Everytime I think about our future, I am hopeful for what it holds. I think about my girls, all grown up, in a world that chooses selflessness over selfishness and to spread kindness whenever possible. I hope my girls use the power of technology to connect to and learn from others, while embracing the diversity that exists around them. 

When was the last time you opened the front door to grab the daily newspaper and flipped through to your favourite section while getting black ink on your hands? It’s more likely that you unlock your phones to open an app, like Twitter, and begin to scroll through the newsfeed as you read updates. How we consume and produce information has changed over the years. I am frequently checking my Instagram account (more than I’d like to admit) and find much of my parenting information on the platform by following experts in the field. The IG algorithm also ensures that more of these similar accounts show up on my feed and I am able to read a compact amount of information in a shorter period of time.  These short statements are typically used on social media platforms to share  personal opinions or experiences. These microblogs have exacerbated the need to constantly share and post about our everyday doings- from taking pictures of our food to tagging our location. I would ask, is it really necessary? By condensing our thoughts into these bite-sized captions, often accompanied by a # or picture, it limits the in depth conversations and analysis, focusing on the entertainment value and the number of likes rather than the content itself. In this regard, I think it alters the way we write, as we want to be perceived in a certain light or to capture a wider audience- adding emojis to make it more light-hearted or tagging a friend/ company to receive a response. Moreso, predictive text takes away some agency over the writing process as it guides the sentence structure and selects the next word to resemble your voice. 

When I was generating my post on IG, the  software was able to correct my spelling errors (notably,  separating the words every-time) and predict a few of my words. Interestingly, as I was typing out the first few letters, it was able to predict the root word but not in the correct grammatical sense. For example, the word ‘hopeful’ came out as ‘hoping’, suggesting that my sentence would continue. Based on the context of the statement, the software was able to decipher my thoughts by drawing from hundreds of words and phrases that were used in the past. Since I used my iPhone for this task, there could also be an abundance of data drawn from a larger dataset from other Apple devices. 

In terms of other textual products, I would argue that these statements are not commonly found in academic articles or novels, it is more likely to find such writings in magazines. That being said, there is potential for algorithms to be used in those contexts in a way that captures the specific topics by selecting from a more discrete data set.  As AI systems learn personal typing patterns, it has the potential to streamline work and to improve productivity. Moreso, for education, this algorithmic technology used to predict text can assist students in writing tasks, particularly for those students who may struggle to start generating ideas by making the process less frustrating. These deep learning software have the potential to become more adaptive with the context and more accurate with its capabilities to target domains by suggesting certain words.  

Upon first glance of the data presented in the graph, it was an overwhelming amount of information spread across the web. With further dissection of the data into communities (and the help of Ernesto), I found myself situated in a group of six with Jessica, Katie, Jacey, Sage and Kayli. It was also helpful to filter by the size of the nodes to determine which targets held the most weight. In our group, we all selected Night Chant and Jaat Kahan Ho as one of the top 10 songs from the Golden Record. There were four more tracks that 5  of us shared in common and each of us had individual outliers that we chose. Upon further analysis of this data by reading my peers’ blogpost on their selection process, it would seem that these commonalities occurred because of our desire to cast a wide range of musical selections with more diversity. As Sage aptly explained in her justification process, “I wanted to create a ratio that was more representative of humanity”. 

This reasoning seemed logical for our grouping but upon deeper analysis of the entire dataset it appeared that these two songs had a high degree of connectivity for the entire class. Therefore, unlike my original assumption, it is not because we share these two songs that we are in the same group. That begs the question, why did the algorithm put us together in Community 4? Looking at the wider scope, it could be because of the number of connections between one or more group members that are similar. Our common null choices could also be considered in the algorithm that created these facets. 

The quantifiable data visualizations do not consider the qualitative nature of the selection process. Whereas each of our group members had different criteria to either include or exclude tracks, whether it be on emotional value, personal experience or ‘decipherability’ as Sage decided, there are multiple paths that can be taken to garner the same results. The political implications of this filtering criteria causes a discrepancy between the intent and the outcome. That is, opposing political groups may reach the same conclusion in different contexts as a result of missing or assumed data that is not aligned with the intent. Take for example the controversial topic of vaccination status.  One might make assumptions about a person’s educational background, political views and even religion without truly understanding the reasoning.  Even a topic as simple as mask wearing can cause tension between groups because of assumptions or misinterpretations for a person’s decision. These choices or results in data are not a direct reflection of one’s identity as it fails to consider the underlying intentions.

*click on the image to go to an interactive map of “My Top 10 Tracks from the Golden Record” on genial.ly*

A snippet of time in space. I was interested to learn how the Gold Record would capture the cultural diversity in music that exists in the world to share the story of humanity with interstellar life forms. There was a variety of musical genres from classical, to jazz and the blues. It also included some prominent languages from around the world.  As noted in the podcast by Dallas Taylor, “Music is non-specific but communicates something to someone”. From this inclusionary lens, I selected a compilation of songs that I was not familiar with. In the selection process, I wanted to ensure that there was cultural representation as I narrowed down the list with a mix of both instrumental and vocal music from around the globe. 

1.Senegal, percussion, recorded by Charles Duvelle

The fast paced beat of the percussion instruments was intriguing. After reading about the traditional African drum music, it was interesting to learn that there are certain elements that have influenced American and Cuban music. In the background, I could also hear a wind instrument joining along with the rhythm of the drums. 

2. Mexico, “El Cascabel,” performed by Lorenzo Barcelata and the Mariachi México.

I chose this upbeat song which happens to be a well-known Mexican folk song that means “Little Bell”. At first, it was hard to decipher the many different instruments playing, then I learned that a mariachi band consists of trumpeters, guitarists and violinists. 

3. New Guinea, men’s house song, recorded by Robert MacLennan. 1:20

The vagueness of the title intrigued me. I imagined the playing of the long flutes told a story amongst the men as a form of initiation. The back and forth of the long flutes sounds as if it were a playful  ‘face off’ between the men. 

4. Japan, shakuhachi, “Tsuru No Sugomori” (“Crane’s Nest,”) performed by Goro Yamaguchi. 4:51

The translation of the title of the song “Crane’s Nest” was fitting to the tune of the flute and woodwind as I began to visualize the graceful movements of the crane as it built its home.

5. Azerbaijan S.S.R., bagpipes, recorded by Radio Moscow. 2:30

My initial assumption was that this bagpipe song would derive from Scotland. But the rhythm and almost soft tones were different from my expectations of the Scottish bagpipes.

6. Stravinsky, Rite of Spring, Sacrificial Dance, Columbia Symphony Orchestra, Igor Stravinsky, conductor. 4:35

I can feel the suspense of this sacrificial dance through the dissonance of the string instruments. The Rite to Spring orchestral movement is the score that accompanies a ballet about a young girl who dances as a sacrificial victim. Viewing the 1989 version of this Rite to passage dance, I found it to be quite offensive towards Indigenous groups and the representation of their culture. 

7. Navajo Indians, Night Chant, recorded by Willard Rhodes. 0:57

The sounds from the singer’s voice were airy and enchanting with the rise and fall of the notes. The repetitive melody were characteristics of a chant. It piqued my interest to learn that the night chant is a healing ritual to restore balance between humans and the universe.

8. Peru, wedding song, recorded by John Cohen. 0:38

The woman’s voice sounds as if she is young and innocent, so it was surprising to discover that the chorus of the song translates to “What a fool I was, stupid fool” to serve as warning about marriage rather than a celebration. 

9. China, ch’in, “Flowing Streams,” performed by Kuan P’ing-hu. 7:37

The sounds of this string instrument were unique. The guqin is a seven string musical instrument from China that is plucked. The quaint sounds emanate the calm of the flowing streams. 

10. India, raga, “Jaat Kahan Ho,” sung by Surshri Kesar Bai Kerkar. 3:30

The translation of the title in English “Where are you going along, girl?” features an Indian female vocalist. The woman speaks as if she is relaying imparting wisdom onto the younger generation.

Click here to view the task.

*Note: Please view it first then click on the links! 

After reading the New London Group article on multiliteracies and applying it to the mode bending task, I drew a connection to  the SAMR model created by Dr Ruben Puentedura as a means to enhance and transform teaching and learning with the use of technology. As Terada (2020) pointed out, educators must determine how and when to apply the strategies to best suit the learners. To engage 21st century learners and a generation of digital natives, there needs to encompass “broad forms of representation” taking into consideration the cultural context and dynamic nature of language (Dobson & Willinsky, 2009). The challenges that exist in using digital technologies to redesign literacy practices involve an understanding on both the production and consumption of the multimodal designs. There is a complex relationship between the design elements. Rather than having to ‘reinvent the wheel’, there is an abundance of knowledge and skills to implement these modes of learning to allow for new meaning-making practices. 

Using the New London Group  diagram of multimodal design of meaning, I began to think of how I could redesign the original visual task to include various other elements of design.  Using the flat 2D image captured with my iPhone as the Available design, I remembered another student’s (Jocelyn Chan) presentation  of the task using the website genial.ly to elevate the design into an interactive sensory experience. Initially, I was searching for a digital tool that would allow me to maximize the modes of representation. Here I will outline how I used some of the modes of meaning: 

• Linguistic Design: On each of the pages in the slideshow, there are subtitles that are meant to ‘nominalize’ the content on the page. I also took a lighter, slightly more humorous approach to this task using metaphors to support the description of the image. 
• Visual Design: In order to produce a visually dynamic piece, I included transitions and interactive elements into the slideshow. Furthermore, I decided to highlight each of the items inside the bag  by focusing on individual or groups of items  rather than a holistic approach with all the items in one layout as it was for the original task. 
• Audio Design: In the background of the slideshow, I included a voice over of a description of each of the elements on the page. This accessible feature would allow for inclusion of a wider range of audiences based on reading ability. 

Arguably, I found it more difficult to include elements of spatial and gestural design into the task. To some degree, there are spatial elements as the audience is redirected to new websites when they click on an image. For example, if you were to click on the image of the board books, it would direct you to a read aloud of the text on YouTube. It takes you to various spaces on the web to gain more information on the topic. The main challenge with this platform was including an audio that fit the timing of the transitions of the page. It took a few tries to ensure that the audio matched with the description. Overall, the use of the platform genial.ly was effective in modifying the original task for the redesign process. 

Reference: 

Dobson & Willinsky (2009) Digital Literacy Cambridge Handbook of Literacy

Terada, Y. (2020, May 4) A Powerful Model  for Understanding Good Technology Integration. Edutopia. Retrieved from: https://www.edutopia.org/article/powerful-model-understanding-good-tech-integration

The New London Group.  (1996). A pedagogy of multiliteracies: Designing social futures.Harvard Educational Review 66(1), 60-92.