Author Archives: James Seaton

Final Project: Describing Communication Technologies – Google Translate

Leave a reply

The History of Google Translate

Machine Translation (MT) and discussion regarding its use in language learning dates as far back as the 1980’s (Garcia & Pena, 2011). Today, the application Google Translate (GT) is ubiquitous within these discussions, and for good reason. In celebrating the 10-year anniversary of its 2006 release, GT Product Lead Barack Turovsky boasted of the incredible reach of the application; among the notable accomplishments highlighted were the range of languages the app supported (103), the staggering number of users (500 million+), and the incredible number of words translated daily (100 billion+) (Turovsky, 2016). Originating as a webpage, the Machine Translation (MT) software is now available as a smartphone application for use by language learners and those navigating language barriers in everyday life (Bin Dahmash, 2020). Ever-improving, a new version utilizing artificial intelligence – the Google Neural Machine Translation (GNMT) – was launched back in 2016 (Tsai, 2020). Even before the introduction of GNMT, the grammatical aspect of the app’s English translations would likely have satisfied the minimum language requirements for many English-speaking universities (Mundt & Groves, 2016).

To view the most recent updates to GT, view Google’s official blog.

Affordances of the Technology

To understand the potential impacts of GT within literacy and education, we need to first look at the designed affordances – what is the technology meant to do? Using a variety of input options – typed text, handwritten text, and voice input (including its conversation feature) – GT offers translations along with both definitions and audible pronunciations. It will also allow you to save translations into your own personal phrasebook for future reference, and for a select set of languages (ten at the time of posting this article), the software will create translated transcriptions that you can also save. Probably the most advanced feature to date, however, is the ability to hover your phone’s camera over a piece of text and have a translation superimpose itself over that text using augmented reality.

It should be noted that the level of support and sophistication varies from language to language. Not all supported languages are available to download for offline use, and less in-demand languages are missing certain functionalities. Additionally, due to structural differences between languages, the app works better for some language combinations (like Spanish and Italian) compared with others (like English and Arabic) (Garcia & Pena, 2011; Läubli & Orrego-Carmona, 2017).

Though a robust tool for providing language translations, the original intention of MT systems like GT was not for language learning (Tsai, 2020), much in the same way that dictionaries are not meant to teach literacy but rather act as a supportive resource. However, the current iteration of GT is a far cry from the early MT systems of the 1980s and according to Bin Dahmash (2020, p. 228), “how people utilize the built-in features of the app to fulfill their unique purposes might not match the original designers’ intentions”.

Within Education and Additional Language Literacy

So what are the implications of GT use within education? For starters – barring some unforeseen circumstance – the app will undoubtedly be heavily utilized by language learners for years to come. Anecdotal evidence compiled by Bin Dahmash (2020) in their study of Arabic-speaking English language learners in Saudi Arabia showed that students initially encountered GT through a variety of sources outside of the classroom including online advertisements, search engines, and close relations. Already viewed by students as a valuable resource before entering the classroom, there is no denying the likelihood of the assumption made by Mundt and Groves (2016), that GT “will be used by student writers, either openly if sanctioned by institutional policies or clandestinely if not” (p. 388). With that assumption, they surmise that universities (which can conceivably be extended to include other types of learning institutions) should develop a set of best practices for integration of the technology into language learning, and establish guidelines for acceptable use.

So how are language learners currently using the GT in ways one might deem worthy of promotion and inclusion within curriculum? Before answering that, let us make a distinction between the tasks of learning another language and being able to produce written work in that language. While there is intersection between the two, they can still be seen as different entities needing distinction.

As previously stated, GT translations can be accessed using various modes of unput. Bin Dahmash (2020) noted that within their study, a determining factor for method of input was the length of text, with learners typing in short sections of text by hand, and utilizing their phone’s built-in camera for longer passages. Students in this study also accessed translations using GT’s conversation feature, which would transcribe and translate conversations between two or more parties from one language to another (in this case, between English and Arabic) (Bin Dahmash, 2020). Through use of GT, students have been able to improve their vocabulary, pronunciation, spelling, grammar, and reading (Bin Dahmash, 2020; Tsai, 2020). It should be highlighted however that the extent to which pronunciation is improved may vary drastically depending on the chosen language and whether Google has invested in human recordings of the language, or if a robot-voiced version is provided as a cheaper alternative.

Regarding writing, Tsai (2020) acknowledges the importance that learning to write in a new language has on language learning due to the indispensable nature of how writing shares “feelings, thoughts, ideas, comments and experiences with others,” (p. 1). In a study looking at the effectiveness of using GT to improve the writing performance of Chinese students studying English, Tsai (2020) found that students could benefit from using GT-produced translations as a resource to improve texts initially self-written in English and then edited with the aid of the translation. His study observed both lower – non-English Major (NEM) – and higher-proficiency – English major (EM) – English language learners. He found that both groups were satisfied overall with the provided translations, both groups successfully improved their writing as a result, and there was a noted appreciation among both groups for the opportunities it afforded them for improved vocabulary use. However, he reported that the levels of satisfaction were significantly higher among the lower-proficiency NEM students, and these students expressed a greater willingness to continue using GT in their writing.

A similar study was conducted by Garcia and Pena (2011), looking at whether MT could aid in the development of writing skills for English-speaking beginner/early intermediate Spanish language learners. Their study worked with a GT-supported program that allowed users to write in English, view the real-time translation of that writing in Spanish, and then perform both pre-edits (to the English) or post-edits (to the Spanish) in an effort to improve their Spanish writing output. Unsurprisingly, they arrived at a similar conclusion, suggesting that “MT helps learners to communicate more, probably in a way that is inversely proportional to their actual mastery of the language: the lower their mastery, the greater the help provided by the MT draft” (Garcia & Pena, 2011, pg. 478).

As suggested by Tsai (2020), “Google Translate can play a role of a second ‘audience’” (p. 15) for language learners as they practice their writing, providing them with advice on both word usage and sentence structure. In language classrooms where students may have limited opportunities to practice their writing and receive meaningful individualized support from teachers, this additional feedback from GT helps students to revise and improve their writing (Tsai, 2020). As helpful as this may be for writing, Garcia and Pena (2011) shared that some students were skeptical about the ability of GT to actually help them learn the language, with concerns regarding overdependence on the technology. Students were also skeptical about just how good the GT translations were, especially among the beginner group (Garcia and Pena, 2011). Tsai (2020) addresses this issue, stating that students need to build up language skills well enough “to read and understand what is translated by Google Translate” (p. 16), especially considering GT still has limitations and students may be hesitant to rely on it too heavily due to grammar issues prevalent within earlier versions.

These studies (Bin Dahmash, 2020; Garcia & Pena, 2011; Tsai, 2020) help show the power of using GT both for learning and writing purposes, but also point to limitations. In doing so, they seem to concur with the sentiment expressed by Mundt and Groves (2016) that if “used carefully, and with imagination, the software can be used to aid the language learning process, not replace it” (p. 398). Put another way by Garcia and Pena (2011) who use the analogy of using a Global Positioning System (GPS): “one might note that while it certainly helps you reach your destination, it does not train you how to get from Point A to Point B autonomously” (p. 486).

What all of that taken into consideration, what if one’s main objective is not language learning but rather communication? To play back to the analogy, what if you are comfortable always using GPS to reach your destination? What possibilities are opened if mastery of a new language is no longer required to study in that language? Mundt and Groves (2016) suggest that as MT technology furthers in its development there may be potential for students to bypass the need to comprehensively learn a new language in order to study in that language, and that schools may need to consider whether language requirements are still necessary. Regardless of whether or not this becomes the case, schools will need to be clear in their guidelines for acceptable use, especially when it comes to cases of plagiarism. While it is an obvious violation to take work from another language and present a translated version as one’s own, there is a question of whether writing in one’s own language and presenting the GT version might also be considered as plagiarism, as “the work they are submitting is theirs, but could not be truly said to be theirs on totality” (Mundt & Groves, 2016, pg. 395).

Conclusion

So what is the future of language learning, and what role will MT systems like GT play? How important will language learning become as communication barriers continue to fall? Mundt and Groves (2016) believe that “GT clearly has an effective part to play in the future of transnational education, and can help students and staff cross linguistic boundaries more easily and faster” (p. 398). The extent to which that happens will undoubtedly depend on how advanced these systems become and how widespread their acceptance is. While it is difficult to see MT systems completely replace the need to learn new languages – or replace the need for professional translators for that matter (Läubli & Orrego-Carmona, 2017) – they are no doubt on their way to leaving a lasting impact on both language learning and interlingual communication.

References

Bin Dahmash, N. (2020). ‘I can’t live without Google Translate’: a close look at the use of Google Translate app by second language learners in Saudi Arabia. Arab World English Journal (AWEJ) Volume, 11. https://dx.doi.org/10.24093/awej/vol11no3.14

Läubli, S., & Orrego-Carmona, D. (2017). When Google Translate is better than some human colleagues, those people are no longer colleagues. https://doi.org/10.5167/uzh-147260 

Garcia, I., & Pena, M. I. (2011). Machine translation-assisted language learning: writing for beginners. Computer Assisted Language Learning, 24(5), 471-487. doi: 10.1080/09588221.2011.582687

Mundt, K., & Groves, M. (2016). A double-edged sword: the merits and the policy implications of Google Translate in higher education. European Journal of Higher Education, 6(4), 387-401. https://doi.org/10.1080/21568235.2016.1172248

Tsai, S. C. (2020). Chinese students’ perceptions of using Google Translate as a translingual CALL tool in EFL writing. Computer Assisted Language Learning, 1-23. doi: 10.1080/09588221.2020.1799412

Turovsky, B. (2016, April 28). Ten Years of Google Translate [Blog post]. Retrieved from  https://www.blog.google/products/translate/ten-years-of-google-translate/

 

Linking Post #6- Task 12: Speculative Futures by Sarah Stephenson

Leave a reply

Link: https://blogs.ubc.ca/etec540ss/tasks/task-12-speculative-futures/

For my final linking assignment, I selected Sarah’s speculative narratives thanks to the interesting questions that they raise. Whereas I avoided using dialogue within my narratives, for Sarah they were a major component. Through these dialogues, she was able to suggest the sort of familial conversations that might be had in the near future as technology takes a stronger hold over our daily lives and the ways in which we live and learn.

In her first narrative, she addresses the idea of playground intelligence being one of the multiple intelligences one can have, modelling a comical situation in which a kid doesn’t understand the basics of a slide (you need to sit at the top to operate, not the bottom). Though I don’t think low playground intelligence will ever be as pronounced as in her scenario, I do think that some more advanced playground activities might be lost to kids in the future if technology dominates their lives to too great of an extent – things like how to throw a ball (well), or climb a tree. Admittedly, I felt a little shame reading this and making connections to the types of knowledge and skills that I don’t have which would have been seen as more necessary within earlier generations, such as how to properly gut a fish or *successfully* change a tire (I know the basic idea, I just haven’t executed it myself as of yet).

As for her second narrative, Sarah looked at the issue that arises when technology creates an imbalance between knowledge and wisdom – in knowing something and having the understanding of how to apply that knowledge. In the scenario, a child is talking with their parent about their new glasses which, rather than being used for sight, are used in a manner more akin to Google Glass. The child is asked about the capital of Sweden, and they in turn look to their new glasses technology to answer the question. I think her narrative speaks to a pedagogical concern that has been raised more and more frequently as of late: what should we teach, now that students have instant access to information? How much will education shift away from information retention towards teaching students the skills to evaluate and use the information they encounter on their own?

Linking Post #5- Task 11: Algorithms of Predictive Text by Dierdre Dagar

Leave a reply

Link: https://blogs.ubc.ca/ideamachinery/2021/03/25/task-11-algorithms-of-predictive-text/

I selected to link to a Dierdre’s post on using a predictive text algorithm partially because this is one of the optional tasks that I selected not to complete my own posting on. The main reason for this was that I had already completed a sufficient number of optional tasks, and not due to some larger preference to avoid it. As such, I was curious to see what the task was all about. Admittedly, I hadn’t completed the week’s lesson prior to making my way through her post. As such, I was very appreciative of the detail that she went into in her post. Instead of simply reflecting on her “dude bro” predictive text and her feeling surrounding it, she provided a lot of background information on the algorithms behind the functionality. Honestly, I am quite impressed that she was able to navigate her predictive text to the point that it not only made sense, but came off as somewhat profound. Spurred on by her experience, I attempted my own predictive text message using the same given prompt “As a society, we are…” and was highly disappointed with the result, which ended up including my favourite author’s name (showing me the personalized nature of the algorithm), though with a misspelling which I must have accidentally used enough times that my G-mail phone app now recognizes the error as being correct.

Dierdre referenced Gmail’s Smart Compose functionality, which made me think of how when composing e-mails using Gmail on my computer (rather than on my phone), the autocomplete suggestions are much more robust and intelligible, often suggested in longer phrases rather than single words. I decided to attempt the same task using my desktop Gmail and found that I wasn’t actually provided with any prompts for the input. I think the reason for such is that it was too off-script from everyday conversational language. Prompts like “Hello, how…” will easily be completed with satisfactory suggestions, but more complex or unique prompts like the one Dierdre and I both selected stray too far from the basic conversational fillers that Smart Compose was designed to provide.

I actually wrote a speculative narrative that involves a subject working to create a database of predictive text phrases that allow users of these technologies to sound less robotic and more like their natural speaking styles while utilizes the technology: Alternate future #2: PRESCRIPTIVE TEXT.

Linking Post #4- Task 7: Mode-Bending by Nathan Bristow

Leave a reply

Link: https://blogs.ubc.ca/mrbristow/etec-540/task-7-mode-bending/

Nathan took a very interesting and entertaining approach to re-addressing the original task (sharing information about himself by answering the question “What’s in my bag?”) in a new format. His video is nearly devoid of all verbal and written language (minus the writing on his allergy pill bottle), instead opting for a common language of gestures and actions. The end result speaks as much to his personality and competencies as a digital content creator as the actual items that he’s included in his bag. In fact, the items that he chose to place in his bag were quite non-descript and not very telling of who he is, at least in comparison to what I’ve seen from various other classmates. I’m curious if there was some intentional editing of the items so as to fit the style of video that he created. As mentioned, his (quite hilarious) video relied heavily on body language rather than speech or text, and I imagine there might be other items traditionally in his bag which he might have found difficult to demonstrate a need for in the same way as say, his deodorant or a rain jacket.

After watching his video, I went and re-watched part of my own Mode-Bending task. It made me that much more impressed with Nathan’s work. I narrated my video thinking that it would help to express more about myself, but I feel as though aspects of the narration fell flat, adding less to the video than I originally thought. Part of the issue is that I feel like I really underutilized the power of tone in communication. Listening back to myself speak, I could tell that I was having a deep-voice kind of day, and I made no real attempts to change my intonation to aid the viewer. I had likely recorded at least 10 different versions prior to the one that I posted, so my enthusiasm was running low and I too focused on not missing any items instead of selecting a few items and addressing them in a more meaningful way.

If I were to go back and re-complete this task, after having watched Nathan’s video, I think I would opt to include a curated soundtrack to my video rather than a narration. Music is more universal than English, making the video more inclusive, and including different songs for different applications may make for a more exciting experience and end up showing off more of my personality. Then again, maybe the monotone nature of my video is an accurate representation of my personality…

Linking Post #3- Task 6: An Emoji Story by Erin Marranca

Leave a reply

Link: https://blogs.ubc.ca/marranca/2021/02/21/task-6-an-emoji-story/

I’m going to present this link a little differently this time. Erin and I had a little discussion on her blog post which I’ve included below here:

I really connected with Erin’s work, the detail that she put into it, and the reflection that showed the decisions she had to make while attempting to convey the ideas behind the film. Reading back over our conversation, I’m reminded of how easily a selected emoji can detract from the overall message if it leads to ambiguities, and how certain emojis can give everything away so easily if unique enough, or if used in unique combinations with others. This has made me think back to some of the other emoji stories my classmates put together, so what I’ve decided to present to you here are what I found were a few of the dead giveaways when reading my classmates’ emoji stories:

https://sites.google.com/learn.sd23.bc.ca/540-course-work/weekly-assignments/task-6 (Chelan H)

Lion + crown –> has to be Lion King

 

https://blogs.ubc.ca/jasmineparentetec540b/2021/02/20/task-6-emoji-story/  (Jasmine P)

“Small” + city, cornfields + aliens –> gotta be Smallville

 

 

https://blogs.ubc.ca/etec540judytai/2021/02/17/task-6-an-emoji-story/ (Judy T)

rat + chef –> As another classmate put it, there aren’t a lot of movies about rats and chefs…definitely Ratatouille.

 

 

Linking Post #2- Task 5: Twine by Sarah Stephenson

Leave a reply

Link: https://blogs.ubc.ca/etec540ss/tasks/task-5-twine/

For starters, I have to say that I think that the extent to which Sarah and I differed in terms of both our approaches and mindsets regarding this task is quite hilarious. Sarah enjoyed the overall process and reflected on it fondly; I did not. The irony is that we both recalled our days reading R.L. Stein’s “choose your own adventure” Goosebumps books in our reflections, but whereas it seemed to bring a sense of nostalgia to Sarah, I recollected how much I disliked the books. Sarah planned out her story ahead of time on paper, whereas I addressed in my reflection that I likely should have done that sort of planning, but ultimately opted against it. As a result, she ended up with a successful story that had nice flow and incorporated educational components relating to Greek gods. I ended up highlighting some of the functionalities of Twine, but without any real educational content.

Overall, I am envious of Sarah’s positive experience using Twine. It’s a reminder for me that my students may appreciate things differently from myself, and as a result I should be slower to dismiss potential learning opportunities that don’t necessarily connect with my preferences. I also appreciate the fact that she addressed the differences in the editing process between the rough draft she put on paper, and her later digital version. Even with limitations on what can be done on paper through editing, it can still be a good place to start when creating writing pieces, especially when storyboarding rough plans. Sure there are tech apps that can help with that process, but they can be cumbersome to work with – especially if you’re new to the technology – and they still don’t allow you to splay out several pages at once in the same way that you can with paper and a big desk.

Linking Post #1- Task 4: Manual Scripts by Greg Patton

Leave a reply

Link: https://blogs.ubc.ca/etec540gregpatton/2021/02/07/task-4-manual-scripts/

In Greg’s reflection on the Manual Scripts task, he talks about how he usually writes by hand as it allows him to express his thoughts faster compared with typing. This extends to his formal writing that is completed on a word processor, but starts off as handwritten rough drafts. He also mentions that he has a favourite pen to write with (one gifted to him by his in-laws) and that he likes having a physical copy of his work in his hand when he completes a piece of writing.

I really resonated with Greg’s reflection and his preference to work with the medium that helps him achieve his tasks faster. For Greg, it is by writing rough drafts with pen and paper, and then converting those over to a digital form. For me, I tend to produce written works quicker with typing, so that is generally my go-to. That’s not to say that I can’t handwrite quite quickly, but the fact that I would then need to retype anything I’d written would likely make the process feel redundant and too slow. At the same time, I’ve always wondered what methods I would take if I were to delve back into the world of fiction writing. I like the idea of creating rough drafts by hand that can’t be easily be edited as a way of just pushing out a lot of content and getting the creativity flowing. It can be so difficult when typing to create content because its too tempting to initiate the editing process immediately before full thoughts have even been formed. Reflecting on this making me curious as to how much Greg edits and alters his work when he converts his rough handwriting into what he refers to as his formal writing.

I also really appreciated the romanticism that Greg has for physical copies of books and other writings, and the fact that he has a favourite pen. Though I don’t have a special pen myself, I have definitely had my preferences over the years, and I actually have a favourite kind of paper that I often buy in large quantities. Just like typing with a keyboard that you aren’t quite used to, the type of pen and paper that you’re working with can make a world of difference. If the pen I’m attempting to write with doesn’t glide the right way, or if the paper doesn’t have just the right firmness and thickness, then I’m off in search of something that will do the job better. If you try to hand me a pencil for anything larger than a grocery list, I’ll be up and hunting for a pen within seconds (or just whipping out my laptop).

Speculative Futures (Task 12)

6 Replies

For this task, I was asked to come up two speculative narratives suggesting future relationships between education, technology, text, and media. As Dunne and Raby (2013) state, even though most future predictions of this nature are unlikely to come to fruition in a meaningful way, they can still be used “as tools to better understand the present and to discuss the kind of future of future people want” (p. 2). I have presented my narratives in slightly different forms below, with one focused more on presenting a potential new form of writing, and the other examining a subject (Alex) and the ways in which his career and life is being impacted by an increased prevalence of artificial intelligence.

Alternate future #1: TEXTOGRAMS

Typing skills for the English alphabet are taught as fluid motions and are studied/recognized as distinct entities in the same way as logograms.

In the year 2031, the majority of writing has gone digital. A decade prior, back in 2021, there was already a shifting emphasis away from handwriting and towards developing strong typing skills. As smartphones got cheaper and cheaper, and students at younger and younger ages were granted access to their own personal devices, new writing skills emerged. Soon, students were doing the majority of their writing simply with their thumbs. As fast as they were (indeed, it could be quite impressive to those non-digital natives) the speed with which they sent messages simply wasn’t as fast as previous generations using computer keyboards. To address that speed deficiency, a group of teachers in Vancouver, B.C. joined together to create a locally-developed texting course called “Intro to swiping – the texts of the future”. What started as a guide to faster texting evolved into an entirely new way of looking at the English language, through the use of logograms (characters representing whole words), similar to what is seen in eastern cultures. Now, alongside courses in typing, the majority of English-speaking countries have courses devoted specifically with learning this new way of texting, as well as the study of the characters (uncreatively called “Textograms”) that have resulted from this new form of writing.

Shown below is a greeting projected onto the board of a typical introductory course teaching Textogram writing. See if you can decipher it, check out the slideshow that helps explain the meaning behind each Textogram. As a minor hint, the dot on each Textogram (aside from for those symbols representing single letter words like “a” or “I”) represents the beginning of the swiping motion required to form each word.

https://docs.google.com/presentation/d/1CWpYlrQU1ghoxV-GcFHvSd_uFg_KcEWQeP1ZVsrdaEU/edit?usp=sharing

 

Alternate future #2: PRESCRIPTIVE TEXT

It’s been a tough few years for Alex. With the ever-changing job market due to the rise in automation, he has struggled to find a role that he can stay in long term. He knew graduating with an arts degree might not have been the wisest choice in the current climate, but he just couldn’t see himself sitting behind a computer coding all day – like the majority of his more successful friends. The irony then is that his current role has him working so closely not only with computers, but with the automation that is making him feel so obsolete.

What does he do, you ask? For the last three months, Alex has been working behind the scenes of Google’s latest initiative – Prescriptive Text. Think of the automated replies and autocomplete suggestions you get in g-mail. It’s like if those two features had a baby with the love child of greeting cards and mad libs – thoughtfully worded messages provided for the user without the care and attention previously required for such things. The eventual goal of Prescriptive Text is to allow users to select a set of emotions and general ideas that they want to convey, and in return they are provide with a set of messages they can use. Automatic replies in g-mail are part way there already, but they lack certain levels of human emotion and rely on too many clichéd and painfully obvious stock responses.

Alex’s job is to bring the humanness to the texts being prescribed. In essence, he has been tasked with creating a database of new turns of phrase for both generalized and specific situations so that the artificial intelligence managing the Prescriptive Text doesn’t sound so, well, artificial. Google’s two-fold goal is to have a rich enough database within the next two years (by 2028) that all writing within the Google suite of products will have the option of being written by Prescriptive Text, and that such messages will be near-impossible to decipher from original, fully user-generated content.

With that timeline, Alex knows that his current position is limited. His only hope at extending this position beyond the two-year timeline is to bring something invaluable to the table. This has meant hours upon hours of researching the latest slang and studying speech patterns of different social groups. His hope is that he will snag a position in the smaller contingency group planned to address the changing needs to the database as the English language continues to evolve. His great fear though – one also expressed by others in his department – is, ironically enough, that if Prescriptive Text becomes too widely used, it may slow down the evolution of language to the point that the contingency group is rendered unnecessary.

References

Dunne, A., & Raby, F. (2013). Speculative Everything: Design, Fiction, and Social Dreaming. Cambridge: The MIT Press.

Attention Economy – Navigating User Inyerface (Task 10)

Leave a reply

The instructions for this task were to navigate through a misleading GUI game – User Inyerface –  designed to distract and manipulate the user by employing what Brignull (2011) refers to as “dark patterns” (essentially, misleading practices involving online interfaces). I ended up navigating through the game in seven minutes and 22 seconds (see below).

Reflection:

I likely could have made it through the game much faster if I had focused less on examining the ways in which it attempted to manipulate and distract me, and instead just earnestly attempted to comply with each item requested of me. Aside from the obvious reason that I knew I would have to create a reflection and therefore wanted to be as perceptive as possible, I didn’t know what the rules of the game were, and whether or not I could lose at some point in time. I was initially concerned that this game might play out in a choose-your-own-adventure style manner like the twine task, and that I would go down a dead-end path that would simply waste my time. However, after not immediately losing the game by selecting (well, attempting to select) the “NO” button on the initial landing page (see “A” below), I decided to worry less about losing by selecting or completing something incorrectly, and focus more on doing whatever I could to satisfy the requirements of each page as quickly as possible to progress to the next.

Overall, I found the game to be way more misleading than manipulative, but at the same time it helped me to reflect on how these dark patterns might be employed in the real world in unethical ways.

As there were so many frustrations that I had while attempting to complete this game – I was very much not a fan of the ways in which it attempted to waste my time – I have decided to focus on four of the major issues:

Issue #1 – Useless Buttons/Hidden Links

A – Not a button, despite all appearances.

B – Apparently “HERE” is a hyperlink despite the signifying characteristics of a hyperlink being associated with the words “click” and “next page” instead

 

C – Not a button. No option provided for those who have a problem with cookie usage.

D – At first glance, this button looks like it will “send” your message, but instead slowly removes a useless chatbot.

Issue #2 – Double Negative/Explicit Choice Issues

E – Double negative aspect to accepting terms and conditions is not intuitive. Though technically users are forced to make an explicit choice (Brignull, 2011) by deselecting the option, some may do so as a force of habit (as they may associated pre-selected boxes with subscribing to email lists)

Issue #3 – Hidden Information

F – Password requirements are not visible initially unless you scroll down the page (and include requirement of a Cyrillic character)

 

Issue #4 – Privacy Concerns

G – Especially considering I didn’t actually read the terms and conditions (that’s another issue altogether), I don’t know what is being done with my photos and information, so I don’t want to share an actual picture of myself: enter Sad Keanu

 

There were a lot of other frustrations that arose as I worked through this game, including some components which weren’t inherently misleading, but were obviously incorporated due to their poor design. Examples of this include placeholder values on submission forms that didn’t easily erase when selected, and the inability to use the tab button to move between different components of those forms.

All in all, it was quite a frustrating experience to work through the game, and I have to say that I am very glad to be done with it.

References

  • Brignull, H. (2011). Dark Patterns: Deception vs. Honesty in UI Design. Interaction Design, Usability, 338.

Examining The Golden Record Curation Data (Task 9)

Leave a reply

A brief introduction: I was tasked – along with my classmates – with curating a list of ten songs from NASA’s Golden Record (see post). Our professor collected this data and produced a graph to visualize our selections. The graph is shown below, with the lighter nodes representing the songs scaled in size relative to the number of times they were selected (denoted by edges attaching songs to the darker nodes which represent each classmates). For example, the song “Johnny B. Goode” was selected by all 23 classmates and is therefore represented by the largest circle (node). That song’s node is also much closer to the center of the graph compared with those rarely-selected songs with few edges, shown on the outskirts. The ten songs set for inclusion would then arguably be those with the highest number of selections – or highest degree of connectivity, according to Systems Innovation (2015).

Right click and select “open image in new tab” to view a larger version of the graph

Subset groups were also formed based on similar selections. I have included a graph showing my personal group. Note that although we selected enough common songs to be grouped together, this is no way expresses how similar our reasonings were.

Right click and select “open image in new tab” to view a larger version of the graph

My reflection on the process and results:

First and foremost, I think that there is so much more to consider when performing such a curation than simply selecting your own personal list of ten. One of the big considerations I had when selecting my own list of ten was attempting to avoid repetition of similar songs. Even if all of my classmates had the very same general thought process, if half of us selected one song and the other half selected another (with the two songs being considered the most similar to each other), both songs would likely make it into our curated list, an outcome none of us would really be in favor of. If you take the same idea but apply it to a group of four or five similar songs, if all classmates hold the same idea of selecting one of those songs and end up splitting the vote too many ways, none of those songs ends up being selected. In both cases, the curated list isn’t reflective of the collective intentions of the group. Another issue is the question of weighting, as there is no way to emphasize the importance of a particular selection (or lack of selection) in the current selection process.

Due to these perceived shortcomings (along with others), I am proposing three methods which could help make the curation process more representative of the collective will of the curators.

  1. Ranking (true majority rule) – as addressed by Dasgupta and Maskin (2008) in Scientific American (see here for a full explanation), ranking candidates in elections is a more accurate way of reflecting the collective will of the people. In deciding between any two candidates, the selection would be based on a direct comparison between how many ballots they ranked above each other in. This may be quite complicated to implement when selecting a larger subset of choices, however.
  2. Ranking (rank-order voting) – also mentioned by Dasgupta and Maskin (2008), this is the system employed by the Heisman Trophy selection committee (“Heisman Trophy”, 2021), in which choices are ranked and points are awarded accordingly (in the case of the Heisman, voters make a selection of first through third place, and those candidates receive three, two or one points as a result). In this case, each curator would rank the songs one through 27, with the inverse value being assigned (a first place vote receives 27 points, and a 27th place vote receives one point, etc.). Those songs awarded the highest number of points would then be included.

With the current curation system, all ten selections were considered equal (and the same could be said of those not selected). However, curators most likely had stronger feelings about some songs being included over others, and either ranking system would better reflect that than the status quo.

  1. Iterative voting – selecting one song at a time, and after each selection is made, voters would have a choice to adjust their voting strategies. This could be done with either a single vote each selection round, or could still be done using one of the ranking systems mentioned above. This would help a lot assuming that most curators voting were attempting for a sense of variety in the curation, or were hoping to include songs from different personal groupings. Personally, there are songs I would not vote to include if similar songs had already been slated for inclusion, so I know this would help better reflect my choices.

In the end, curating such a list will never fully satisfy all parties involved. However, with some adjustments like incorporating aspects of the methods proposed, we can do a better job of supporting the rationales behind the selections.

References

Dasgupta, P. & Maskin, E. (2008, October 6). Ranking candidates is more accurate than voting. Scientific American. https://www.scientificamerican.com/article/ranking-candidates-more-accurate/

Heisman trophy. (2021, March 14). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Heisman_Trophy&oldid=1012034262

Systems Innovation. (2015, April 19). Network Connections. Retrieved from https://youtu.be/2iViaEAytxw