Category Archives: Linking Assignment

Task 12: Speculative Futures

When I checked out Nick’s Speculative Futures task, I was drawn to the illustrations in his post.  I think using a storybook format was a great ideas.  I have never created a storybook like this before.  When I completed this task, I did not consider the possibility of adding illustrations to my stories.  When I was thinking about our futures, I was looking at education and tried to imagine what it would be like if I were to return to my current school in 30 years.  Nick took a more holistic view of the future.   

I found it interesting that the same character in each story lived a very similar, video game type life, with access to the same technologies and yet how he  interacted with those technologies was so different.  When I read the stories of the future, I found the control exerted by Surli over the main character to be rather disturbing.  But, when I really think about it, many aspects of our lives are controlled by technology today.  We have alarms and timers reminding us when to wake up in the morning, when we have to be in class, when to take our medications, when to watch a particular TV show.  We have smart watches and Fitbits that count calories and tell us when we need to exercise more.  We have apps and cards to pay for things, without ever seeing real money.  Maybe Nick’s future is right now.

Task 11: Algorithms of Predictive Text

This was one of the tasks I chose not to complete, so I wanted to read a post from someone who chose to complete it.  I found it interesting to read about Braden’s experience with algorithms for predictive text.  I personally have never had very good luck with predictive text on my phone or tablet.  Rarely does it predict what I am actually trying to say and it seems as if Braden had the same experience on his iPad.

Braden made an important observation about the difference between the predictive text on his phone or iPad compared with that on his laptop, even while using the same program.  It does make sense, given the different uses for each of the devices.  The only real predictive text that I have on my laptop is when I am using Google Docs or Slides to do work for school.  There, the predictive text is fairly accurate.  The subject matter is quite specific and mostly limited to math and science.  The wider variety of activities my phone and tablet are used for would make it much harder for an algorithm to come up with the correct words.

It was interesting that Braden had experience with students completing assignments using predictive text, to the point where the assignments did not make sense.  This is something I have never experienced.  I question how we, as teachers, can monitor this kind of use of technology.

Task 7: Mode-Bending

I was interested in this post because, in her mode-bending assignment, Marlis took a similar approach to me.  We both chose to record a slide presentation about the objects in our bags.  I described how I would use many of the items in a day, while Marlis had a different spin on her presentation.  Rather than the story being about how she found the objects useful, she made the objects in her bag characters with a voice.  They spoke of their perceived usefulness to their owner.  To add to the effect, she also used background music and sound effects.  All of these decisions made her presentation interesting and effective and a much different experience than my own presentation.

In her reflection, Marlis commented about the ability to include more information in written form than in a video, and I totally agree with this.  She found that she had to leave out some information which she had included in the original task of “What’s in My Bag?”  Watching her video, I didn’t get the feeling that there was anything missing, though.  I found that I also left out some of the information that I had in the initial task.  I did not include all of the objects that were originally displayed.  My video would have been much longer, and honestly, some of the objects were not that significant in my everyday life (maybe they should come out of my bag).  With a concise, well-written piece, it is easier to convey more of what one wants to say.   

Task 5: Twine

When I started reading DeeDee’s post, I was drawn in by the question about vomit and dirt flavored jelly beans.  I just had to check out her game.  Then she explained that she was teaching about dichotomous keys.  I have been trying to come up with other ideas of how I could use Twine in my Biology classes.  I had used Twine in one of my other courses to create a game based on the circulatory system.  Using it for dichotomous keys seems like such an obvious idea based on the structure of the game, and yet I hadn’t come up with the idea before.  I am definitely stealing this for the next time I teach the topic.

I thought that it was a good idea to use the jelly beans from Harry Potter as the basis of the game and learning about dichotomous keys.  Students know about and can connect with Harry Potter.  Using a variety of effects and media in the game: coloured font, font effects, photos, sounds, and video clips, as DeeDee did, is effective at engaging learners.   

When I approached this task, I considered trying to make another educational game, but then I decided to have a bit of fun and do something entirely different than what I would normally do.  I appreciate the effort that DeeDee put into her game to make something that was both useful for her as teacher, but also a fun, educational experience for her students.

Task 4: Manual Script

I chose this particular post because for Task 4, I chose to complete the potato print option as it seemed more appealing to me than doing some more writing by hand; however, I was interesting in learning about someone’s experience with the other option in this task.  Even though I did not complete this portion of the task, I have done enough handwriting that I could relate to some of Victoria’s experience.  Even with access to technology, I still complete some tasks on paper in handwriting: notes on assigned readings, lists, notes at meetings, and even planning and notes for assignments.  I find that there is a connection between my brain and the pen that allows me to plan better while handwriting.  Sometimes, I pay careful attention to my writing, other times, not so much.  Sometimes it is well-formed cursive writing and other times it is a strange hybrid of writing and printing.  It usually depends on the speed at which I am trying to record ideas and also the intended audience of the writing.

Victoria made a valid comment about the difficulty in editing hand-written work.  I usually don’t spend much time editing writing done by hand.  I don’t have the kind of time it takes to edit an entire written document.  For writing that will require editing, I definitely also prefer using a word processing program, which is usually Google Docs.  

Task 3: Voice to Text

I was interested in Graeme’s post because he took a different approach to the task than I did.  Rather than just giving everyone a written copy of his narration like I did, he shared audio files.  We got to listen to his narration of his story and the Microsoft Read Aloud version of the transcript.  I found that his choice to present the narrations this way was very effective, more so than just having the written transcript.  With my written transcript, there was nothing for others to compare it to.  There is neither a written record of what I said nor an audio recording.  It is interesting that he even compared two different voice to text programs using the same audio recording for each and obtained very similar results.

With the Microsoft Read Aloud, the lack of punctuation in the written record was emphasized.  A speech-to-text program cannot record the emphasis and enthusiasm of the story-teller.  It didn’t capture any of the humor or urgency present in the story or the tone of or inflections in Graeme’s voice.

Graeme describes the issues with the written text and I had similar issues: missing punctuation, inconsistent verb tenses, and even incorrect words altogether.  The tool took the oral language and translated it into what it determined was the most “correct” version of what was being said.  In any case, it was definitely not perfect.