focused on creating machines capable of general problem-solving and learning.

Herb Simon (1916-2001)

My response:

Herb Simon was an American political scientist and one of the founding fathers of AI. He was famous for his views on decision-making which he believed was a crucial part of intelligence (UBS, n.d.). Simon proposed that machines could have emotional cognition which was mostly ignored then eventually became influential (Herbert Simon, 2024).

ChatGPT – Prompt: “Who was Herb Simon and how did he view intelligence in 50 words or less”

Herbert A. Simon (1916–2001) was an American economist, cognitive psychologist, and Nobel laureate. He viewed intelligence as bounded rationality, emphasizing that human decision-making is constrained by limited information and cognitive resources. Simon’s work laid the groundwork for behavioral economics and influenced the study of artificial intelligence and cognitive science.

Marvin Minsky (1927-2016)

My response:

Marvin Minsky was an American cognitive and computer scientist who believed machines can think and intelligence in humans and computers is equal (Chollet, 2019; Crawford, 2021). He co-developed the Logo “turtle” and the Society of Mind theory with Seymour Papert that explains how intelligence is task-focused and is a product of interacting components (Chollet, 2019; Marvin Minsky, 2024).

ChatGPT – Prompt: “Who was Marvin Minsky and how did he view intelligence in 50 words or less”

Marvin Minsky (1927–2016), a pioneering AI researcher, viewed intelligence as a collection of interconnected processes, emphasizing the role of perception, learning, and problem-solving. Co-founder of MIT’s AI Lab, Minsky explored the idea of “frames” and “agents” in AI, contributing to the understanding of complex cognitive functions and machine learning.

Timnit Gebru (1982)

My response:

Timnit Gebru is an Ethiopian-born computer scientist who researches AI, biases in AI, and data mining. She advocates for ethical uses of AI and diversity in technology. Gebru was fired from Google after criticising their biased AI work. Gebru views intelligence as diverse and should benefit all humans (Al Jazeera English, 2022).

ChatGPT – Prompt: “Who is Timnit Gebru and how does she view intelligence in 50 words or less”

Timnit Gebru is an Ethiopian-American computer scientist known for her work in artificial intelligence ethics and bias. She co-authored influential research on bias in facial recognition technology. Gebru advocates for ethical AI development and has been an influential voice in addressing issues of fairness, accountability, and transparency in AI systems.

2. How do “machine (programming) languages” differ from human (natural) ones? (~100 words).

My response:

Machine and human languages are both systems of communication. Human languages are ever-evolving and help us interact with others. Humans are generally better than computers at discerning meaning by using context and inferences (Jones, 2019, p. 27). Natural languages, like English, evolve over time and can be spoken or written, and even include gestures (Language, 2024). Machine languages differ from human languages because they are special languages built on algorithms and are executable (Galloway, 2009, as cited in Jones, 2019). Machine languages are a set of sequential instructions that tell the computer’s CPU what to do (Machine code, 2023). Machine languages are built by humans and they only understand what humans tell them to understand.

ChatGPT: How do “machine (programming) languages” differ from human (natural) ones? (~100 words).

Machine (programming) languages and human (natural) languages differ fundamentally in their purpose and structure. Machine languages, such as Python or C++, are designed for computers to execute specific instructions, focusing on precision and efficiency. They use syntax and logic tailored for programming tasks. In contrast, human languages, like English or Spanish, serve communication between people and encompass diverse nuances, emotions, and abstract concepts. Human languages are expressive and context-dependent, emphasizing the richness of communication, while machine languages prioritize clarity and exactness for computers to interpret and execute instructions accurately (OpenAI, 2023).

3. How does “machine (artificial) intelligence” differ from the human version? (~100 words).

My response:

Many attempts have been made to define intelligence yet it has been difficult to develop one definitive definition (Chollet, 2019). Chollet (2019) discusses how intelligence can be perceived as task-specific skills or a general learning ability, then uses Algorithmic Information Theory to propose a new definition of intelligence. Human intelligence uses pragmatics to make sense of situations (Jones, 2020). Machine intelligence follows the instructions that humans have programmed it with the hopes of mimicking human intelligence and can recognize patterns that humans do not have the capacity to process, as we saw in the large amounts of data analyzed with the Cambridge Analytica example of “pragmatics on steroids” (Jones, 2020).

ChatGPT – Prompt: How does “machine (artificial) intelligence” differ from the human version? (~100 words).

Machine (artificial) intelligence differs from human intelligence in several key aspects. While artificial intelligence (AI) excels at specific tasks through programmed algorithms and data analysis, it lacks the depth and breadth of human intelligence. Human intelligence encompasses emotional understanding, creativity, social awareness, and complex problem-solving, whereas AI is task-oriented and lacks genuine consciousness. Human intelligence is dynamic, adaptable, and driven by consciousness, self-awareness, and subjective experiences, which remain elusive in artificial intelligence. While AI can perform specific tasks efficiently, it falls short of replicating the holistic and multifaceted nature of human intelligence (OpenAI, 2023).

4. How does “machine learning” differ from human learning? (~100 words)

My response:

As we learned in ETEC 512, there is no best or correct theory that can account for all aspects of human learning – human learning is complex and is based on many factors such as biology, prior experiences, and sensory input. Human learning uses intelligence that can be used to program machine learning through using data and models. Machine learning is a branch of AI where machines can learn to process data without explicit instructions using algorithms and they can “learn” to generalize in novel situations as they are exposed to more data over time (Machine learning, 2024).

ChatGPT – Prompt: How does “machine learning” differ from human learning? (~100 words)

Machine learning and human learning differ in their processes and origins. Machine learning involves algorithms and computational models that enable computers to improve their performance on a task through experience or data. It relies on statistical patterns and mathematical optimization. In contrast, human learning is a complex, multifaceted process that involves cognitive, emotional, and sensory aspects. Humans can generalize knowledge, apply intuition, and learn from a wide range of experiences, adapting to new situations with flexibility and creativity. While both involve learning from data, the mechanisms and scope of machine learning and human learning are distinct. (OpenAI, 2023)

5. And for your LAST challenge, a version of the Turing Test: how do YOUR answers to these questions differ from what a machine could generate? make sure you reference the specific differences between your answers and ChatGPT’s. (~200 words)

There are many differences between my answers to the above questions and the answers generated by ChatGPT.

First, ChatGPT is fast. ChatGPT took seconds to process in the specified number of words, whereas my answers took many hours of reading, synthesizing, and staying within the word counts.

Second, ChatGPT did not provide references to its sources of information, whereas my answers have citations. I pulled together references using APA formatting from the course readings whereas ChatGPT did not use the specified readings, nor do I have a way of checking for its plagiarism or find the primary source of information to refer to.

Third, ChatGPT does not understand the nuances within the context of this course, the relevance to the MET program, or to the fields of Educational Technology.

Lastly, ChatGPT does not factor in the rubric as provided by our ETEC 511 professor which is a personally relevant consideration in this assignment.

The end result is that these answers are not that different from each other but I do hope my answers come across as more “human” as they are based on my personal reflections, experiences, and the nuanced expectations of this course.

References

Al Jazeera English. (2022, August 5). Timnit Gebru: Is AI racist and antidemocratic? | Talk to Al Jazeera [Video]. YouTube. https://www.youtube.com/watch?v=vUJVzIdRSnQ

Alan Turing. (2024, January 19). In Wikipedia. https://en.wikipedia.org/wiki/Alan_Turing

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

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

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

Crawford, K. (2021). Atlas of AI. Yale University Press. (Introduction: pp. 1-21)

Herbert A. Simon. (2024, January 23). In Wikipedia. https://en.wikipedia.org/wiki/Herbert_A._Simon

John McCarthy (computer scientist). (2023, December 5). In 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.

Language. (2024, January 28). In Wikipedia. https://en.wikipedia.org/wiki/Language

Machine code. (2023, December 10). In Wikipedia. https://en.wikipedia.org/wiki/Machine_code

Machine learning. (2024, January 29). In Wikipedia. https://en.wikipedia.org/wiki/Machine_learning

Marvin Minsky. (2024, January 4). In Wikipedia. https://en.wikipedia.org/wiki/Marvin_Minsky

McCarthy, J. (2007, November 12). What is Artificial Intelligence? https://www-formal.stanford.edu/jmc/whatisai/node1.html

Timnit Gebru. (2024, January 16). In Wikipedia. https://en.wikipedia.org/wiki/Timnit_Gebru

Turing’s proof. (2024, January 8). In Wikipedia. https://en.wikipedia.org/wiki/Turing%27s_proof

Turing test. (2024, January 7). In Wikipedia. https://en.wikipedia.org/wiki/Turing_test

UBS. (n.d). Meet the Nobel Laureates in economics: Do we understand human behaviour? https://www.ubs.com/microsites/nobel-perspectives/en/laureates/herbert-simon.html

ETEC 511 – IP #3 – Algorithms

Introduction

Google impacts many people around the world everyday and could rightfully be called the world’s most powerful company (Jack, 2017). Google’s products include online advertising, search engine technology, cloud computing, artificial intelligence, e-commerce, consumer electronics, and more (Google, 2024).

When most people search for information on the internet, Google’s search engine is usually top of mind. Behind Google’s search algorithms, however, is a darkside that is controlled by artificial intelligence such as recommender systems, priority cutes, algorithmic bias, and a PageRank system that can be blamed for societal inequities, racism, and oppression through how its content prioritization algorithms work behind the scenes (Nobel, 2018, p. 33).

Content Prioritization

Content prioritization is a way of ranking what the product designers think is important to the end user of the information so that the most important items are displayed first. Content prioritization is used in education when the teacher wants to highlight the important concepts first. In search engine content prioritization, however, the information that is presented first is usually what will generate the most clicks or the most revenue.

Content prioritization is meant to capture the end users’ attention and is found in digital or print media, like magazines, email marketing, newspapers, where the most important stories are displayed first and less important information would follow. Content prioritization can also be found in digital entertainment streaming platforms such as Spotify and Netflix.

Controlling the largest digital repository in the world

The content prioritization algorithms that companies like Google use are helpful when getting important information out, however, these same algorithms can also be responsible for perpetuating misinformation, as we saw in the Cambridge Analytica scandal (Jones, 2020). These algorithms also punish those without the resources to advertise, like Nobel (2018) exposed Kandis’ Yelp experience where her low rankings punished her business and also punished potential clients by having to travel long distances to find a hairdresser who was knowledgeable about Black hair, further oppressing an already marginalized group of people. This form of algorithmic oppression is “loaded with power” and continues to be true today as they continue to perpetuate racism, hegemonic ideals, and inequalities through these content prioritization systems (Nobel, 2018, p. 171). Nobel (2018, p. 6) describes that the internet is “the most unregulated social experiment of our times” and the importance of public policy to prevent algorithms, whether these biases are programmed intentionally or not, from further inequities.

Impact on my professional life

Content prioritization algorithms can have positive and negative impacts on my professional life. They can be useful for some tasks as it can find information faster, focusing on the most relevant information. The content prioritization systems can help highlight what is most important and reduce overwhelm by having the algorithms do it for me. These algorithms will hopefully display what is personally relevant based on my previous searches and relevant recommendations (OpenAi, 2023). The downside to these algorithms choosing the information that gets displayed to me is that I don’t know what goes on behind the algorithm from Google, an advertising company that is driven by profits. Google controls what information I see and therefore will display results that prioritize their interests above mine, leaving those people with less money, power, and resources without a voice. This is a form of oppression by prioritization of power.

PageRank

PageRank, named after its co-founder Larry Page and the term web page, is one of the algorithms used by Google Search. PageRank measures how important a web page is to display their search engine results to the user. Each web page gets a numerical weighting, that is not known to anyone other than the algorithm, based on a probability distribution algorithm that determines its importance. The higher the ranking, the more important the link is according to PageRank (PageRank, 2024).

PageRank impacts my personal life because anyone who searches my name will easily see information about me. When I search my own name on Google, there is a lot of personal information that comes up, for example my social media profiles, charities I work with, and my professional website.

One thing I did not consider was how changing my last name when I got married impacted my online searchability. When I search my maiden name, my accomplishments and information have fewer pages over the years so potential employers are not able to see publications I co-authored, my college volleyball stats, and other information under my maiden name, unless I provide these links directly. It makes it a bit more difficult to find information on me which I view as a positive and I have a bit more control over the information that is displayed about me.

Conclusion

Algorithms are increasingly shaping how we interact with products made by companies such as Google, Apple, and other tech giants. We usually do not see how the algorithms work but we do need to be aware of how they make decisions for us and increase our awareness of the biases underlying them so we can fight against them (Slavin, 2011).

References

Crawford, K. (2021). Atlas of AI. Yale University Press. (Chapter 3L Data, pp. 87-121)

Google. (2024, January 30). In Wikipedia. https://en.wikipedia.org/wiki/Google#cite_note-11

Jack, Simon. (2017, November 20). Google – powerful and responsible? BBC News. https://www.bbc.com/news/business-42060091

Noble, S. U. (2018). Algorithms of oppression. New York University Press. (Introduction, Chapter 1, Conclusion)

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

PageRank. (2024, January 31). In Wikipedia. https://en.wikipedia.org/wiki/PageRank

Slavin, K. (2011). How algorithms shape our world [Video]. TEDGlobal.

Taylor, A. (2021, February 2021). Are streaming algorithms really damaging film? BBC News.

ETEC 511 – IP #4 – Media Convergence

ASSIGNMENT DETAILS

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.

Production format

A (digital) mind map and a <5-minute video walkthrough explaining your mind map.

Click on the video below to view my assignment (see text below for full transcript).

Final Mind Map for Media Convergence

ETEC 511 - IP #4: Media Convergence Mind Map — Click to view larger image.

Transcript for the video:

ETEC 511 – Module 4 Notes: Media Convergence

In this video, I will walk you through the mind map I created for my IP#4 on Media Convergence.

I’ll outline what media convergence is, how Henry Jenkins categorizes media convergence into 5 different processes, and its educational implications.

SO, WHAT IS MEDIA CONVERGENCE?

Media convergence is where all media are blending into one.
It is not a hypothetical black box, but instead an ongoing process that changes how media is both consumed and produced.
But what’s happening is that we’re living in this diverse media world, where media consumption is making users participants that engage with different forms of media, often at the same time.
Media is all around us and we’re developing new skills to manage all this information.
There is the coming together of these previously separate technologies that are now sharing tasks and created this place where media, industries, content, and audiences intersect.

JENKINS’ 5 PROCESSES OF MEDIA CONVERGENCE

Jenkins states that when we talk about media convergence, we’re usually referring to 5 processes

Technological Convergence
- Technological convergence is the digitization and integration of many forms of media into one ecosystem
- We expand these relationships between digital media and enable them to flow across platforms allowing different kinds of technology to merge.
- EXAMPLES ARE
  - Smartphones, IoT, smart home technology, wearable devices, AR/VR, cloud computing, autonomous vehicles, e-books, gaming consoles
- So phones are no longer just for making phone calls, and video game consoles aren’t just for playing video games – we can use smartphones to play video games, and also watch shows, access the internet, text friends, and more; and we can use video game consoles for gaming, social media, or streaming services.
Economic Convergence
- Economic convergence is when companies control entertainment franchises across multiple forms of media; or, as Jenkins describes, the horizontal integration of the entertainment industry
- It involves the financial and business aspects of integrating different media types.
- EXAMPLES
  - Broadcast tv (Fox), cable tv (Nat Geo), digital advertising, subscription models, news papers now online news companies (NYP, WSJ)
Social or Organic Convergence
- Social or organic convergence is when people multitask and integrate technology across many platforms.
- It reflects how individuals blend and share media content in everyday communication.
- EXAMPLES
  - Watching netflix on a tv, texting friends on an iphone, writing a paper on a laptop
Cultural Convergence
- Cultural convergence is where culture is merged with many forms of media through participatory culture and storytelling.
- Anyone with access to the internet can use the affordances of different types of media to tell stories or engage with other people’s content by commenting, sharing, etc
- EXAMPLES
  - Books becoming TV shows or movies; fan communities creating fan fiction, art, videos; remix culture where existing content is recombined and repurposed; globalization or pop culture through the spread of music, movies, and TV across borders; interactive museum exhibits combining traditional artifacts with digital technology for immersive experiences (ChatGPT)
Global Convergence
- How media is interconnected around the world and when geographically distant cultures are able to influence one another
- Can have its advantages but could also just perpetuate the dominating structures at the centre of the system
- EXAMPLES
  - Social media platforms (Facebook, Twitter, Instagram, YouTube, TikTok), streaming services (Netflix, Amazon Prime, Disney+; access content anytime, anywhere, any device with internet access)), global news networks (CNN, BBC, Al Jazeera English – 24 hour worldwide news coverage), digital journalism (Huffington post, Buzzfeed, The Guardian), Global advertising campaigns (Google ads, Facebook ads – allows advertisers to target specific demographic groups and geographic regions with personalized ads), viral content and memes (transcending language and geographic barriers – on TikTok, Reddit, other social media platforms), cultural influences and trends (Hollywood movies, K-pop, etc)

So, Let the DIGITAL RENAISSANCE begin…

Jenkins argues that these forms of media convergence are leading us toward a digital renaissance – a period of transformation that will affect all aspects of our lives.
And that a new cultural order is going to emerge, for better or worse.

We’re still unsure how these different types of convergence will affect society and individuals, including the educational impacts.
The POSITIVE educational implications include
- Having better access to information (there are no longer gatekeepers of information);
- Being more cost effective to access information (no more expensive university tuition);
- We are likely to see more innovation and diversification of content;
- And that this content can be personalized to the learner.
However, there are NEGATIVE implications to education that may happen such as
- Having information overload;
- It creating an even further digital divide with learners;
- And the homogenization of information as powerful companies have monopolized media platforms

I guess we’ll see how it all unfolds as media convergence as we experience this digital renaissance together.

Thanks for watching!
/ end of transcript

References:

Jenkins, H. (2001, June 1). Convergence? I diverge. MIT Technology Review.

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

University of Minnesota. (2016). Understanding Media and Culture: 1.4 Convergence. https://open.lib.umn.edu/mediaandculture/chapter/1-4-convergence/

ETEC 511 – IP #5 – Global Health

Disclaimer: Some of the formatting is not standard for APA formatting due to the restorations of the webpage format.

Global health includes an interconnection between humans, animals, plants, and the environment around us.

Source: https://oia.osu.edu/media/japhavmq/onehealthvenn.png

Global Health and Its Impact on Education

The COVID-19 global pandemic was a reminder that global health directly affects global education. When we refer to global health, we should ensure we include humans, as well as animals, plants, and the environment and the interrelationship of all of these elements.

Investing in education can contribute to improved overall global health because they are both interconnected in several ways. Education can impact global health positively as advancements in research, new treatments, vaccines, and health care can help improve the health quality of our communities. Education can be useful to prevent people contracting infectious diseases, expand their knowledge of nutrition, and provide resources to access adequate health care. Education, including but not limited to educational institutions, can be used to prevent some diseases or expand our knowledge of health issues such as nutrition, hygiene, and reproductive health. Global economics are affected because of the increased health care costs around illnesses, sometimes causing families to suffer financially or make sacrifices such as school or job absenteeism, or dropping out of school to contribute financially or to care for sick family members. Global health affects the quality of education because it impacts the cognitive development and academic performance of students; if basic nutritional and health needs are not being met, students will struggle to learn. It can also affect students and teachers who do not want to return to in-person learning as they would be putting themselves at risk for infections. Global health also disproportionately affects marginalized communities, including Indigenous communities and women causing even more detrimental health disparities (OpenAI, 2023).

It is undeniable that the COVID-19 pandemic disrupted education, at least our traditional view of what education is. Children were at home with caregivers while educators attempted to adapt to provide continuity of learning. Interestingly, the COVID-19 pandemic brought to light assumptions and biases about what “education” actually is, exposed those who were unwilling to adapt, and highlighted “many pre-existing poor teaching practices (Czerniewicz et al. 2020, as cited in Boys, 2021, p. 17).

Prior to the pandemic, an assumption about effective education was that learning could only happen in-person, face-to-face, even when there was research that concluded otherwise (Boys, 2021, p. 19). It also highlighted what constitutes evidence of learning, who gets noticed, who has “preferred character traits”, and who is “invisible” (Boys, 2021, p. 19). It also highlighted assumptions on who is valued and deserving of education, leaving certain genders, race, classes, abilities, or sexualities seen as inferior and therefore excluded or “misfitting” for education (Boys, 2021, p. 18). Examples of this would be children of different abilities excluded from the traditional classroom setting or if a school is an unsafe space for students who are gender expansive. This highlighted the need for developing new, innovative, and equitable learning for all as we lived through a global pandemic together. Researchers and educators realized that there were problems with how education has been traditionally carried out and alliances such as COVIDEA have been created post-pandemic because they believe digital tools and resources should be easily accessed by all to help transform our education system. We have moved beyond seeing education as an intellectual process and are moving towards a holistic, interconnected view between humans, non-humans, and the global environment around us as well as elements that promote collective well-being such as character education, judgement, resilience, social awareness, responsible and active citizenship (COVIDEA, 2020; Ewart, 2024).

Lessons we learned from the pandemic included how education children received when they were at home was inadequate, math achievement scores suffered, educators were not held accountable for their students’ learning outcomes, and the mental and physical health of educators and students suffered since people were working in isolation (Kuhfeld et al., 2020; Bennette, 2020). Unfortunately, since administrators had not been through his before, there was no unified plan on what the expectations from either students or educators were and therefore students were not incentivized to continue learning when marking practices were removed. The most disheartening issue illuminated was that the “digital divide” was more obvious than in the past and it was the lower income communities without access to internet, technology, or the means to help children at home struggled most (Burgess et al., 2020; Bennette, 2020). Additionally, it challenged underlying assumptions that that online education is “inherently inferior to face to face” (Boys, 2021, p. 16).

The COVID-19 pandemic that began in 2020 is still affecting our health and education systems and probably will for years to come and is providing a “new foundation” on which educational technology is being built. The pandemic highlighted the interconnectedness of global health and how a pandemic affects and influences our education systems at all levels – from early to higher education. It shone a light on what was already happening in education – for better or worse. It challenged assumptions of what “education” is and that it does not have to be in-person. Some programs and universities, such as the UBC MET program, were already effectively using technology as a tool for education pre-pandemic and utilized the affordances of technology for learning. With more research being done post-pandemic and realizing where education can be reformed and improved, we can make changes using an evidence-based approach and “suitable learning design” to teaching and learning (Boys, 2021, p. 27).

References

Bennette, P. W. (2020, July 20). The educational experience has been substandard for students during COVID-19. Policy Options.

Boys, J. (2021). Exploring inequalities in the social, spatial and material practices of teaching and learning in pandemic times. Postdigital Science and Education, 4(1), 13-32.

Burgess, S., & Sievertsen, H. H. (2020, April 1). Schools, skills, and learning: The impact of COVID-19 on education. Vox.

COVID Education Alliance (COVIDEA). (2020, October). COVID EDUCATION ALLIANCE (COVIDEA) Adapting education systems to a fast changing and increasingly digital world using relevant technologies and online resources. COVIDEA Primer.

Ewart, K. (2024). Global Health [Class handout]. WebCampus. https://canvas.ubc.ca/courses/130938/pages/global-health?module_item_id=6328308

Koplan, J. P., Bond, T. C., Merson, M. H., Reddy, K. S., Rodriguez, M. H., Sewankambo, N. K., Wasserheit, J. N., & Consortium of Universities for Global Health Executive Board. (2009). Towards a common definition of global health. The Lancet, 373(9679), 1993–1995.

Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., & Lewis, K. (2020, December 3). How is COVID-19 affecting student learning? Question 2. Brown Center Chalkboard, Brookings.

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

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