Category Archives: ETEC 540

Writing by Hand in a Digital Age (Task 4)

 

Here are two samples of my handwriting in English and Persian.

I usually, or perhaps I should say most of the time now, type instead of writing by hand. Especially after immigrating, my habit of using a pen has become very rare. I notice this most clearly in my personal diary. Nowadays, handwriting has become difficult for me. Holding a pen and moving it across the paper feels strange and tiring. I also find that I can no longer form letters as beautifully as before. If you look at a page from my diary (Persian one), you will see how my handwriting has lost its neatness and flow. Even though I do not type with the full ten finger method, I still rely on typing almost every day. My typing is not professional, but it works. I am forced to depend on typing because almost everything I need to prepare for assignments, proposals, or documents to be sent, must be submitted in Word or PDF format. Because of this, typing has become the natural and mandatory choice for me, while handwriting has become more of a rare, nostalgic practice.

When I write my diary, if I make a small mistake, I usually just ignore it and continue writing. But if the mistake changes the meaning of sentence, I have no choice but to correct it. I cannot erase the word. Instead, I cross it out with a line and write the correct word above or below it. Editing by hand is therefore more manual and visible, and the corrections themselves become part of the page (Clement, 1997).

When I type, the process is very different. I use Word software that has autocorrect and spell-check features. This can be very helpful. However, it can sometimes create new mistakes. For example, if I am not careful, the autocorrect feature may change a word into something completely different, with a different meaning (Lekamge & Smith, 2025).

Because of this, I think the medium plays a very important role in how I edit my work. With handwriting, I am responsible for finding and correcting every mistake myself, and my corrections stay visible on the page. With typing, the computer helps me, but it also sometimes makes errors that I must fix. In both cases, editing affects the overall beauty and clarity of the text, but in different ways (Lamb & McCormick, 2020, May 26).

From my perspective, the most important difference between handwriting and mechanized writing is the way the flow of thought is affected. When I type, the presence of autocorrect sometimes interrupts my idea rhythm. The software may change or suggest something that alters the structure of the sentence. This can break the natural stream of my thinking and shift my focus from meaning to correction. When I write by hand, even though it feels harder for me now, my thoughts and my hand move together. I do not worry too much in the moment about whether the word is perfectly correct; instead, the fluency of the sentence and the clarity of the idea show themselves more naturally. With practice, handwriting can also be very enjoyable because of the beauty of the script itself (Kiefer et al., 2015).

I also feel that handwriting leaves a kind of trace of my learning. The small corrections and crossed-out words remain visible on the page, like a memory of my past mistakes. When I look back, I can see where I wrote a wrong word or made a grammatical error, and it reminds me that I am improving. With typing, this does not happen. Because of this, I personally prefer handwriting, since it carries both aesthetic value and a visible record of growth, even though typing is more practical in my daily work.

References:

Clement, R. W. (1997). Medieval and Renaissance Book Production. Utah State University.

Kiefer, M., Schuler, S., Mayer, C., Trumpp, N. M., Hille, K., & Sachse, S. (2015). Handwriting or Typewriting? The Influence of Pen- or Keyboard-Based Writing Training on Reading and Writing Performance in Preschool Children. Adv Cogn Psychol, 11(4), 136-146. https://doi.org/10.5709/acp-0178-7

Lamb, R., & McCormick, J. (2020, May 26). From the vault: Invention of the book, part 1 & 2.

Lekamge, R., & Smith, C. (2025). Impact of Auto-Correction Features in Text-Processing Software on the Academic Writing of ESL Learners. English Language Overseas Perspectives and Enquiries, 22(1), 69-91.

 

My Journey with a Voice-to-Text Story (Task 3)

My Journey with a Voice-to-Text Story (Task 3)

This week, for maybe the second or third time, I tried using apps that change voice to text (https://speechnotes.co/). Because of this, I decided to share a memory about the first book I prepared and published about thirty years ago. I read the story in simple English into the app. Of course, English is my second language, so some of my pronunciations may not have been clear. The result was very surprising for me. Below, you can see the text that the app created. After that, I will share my thoughts about the text and how the app changed my words. At the end of my post, I will also include the edited version so you can compare and see the differences.

                 

The voice-to-text story feels different from normal written English. It sounds more like someone talking than like something written down. The sentences are long and run together without punctuation, so it’s hard to tell where one idea ends and another begins. Sometimes small words like “the” are missing. There are also grammar mistakes and odd word choices. For example, “library well very strict with big box light Britannica” or “many of their Ray sources” don’t make sense. These are mistakes from the speech-to-text program. In proper writing, sentences would be shorter, the verb tense would stay the same, and the words would be clearer.

Still, the text is not all wrong. There are problems like run-on sentences, wrong verb forms, misheard words, missing punctuation, and confusing sections. It doesn’t show the tone of voice or body language you’d expect when someone is speaking. But the story itself makes sense. It flows in a way that feels natural. It shows feelings of nostalgia, effort, and pride. The voice feels personal and conversational, which makes it sound like a real person telling their own story. Most importantly, the main ideas still come through. We can see how hard research was before the internet and how much patience and passion it took.

The most common mistakes are easy to spot. The sentences run on because I don’t talk in neat, written chunks. Misheard words happen because the app guesses at sounds and sometimes gets them wrong. Missing words show up because the app often skip little words like “the or “to when they talk. Verb tense changes appear because I jump between past and present without noticing. These are considered mistakes because they don’t follow the rules of written English. That makes the text harder to read and less exact.

If the story had been scripted, it would look very different. I made it shorter and easier to read, with correct grammar, punctuation, and spelling. I try to stick the main points like the libraries, the copying rules, the translations, and the handwritten drafts. Also, there wouldn’t be as much repetition or side-tracking. But something would also be lost. The natural emotion of the spoken version might disappear. Oral storytelling has a kind of life and feeling that a scripted version can’t always capture.

This shows how oral storytelling is not the same as written storytelling. Oral stories feel alive because they use tone, pauses, and even body language to make them real. They can wander a little, repeat things, or change tense, and that’s okay in speech. Written stories are different. They need structure, spelling, punctuation, and grammar to make sense. They aim to be clear because the reader can’t hear the voice or see the gestures. Oral storytelling connects with people in the moment. Written storytelling is built to last.

   

Project Retrospective Group 3: Meet the Met-MET’s Online Student Centre (MOSC)

Short review on design and development of Meet the MET

At the outset of the MET program, an online session was convened to introduce and acquaint participants with the program’s objectives, and to gain insights from the perspectives of faculty and alumni. It became apparent that despite the presence of various UBC MET social media, there was a necessity for a interactive platform where they could share their questions and opinions among themselves, as well as with alumni and instructors. The primary challenge identified was the lack of interactivity. Consequently, we decided to address this need by launching a website. Throughout numerous online sessions and the formulation of performance standards and policies, my responsibility encompassed evaluating the usability and sustainability of this tool, along with designing two “Collaborative Virtual Study Hub” and “Showcase” webpages.

My own role in Meet the MET

  1. Usability and Sustainability of Meet the MET

In addressing usability concerns, I endeavored to adapt our approach based on course materials and the needs of our target audience, prioritizing accessibility, and user-friendliness. Our design aimed to meet key usability criteria (Issa & Isaias, 2015), including flexibility, efficiency, satisfaction, and learnability, while also resolving program issues as they arose during execution. The preference for website design over an application was due to usability and accessibility, as applications face limitations in installation and execution on some androids devices (Apple, 2022).

Sustainability was a crucial consideration in our decision-making process, as websites require fewer updates compared to applications, reducing maintenance efforts and costs. However, sustaining our educational tool in the digital realm requires collaboration and support, particularly in areas where our team lacks IT expertise. Additionally, further refinement is needed in the activation method for icons and links to course units, requiring additional time for completion. To enhance efficiency, I propose streamlining our website by reducing the number of pages and integrating content to improve user-friendliness.

  1. Webpages (Collaborative Virtual Study Hub and Showcase of MET Projects)

In my opinion, the “Collaborative Virtual Study Hub” webpage serves as a summary of the overall design of this educational tool because on this page, by clicking on each course icon, the user is directly taken to the relevant course. There, they can share their opinions, needs, and questions about that course with other students and alumni. Briefly, the MET Students Virtual Study hub is designed to facilitate collaborative learning and academic support for MET students. Through shared resources, study sessions, and peer interaction, it promotes success within the MET curriculum.

In the Showcase of MET Projects webpage, which could be considered as an extension of the Virtual Study Hub webpage, users not only have access to links and blogs of other MET students and alumni but also become familiar with their projects that they were done during their study period. Additionally, this page provides access to other educational institutions and job centers, thus enabling students to gain a proper understanding of available job opportunities. Furthermore, we hope that this website serve as a reference for recruiting companies to engage in discussions with students based on their projects and work experiences for potential employment within their companies.

In the end, this teamwork was a new experience for me because it was a practice in coordinating tasks virtually, with its challenges and unique joys.

References:

Apple. (2022). Can any apple apps work for android phones to? https://discussions.apple.com/thread/253785573?sortBy=best

Issa, T., & Isaias, P. (2015). Usability and Human Computer Interaction (HCI). In T. Issa & P. Isaias (Eds.), Sustainable Design: HCI, Usability and Environmental Concerns (pp. 19-36). Springer London. https://doi.org/10.1007/978-1-4471-6753-2_2

Udemy: an online platform for distance learning

Udemy is a platform where people can learn from experts in diverse subjects such as data science, business, lifestyle, and teaching. Udemy like other online platforms spends both direct and indirect expenses to sustain and grow its educational products. These costs cover various aspects such as content creation, platform maintenance, marketing, and technological enhancements. Moreover, some expenses outside of this platform are paid by major IT companies to update systems for increasing data storage capacity, data processing speed, and expanding coverage of internet services. While there are several articles and editorial columns across journals and social media, discussing the benefits of these educational platforms, here we are exploring the obstacles to the sustainability of such tools. If not addressed, these challenges could not only disrupt the growth and efficiency of these platforms but also inflict irreversible damages on societies and ecosystems. It must be noted that discussing the tangible and intangible costs of this platform or similar ones does not imply neglecting the benefits of these educational methods and reverting to traditional teaching approaches. The traditional learning systems are gradually moving away due to the high costs for ecosystem.

In a review of clear costs of this platform, Udemy pays for production costs like the expenses of making and delivering course content. This includes investing in video production and integrating various multimedia elements into the courses. Platform development and marketing costs constitute an important aspect of Udemy’s expenditure. Udemy should pay to other media for advertising and promotional activities to attract instructors and learners to the platform. Administrative, operational, and office-related costs are essential for the day-to-day functioning of Udemy. For enhancing the platform’s content, Udemy might form partnership with other organizations, institutions, and content creators. However, determining the real resource costs of Udemy’s program is challenging due to several factors like private company status, confidentiality, and unique cost models that are not easily comparable to traditional industries. Well, I have tried to clarify that contrary to common belief, online education is not necessarily entirely free or low-cost, and on a production and infrastructure level, tens of thousands of people are involved. Now, I would like to address into the non-measurable costs of these platforms. So, before the rush to expand such platforms, we need to minimize the imposed costs on societies and ecosystems. We can discuss this in two domains: the human domain and the environmental domain.

In the context of human domain, although these platforms try to enhance individuals’ capabilities and knowledge, their long-term effects on human behavior will not be without consequences. Unlike physical classrooms, these platforms do not encourage discussion and questioning of course material, and the transfer of information is predominantly one-sided from the instructor to the learner. Therefore, the space for raising questions and subsequently sharing classmates’ opinions is limited. In the long time, users become a massive data storage source (like a hard drive). Additionally, the practice of actively listening to others’ opinions, and accepting or refuting them is neglected. In the future, capable individuals with high self-confidence may experience a reduction in their creative power, lose the ability to exchange ideas in a group, and perhaps even witness the emergence of a new common phobia known as “group learning phobia or anxiety of learning”(Downing et al., 2020). The mechanisms and standards for monitoring the scientific content of productions on these platforms are not feasible. Additionally, the assessment of the effectiveness of their materials in enhancing users’ capabilities requires reconsideration and study. Also, Educational institutions are generally considered places for educating sciences and upbringing human qualities (Bayanov, 2020). The daily presence at specific hours in a place called the classroom demonstrates the practical importance of having order in social life. Presence in class with other students provides another practical lesson in respecting values and opinions of others, forming friendships, and emotional growth. With the increasing popularity of online platforms like Udemy, the emphasis on teaching skills may surpass the focus on behavioral development and socialize.

In the context of environmental domain, Sustainable IT practices refer to environment-friendly approaches to design, implementation, manufacturing, and disposing-off of their products. Educational platforms should try utilizing energy-efficient systems for visual elements of their programs to reduce energy consumption during downloading and running the program. Now, let me ask a question; how many laptops or smartphones are made each year? 300 million computers and 1 billion cellphones (TheWorldCounts, February 25, 2024). Approximately 40 million tons of electronic waste is produced annually on a global scale. Only 12.5% of E-Waste is recycled (TheWorldCounts, February 25, 2024).The reason for raising this question and providing the answer here is that the primary tools for utilizing online platforms are smartphones and laptops. Therefore, for evaluating the sustainability of the online educational platforms, we must undoubtedly consider the sustainability of their infrastructure. The high volume of e-waste production is significant for two reasons. Firstly, due to the inability to recycle it, the accumulation of waste and its chemical components in the soil and water lead to the extinction of various vital species and posing a threat to human health. Secondly, the growing need to replace these tools implies an increased production by relevant companies. More production generate various toxic gases and consum more energy. Moreover, companies need more extraction of raw materials from mines. Mining for additional raw materials not only produces polluting gases from machinery but also creates significant changes in the ecosystem, possibly causing more damage than deforestation for paper production.

In coclusion, securing sustainability for online learning platforms is vital to advance their programs. These platforms must take care in both human and environmental fields to demonstrate their values ​​to the users and stockholders by reducing environmental impacts and emphasizing social behavior alongside individual training.

 

Reference:

Bayanov, K. R. (2020). The Function of The System “Education Plus Upbringing” In the Context of The Purpose of a Person. SHS Web of Conferences: Theory and Practice of Project Management in Education: Horizons and Risks, 70(03001). https://doi.org/10.1051/shsconf/20207903001

Downing, V. R., Cooper, K. M., Cala, J. M., Gin, L. E., & Brownell, S. E. (2020). Fear of Negative Evaluation and Student Anxiety in Community College Active-Learning Science Courses. CBE Life Sci Educ, 19(2), ar20. https://doi.org/10.1187/cbe.19-09-0186

TheWorldCounts. (February 25, 2024). Electronic Waste Facts. https://www.theworldcounts.com/stories/electronic-waste-facts

Global Health, Education, and Educational Technology

Global Health, Education, and Educational Technology

Even though we have managed to get the COVID-19 pandemic under control, it is time to consider the global health impact on education and educational technology. In this discussion, I will explore two global health dilemmas – the COVID pandemic and climate changes- and their impacts on educational systems.

COVID

During COVID pandemic, the lockdown forced schools worldwide to adapt rapidly and showed the advancements and weaknesses of traditional educational systems. The shift from in-person classrooms to remote learning brought about the use of online platforms, replacing traditional lessons. Interactive learning was further enriched through the integration of advanced applications. Collaboration among institutions resulted in the development of open educational resources. Notably, digital technology elevated the role of teachers, transforming them into co-creators of knowledge, coaches, sand mentors (Janssen, Feb. 11, 2024).

While online course delivery has seen significant improvements, certain challenges have surfaced, requiring a reevaluation and redesign of systems. Some challenges arose in how exams were done, classrooms accessibility, and keeping students safe. Education institutions faced difficulties figuring out ways to prevent cheating and plagiarism in online exams (Salmi, 2020). Another major challenge for remote learning was the limited availability of computers and the internet in developing countries (Janssen, Feb. 11, 2024). To tackle this issue, worldwide cooperation made sure that innovative resources became affordable and available, ensuring education for everyone even after the pandemic.

Distance learning has lessened the connection between parents and educators in guiding students’ learning. Yet, the growing use of technology to connect teachers with parents provides more opportunities for parents to engage in their children’s education (Huck, 2021). Moreover, the pandemic led to worries about mental health, obesity, and students leaving school (“How COVID-19 caused a global learning crisis,” April 4, 2022).

Throughout and following COVID, the primary focus of educational technologies was on improving students’ education. However, it’s crucial to recognize that schools serve purposes beyond academics. They contribute diverse social interactions, encourage relationship-building, facilitate extracurricular activities, and promote a balance in physical and mental health (“How COVID-19 caused a global learning crisis,” April 4, 2022). School hours not only offer a pleasant experience but also play a role in developing social skills and awareness (Burgess, 2020). In the future, educational technologies should focus on and incorporate these aspects more prominently into their products.

The Canadian Commission for UNESCO holds the view that online learning can never replace the role of teachers and schools (“COVID-19 Is Creating a World Crisis in Education,” 20 APRIL 2020). While online learning can protect the health of students and faculty, it has been found to be less effective than traditional learning. The success of online learning depends on factors such as the amount of interaction between students and teachers, technical proficiency, and the suitability of learning content for online courses and group activities (Xia Y, 2022).

Some researchers argue that COVID-19 has created an opportunity for private technology companies like Google or Microsoft to advocate for more involvement in public schools. They believe that corporations are using this health crisis to promote policies and technological infrastructure that will be used to rationalize ongoing online learning (Moore, 2021). Further studies in the future may confirm or rule out this perspective.

Climate change

Global health isn’t just about COVID-19. It’s also about climate change, tobacco control, micronutrient deficiencies, obesity, injury prevention, and work safety. Many articles and seminars have been published on the impact of COVID on education and educational technologies. While COVID-19 is an immediate crisis, climate change has long-term effects on ecosystems, weather patterns, and health challenges, and worsens socioeconomic disparities.

 Most studies highlight notable progress in educational technology during the COVID era. While these advancements cannot be overlooked, it might be said that they are not necessarily new innovations but rather the widespread use of existing discoveries. Online courses and educational apps were present before COVID, and software programs for remote meetings, like Facebook or Zoom, already existed.

Let’s consider another global health dilemma, such as climate change, for instance. Climate change is a global issue that affects everyone. While the immediate effects may not be severe, in the long run, changes in ecosystems can cause irreversible damages to life on Earth, affecting not only humans but also other beings. It may even be impossible to compensate for these damages through the application of a vaccine or lockdown. Despite researchers and policymakers being aware of the seriousness of the situation, practical actions to prevent the destruction of natural resources and address this problem seem limited. Educational technology industries can play a significant role in addressing this issue. For example, many schools worldwide still use paper for assignments, and teaching often involves chalkboards. While industries generally consider laptops as alternatives, they come with high costs and require high-speed internet. However, if educational aid industries design devices like ‘eBook readers’ to function as ‘eNotebook writers,’ allowing writing, highlighting, and saving results, these devices could operate offline. These devices can be somewhat connected to a mobile network, allowing for the exchange of information among students and even with the teacher without the need for the internet. As of the end of 2021, a mobile broadband network covers 95% of the world’s population (Bahia, Oct 2022). Additionally, designing large monitors for classrooms that enable writing and drawing could be a more economical option than laptops and computers. These measures could reduce the need to destroy forests for paper production and decrease the consumption of materials like chalk and markers. So, digital technologies are valuable tools that can help protect the environment by reducing paper usage, air pollution, and water pollution associated with the production of paper.

In conclusion, the COVID-19 pandemic has caused advances and challenges in educational technology, emphasizing its impact on education. Furthermore, the climate change issue, emphasizing the need for sustainable solutions in educational technology.

References

Bahia, K. (Oct 2022). The State of Mobile Internet Connectivity 2022. GSMA Intelligence.https://data.gsmaintelligence.com/research/research/research-2022/the-state-of-mobile-internet-connectivity-2022

How COVID-19 caused a global learning crisis. (April 4, 2022). McKinsey & Company. https://www.mckinsey.com/industries/education/our-insights/how-covid-19-caused-a-global-learning-crisis#

Janssen, L. (Feb. 11, 2024). How COVID-19 exposed challenges for technology in education. G.STIC. https://www.gstic.org/expert-story/how-covid-19-has-exposed-the-challenges-for-technology-in-education/

Moore, S. D. M., De Oliveira Jayme, B. & Black, J. . (2021). Disaster capitalism, rampant edtech opportunism, and the advancement of online learning in the era of COVID19. Critical Education,12(2), 1-24. https://doi.org/10.14288/ce.v12i2.186587

Xia Y, H. Y., Wu C, Yang L and Lei M. (2022). Challenges of online learning amid the COVID-19: College students’ perspective. . Front. Psychol. , 13, 1-13. https://doi.org/10.3389/fpsyg.2022.1037311

Content Prioritization: Personal and Society impacts

Content prioritization – as a resorting algorithm- means deciding what information are most important and should be displayed first or received more attention. For a correct understanding of this term, it may be helpful to state some concrete examples in everyday life.

Chain stores increase sales by creating an attractive display, using window displays effectively, and highlighting discounts. Strategic placement of discounted products helps improve the shopping experience. Seasonal adjustments are also done to keep customers’ attention. Stores also, considering the customers’ interest in a specific type of product, place it more prominently in view or at the store entrance. Similarly to computer science world, stores consider the sales volume and people’s preferences, rearrange, and update the layout of supermarket products, aligning with past seasonal sales. In chain stores like Walmart or Safeway, these arrangements are simultaneously implemented across all branches.

In computer science, it could involve organizing a website to showcase crucial buttons at the top or arranging files to find easily. In computer science, it’s about choosing what information to showcase first. It’s like putting the most important stuff upfront for a better experience.

Major online platforms like Facebook, Google, and others have transformed the world, but they have some social disadvantages like racial discrimination and misinformation due to reducing information privacy. Black and Brown people are vulnerable against equitable opportunities in housing, employing, and studying due to biased data. Low-income people may decline privacy protections due to use cheap data-secure devices and services. Currently, personal information, particularly residential addresses, can be used to determine socioeconomic status. (Allen, 2022). For example, the algorithm used to estimate kidney function is developed from biased data sets or rely on incorrect assumptions. There is a concern that it results in care disparities for racial and ethnic minority groups. This healthcare algorithm shows Black people have healthier kidneys compared with White people. So Black people’s access to kidney transplants is restricted. (Jain et al., 2023). Also, face recognition algorithms with high classification accuracy  has a divergent error rates in female, Black and 18-30 years old (Najibi, 2020).

In my professional work as a research assistant, I use google to find new information about tools that we use in our lab. Google search engine put big companies in the top of its result list. Based on this type of algorithm, new start-ups lose the opportunity to participate in marketing and the market is monopolized by a few big corporations. I trust the results of this hierarchical research algorithm, but I heard that some companies or persons by paying a fee could be moved to the top of the search list. As a result, trust in the results could be shaken. Also, I usually use university library and some professional scientific website. Sometimes, I use google scholar to complete or check my search from other websites. I do not consider any evidence of discrimination in this filed. However, it should be kept in mind that English-language journals and journals published by European and American centers are more accessible in the initial search than journals published from other parts of the world. So, this can lead to discrimination in the reading and referencing of such articles.

PageRank

PageRank is a google algorithm to display search results based on some defined order. In network world, increasing the PageRank score of a web page will mean that page is more visible and place higher than other pages in a search list.

In personal life, this algorithm helps me to find more match results based on my search. For example, in online shopping, based on my keywords and filters, it presents me the best quality and good price products and more reliable buy/sell sites. Therefore, by trusting its scaling, I not only get the goods with high quality and reasonable price, but also save my time. Or, for example, when choosing a restaurant to have dinner with the family, with a quick search, in addition to the suitable restaurant in terms of space ranking and other facilities, you can even choose the most favorite food of that restaurant. However, these results sometimes could be popular not factual because some companies offer to sell high PageRank links. Another example is searching for the most watched movies of year. PageRank with the list of active websites in the field of movie based on the most used site, makes it possible to avoid wandering in checking multiple sites and not finding the desired result. But we should be aware of its limitations such as older pages may have higher rank (“PageRank,” Feb 5, 2024) or uncontrolled posting of spam links (“Limitations of PageRank,”).

Not only PageRank impacts our life, maybe we can impact it. We need to remember that changing the PageRank is not directly in our control. PageRank is changed algorithmically based on the quantity and quality of its incoming links. But we can indirectly contribute to a website’s online presence like as sharing the website’s content on social media to increase its visibility, or if we have a personal website (or blog, I am not sure it works) we can link to the other sites to increase their visibility and ranks.

Reference:

Jain, A., Brooks, J. R., Alford, C. C., Chang, C. S., Mueller, N. M., Umscheid, C. A., & Bierman, A. S. (2023). Awareness of Racial and Ethnic Bias and Potential Solutions to Address Bias With Use of Health Care Algorithms. JAMA Health Forum, 4(6), e231197. https://doi.org/10.1001/jamahealthforum.2023.1197

Limitations of PageRank. https://salomewairimu.github.io/essays/pagerank.pdf

Najibi, A. (2020). Racial Discrimination in Face Recognition Technology. Blog, Science Policy , Special  Edition : Science, Policy, and Social  Justice https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/

PageRank. (Feb 5, 2024). Wikipedia, the free encyclopedia. https://en.wikipedia.org/wiki/PageRank

Human intelligence vs. Artificial intelligence

Human intelligence vs. Artificial intelligence

Human answer AI answer (Chat GPT)
1. 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?

 

 

Alan Turing (1912-1954): Alan Turing, a British mathematician, is known as foundational research of computer science. In 1950, he proposed to consider the question, “Can machine think?”(Turing, 1950). His works on the code- breaking process leads automatic computing engine. His experiments on the issue of AI known as the “Turing Test”. He was considered the father of artificial intelligence.(Editors, 2020)

John McCarthy (1927-2011): He is one of the “founding fathers” of artificial intelligence. He developed machine languages such as Lisp and ALGOL. He invented garbage collection ( automatic memory management)  and time-sharing technology (servers). He commented that “human material progress is desirable and sustainable.” In 2001, he released his predict on robot’s emotions and social networking in the future. (“John McCarthy – computer scientist,”)

Herb Simon (1916-2001): He is a pioneering in AI. In 1950s, he worked on AI and cognitive science. Simon’s models of decision-making influenced early AI and lead him to win Nobel prize. In the Sciences of the artificial he framed intelligence as problem solving. Simon concentrated on digging deeper to understand data.Simon was interested in how humans learn and, with Edward Feigenbaum, he developed the EPAM (Elementary Perceiver and Memorizer) theory, one of the first theories of learning to be implemented as a computer program.(“Herbert A. Simon,” ; “Herbert Simon,”)

Marvin Minsky (1927-2016): As one of the pioneers of robotics and telepresence, Minsky used computational ideas to understand human thinking processes and simulated it in machines. His studies have led to practical advances in the fields of artificial intelligence, cognitive psychology, and neural networks. Minsky proposed the “community of mind” theory, which is influenced by the developmental psychology of children and insights gained from the field of artificial intelligence. He mentioned that intelligence is the result of the interaction of diverse specialists. In 2006, he introduced The Emotion Machine, which includes a framework to explain human emotions and conscious thoughts in various (“AI pioneer Marvin Minsky dies aged 88,” 2016).

 Timnit Gebru (1982): A computer scientist born in Ethiopia, as one of the founders of Black in AI, has made extensive efforts to promote the active participation of Black and people of color in the field of artificial intelligence. As an expert in the ethics of artificial intelligence, she tries to enhance the capabilities of AI for the improvement of society. Her studies on large language models have highlighted the risks associated with using them in understanding fundamental concepts and the potential for misuse. She mentioned that AI models could generate misinformation or mistranslation, leading to laziness in people due to the convenient use of these models (Hao, 2020).

Alan Turing, a pioneering computer scientist, significantly shaped the foundations of artificial intelligence. His Turing Test proposed a criterion for determining machine intelligence, emphasizing indistinguishability from human responses. Turing’s seminal work laid the groundwork for AI development, influencing how we perceive and define intelligence in computational systems.

John McCarthy, a key figure in AI history, coining the term “artificial intelligence” in 1955, propelled the field. His influence extends to developing the Lisp programming language and founding the Stanford AI Lab. McCarthy advocated for the idea that intelligence is computational and introduced the concept of “computer as a universal machine for all cognitive processes.”

Herb Simon, a Nobel laureate and AI pioneer, emphasized cognitive psychology’s integration into artificial intelligence. His work on problem-solving algorithms and the “thinking machine” concept shaped AI’s early landscape. Simon proposed that intelligence is a product of rule-based symbol manipulation, influencing AI research and defining intelligence as a computational process.

Marvin Minsky, a trailblazer in AI, co-founded MIT’s AI Lab and developed foundational ideas in perception and learning. His contributions include the concept of neural networks and co-authoring the influential book “Perceptrons.” Minsky viewed intelligence as a collection of interacting mechanisms, inspiring diverse AI research and shaping our understanding of machine intelligence.

Timnit Gebru, a leading AI researcher, co-founded Black in AI to address diversity in the field. Her work at Google focused on ethical AI, shedding light on bias and accountability. Gebru advocates for inclusive AI development and redefines intelligence by emphasizing fairness, transparency, and social responsibility in machine learning systems.

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

 

Human language, developing across centuries, functions as a tool for everyday emotional communication shaped by society. In contrast, machine language, designed by experts for efficient command execution, lacks the depth of human linguistic evolution. Human communication is rich and influenced by societal factors, while machine language focuses on specific instructions. Humans talk for many reasons, but machine language is all about clear commands, showcasing the unique roles each language has (Tsujii, 2011). Also, the human language is corrected and adjusted by the members of the society, but the machine language is improved by the computer experts according to the users’ requests.

 

Machine programming languages, like Python or C++, are artificial, designed for computers to execute specific tasks efficiently. They employ strict syntax and are devoid of ambiguity. In contrast, human natural languages, such as English or Spanish, are nuanced, context-dependent, and expressive. Machines lack innate understanding of human language subtleties, relying on precise instructions. While humans use languages for communication, conveying emotions, and abstract ideas, machines interpret languages algorithmically. Bridging this gap is a challenge in AI, with ongoing efforts to develop natural language processing capabilities for machines to comprehend and generate human-like language.
3. How does “machine (artificial) intelligence” differ from the human version? Human intelligence, marked by emotional acuity and social adeptness, could understand complex emotions and varied concepts. In contrast, artificial intelligence encounters challenges in accurately decoding complex emotions during feelings analysis (Kumar & Martin, 2023). Human intelligence is adept at multitasking and setting connections across diverse domains, unlike artificial intelligence, which, guided by predefined algorithms, often struggles with tasks beyond its specific focus that prevents innovative capacities (Lake et al., 2017). Additionally, human intelligence has inherent creativity, while artificial intelligence relies on algorithms and data, lacking the spontaneous initiative evident in human cognition. Also, human intelligence consistently displays intelligent behaviors, even during sleep (dreaming) (Horowitz et al., 2023), while turned-off machines remain silence without any analytical processes. Machine intelligence, while powerful, differs fundamentally from human intelligence. Machines excel at data processing, pattern recognition, and executing predefined tasks with speed and precision. However, they lack human qualities such as emotional intelligence, creativity, and holistic understanding. Human intelligence encompasses complex emotions, adaptability, and nuanced decision-making shaped by experience and empathy. Machines follow algorithms, while humans navigate a vast array of cognitive processes. The challenge in artificial intelligence lies in bridging this gap, striving to imbue machines with more nuanced, adaptable, and human-like intelligence, ultimately aiming for a harmonious integration of artificial and human cognitive abilities.
4. How does “machine learning” differ from human learning? Human learning is a complex process that influenced by thoughts, emotions, and cultural factors. People learn through their feelings, experiences, and cultural contexts (Vygotsky, 1978). Artificial Intelligence learning uses specific designed programs to analyze data and follows defined algorithm regardless considering diversity of cultures and human emotions. AI learning only focuses on user satisfaction about accessibility, practicality, and cost-effectiveness. In human learning, the attitude of both the teacher and the learner influence how content is presented. However, in AI learning, there is no consideration for the attitude of the learner and some differences like as gender, age, IQ level, and language. AI learning is predefined and lacks flexibility. It could reinforce social inequalities (Zou & Schiebinger, 2018).

 

Machine learning differs from human learning in its method and scope. While human learning involves complex cognitive processes, intuition, and context awareness, machine learning relies on algorithms to identify patterns and make predictions from data. Humans can generalize knowledge across diverse scenarios, adapt to new information seamlessly, and possess common-sense reasoning. In contrast, machine learning models excel at specific tasks but lack the holistic understanding and flexibility of human cognition. Bridging these distinctions is a key challenge in advancing artificial intelligence, aiming to imbue machines with more human-like learning capabilities for enhanced adaptability and comprehension.
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.

 

Both machine and human tasks begin with an internet search algorithm. The machine, utilizing advanced predefined processes, quickly produces a coherent result by searching millions of words. Conversely, human step-by-step searches evolve, guided by individual knowledge and opinions, allowing for expansion, or revisiting as needed for personalized exploration and understanding.

I will categorize the differences between these two into three categories. Regarding language, both machine and human discussions encompass the diverse applications of their respective languages. However, human responses explore the origins of language creation, emphasizing that human language evolves from thousands of years of collective experience, with a focus on socialization. In contrast, machine languages, are made by computer experts, serves specific tasks, so human communication falls a secondary priority. In terms of intelligence, human responses highlight the significance of emotion, human concepts, creativity, and dreaming. In the context of learning, human responses focus attention on the role of bidirectional communication between learners and educators, integrating explanations that take into consideration structural variations such as age and gender.

Generally, human responses prioritize human behavior’s impact, while machine responses prioritize correctness, regardless of cultural diversity, emphasizing a fundamental distinction in their perspectives and considerations.

 

References:

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

Editors, B. c. (2020). Alan Turing Biography. A&E; Television Networks. https://www.biography.com/scientists/alan-turing

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

Herbert A. Simon. https://en.wikipedia.org/wiki/Herbert_A._Simon

Herbert Simon. https://www.ubs.com/microsites/nobel-perspectives/en/laureates/herbert-simon.html

Horowitz, A. H., Esfahany, K., Gálvez, T. V., Maes, P., & Stickgold, R. (2023). Targeted dream incubation at sleep onset increases post-sleep creative performance. Scientific Reports, 13(1), 7319. https://doi.org/10.1038/s41598-023-31361-w

John McCarthy – computer scientist. https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)

Kumar, H., & Martin, A. (2023). Artificial Emotional Intelligence: Conventional and deep learning approach. Expert Systems with Applications, 212, 118651. https://doi.org/https://doi.org/10.1016/j.eswa.2022.118651

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behav Brain Sci, 40, e253. https://doi.org/10.1017/s0140525x16001837

Tsujii, J. i. (2011, 2011//). Computational Linguistics and Natural Language Processing. Computational Linguistics and Intelligent Text Processing, Berlin, Heidelberg.

Turing, A. (1950). Computing Machinery and Intelligence. Mind, 49, 433-460. https://redirect.cs.umbc.edu/courses/471/papers/turing.pdf

Vygotsky, L. S. (1978). Interaction between learning and development. Mind and Society, 79-91.

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

Users, Uses, and Usability

Mostly, usability is described in the context of human-technology interaction. Usability is not an independent entity; rather, it is considered as part of an ongoing process -a continuous cycle of production, evaluation, and reproduction. From the user’s viewpoint, usability means carrying out a task accurately and with enjoyment. It must be simple to learn, easy to use, and minimizing errors. Technology specialists consider usability in terms of organizational, social, and psychological factors during the design process and quality assessment.

Educational products should not only be easy to use but also support teaching strategies and learning outcomes. Educational environments are characterized by diverse learners with varying needs and abilities. In the realm of education, users fall into two distinct groups with different needs. The first group comprises teachers who use educational technology for teaching. Since these technologies are at their disposal throughout the teaching period, they actively provide feedback. The second group consists of students who use these tools for completing assignments and studying. Here, two issues arise: firstly, at each educational level, a student typically uses the tool only once, and they won’t revisit it, leading to less inclination to provide feedback. Furthermore, the disparity in individuals’ awareness at a particular educational level can result in diverse opinions, making it challenging to derive comprehensive feedback. Therefore, there is a need for a shift towards a student-centric approach in the future.

There is a need for redefining usability concerning instructional methods. Educational usability is a comprehensive framework that goes beyond traditional usability concepts and focuses on instructional design models with pedagogical objectives, user-centered design, feedback mechanisms, and assessment features. System designers for educational products often rely on the feedback from teachers and a small group of students. This limitation can potentially lead to significant differences in the usability of their products.

In reviewing Woolger’s article, several examples show that a usability study could end up configuring users. For example, he demonstrated that “Observers frequently intervened to explain the origin of a problem in terms of a machine fault, where this prevented (or made difficult) the completion of the task by the subject.” 1 (Page 85)

Repeated interventions during testing to correct errors lead to the user’s uncertainty and doubts about the device’s capabilities. It interferes with subsequent actions in the next user’s steps. User dissatisfaction with pressure of frequent interventions causes a lack of focus to follow procedures as an ordinary user. Additionally, the continuous presence of observers decreases the sense of being in the real work environment. The quick intervention of the observers leads to a misunderstanding of the technical problem of the device, not only overlooking the user’s problem but also diverting resources to the support the device. Finally, this feeling in the user reinforces the hypothesis of attributing the problem to the machine rather than the user, so, in the next stages, the observers will have incorrect results of the affected user’s behavior.

The second example involves the impact of confidentiality on the usability process. Due to competition in capturing consumer markets, companies try to disclose the least amount of information about their product until its release. This limitation extends across various stages, from design to production and distribution. Because of this constraint, companies for obtaining feedback from users, must hire non-real users, such as someone inside the company or experienced college students. This approach interference with the goal of working with beginner users and the obtained results do not perfectly align.

Finally, two quotes (Issa & Isaias, 2015 and Woolgar, 1990) are compared to highlight their differences in the uses of ability. Issa & Isaias’ quote3 about the usability evaluation stage focuses on the stage of usability (it refers to the “quality of the interaction”, “improving methods”, and “assessing effectiveness”) evaluation only restricted to tool making process. It suggests that during development process, the user plays an important role in shaping usability, but this involvement fades or stops after the product is released to the market.

Therefore, there is a timeframe for user’s role, and the company, until the next product does not need any post-market monitoring and evaluation. However, Woogler4 believe that for making a new product, there should be special attention to the user’s configuring process from the design time until after releasing it to the market. Thus, it is not a limited timeframe. In this perspective, the user’s role is not only prominent in design and production but should also be considered for long term.

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  1. Woolgar, S. (1990). Configuring the user: The case of usability trials. The Sociological Review, 38(1, Suppl.), S 85.
  2. Woolgar, S. (1990). Configuring the user: The case of usability trials. The Sociological Review, 38(1, Suppl.), S 87.
  3. Issa, T., & Isaias, P. (2015)Usability and human computer interaction (HCI).In Sustainable Design (pp. 29). Springer. “
  4. Woolgar, S. (1990). Configuring the user: The case of usability trials. The Sociological Review, 38(1, Suppl.), S 59.