Chris Rugo’s ETEC 540 Blog

Scenario Prompt

This week’s task involved writing a scenario based on a prompt generated through a version of Situation Lab’s (n.d.) game, The Thing From The Future.

Describe or narrate a scenario about a pill found a few years into a future in which society as we know it has come apart. Your description should address issues related to the government and elicit feelings of disgust.

Scenario

Reflection

When thinking of a scenario that could “elicit feelings of disgust” and address “issues related to the government” in a world “where society has come apart”, the climate crisis was one of the first issues that came to mind. At first, this seemed like an interesting prompt and I decided to run with it, keeping in mind Dunne & Raby’s (2013) suggestion that speculative scenarios “are by necessity provocative, intentionally simplified, and fictional” (p. 3). However, while working through the the exercise, I wondered if my scenario was falling a bit too much into what Auger, Hanna & Mitrović’s (2021) describes as a “dystopian spectacle” (p. 4) – something they suggest that we move away from in speculative design. In an attempt to counter this, I tried to take an optimistic approach by imagining a technology-enabled, scientific solution that could address some of the damage brought on by climate change.

Reflecting on Dunne & Raby’s (2013) distinction of “probable . . . plausible . . . possible . . . [and] preferable” (p. 2) futures, I believe that my scenario would be classified as “possible” future. This is because there is an element of science fiction at play, with the inclusion of an somewhat contrived scientific solution that would make it possible for crops to thrive in harsh weather conditions. While my scenario doesn’t get too deep into the actual science that makes this compound possible, I do point to Harari’s (2017) and Vallor’s speculation of how AI might change the conditions for scientific research and discovery through the automation of routine tasks (Santa Clara University, 2018, 05:25).

In spite of my scenario’s somewhat dim scope, I enjoyed this exercise and experimenting with visuals to support my scenario. For this task, I tried playing with DALL-E 2 (Open AI, n.d.) to generate an image that I couldn’t find elsewhere. Using the prompt ‘pill on soil with seed sprout’, DALL-E 2 was able to come up with an image that I thought worked well to support my scenario. While I need more time to learn about AI image generation tools and it’s capabilities and limitations, it was interesting to think about it’s potential use for speculative design.

References

Auger, J., Hanna, J. & Mitrović, I. (2021). Chapter 1: Beyond speculations. In Mitrović, I., Auger, J., Hanna, J., & Helgason, I. (Eds.) Beyond speculative design: Past – present – future (pp. 12-23). SpeculativeEdu. https://speculativeedu.eu/wp-content/uploads/2021/06/Beyond-Speculative-Design.pdf

Citytransportinfo (2020, April 17). Empty shelves in a supermarket during the spring 2020 Covid-19 pandemic [Photograph]. Wikimedia Commons. https://commons.wikimedia.org/wiki/File:Empty-Shelves-P1630832.jpg

DALL·E 2. (2023). Pill capsule next to a sprout growing from soil [Image was created with the assistance of DALL·E 2].

Dunne, A., & Raby, F. (2013). Speculative everything: Design, fiction, and social dreaming. The MIT Press. https://webcat.library.ubc.ca/vwebv/holdingsInfo?bibId=7411587

Harari, Y. N. (2017). Reboot for the AI revolution. Nature International Weekly Journal of Science, 550(7676), 324-327. https://www.nature.com/articles/550324a

Jenner, E. (2020). Woman using the microscope [Photograph]. Pexels. https://www.pexels.com/photo/woman-using-the-microscope-4031321/

Lab, S. (n.d.). The Thing From The Future. Situation Lab. Retrieved March 29, 2023, from https://situationlab.org/project/the-thing-from-the-future/Links to an external site.

Mont, U. (2020). Woman buying lettuce in market during coronavirus pandemic [Photograph]. Pexels. https://www.pexels.com/photo/woman-buying-lettuce-in-market-during-coronavirus-pandemic-6280410/

Open AI (n.d.). DALL·E 2. Retrieved April 2, 2023, from https://labs.openai.com/

Pixabay. (n.d.). Electric towers during golden hour [Photograph]. Pexels. https://www.pexels.com/photo/air-air-pollution-climate-change-dawn-221012/

Santa Clara University. (2018, November 6). Lessons from the AI Mirror Shannon Vallor [Video]. YouTube. https://youtu.be/40UbpSoYN4k

Spiske, H. (2019). Woman holding environmental protest sign [Photograph]. Pexels. https://www.pexels.com/photo/woman-in-blue-jacket-holding-white-and-black-i-am-happy-to-be-happy-print-paper-2559749/

This week’s task involved engaging with a simulation called Detain/Release (Porcaro, 2019), which involves taking on the role of a pre-trial judge and ‘ruling’ on 24 simulated cases. Rulings are based on simulated defendant profile, scaled AI risk assessments, and prosecution recommendations.

I attempted the simulation twice, as my connection failed towards the end of my first attempt. On my second attempt, I got as far as 21 rulings before receiving the following message:

Detain/Release proved to be a bit of an ethical challenge for me as I made my way through the simulated rulings. My own personal thoughts on the U.S/Canadian criminal justice system aside, there was too little information available through the interface to further contextualize the risk assessments generated by the simulated AI system. In particular, there were no controls available for me to review the source information that informed the AI’s risk assessment. As someone who prefers to make informed decisions, the broad categorizations of ‘crime’ and the lack of transparency afforded by the system made me feel uncertain and apprehensive when ruling each case. Most of the time, I found myself disagreeing with the simulated prosecutor’s recommendations to detain for cases that didn’t involve violent crime. In retrospect, this is likely why I lost before the 24 rulings were up.

This simulation made me think more deeply about the role that algorithmically powered decision-making technologies play in the criminal justice system; and specifically, crime prediction tools that are used by both Canadian and U.S. police institutions (O’Neil, 2017; Robertson, Khoo & Song, 2020). Given that AI systems are trained upon large data corpuses inputted by a system architect (Storied, 2021, 07:16), the question becomes who is or isn’t represented in each corpus, and what kind of biases shape them. This becomes further problematized in the context of the criminal justice system, an institution that has a long history of systemic racism (House of Commons, 2021), where inaccurate and biased data sets are being used to make high-stakes predictions and risk assessments (O’Neil, 2017).

In their TED article, O’Neil (2017) unpacks the implications that police bias in data has had in multiple police jurisdictions across the U.S. In particular, O’Neil (2017) describes how neighbourhoods with low socioeconomic status are overrepresented in many data sets due to the policing of minor “nuisance” crimes in these neighbourhoods. O’Neil (2017) goes on to address how data points associated with minor crimes tend to skew the predictive models, thereby perpetuating a “feedback loop” of over-policing in low-income, racialized communities. While reading O’Neil (2017), I’m also reminded of a UN report that found the unregulated use of AI technologies in policing and immigration, such as facial recognition, is at risk of violating human rights policy by way of reinforcing racist and xenophobic biases. (Cummings-Bruce, 2020).

In the context of education, there has been an uptake of algorithmically-powered technologies to support automated assessment, writing, and proctoring. Furthermore, there has been interest in how GPT models will play a role in pedagogical practice and academic integrity. As AI-powered tools become more ubiquitous in education, there is a need for greater scrutiny on the part of practitioners to understand whose data is being used and how. The presence of oppressive algorithmic systems is not just limited to policing. In the podcast 25 Years of Ed Tech, Pasquini and Gilliard (2021) discuss how a risk assessment tool was widely adopted across U.S. schools with little training and oversight, and used biased data sets to disproportionately categorize racialized students as ‘at-risk’ (17:40). Much like the policing prediction models explored in this unit, AI tools designed for education (especially ones related to high-stakes assessment) can perpetuate harmful outcomes for equity-deserving groups. There is a clear need for educators to develop critical pedagogical practices and understand around how these tools operate.

References

Cummings-Bruce, N. (2020, November 26). U.N. panel: Technology in policing can reinforce racial bias. The New York Times. https://www.nytimes.com/2020/11/26/us/un-panel-technology-in-policing-can-reinforce-racial-bias.html

House of Commons. (2021). Systemic racism in policing in Canada. https://www.ourcommons.ca/DocumentViewer/en/43-2/SECU/report-6 

O’Neil, C. (2017, April 6). Justice in the age of big data. TED. Retrieved August 12, 2022. https://ideas.ted.com/justice-in-the-age-of-big-data/

Pasquini, L. & Gilliard, C. (Hosts) (2021, April 15). Between the chapters #23 looking in the black box of A.I. with @hypervisible (No. 52). [Audio podcast episode]. In 25 Years of Ed Tech. Laura Pasquini. https://25years.opened.ca/2021/04/15/between-the-chapters-artificial-intelligence/

Porcaro, K. (2019). Detain/Release [web simulation]. Berkman Klein Center. https://detainrelease.com/

Robertson, K., Khoo, C. & Song, Y. (2020, September 1). To surveil and predict: A human rights analysis of algorithmic policing in Canada. The University of Toronto Citizen Lab. https://citizenlab.ca/2020/09/to-surveil-and-predict-a-human-rights-analysis-of-algorithmic-policing-in-canada/

Storied. (2021, May 5). From Alan Turing to GPT-3: The evolution of computer speech | Otherwords [Video]. YouTube. https://youtu.be/d2UccTPnl4w

This week’s task involved playing the online game User Inyerface (Bagaar, 2019) and reporting back on our experience.

I found this task to be both fun and frustrating, and in many ways, reminded me of an escape room challenge in the form of web page. The game managed to subvert my expectations a number of times, and in the end I only got as far as part 4, despite following all of the instructions. After about 5 minutes of trying to get through part 4, I decided to call it quits. I then watched a speed run of the game by Kittrz (2019) to understand what went wrong, only to discover that the successful course of action (checking all boxes on the page) contradicts the game instructions.

Contradictory instructions is just one example of how User Inyerface makes use of deceptive and/or inaccessible web UI to prevent users from achieving their goals and purposes. This is also achieved through the game’s use of broken and/or mislabeled UI elements, such as the green, circular ‘no’ button on the main screen, or alerts like the cookies notification and timer warnings. Other UI elements were simply unintuitive (e.g., checkboxes and text entry fields that wouldn’t clear by default) or inaccessible (e.g., no tab key functionality to navigate between elements).

User Inyerface (Bagaar, 2019) effectively demonstrates the capacity for web-based artifacts to employ deceptive, inaccessible and simply counterintuitive designs. Some of these designs reflected what Brignull (2011) describes as dark patterns, which are user interfaces intentionally designed to deceive users and to serve commercial interests. For example, the confluence of deceptive and prohibitive UI in User Inyerface (e.g., confusing instructions, disabled buttons/links, checkboxes that contradict the labels, etc.) resembles a pattern that Brignull (n.d.) refers to as the “roach motel”, which is designed to lures users in and make it difficult to escape. Similar to how Amazon uses this pattern to make it difficult for users to cancel their membership (Nerdwriter1, 2018, 00:19), User Inyerface does so to prevent users from completing the game. In addition to the use of dark patterns, the inaccessible interface elements in the game (e.g., inability to tab navigate between elements) reflects the inequitable conditions that users with disabilities may encounter on the web.

Overall, this was an interesting exercise and made salient the need for more transparent, accessible, and user-centered interfaces (Brignull, 2011). In particular, it raises a number of considerations for my own work in the domain of post-secondary education:

• The vetting of any commercial/proprietary technologies used in the learning environment (software/tools/subscriptions), so that learners are safeguarded by dark patterns (or at minimum, afforded transparency and alternatives).

• Designing instruction with usability principles in mind, so that learners are able to successfully achieve their purposes (e.g., online modules, activities, resources, the LMS).

• Adhering to (and advocating for) web accessibility standards when designing web-based learning environments.

• Ensuring that critical thinking and awareness of dark patterns is reflected in digital literacy instruction.

References

Brignull, H. (2011). Dark patterns: Deception vs. honesty in UI design. A List Apart, 338. https://alistapart.com/article/dark-patterns-deception-vs-honesty-in-ui-design/

Brignull, H. (n.d.). Roach motel. Deceptive Designs. https://www.deceptive.design/types/roach-motel

Bagaar. (2019). User Inyerface [web game]. https://userinyerface.com/

Kittrz. (2019, July 4). User Inyerface speedrun [Video]. YouTube. https://youtu.be/SH84z78F9gY

Nerdwriter1. (2018, March 1). How dark patterns trick you online [Video]. YouTube. https://youtu.be/kxkrdLI6e6M

This task involved analyzing a network based on each class member’s song choices for Task #8: Golden Record Curation using Palladio, a data visualization tool (Stanford, n.d.).

Analysis

Given that I have very limited experience with network visualization, let alone Palladio, the video tutorials by Systems Innovation (2015) were a useful primer to become more accustomed with graph theory and network terms like ‘edges’, ‘verticies’ and ‘nodes’.

Upon importing the data set into Palladio, the initial display of information was a bit chaotic and unclear. By manually pulling out the student-named nodes to the periphery and the track nodes in the centre, I was able to start making better sense of the visualization. However, I still found it a challenge to distinguish which nodes had a “high degree of connectivity” (Systems Innovation, 2015).

I turned to this video by Raynor Digital Scholarship Lab (2018), which helped me better understand the functions within Palladio, and in particular, how to customize the size of nodes based on the number of edges. In doing so, I could see that track #20 (Night Chant) had the highest degree of connections, and that track #11 (Izlel je Delyo Hagdutin) had the lowest degree of connections.

The facet filters labelled ‘modularity_class’ listed 5 groupings, each of which further reduced the scope of the data set. I found myself in ‘group #1’ along with two other peers. I once again manipulated the data and re-sized the nodes to better interpret the network.

Based on this smaller network, I’ve made the following observations:

• The three of us shared 2 tracks: Night Chant and Dark Was the Night.
• Kristine and I shared 4 tracks
• Deborah and I shared 2 tracks
• Deborah and Kristine shared 1 track
• I selected 2 distinct tracks
• Deborah selected 5 distinct tracks
• Kristine selected 3 distinct tracks

Reflection

Is the visualization able to capture the reasons behind the choices?

Based on the network visualization alone, there isn’t enough information available to draw any strong conclusions about the reasoning of each of our song choices and the commonalities between them. While we can quantitatively assess the degree of connectivity between songs and class members, one can only make assumptions about the reasons for these connections. One would need to refer to the source material to further contextualize any observations based on the visualization. A more accurate visualization might involve an expaded data set that includes coded information on the selection criteria, which would require analysis of the source material.

Upon examining the blog posts of my peers within this group, it doesn’t seem that we have much in common based on our curation criteria. My own criteria was centred around equity and inclusion (Rugo, 2023), while my peers based their curation on human evolution (Wolf, 2023) and subjective musical preferences (Lachance, 2023). As such, if one were to make assumptions from this data set without examining the source material, they would likely draw false conclusions.

Reflect on the political implications of such groupings considering what data is missing, assumed, or misinterpreted?

Given the lack of data points related to selection criteria, we can only draw assumptions or generalizations from this network- which is, of course, not conducive to critical analysis. Furthermore, the formation of group categories is based on limited information – which can be problematic when viewed in the larger context of algorithmic bias and content prioritization systems on the internet, such as PageRank (Code.org, 2017, 03:02).

In her book on algorithmic oppression, Noble (2018) addresses how online content prioritization algorithms can perpetuate and exploit racist and misogynistic ideologies by design. According to Noble (2018), unvetted data prioritization results can the exclude and further suppress equity-deserving groups, while prioritizing results that reflect privileged identities. As such, the political implications of drawing assumptions based on missing, assumed or misinterpreted data (whether intentional or not) can actually be quite detrimental to the work of equity and inclusion. As someone working within the domain of academic libraries and instructional design, I recognize my role in advocating for critical digital literacies and awareness of the biases that influence and inform the systems that we work with on a daily basis, including data sets and search tools.

References

Code.org. (2017, June 13). The Internet: How search works. [Video]. YouTube. https://youtu.be/LVV_93mBfSU

Lachance, K. (2023, March 5). Task 8: Golden Record curation. ETEC 540 – Text Technologies. https://blogs.ubc.ca/kristinelachance/2023/03/05/task-8-golden-record-curation/

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.

Raynor Digital Scholarship Lab (2018, April 18). Palladio 3-Data visualization. [Video]. YouTube. https://youtu.be/m-OVVAm0utE

Rugo, C. (2023, March 5). Task 8: Golden Record curation. Chris Rugo’s ETEC 540 Blog. https://blogs.ubc.ca/rgch540/2023/03/05/task-8-golden-record-curation/

Stanford. (n.d.). Palladio. http://hdlab.stanford.edu/palladio/

Systems Innovation. (2015, April 18). Graph theory overview. [Video]. YouTube. https://youtu.be/82zlRaRUsaY

Wolf, D. (2023, March 5). Task 8 – The Golden Record. ETEC540 65A: Text Technologies. https://blogs.ubc.ca/dwolfetec54065a/2023/03/05/task-8-the-golden-record/

This week’s task involved curating 10 musical tracks from the original 27 that were included on the Voyager Golden Record. Listed below are the tracks that I’ve selected:

• Track 3: Cengunmé – also known as Senegal percussion (NASA, n.d.).
  
  
• Track 4: Pygmy Girls’ Initiation Song – also known as the Alima Song, performed by Indigenous peoples in the Congo Rainforest (Taylor, 2019)
  
  
• Track 5: Australia Barnumbirr (Morning Star) and Moikoi Song – Two Aborgine songs from Australia (Taylor, 2019). Contrary to what’s listed on the NASA website (n.d.) , Gorman (2013) suggests that it is “Moikoi Song”, not “Devil Bird”, that is actually on the Voyager Record.
  

• Track 6: El Cascabel – a Mariachi song (Taylor, 2019).
  
  
• Track 8: Mariuamangɨ – A traditional New Guinea folk song (Taylor, 2019), also known as “men’s house song” (NASA, n.d.).
  
  
• Track 9: Sokaku-Reibo (Depicting the Cranes in Their Nest) – A Japanese folk song (Taylor, 2019), also known as “Tsuru No Sugomori” or “Crane’s Nest” (NASA, n.d.).
  
  
• Track 20: Night Chant – Also known as “Yeibichai Dance” (Taylor, 2019), performed by Navajo Indians (NASA, n.d.).
  
  
• Track 22: Naranaratana Kookokoo (The Cry of the Megapode Bird) – From the Solomon Islands (Taylor, 2019), also known as “Panpipes” (NASA, n.d.).
  
  
• Track 25: Jaat Kahan Ho – From India (Taylor, 2019)
  
  
• Track 26: Dark Was the Night, Cold Was the Ground – A blues song recorded by Blind Willie Johnson (NASA, n.d.).

Reflection

When approaching this task, I was reminded of Dr. Smith Rumsey’s recorded talk at Brown University (2017), which addresses the preservation of digital information in the face of surplus information and economical limitations. Something that stuck with me during her talk was her point that the goals of preservation should be to “create an honest record” (Brown University, 2017, 00:34:00). Specifically, Smith Rumsey speaks to Carter Woodson’s work of maintaining a community archive of African-American materials to ensure that an accurate cultural narrative was not being misrepresented through scholarly institutions (Brown University, 2017, 00:27:00).

This gave me pause to consider the colonial erasure of Indigenous cultures and knowledge systems and it’s replacement with Eurocentric/Western knowledge systems and narratives. Addressing my own context within Canada, forms of erasure were enacted and perpetuated through the Canadian residential school system, which contributed to what Tuck & Gaztambide-Fernández (2013) describes as a “curricular project of replacement, which aims to vanish Indigenous peoples and replace them with settlers” (p. 73).

With this in mind, I’ve tried to take a reparative approach to my curation of the Golden Record so that erasure of historically underrepresented cultures is not further perpetuated. As such, I decided to decenter and exclude the white, European composers (i.e., Bach, Mozart, Beethoven) found on the original record. I then prioritized the inclusion of compositions originating in Indigenous communities (i.e. Aboriginal Australians, Congo Pygmy Peoples, Navajo Indians). From there, I attempted to capture a wide range of cultural narratives that have been informed by or impacted in some way by settler colonialism and oppression.

References

Brown University. (2017, July 11). Abby Smith Rumsey: “Digital memory: What can we afford to lose?” [Video]. YouTube. https://youtu.be/FBrahqg9ZMc

Gorman, A. (2013). Beyond the morning star: the real tale of the Voyagers’ Aboriginal music. The Conversation. https://theconversation.com/beyond-the-morning-star-the-real-tale-of-the-voyagers-aboriginal-music-18288

NASA. (n.d.). Voyager – music on the golden record. NASA. Retrieved March 3, 2023 from https://voyager.jpl.nasa.gov/golden-record/whats-on-the-record/music/

Taylor, D. (Host). (2019, April). Voyager golden record. [Audio podcast episode]. In Twenty thousand hertz. Defacto Sound. https://www.20k.org/episodes/voyagergoldenrecord?rq=voyager

Tuck, E., & Gaztambide-Fernández, R. A. (2013). Curriculum, replacement, and settler futurity. Journal of Curriculum Theorizing, 29(1), 72- 89. https://go.exlibris.link/3Rxs1XGz

This week’s task involved changing the semiotic mode of our week 1 task. I’ve opted to redesign my original task by creating an audio-driven trivia game using Twine. During the game, users are invited to guess the items in my bag based on the sounds that they make.

Twine Game: Play ‘What’s in My Work Bag’ on itch.io

Project File: Download ‘What’s in My Work Bag’ (*.zip)

Development

Audio Recording

In the process of changing the semiotic mode of my original task, I took it upon myself to record the sounds made by each of the items in my bag. To do so, I used the Voice Notes app on my iPhone to create the recordings. By positioning and manipulating each item close to my phone’s microphone, I attempted to capture each sound in a distinguishable way. I then brought each sound into Audacity to edit out any excess noise. The only items that I did not include from my original task #1 were the face mask and the laptop charger, as the recorded sounds were not distinguishable enough.

Twine Game

To tie each sound together and create a unified multimodal experience, I decided to produce a simple hypertext trivia game in Twine. This was an opportunity for me to learn and experiment with Twine for the first time, especially since I had more bandwidth this week to do so. Along the way, I found myself relying on my knowledge of HTML and CSS, the Twine user-guide, and findings that my peers made with their own Twine objects. For example, Elaine Lee’s (2022) task #5 post references Fry’s (2017) video on hosting and embedding images in Twine, which I also drew upon to add some visuals from the Noun Project.

To integrate my sound effects into Twine, I uploaded each sound to Google Drive and then pasted a public embed link for each sound.

In order to add the score counter, I referred to a discussion post by Chapel (2017) which introduced me to the $score variable. While this was originally written for Twine Sugarcube, I was able to modify Chapel’s (2017) approach for Harlowe with the help of Twine’s debug tool.

Reflection

In my original week #1 task, I shared the contents of my bag using a combination of modes including visual (photo), linguistic (text), and spatial (content arrangement/page layout). While approaching this task, I was reminded of the New London Group’s (1996) “designs of meaning” (p. 73) framework, which speaks to the ever-changing and contextually-driven relationship between “available designs, designing, and the redesigned” (p. 74). I can see now how my use of visual, linguistic and spatial modes in task #1 was drawing on my existing familiarity and knowledge of “available designs” (NLG, 1996, p. 74) for sharing information in a blog environment.

As such, the “designing” (NLG, 1996, p. 73) process challenged me to reimagine the representation of task #1 by modifying the mode of communication. With my “redesigned” (NLG, 1996, p. 73) task, I relied more on aural modes (rather than solely linguistic and visual) to do the ‘heavy lifting’ of meaning-making. Rather than describing the object using visuals and text, the user must interpret the sound of each object to guess what it is.

The use of hypertext allowed me to modify the arrangement and presentation of audio clips in a more participatory way. While my redesigned task is more of a proof-of-concept, I think it successfully demonstrates how users can express their existing knowledge in a new way (i.e., drawing on knowledge of different sounds and the trivia game rules and format) when compared to the more passive nature of the original task.

Thinking through this mode-changing exercise and its broader connection to digital literacy, I’m reminded of Dobson & Willinsky (2009) suggestion that digital literacy has the potential to “[further] educational and democratic as well as creative and literary ends” (p. 303). In particular, I can see how the practice of mode-changing can work towards advancing 21st-century skills and student-centred learning by providing exposure to modes of representation and expression that go beyond written text. After completing this exercise, I feel more confident with approaching a multimodal redesign of my own instructional content in a way that would support digital literacy.

References

Chapel (2017, June 29). Keeping track of points [Online forum post]. Twine Forum. https://twinery.org/forum/discussion/comment/23447/#Comment_23447 

Dobson, T., & Willinsky, J. (2009). Digital literacy. In D. R. Olson & N. Torrance (Eds.), The Cambridge handbook of literacy (pp. 286-312). Cambridge University Press. https://go.exlibris.link/7G5xqPXN

Fry, J. (2017, March 27). Twine – Adding Pictures [Video]. Youtube. https://www.youtube.com/watch?v=DRETz9wnOkk

Lee, E. (2022, February 13). Task 5: Twine story. ETEC 540 Reflections. https://blogs.ubc.ca/etec540elainelee/2023/02/13/115/

The New London Group. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review 66(1), 60-92. http://newarcproject.pbworks.com/f/Pedagogy%2Bof%2BMultiliteracies_New%2BLondon%2BGroup.pdf