IP 5: Global Health  

Global health creates a new starting point for the growth and future of educational technology. This is because global health shapes the way educational technology is developed and used, and vice versa. Technology provides educational resources across borders and geographical boundaries to those with the required technology. These tools include a vast array of information including public health data, sexual education, nutrition, research, and health care professional training. Ultimately, improving global health by providing essential information and education. Conversely, global health frequently shapes the creation of educational tools as they are often designed in response to a specific need. An example of this is the digital tool created by the University of Michigan students, aimed at educating people on different aspects of Malaria to help eradicate the disease (Magdalena, 2023). Additionally, we saw many examples of digital tools being created out of necessity during the COVID-19 pandemic. For instance, education apps such as Seesaw and Kahoot, the COVID mobile App allowing people to track their symptoms and find updated information, and Telehealth and digital doctors became common place. 

COVID-19 accelerated the integration of technology prompting educators to adapt to the changing circumstances and meet the evolving needs of learners. Educational technology has been instrumental in enabling remote learning, facilitating collaboration among students and educators, and providing access to educational resources amidst the disruptions caused by the pandemic. This global health event impacted education greatly, taught us valuable lessons, and proved there is still much to be learned.  

First, it emphasized that without health everything in life becomes secondary, urging a shift to a more holistic approach to learning where value is placed on not only academic outcomes, but physical, mental, and emotional well-being. Bennette’s (2020) article criticizes educators during the pandemic. I do agree with Bennette that there were areas that could be improved such as having more robust technology infrastructure (Bennette, 2020). However, the pandemic showed many educators are creative, resilient, and adaptable. Bennette overlooks the fact that teachers are notoriously overworked and provided limited resources. Not to mention, they themselves were navigating a global health crisis impacting their own safety and their families. I hope one lesson learned worldwide was that global health can be greatly supported and driven through educators. This accentuates the importance of valuing teachers and providing essential resources to ensure our future leaders and innovators are being supported.  

The pandemic also highlighted the importance of building adaptive and resilient education systems, educators, and students. This could be supported by teaching students in a variety of learning environments, implementing both group and individual work, and embracing innovation and technology. Access to technology plays a crucial role in providing accessible education globally. This is particularly significant for those regions with limited access to traditional education. However, as mentioned in the article “Technology can transform global health and education, but it’s no silver bullet,” by the University of Oxford (2019); technology implemented incorrectly will be ineffective and costly. However, “when technology is deployed thoughtfully and judicially, positive disruption on a large scale is entirely possible” (University of Oxford, 2019). This underscores the importance of integrating technology effectively to enhance global health outcomes. 

Amidst the pandemic, inequalities became even more evident as individuals with lower socioeconomic status found themselves further marginalized, experiencing unequal access to not only health care and vaccinations, but crucial student learning support. As Burgess and Sievertsen note, “there will likely be substantial disparities between families in the extent to which they can help their children learn” (Burgess & Sievertsen, 2020). These disparities are influenced by factors including parents’ work schedule, capacity to afford tutors, parental education, and access to technology. 

Communities who face greater challenges in accessing online learning resources were particularly disadvantaged. Addressing the digital divide and ensuring equitable access to technology and internet connectivity are critical for inclusive education and global health. “While digital technology undoubtedly enables communication, collaboration and online education, a more permanent shift to remote learning could exacerbate inequalities if adequate digital tools and technological options are not affordable or easily accessible” (COVIDEA, 2024). This is a lesson that governments and education policies need to address and prepare for in case of another global pandemic. 

Another important lesson acquired post pandemic is the re-evaluation of the metrics used to evaluate a ‘good’ student to include diverse learners. As Boys (2021) highlights, teachers search for evidence of an engaged student such as body language and participation. However, the pandemic has shown us engagement and learning can look different for individual students. When the removal of face-to-face learning occurred, everyone thought it was the demise of education. “This is despite the fact that for the majority of on-campus students, most of their study is already not done face-to-face” (Boys, 2021, pg 15). This shift requires a willingness to adapt traditional evaluation methods to better accommodate the needs and preferences of individual learners, ultimately fostering a more equitable and healthy learning environment for all. 

In conclusion, the relationships among education, technology, and global health are multifaceted and interconnected. The COVID-19 pandemic emphasized the significance of leveraging educational technology to address global health challenges and adapt to changing educational landscapes. “The pandemic’s disruptions have had significant implications for student learning, with estimates suggesting that students may have begun fall 2020 with only approximately 70% of the learning gains in reading compared to a typical school year” (Kuhfeld et el., 2020). Moving forward, it is essential for education systems to learn from this life altering experience to build more resilient, inclusive, and innovative educational environments. By doing so, we can better prepare ourselves to meet the challenges of the future while ensuring equitable access to education and health for all. 

Works Cited 

Bennette, P. W. (2020, July 20). The educational experience has been substandard for students during COVID-19Links to an external site.. 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 educationLinks to an external site.. Vox. 

 COVIDEA (COVID Education Alliance) – foggs.org. (n.d.). Retrieved February 12, 2024, from https://www.foggs.org/covidea/ 

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

 Magdaleno, R. (n.d.). Students develop digital tool to help the fight against malaria | Pursuit | University of Michigan School of Public Health | Malaria | Infectious Disease | Epidemiology | Student | BS | BA | Epidemiology | Undergraduate. Retrieved February 11, 2024, from https://sph.umich.edu/pursuit/2023posts/students-develop-digital-tool-to-help-the-fight-against-malaria.html 

 Technology can transform global health and education, but it’s no silver bullet | University of Oxford. (2019, May 29). https://www.ox.ac.uk/news/2019-05-29-technology-can-transform-global-health-and-education-its-no-silver-bullet 

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) 

 Explain in your own words what “content prioritization” (Noble, p. 156) means (give some examples) and how (in lay terms) content prioritization algorithms work.  

Content prioritization is giving preference, highlighting, or showing certain types of digital content before others. These prioritization algorithms follow rules and organized patterns that determine what you see. These patterns are based off relevance to you, your previous searches and online behaviour, and what is currently popular. For example, if you often search articles about sports, open news about sports, like posts about sports, the algorithm will learn this behaviour and then prioritize sports-related content for you. Another example is Tik Tok. Depending on how long you spend watching a video, Tik Tok will determine your interest level in the post and continue to prioritize similar posts. Other examples can be seen on your social media feeds, your news apps, even my Gmail prioritizes certain mail and files what is considered lowest priority into my junk mail. Many of these algorithms can seem positive by increasing efficiency and steering you towards areas of interest, however they also result in people seeing more of the same, causing ‘filter bubbles’. Merriam webster defines these as “an online environment in which people are exposed only to opinions and information that conform to their existing beliefs (Definition of FILTER BUBBLE, 2023). This has many consequences for the user and others in society, particularly those often marginalized. 

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) 

Noble highlights Google as an example of a private institution that has massive control since declining state memory institutions. If a corporation, such as Google prioritizes specific content, it goes against the principles of net neutrality. In a net-neutral environment, content should not be chosen based off of what it is or who is viewing it. Without this neutrality, there is no longer an even playing field for all content and viewers. Instead, there is the opportunity for discrimination and bias. 

For example, “Google focused on its prioritization of high-paying advertisers that were competing against small businesses and entities that do not index pages on the basis of the pay-per-click advertising model” (Noble, 2018, pg 158). Small companies that are already facing obstacles are therefore further disadvantaged. Prioritization algorithms are also based on previous data. Therefore, if historical data includes stereotypes or bias towards certain people, then this could be perpetuated. For example, if you were to search ‘doctor’ or ‘engineer’ and saw only pictures of males you may be influenced to believe that they are male dominated professions. If you were seeking to learn about a certain culture, your online searches may only yield stereotypical pictures and information furthering an inaccurate representation. 

These algorithms impact my professional life as a teacher in many ways. They can make it harder to find resources that are diverse. This is especially hard as a Social Studies teacher, trying to stay up to date with current events. Algorithms make it much more difficult to ensure I am getting an unbiased understanding of what is happening across the world. Additionally, these algorithms impact curriculum and lesson planning. Administration and I must implement this learning into curriculum, so students understand what they see online is not the whole picture teaching skills such as triangulation and fact checking to combat their ‘filter bubbles’ and misinformation. 

PageRank is essentially a popularity contest for websites. It looks at how many other websites link to a particular site and considers those links as votes. The more and higher quality votes a site gets, the more important Google thinks it is, and it shows up higher in search results. This impacts my personal life in many ways. I rely on research and information to form many decisions in my life. For example, my finances. I search for information online about investing, saving, mortgages etc. So, PageRank would show me certain content perhaps because large corporations have paid to have the highest rank to seem the most reputable. This ranking system can be seen very clearly in social media, something I use daily. If a post on Instagram or TikTok gets a high number of likes, it will get higher visibility on my feed. However, just because something is popular does not make it accurate or credible. Certain posts act as click bait and contain misleading information. Also, popular and wealthy people such as celebrities or content creators continue to get more popular instead of providing equal opportunity to those with less of a platform but valuable things/ideas to share. Therefore, I can impact PageRank by being conscious about what I click on and share and ‘liking’ diverse posts. 

 Definition of FILTER BUBBLE. (2023, December 7). https://www.merriam-webster.com/dictionary/filter+bubble 

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press. https://doi.org/10.18574/9781479833641 

IP 2: Artificial Intelligence

Alan Turing

Alan was a British Mathematician who developed the first ‘computer’ or what came before the computer. He proved that not all mathematical problems had a definitive answer. He thought intelligence could be defined by how close AI can complete tasks similarly to a human.(Alan Turing – Education, Movie & Quotes, 2020) https://www.biography.com/scientists/alan-turing

Chat GPT:  Alan Turing (1912–1954), a British mathematician, contributed to artificial intelligence (AI) by proposing the Turing Test in 1950. He believed intelligence could be identified through observable behavior. If a machine’s conversation was indistinguishable from a human’s, it was considered intelligent. Turing’s pragmatic approach influenced early AI development, shaping natural language understanding. 

John McCarthy was an American computer scientist who coined the term ‘Artificial Intelligence.’ He believed intelligence was “the computational part of the ability to achieve goals in the world” (Sutton, 2020). Or in other words, using thinking and problem-solving skills to achieve goals.

 

Sutton, R. S. (2020). John McCarthy’s definition of intelligence. Journal of Artificial General Intelligence, 11(2), 66-67. 

Chat GPT: John McCarthy (1927–2011), an American computer scientist, coined “artificial intelligence” and played a pivotal role in AI’s formalization. He organized the 1956 Dartmouth Conference, marking AI’s inception. McCarthy identified intelligence through symbolic reasoning and problem-solving, shaping the development of AI systems with advanced cognitive abilities. 

Herb Simon was one of the founding fathers of artificial intelligence. His main area of interest was decisions making. He defined intelligence to be when computers solved problems like humans. 

Chat GPT: Herbert Simon (1916–2001), an American scholar, contributed to artificial intelligence (AI) by pioneering problem-solving and decision-making research. He developed the Logic Theorist, an early AI program. Simon identified intelligence through problem-solving capabilities, emphasizing symbolic reasoning. His work laid the foundations for AI systems with advanced cognitive abilities. 

Marvin Minsky was an American computer scientist and mathematician who contributed to the field of AI in robotics, mathematics, and cognitive science. He thought intelligence was based on common-sense reasoning and if systems could function with this human trait. 

Chat GPT: Marvin Minsky (1927–2016), an American cognitive scientist and computer scientist, co-founded the MIT Artificial Intelligence Project and made pioneering contributions to AI. He emphasized neural networks and perception in his work, believing that intelligence could be identified by emulating human cognitive functions, particularly in the realms of perception and learning. 

Gebru is an ethics researcher that focuses on biases and inequality in AI in areas such as facial recognition, energy use, and the impact on climate change. Although I could not find a definitive answer, I can assume she would value AI intelligence as one that can recognize and account for human diversity and aid in equality, or at least not support/exacerbate inequality. 

Chat GPT: Timnit Gebru is a computer scientist known for her work in artificial intelligence (AI) ethics and fairness. She co-founded the group Black in AI and has researched biases in AI algorithms. Gebru emphasizes the need for diverse perspectives in AI development to ensure fairness and avoid biases in intelligent systems. 

 Machine (programming) languages” vs human (natural) ones?  

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

How does “machine learning” differ from human learning?  

 Do YOUR answers to these questions differ from what a machine could generate?  

My answers to these questions differ from what a machine could generate in a few ways. Firstly, I used examples of experiences in my life to formulate answers and draw conclusions. I used my own assumptions and intuition. For example, when researching Timnit Gebru. I could not find her definition of intelligence and AI did not find me one either. I had to use my common sense and reasoning skills to come up with a potential definition. Additionally, I used references to articles to support my writing whereas AI does not have that capability (at least the version I am using). This makes the responses less specific and more general in nature. Lastly, the machine answers are better at writing more precisely and sound very professional, however the questions asked are more specific and factual requiring less opinions or problem-solving skills which are easier for AI to answer. 

1. Formulate a conception of usability (based on the Issa and Isaias (2015) chapter on HCI and Usability).  

HCI stands for Human-Computer Interaction, and it is a multidisciplinary field that focuses on the design and use of computer technology, emphasizing the relationship between humans and computers. Central to HCI is the concept of usability. This term includes many factors but essentially aims to create effective and user-friendly systems. This means that they should strive to be easily learned, navigated, individualized, memorable, and enjoyable for their users. To ensure usability, HCI should incorporate users in all stages of design.  

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.  

The concept of usability mentioned above may not account for certain aspects of education. To start, usability as discussed by Issa & Isaias (2015), is often evaluated by speed, completion, and ease of use. However, in education evaluation of success surrounds learning concepts such as critical thinking and problem solving. These skills are not quick to develop and very difficult to assess. Additionally, learning is not meant to be easy. More traditional education such as memorization and regurgitation may fit with the concept of usability, however more contemporary education has shifted to collaboration and higher-level thinking. Educational usability would need to focus more on tools which promote learning, support diverse learners and educators, and have less concern for user satisfaction but rather users reaching their learning outcomes. In conclusion, when considering educational usability, a more comprehensive perspective is required compared to the conventional usability criteria. This entails considering the specific objectives and desired learning outcomes in the educational context. 

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. 

This paper uses examples from studying computers to explain how creating a new thing, like a new type of computer, involves shaping the people who use it. This process, called “configuring,” includes defining who the users are and putting limits on what they are likely to do in the future. By analyzing recordings of usability trials, where people test how easy it is to use the new machine, the paper suggests that deciding how well the machine and user relate to each other is influenced by the idea of setting boundaries between them. These boundaries can affect how users are perceived, how their actions are constrained, and how the machine is defined in relation to them. 

 Example 1  

In the story about the “wrong socket” incident, Ruth is trying to connect a machine to a printer. What stands out here is that the evaluation isn’t about Ruth’s abilities. Instead, it’s about checking if the instructions on how to use the machine effectively guide or shape the user, like Ruth, to complete the task correctly (Woolgar, 1990, pg 86). However, because the computer is the wrong type for the plug, we see how instructions and machines are shaping both Ruth and the observers when usability goes wrong. 

Example 2  

The manuals provide information for the user on what steps and actions they are to perform to use the machine. Therefore, “these captions configure the user by defining the correct courses of interpretation and action to be followed” (Woolgar 1990 pg 81). Because the machine was not complete the assessment involves considering whether the machine is behaving like a real machine and whether the user is acting like a real user in this evolving project. 

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. 

“…the usability evaluation stage is an effective method by which a software development team can establish the positive and negative aspects of its prototype releases, and make the required changes before the system is delivered to the target users”  (Issa & Isaias, 2015, p. 29). 

 

“…the design and production of a new entity…amounts to a process of configuring its user, where ‘configuring’ includes defining the identity of putative users, and setting constraints upon their likely future actions” (Woolgar, 1990). 

The difference between the 2 excerpts is how the user is viewed. In Issa and Isaia’s (2015) quote the user is utilized in the stages of development and assessment. They are the focus that the product will then change and evolve around for increased usability. Alternatively, Woolgar’s quote positions the user as something that will be configured to fit the machine. By placing constraints on the user, usability is determined by if the user can use the product within these set constraints. 

Sources:

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. 

TandR (2)