Month: November 2019

This speculative narrative features a boy named Fredericton who is experiencing education at its finest in the year 2042. Reading has transformed itself into a comprehensive process in which augmented reality enhances the experience. Textbooks have been uploaded into headsets that can project text onto any space. The headset will also now read the text aloud to Fredericton as the text that is being projected lights up to indicate what is being read in the headset. Fredericton is “extremely engaged” as the challenge of reading and comprehending is something that is presented to him every day.

The second speculative narrative takes place eight years later.Fredericton is in his final year of high school. He is asked to write his final thought paper about his experience throughout his formative educational years. The paper requires him to be thoughtful, and provide photographic artefacts to demonstrate his thoughts. The minimum word count for this assignment is 3000 words.This process should take Fredricton no more than 20 minutes. A truly “thoughtful” process.

In this video that I just created, the reason I use Twitter was that I view a 280 word limit as synonymous with this platform. Generally, most microblogs I have seen are twitter based, and even blog websites reference twitter for quotes from important relevant figures. I think that these generated statements are different from how we normally express ourselves as stating our point in 280 characters can be quite challenging. Considering the brevity that is 280 characters, one has to be precise and concise with their wording. Since I am no expert Twitter user, I was unable to fully convey my thoughts about “What education is not…”. I could not even complete it with hashtag without going over the limit. What I did find interesting was the predictive hashtags that came up when I did try to use them. I wasn’t even trying to use the hashtag #DonCherryisRight but it popped up in my list of options as Twitter does so based on current trends. This made me wonder about other predictive algorithms in the keyboarding aspects of Twitter. Does it Twitter have a similar code when it comes to the words you are presented with for non hashtags? If so, is there an agenda in how words are presented? How does that affect the thought patterns of the general public that is under the impression that they have agency in word choice? Are there words or patterns of words that are avoided in the algorithm in order to steer thoughts in a particular direction? Do these algorithms exist in other keyboarding apps? These ethical questions are similar to that of the attention economy module and how news feeds are selected for our mobile apps. It is scary to think that predictive text technology could affect our freedom of speech. However, when I type on my mobile device without autocorrect and predictive text, I feel completely inefficient and quickly turn it back on in my settings. Ignorance is bliss.

I don’t think I’ve ever been so frustrated playing a game. This is the screenshot where I gave up on trying to complete this task within one minute. I realized it was designed to be impossible. Through multiple attempts and failures, I thought I could bypass each page by copying and pasting the word fake into every bar… memorizing where all the weird little pitfalls like the reverse gender from male to mrs. etc. Even the designed helped button is meant to be another distraction. An annoying yet powerful way to convey the message of how attention is consumed.

after viewing the discussion board. I figured it out thanks to TUO.

Analyzing the data from the curation quiz was quite interesting. I happened to match up with Jessica, Ana and Eva according to the colour grouping. However, upon viewing the sites, I recognized that my choices also matched up with Tanya, Barbara and Stephanie as well since my proximity to them in the data was about the same. I came to realize two things in reviewing the data while qualifying the relevant websites. First, we were grouped based on strictly quantifiable parameters of matching music selection and the data did not explicitly state how closely our choices matched. After doing my own
examination, I could only notice an average of 60-70 percent commonality of music selection with the other members of the blue group. The second thing I noticed is that each of us had different qualifiers for how we selected music. The criteria ranged from seeking diversity in cultural soundscapes, instruments, emotions, time period, and power structures. I was a complete outlier in my criteria for music selection as I sought to establish Earth as a non-invadable planet through showing strength in our music. With this, I realized that data can easily be misinterpreted as in this example, I was clustered into a group with completely different views based on non-exact matches. Furthermore, the data doesn’t represent the reasons for our null choices. This exercise definitely shows that interpreting data needs to be done without assumptions and a critical lens.