Please view the VideoScribe link for animation:

https://www.videoscribe.co/app/preview/382477a6-9d41-4193-8956-2c17d6e12a90

References:

Crawford, K. (2021). Atlas of AI Yale University Press.

Duffy, B. E. (2017). Entrepreneurial Wishes and Career Dreams. (Not) getting paid to do what you love: Gender, social media, and aspirational work. (p1-11)Yale University Press.

Ozimek, A (2019) Outsourcing Digital Game Production: The Case of Polish Testers. Contested Formations of Digital Labor. Television & New Media.

Group Members: Jocelyn Fung & Nicolas Robitaille

Topic: The displacement of analog portfolios to digital portfolios

Student portfolios have long been used as a form of assessment and documentation to showcase products of learning and evidence of growth over time. Before digital portfolios, the collection of student work would primarily be analog, like the submission of a final assignment that is tucked inside a physical folder bursting with papers for parents to observe at certain checkpoints throughout the year. In many cases, these physical portfolios were assembled by teachers themselves or highly curated by them, rather than the learners. Digital portfolios, by contrast, are versatile learning tools that allow for the inclusion of multimodal elements  which gives students agency and voice to visualize their learning progress. We will examine how the technological change from analog to digital portfolios has impacted education from an attentional lens and forms the foundation for student agency.

Welcome to Digi Citi! An online platform for K-5 students to learn about digital citizenship. Our team proposal is below:

ETEC511_ Group Project Proposal

Who were these people, and how did/does each contribute to the development of artificial intelligence? 

How do “machine (programming) languages” differ from human (natural) ones?
The main purpose of language is to communicate- the way in which machines and humans perform this task differ completely, though both are logical with their own devised syntax and structure. Arguably, human language is more complex with underlying emotions and nuances involving non verbal cues and inferential reasoning for understanding (Jones, 2020). Machines are programmed by humans with defined rules that do not deviate from its algorithm. As such, machines make meaning that are distinct to the words, such that each command has one meaning. Jones (2020) suggests that machine language is performative such that the code executes its function to perfection whereas human language can take on a variety of meanings given the context.

How does “machine (artificial) intelligence” differ from the human version?
“Artificial” being the key difference between machine and human intelligence. One is created by and mimics humans with the input of information. Human intelligence requires cognitive processes that are both unconscious and subconscious, whereas machines require direct instruction for processes (Dreyfus, as cited in Crawford, 2021). Chollet (2019) theorizes that AI can be measured by skill acquisition efficiency in comparison to a more in depth measure for humans, the Intelligence Quotient, that considers brain volume, speed of neural transmission and working memory (Stangor & Walinga, 2014). The unpredictable nature of the human brain involves the ability to think whereas AI is dependent on datasets.

How does “machine learning” differ from human learning?
Humans learn from experience and we learn from one another. Alternatively, machines are trained by processing a large set of data and noticing patterns within the dataset to make predictions (Heilweil, 2020). Inherently, both humans and machines face biases, the differences being in how they are learned. Algorithmic biases exist based on the biases of the programmers who design them as well as the bias in the dataset. Machine learning lacks foresight as the information that informs the actions are based on historical and past data.

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?
In theory, a machine would be able to generate answers that are similar to these, however there are a few differences between ‘us’, whether it be a positive or negative attribute. The most obvious being the time spent on these responses. A machine would be able to generate answers fairly quickly with its vast availability of data online; whereas the time I spent was drawn out across several hours in the week. There would also be discrepancies with the accuracy of the responses, notably in the style of writing, the formatting requiring APA and the references to other sources. Notably, I chose a multimodal format to display the biographies, whereas a machine would likely choose a conventional written format, which is also efficient. Lastly, my personal background, experience and knowledge contributes to the answers, where the machines would rely on data across the web.

References:

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

Crawford, K. (2021). Atlas of AILinks to an external site.. Yale University Press. (Introduction: pp. 1-21)

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

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

Stangor & Walinga, 2014. Introduction to Psychology – 1st Canadian Edition, part of the B.C. Open Textbook Project

Usability is a method of evaluating how easy it is for a user to interact with a system to execute a task or to achieve a desired goal. As the breakdown of the word itself implies in the Human-Communication Interaction field, it is the ability to “efficiently, effectively and safely” (p.24) use and learn a system by means of understanding human actions towards it and making adjustments to the input and output technologies to generate a more seamless interaction (Issa & Isaias, 2015). Through the iterative design process, players including users, designers and analysts consider specific criteria and requirements of usability to increase the likelihood of returning users and satisfaction of use. In turn, this creates customer retention by making improvements to the system based on the feedback to develop a functional system (Issa & Isaias, 2015). By implementing usability testing, it ensures a quality system that is centered around the users’ needs to incorporate certain attributes that lead to a user-friendly design (Nielsen, 2003). 

Wherein education is concerned, the term usability serves a different function as it pertains to the user. Moving away from the corporate sense of a user as a customer, in education the user is the student and the goal of the system is to acquire knowledge. Thus, accessibility becomes the focus in order to reach a wide range of learners with varying levels of abilities. If authentic learning is to occur and opportunities are presented by learning from mistakes, then the design must fit the context and adapt to the learning process. With that in mind, educational usability recognizes the purpose and context for learning to design an interface that is actively changing to the users’ needs in a relationship that is mutually exclusive of one another. The system has the ability to shape the learners’ attitude in a positive or negative manner and vice versa. As Woolgar (1990) argues, there is a cooperative relationship that exists between the user and the machine; the implications this has on educational usability affords technology the opportunity to provide users with a differentiated learning experience. 

One example of usability gone wrong is in the Wrong Socket Episode in which Ruth, the subject, is tasked with connecting a printer to the DNS with the support of an instruction manual. After a failed attempt, Ruth resorts to asking Nina, the tester,  for help. Throughout the interaction, Ruth physically moves herself and the manual around the machine in hopes of finding a connection. Despite an error with an incompatible model, it becomes apparent that within the constraints and boundaries of the DNS it was, in fact, Ruth being assessed rather than the system itself. 

Another scenario, Constructing Natural Users, undermines the process of usability by having the testers intervene with unnatural commentary thus altering the interaction of the user with the system and the usefulness of the trials. The testers essentially controlled the outcome by speaking on behalf of the DNS rather than allowing the user to interact and learn how to use the system in an uncontrolled environment. In these examples, the users are designed to fit the designated task by setting specific parameters (Woolgar, 1990). There exists a network of relationships between the computer and human such that these machines are not passive. 

In the two excerpts on usability, Issa & Isaias (2015) emphasizes the responsibility of the development team to design a system that is functional and usable for the users. This is in contrast to Woolgar’s (1990) argument wherein the focus is on determining the user in order to configure their actions. Both perspectives have an unchanged variable in the assessment of usability. For Issa & Isaias (2015), the users’ experience with the system remains constant and changes are made to the system according to the responses of the user. Woolgar (1990) maintains the system to be constant and the actions of the user to be changed in respect to their relationship with the machine. Arguably, in Woolgar’s (1990) case, it is more challenging to make changes to the user’s ability rather than altering the design of the system to make it more accessible for users. 

References: 

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

Nielsen, J. (2003). Usability 101: Introduction to usability. Useit. 

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

As an educator with the Toronto District School Board, we have access to resources available through the Indigenous Education site.  One of which is the article Decolonizing our Schools: Indigenous Education in the Toronto District School Board; a research report on findings from the Talking Stick Project by the Urban Aboriginal Education Pilot Project (UAEPP) with data collected between April 2009 and September 2010. This open source document provides valuable information to educators about the impact of Aboriginal education in urban settings on Aboriginal and non- Aboriginal students and how various stakeholders including teachers, administrators and Board employees have a responsibility in decolonizing and indigenizing learning spaces to ensure success, or specifically, well-being of Aboriginal students (Dion, 2010).  The principal investigator, Susan Dion along with a group of esteemed researchers, offer an analysis through the assessment and evaluation on  the effectiveness of the UAEPP. For the purpose of this task, I focused on Chapter 1: Introduction Aboriginal Education in the TDSB. This chapter in the document provides data and statistics on the population of Aboriginal students in the TDSB, shedding light on student achievement in the formal school setting and moving towards a student well-being model of success. 

From a teacher professional development standpoint, my question is: how can educators use this document to reflect on and improve their current pedagogy and practices around Aborginal education? More specifically,  how might this article educate the educators in the history of Aborignal education and the specific data related to Aboriginal students in the TDSB? Some of the search terms that were provided in the example were not applicable to the current article, as it was written in the past decade and terms such as Indian are outdated and not used in the context of the project. However, you will notice that the term Aboriginal is prevalent to encompass the community and population of students in the urban, Toronto setting of First Nations, Metis and Inuit peoples. The additional search terms decolonize and indigenize hold significance in the article to describe roles and responsibilities of teachers. The emphasis on decolonization includes the recognition and discussion surrounding the history of colonialism in Canada. Wherehas indigenization involves teachers integrating Aboriginal perspectives and experiences into the curriculum. These terms hold value into the recommended strategies that are to be employed to alter teacher practices in Aboriginal education. Finally, the additional term Miigwetch was selected to incorporate Ojibwe terms. 

After reading the article and selecting these terms, a new question arose: Since the TDSB is the largest school board in Canada and one of the largest in North America, how can this research be applied to other settings, including rural areas or smaller districts? 

Note: The search for these terms was conducted  on a peer review of the original article by Dion (2010). The original article was not searchable for words. This is not an accurate representation of the terminology presented as the introduction itself had the words “decolonize” and “indigenize” over 5 times as I read through. Furthermore, in the acknowledgments, specific Indigenous groups were recognized, thus increasing the count for the number of words including First Nations, Metis and Inuit. 

Given the recent publishing date of this article and the team of dedicated researchers in Indigenous studies, it is apparent that the wording and terminology used is sensitive to the topic and there was various input from the Indigenous community. 

References: 

Bentley, S.  Decolonizing our Schools, Aboriginal Education in the Toronto District School Board. Retrieved from: https://www.academia.edu/21521734/Decolonizing_Our_Schools_Aboriginal_Education_in_the_Toronto_District_School_Board

Dion, Susan D.; Johnston, Krista; Rice, Carla, M (2010) Decolonizing Our Schools Indigenous Education in the Toronto District School Board. (Toronto)