Year in Review: Goodbye, 2024

 It's time once again for the year in review! This'll be a short one, as this year was a fallow one for lessons spotted that were recorded in the blog (though certainly never short on lessons for me). 

Plug and Play Miniseries Lesson Spotting with AI: Designing Prompts (but not like that) Take My Advice: If You Have Trouble With Tech

What have I been doing other than (slowly, slowly) writing these posts? Well, it's hard to believe, but early last year, I got an eagerly sought but unanticipatedly early promotion, going from "Associate Director of Faculty Engagement" to "Associate Director of Faculty Engagement-- and also Faculty Engagement Principal," essentially taking on a leadership role for our small but mighty Faculty Engagement functional team at Everspring. Looking back from the turn of the year, it feels like an incredibly long amount of time to be accomplished in such a short time, if that makes any sense. I've gone from awaiting the chance to be certified in things to running the certification process, which is a cool thing to think about. 

It's also been a challenging year personally and politically. From where I sit right now, the coming year looks incredibly long, and full of uncertainty. So, I've been trying to find my way back to the small and helpful-- the small lessons that we can give and receive, and the small actions that may have ripple effects. If I spot any worth sharing, you'll hear it here first. 

Take My Advice: If You Have Trouble With Tech

Sometimes technology betrays us! It’s happened to us all, usually at a point when we are actively trying to seem competent. If you ALWAYS have problems, however, there might be something on your end you could change. There are some common problems I’ve identified from my time supporting faculty and graduate students with technical issues; these issues not only also affect students (and thus could be usefully passed on to them for their own tech needs) but also characterize a decent portion of my own issues when I stop to reflect. Some of the biggest below:

You’re going too fast. Technology can often allow us to go faster! But going too fast with a technology can not only lead you to make mistakes (guilty), it is also sometimes simply too fast for the tool to keep up. For example, Canvas often needs a refresh to show an updated grade when grading quizzes– it simply won’t update its view for you immediately upon the updating of a quiz grade. And, it’s of no use to start reviewing New Analytics in Canvas immediately; it takes time for it to analyze student activity and grade data and present it to you. It can help if you think of technology not as an instant communicator, but as a collaborator who might take a little time to get back to you. 

You’re using the wrong browser/old hardware. Imagine the software you’re trying to use is a gold-plated, top of the line refrigerator. It’s very nice, it functions perfectly, and it has a lot of features that you’re looking forward to using. Now imagine you give that refrigerator to a Pekingese and tell it to carry it up the stairs, install it, and get it functioning.

A Pekingese who would try really hard, though. Image by No-longer-here via Pixabay.

Not only is that Pekingese not going to be able to lift that refrigerator, it’s also not going to have any idea of what you’re talking about. Using incompatible hardware or software to get your tool working is similar– generally, it’s either not strong enough to carry the task, it doesn’t have the capacity to understand what it would need to understand to carry out the task, or both. 

You’re using a nonstandard tool for the job. Imagine that refrigerator again. It has a door, right? It plugs in. You put food in it. All these qualities are very similar to a microwave, but if you try to use the refrigerator as a microwave, you’re going to be disappointed. Similarly, looking at what a tool was designed for and evaluating if that is in line with your ambitions for the tool can be helpful. That’s not to say you can’t use a tool at sideways purposes– I would argue half of my discussions of Twine are sideways to its intent. (Incidentally, the medium of Interactive Fiction also has a lengthy history of using tech in ways contrary to its intention for creative purposes, so I feel I’m part of a rich tradition.) However, you’ll experience less frustration if you do so intentionally, rather than realizing that you’re trying to give feedback with a software that’s really only meant for file sharing, or facilitate group collaboration with a tool that only allows one or two users at a time. 

I’m in an interesting predicament at the moment where I’m trying to use AI tools to complete some data analysis. I am reading about good prompting strategies and trying to use them. I’m trying to build a foundation and then build on top of it. I’m trying multiple different strategies to see if I can come to some different results, even if only to give me some insight into how to better get the results I want in future. Yet, at the moment, it’s been more time checking and evaluating and figuring out where it’s getting its ideas than actual time saved in the analysis from the way I normally do it. 

I don’t think I’ve been going too fast, although I admit sometimes I see the demos of how quickly AI can pop together information and read reports of how it’s dramatically cutting time spent on certain tasks and I’m like “shouldn’t it just be able to do this for me already??” My hardware and software don’t seem to be the limiting issue– it’s not that it’s too slow, it’s that the conclusions are incorrect or partially correct or inventing some information that is not quite there. And the tool by all reports seems to be the tool for the job (see above demos), although perhaps a lesson I may end up learning is that some parts of the job are beyond it. I think the issue is actually a number 4, which is that the tool is more complicated to use than I am treating it as, and I don’t know enough yet about using it effectively. Straightforward and simple UX is powerful, but it can come at the expense of tailored, expert usages– think about what can be achieved by a pro using HTML to create a website versus a novice using Blogger or WordPress, or by a musician versus the prerecorded tracks on a child’s toy keyboard. The problem I’m trying to solve, then, may simply be one that is more “improvisational jazz” than “Old MacDonald.”

 

The strategy that's been most helpful to me so far is one that can be difficult for a lot of us to use: ask someone else to make an attempt at it, and learn from what they do. When in doubt, have someone else try! Either they have success, which you can learn from, or they don't, in which case you can at least feel a bit better that you are not alone.

Any of these tech issues plaguing you, or others? Let me know in the comments!

Plug and Play Miniseries Lesson Spotting with AI: Designing Prompts (but not like that)

Photo by Işıl via Pexels.

In my last feature in this series, I talked about using GPTs to help in the process and clarification of your writing. This entry is a bit of a cheat in this series, since it doesn't actually involve prompting a GPT at all. 

Good UX always inspires me to think about how to use some of these strategies more broadly. I remember being mildly affronted during the CITL reading group I attended when I first became interested in better teaching-- did I spend that whole several weeks affronted? Apparently! Clearly it was challenging me in some useful ways-- because one of the resources we read discussed using advertising principles to make learning "stick."  Advertising? That soulless capitalist enterprise? Could help me teach the intellectually rigorous discipline of history? Pish-posh! 

Of course, the only thing we do if we eschew these strategies is ensure that what they're learning in our class presently doesn't seem quite as memorable as literally any reasonably well-crafted local commercial they saw roughly a decade ago, which is what I eventually realized as I considered the concepts that were "sticky" in my brain and why.

With that in mind, you might reflect on ways that new tools introduce themselves to you, and see if that sparks an idea about how you might in turn introduce a concept that is very familiar to you in a way that seems approachable to a new learner. I'll use the original starting page for ChatGPT as an inspiration to explain something, such as instructions or expectations for an assignment. One thing that is inherently challenging is to explain something you know very well to someone else. It can be more challenging than explaining something you only know kind of well., as you have to identify the most significant information out of all the information you know and explain it clearly.

Let's take a look at how ChatGPT introduced itself when I encountered it (this has since changed as public familiarity with the tool has increased):

Original ChatGPT intro screen. Image via Datamation.

This screen introduces the tool by offering a breakdown of examples of things you could ask it ("Explain quantum computing in simple terms"), capabilities the tool has ("Remembers what user said earlier in the conversation"), and limitations the tool has ("May occasionally generate incorrect information.")

What if you tried a Examples, Capabilities, Limitations breakdown for assignment instructions?  I've seen many, many pagelong or multipage prompts for a paper that's only 3-5 pages long. Rather than paragraphs of context and/or admonitions based on past experiences ("12 point font and 1.5 inch margins this time-- I'm talking to you, Bradley"), what might it look like to organize a prompt arount the following structure: 

1. Examples (of excellent work or strong approaches to the work),
2. Needed Capabilities (of the deliverable students will produce), 
3. Limitations (or parameters they need to work within to produce this work)

Obviously a prompt doesn't have to stay in this format-- I'm not suggesting that the original ChatGPT welcome page cracked some kind of fundamental educational code. But instructions that you find clear or helpful in introducing you to a new topic may be useful in turn to incorporate into your own teaching strategies. 

Year in Review: Goodbye, 2023!

It's time once again for the year in review! This has been a strange year for the blog (and for me!) 

Flashback Post: Embracing Options 

I took a bit of a hiatus early in 2023 to focus on other projects (like Winnie!) and to recover some steam while doing a lot of work-related writing. During that period, I featured a post that has always been helpful for me to return to when trying to write or teach writing. 

Plug and Play Miniseries: Lesson Spotting with AI

Introduction

Think Pair Share

Talking to Write

Like about a thousand others this year, AI was a pretty common theme for the blog this year. In Fall, I returned with an ongoing miniseries on incorporating AI into teaching with my usual focus on flexibility and ease of use.
 

More to come in this series in the new year! I hope 2024 allows us all to spot all the lessons we can handle. 

Plug and Play Miniseries Lesson Spotting with AI: Talking to Write

Photo by Işıl via Pexels.

Despite widespread enthusiasm about having AI help generate content, that's not in my experience the best way to use it. One, it's doubtful it knows as much about what you want to convey on your topic as you do. Two, conversely, you're probably better at the content than you are at communicating it clearly. In this post, I'll offer a few suggestions for how to use AI to help with writing and clarity, which could be useful for both instructors trying to create educational/assignment content and students attempting to frame their ideas for coursework.

I tend to think as I talk-- the talking is the thinking, and talking through a topic helps me write it down. When I'm preparing to lead a workshop or give a presentation, I often begin by recording myself trying to give it off the cuff based on the ideas I have right now before I start outlining or writing things down. Then, I use the talk-through as the basis for the outline or script or notes. Sometimes I do this in the opposite direction; a brief and sketchy outline that I talk through and then edit based on what I said. In the past, I've always done this using Zoom and then watching back the recording.

More recently, I've started using a combination of two technologies to begin creating written work. First, I speak the ideas into text-to-speech, like dictation.io. Then, I take that text and ask ChatGPT to make it grammatically correct and separate it into sensible paragraphs. This is so much easier for me than fully typing out all the same ideas-- I can talk just about as fast as I think, while I am a decidedly slower typer despite years of practice at Typing Tutor. This strategy works best if you speak in small chunks and confirm that dictation.io is absorbing it all; I've noticed dictation.io does not capture everything I say if I speak for a long time. It's also important to communicate clearly with ChatGPT or your AI chatbot of choice-- it will attempt to smooth the language and potentially add (too many) adjectives by default, so if you want language adjustments, give clear parameters; if you just want the text to be given appropriate puncutation and capitalization, say so.

Most recently, I'm intrigued by some of the AI offerings within Zoom to transcript and summarize meetings; in theory, this could mean that a recorded first draft could have a relatively coherent text component to work from. If you have a paid Zoom account, it's worth playing with these features and seeing if they do anything for your process.

Plug and Play Miniseries: Lesson Spotting with AI - Think Pair Share

Photo by Işıl via Pexels.

Think-pair-share-- it's the easiest, most go-to way to get a conversation going in a room of people. It works well in rooms that don't have flexible seating for gathering into larger groups, it's a low amount of investment in setup and explanation, and it gives literally everyone in the room a chance to talk (unless one half of the pair is either real shameless or charmingly enthusiastic). And if done well, it's an "activity" that makes such intuitive sense it doesn't have to feel like an "activity."

If you haven't done think-pair-share before as a leader or participant, this description of the activity and FAQ about it is a nice introduction.

Normally, think-pair-share is conducted by humans (for NOW), but an AI chatbot can be one half of your pair or a supplement to a human pair. This was one of the first uses of ChatGPT I wondered about when it became a popular subject of conversation, and I wasn't alone! Several pieces offer ideas about incorporating AI into your Think-Pair-Share time (for example, it's one of Ditch that Textbook's 20 Ways to Use [Chatbots and Artificial Intelligence] as a Tool for Teaching and Learning). Most treatments of this idea I've seen are riffing off of this widely shared tweet by Sarah Dillard.

[An aside: one of the really funny things to me about the surge in conversations about AI in teaching is how often I now see people talking, writing about, and sharing tweets as though they're academic articles in circles where that might never have been the case before. When something is very new, many standard expectations of what kinds of things are acceptable or useful to cite shift; I imagine this is less wondrous to folks doing more present-focused work, who have frequently encountered the wonderful world of citing social media before, than to those of us who spent seven years citing while fighting microfilm-induced nausea.]

For some real zaniness, and for more insight into the tools themselves, you could have students fire up two chatbots and give them instructions, then feed their responses to one another. This can take careful prompting that will depend on the topic of discussion; it also may shed some light on the boundaries of the chatbots-- it's likely that their conversation will become a bit circular, as they tend to declare everything up front rather than have an evolving conversation (similar to some of the worst human small group activities I've been part of, really).

For more complex use of a Think-Pair-Share type framework, I love this set of options from Acadly, which suggest alternatives to insert into the process like think-write-pair-share, which encourages fuller consideration of the issue before the pair stage, and think-vote-pair-vote-share, which would work well for a question on which minds are likely to change after some collaboration or conversation on the topic. Generative AI could easily be incorporated into these steps to provide some real value; for example, comparing the written thoughts in think-write-pair-share with how ChatGPT might respond to the prompt, or asking the chatbot for reasons why someone might disagree with one's original vote in the think-vote-pair-vote-share framework.

Plug and Play Miniseries: Lesson Spotting with AI

Photo by Işıl via Pexels.

If you're part of the educational space, you've probably noticed the tumult about artificial intelligence and AI chatbots and what they mean for how we teach. I'm not going to round it all up here because there's lots of other places to read about it, but suffice it to say there's a lot of chat about what the surge in AI tools and their availability, particularly natural language processing tools like ChatGPT, means for things like academic integrity, assessment, and what it means to be well-educated, or to produce good and ethical writing. 

Naturally, nobody is more into AI than historians! Just kidding, they hate it. At least, that's what I've observed in an entirely unscientific study in which I read an article about this issue and observe what people with "History" in their job titles tend to say. Obviously this is not true across the board, but it does feel as though the novelty of the past several months has been recieved as a harbinger of doom rather than anything at all helpful. 

Undoubtedly skepticism is justified, even beyond the issues of plagiarism and equity and  scary future dystopiae (ChatGPT told me the correct word here is "dystopias," which I agree with and have also rejected for reasons of vibes). For example, I gave ChatGPT-3 Jourdan Anderson's letter a while back, and it's not great at understanding satire-- it read Anderson as deeply sincere, which is, as you may know, not the best reading of this source. Read it yourself if it's new to you and see what you think the author intends! Moreover, it also makes stuff up, which as you might have guessed, can be a problem. It's great at assembling a bibliography, for example-- I tried it for sources about one of my main subjects of my disserations, Joe F. Sullivan, and it came up with a handy list. However, as Aaron Welborn notes on the Duke University Libraries blog, it's also wildly capable of making up sources out of thin air! So, great at making a bibliography, not so great at making a bibliography of sources guaranteed to actually exist. It also can't always be trusted even just with making a list-- for example, of presidents. That's just how it works. So it's only really a starting point you then need to guess and check. And let's not forget "Historical Figures Chat", which attempted to make AI chatbots of notable historical figures based on ChatGPT, thus incorporating some ahistorical and incredibly odd "PR nonsense" into responses from multiple architects of historical atrocities. (This tool would have been an interesting concept to ponder in my iteration of the Fiction and the Historical Imagination class, in that  many of the less valuable approaches to historical fiction that we looked at dealt with a cultural penchant for believing we can know a person in the past, and mythologizing them based on the image we've created of them, and also in that this thing is effectively creating very specific little historical fictions). 

But! As you probably know, my wheelhouse is less locking down and preventing people from using stuff and writing dramatic take-downs of concepts and more about poking into and exploring what things can be good for, even if I, you, and/or the profession in general isn't psyched about them immediately. So, throughout the rest of the year, I'll be releasing a grab bag of AI-related options-- things teachers can do with the current AI tools without either totally forbidding them or making them central to your course creation. Some will help you plan to teach or create teaching materials, while others will be ways you can assign AI usage as part of the learning process. Some I've come up with, while others I'll feature have been suggested by others. 

I'll note here that due to my own biases as a trained humanist, I've avoided options that mostly boil down to "have the thing create a thing"-- I tend to find that the writing that text-focused AI tools produce is a little uninspired; many of them literally work by stringing together words in the manner and order that they are frequently strung together, so that's not terribly surprising. It's also the opposite of what I'm usually trying to accomplish in my classes. I've always tried to teach and to get students to find new connections, words that haven't been put together that often, ideas not commonly associated. As a result, the more the tool is creating something toward some sort of final product, the less the options for creating material are interesting to me, though there are undoubtedly uses in that line you can find recommended elsewhere.

Finally, if you have an AI usage for teaching you'd like to share, I'd love to hear from you! This series will be percolated on and added to periodically for as long as I keep thinking of or coming across uses that seem useful and exciting to me. 

Flashback Post: Embracing Options

Hello gentle readers! At present, I’m not doing much blogging– I’ve been spending more time on other projects in my off time (my 80-day Duolingo streak suggests a greater degree of achievement in Korean language study than my confinement to the words “hello,” “friend,” and “ice cream” reveals) as the amount of writing and research I do in my day-to-day work has increased. So, as I continue to generate “future blog post ideas” that are really opaque sentence fragments, I wanted to revisit an old blog post on the writing and prewriting processes– for my own benefit, if no one else’s! And as always, if I can support any of your processes, feel free to schedule time on my Calendly (link in sidebar).

A row of white doors. They'll all get you where you're going!

Whenever I am struggling to gather my thoughts about a project, I like to attend a workshop or a lecture. Though one often goes to a talk to hear about the presenter's thoughts on the subject presented and learn something new (a good reason to go, as a rule), I find that they are also useful for reminding me of things I've forgotten or have neglected, and for giving me the space to think over a problem. By taking in new arguments and information, I have the chance to look at new things in a new way, and am often inspired to look at an old thing I've been turning over in my mind in this new way as well. 

Sometimes I gain a new understanding of what I want to say; sometimes I realize something I've already been expressing, but which has remained opaque to me. One of the most amusing events in my academic life was attending a wonderful talk on campus by Natalie Lira and not only learning quite a bit of historical information but also coming to some realization about my project (I've now long forgotten exactly what it was). When I told my advisor enthusiastically, she said, "Yes, I knew that's what you were doing." It was apparently clear in my writing, but it wasn't clear in my head until I'd had the chance to reflect on it from a different angle. 

An Example: A UIUC Writers Workshop Workshop

So, after a difficult spring, I found myself in an hourlong presentation held by the UIUC Writers Workshop: "Staying on Track With Thesis and Dissertation Writing," facilitated by Logan Middleton and Adrienne Pickett. You can probably imagine what this sort of thing consists of-- a discussion of the various stages of writing (from research and organization to prewriting and drafting to revision and writing groups), some recommendations about writing consistently, some sharing stories around the room about how we organize our notes, how we sit down to write, how we deal with feedback. It had all of the ingredients that you might expect, and little information that was brand new to me.

If I wasn't confronted by wildly revelatory information, why did I find the writing workshop so helpful? Some brief factors:

• It's sometimes necessary to be reminded of things you kind of know already because as humans our memories are short. Anyone who's discussed anxiety with a professional knows that they should do their breathing techniques in times of struggle; anyone who's tried to manage anxiety over an extended period knows the feeling of sheepishness they get when reminded that breathing is an option available to them which they have rudely cast aside.
• Several organizational choices made by the facilitators were also a key factor: beginning the workshop with time to reflect on our own struggles with or approaches to writing; the opportunity for attendees to engage in conversation about each topic as the program proceeded. These tactics created a casual but engaged atmosphere.
• It's also nice to devote some time just thinking about the process of a large task you need to do, to have some outside confirmation that the strategies you are using are considered worthwhile by others.

But these are all preludes to the main topic I want to discuss here. More than any of these other aspects, I was heartened by the ways in which Pickett and Middleton offered options about writing, rather than prescriptions for writing. 

What do I mean by this? Let's take the portion on drafting the dissertation as an example. The facilitators suggested that one should consider scheduling a time to write, and that one should try to schedule more frequent short periods to write rather than fewer long stretches of time. They gave specific examples of this concept: try two hours of writing time four days per week, rather than eight hours on Saturday-- same number of hours, but more spread out. Additionally, they advised setting "small achievable goals"-- for example, 300 words per day; or an hour revising the introduction.

Set goals based on time or words; be consistent-- this is advice that is common, yes, but is also commonly given like this:

• "Oh, you have to write first thing every day if you want to get anything done."
• "You need to write 500 words per day. Every day. Even if you are going to Disneyland."

In other words, many offer prescriptions for what exactly you must do to achieve your larger goal. They take the specific application of a general principle and make it the sole factor in success. Without doing this one weird trick, you'll never make it! (Weirdly, the one weird trick is different for basically everyone in its specifics, and yet everyone persists in believing that theirs will work for everyone else.) This kind of thing always stresses me out more than it motivates me to try it because I can already envision the many ways in which I could fail to carry out the maxim. Middleton and Pickett, conversely, offered parameters for what those goals can look like, which gave us the tools and the freedom to determine our own goals.

I don't mean to suggest that this workshop is the only place to get an option-based approach to writing, or that one can't find success with a prescriptive approach. But it did inspire me to reflect on what I found compelling about the style that the facilitators used and reflect on how I could incorporate this into my own teaching. 

What's the Point? 

It should be no surprise to any consistent reader of this blog by now that in many ways, I teach by learning; that is, my first step is to consider how I have learned and try to mimic that process. Fortunately or unfortunately, variety is a big part of how I learn-- switching settings while writing or studying, typing some notes and handwriting others, coming up with new ways to read or annotate when I have tired of the old ones midway through a semester. 

I tend toward emphasizing variety in my teaching as well. I like to switch between different types of sources to see how something looks different as told through different media or by different types of people. I don’t like to do small group work often because it tends to fall into the same types of patterns, preferring instead the random observations that tend to come out of large class discussions or paired conversations. 

So obviously I liked this idea of emphasizing options rather than prescriptions about completing assignments-- it certainly offers variety! It's hard to provide writing support in our classes because writing is such a solitary activity, and there's something endlessly mystical about it. How does something get written? How does a good something get written? The answer, of course, is that there are as many ways that this happens as there are people who write good somethings. 

When teaching first- or second-year level courses, we are also confronted with the idea that part of our goal is to teach good processes for writing and other forms of analytical work so that students can maintain them for the duration of their lives. How can we encourage the divergent work processes of people who haven't necessarily developed an effective work process yet? Faced with the enormity of this, we tend to double down in our teaching on the things that we can know or declare: we want thesis statements, we want topic sentences, we want evidence and analysis. The only thing we say about the process is, "Don’t start it the night before! We can tell!" 

Incorporating Options

So, how do we lean toward options rather than prescriptions in our classes, while still maintaining clarity about expectations and assignments? 

Outside of Class

I've talked about this a bit before in terms of assigning unstructured research time-- offering students an "excuse" to spend some time thinking over a topic without the pressure of creating some artifact for grading. In a course with a big final paper or a consistent smaller written assignments, this could be broadened into an assignment which encourages students to reflect on what kind of goals might work well for the writing assignments they intend to complete and the time they have to work with. If there is a paper due every other Friday, a student might make a goal to spend fifteen minutes jotting down the main points of every reading after completing it so that she can remember them. Another student might focus on the actual drafting of each paper, committing to have a page written by the Wednesday before the due date each week. And another student might wish to focus on actually having time to edit the paper, and resolve to commit an hour on Friday mornings for a final readthrough. 

This process should also incorporate a continual renegotiation of those goals based on changing circumstances. For example, the student who wished to focus on editing may realize a month into the semester that his work schedule or his other courses never allow an hour for editing on Friday, and so he may wish to alter his goal to be more achievable.

Inside of Class

As far as in-class activities go, lots of teachers do this by spending time on prewriting strategies. Prewriting is perhaps the most wiggly step of the writing process and there are a lot of good resources out there about the many ways that one can prewrite-- brainstorming, freewriting, journaling-- a lot of these are really just fancy words for "starting to write and seeing what happens," which in my experience isn't so far removed from "drafting an undergraduate paper." There are also approaches that diverge from these, like clustering, which is a bit more graphical; and outlining or journalists' questions (what we used to call the 6 w's-- one comes at the end, see?). I like presenting these as options for beginning an independent writing project, and I also like the idea of fostering the opportunity for students to dabble in several of these in class and discuss how each worked for them. Perhaps it doesn't come naturally for someone to cluster (it certainly doesn't to me), but it’s a wonderful thing to try in class when you're not actually scrambling to get the paper done right at that moment.

However, I must admit that I remember finding these sorts of activities only minimally helpful when I've been in classes where they've happened, and I think much of the difficulty there is timing. You've gotta talk about prewriting early, while people have time to benefit from it before they really should be writing the paper, but it's also hard to do prewriting before you've done any research, which I had rarely done at that point. So, I think a spirit of experimentalism is best-- ask students to use prewriting activities to brainstorm a hypothetical paper based on something you've all already discussed together in the course, and talk about the results. 

How do you balance options and prescriptions for writing, as a teacher or a student?

Year In Review: Goodbye, 2022!

This year brought a lot of excitement and growth! A key theme of this year's work on the blog was technological curiosity and a willingness to experiment. I focused on fewer posts and more continuity between them, particularly the introduction of and support for the Source Analysis Template. I celebrated my first anniversary in my current position. And, I finally wrote about rubrics after teasing it for roughly three years. Below, find the lessons spotted this year:

Take My Advice: Four Tech-Fueled Efficiency Tips I Wish I'd Known Four Years Earlier

Plug and Play: My Much-Anticipated Proclamation of Love for Rubrics

Flashback Post: Celebrating One Year at Everspring

Source Analysis Template Series

Plug and Play: Source Analysis Template

Enough About Me: Source Analysis Template Walkthrough 

Enough About Me: Putting Your Source Analysis Template to Work

Looking forward to more in the new year! As always, if you'd like to reach out for more support with anything mentioned on the site, don't hesitate to schedule time on my Calendly. 

Enough About Me: Putting Your Source Analysis Template to Work

 So, you’ve created a source analysis activity using this template– congratulations! Now you’re ready to make it available to others so they can, you know, analyze the source. 

Regardless of which method you choose, the first step is the same: From the story view, you’ll click the title of your activity at the bottom left, then click “Publish to File” in the menu that appears. This will bring up a window where you can confirm what you’d like to name the file and where it should be saved; an HTML file will then be created.

The menu that appears when clicking the title of your Twine story. 

The nifty thing about an HTML file is that, if you open it in a browser like Google Chrome or Safari, it will open as your game, playable and looking just as you designed it to look. If you open it in Notepad or something similar, however, it will be code– code that you can use! 

This possibility gives us a lot of options for how we might wish to share our activity. I’ve talked about mechanisms for doing this a bit previously, but it’s worth revisiting here to address one of the spaces almost all teachers already have access to: an LMS space. If you don’t already have one, you can usually create one for free, and many receive these spaces from their institutions automatically.

Here is a quick walkthrough on how to incorporate a Twine activity into the Canvas LMS, with concept and code courtesy of Laura Gibbs:

1. Create your HTML file. 

2. Pop that file into the Files space. You could make that file visible to your class, but would probably rather hide it from students.

3. In a new tab, open the Edit view of the Assignment or Page you’d like to add an activity to. 

4. Click the </> button at the bottom right hand side of the Rich Content Editor in order to open the HTML view. 

5. Copy and paste this code into the text entry field:

<iframe src="https://___/courses/___/files/___/download" width="100%" height="600"></iframe>

6. Go back to the tab in which you have Files open. Right click on the HTML file you just uploaded and select “Copy Link Address” from the dropdown menu that appears. 

7. Go back to the tab in which you have the Edit view of your assignment open. Select the bold text, right click, and paste the link you’ve just copied. 

8. Save and publish. 

To see this in action, enroll in this demo Canvas space and experiment with the items available in the Source Analysis Activity module (feel free to use a pseudonym). Into this space, I’ve added some examples of what the Source Analysis Activity can look like incorporated into an LMS as a page or an assignment. In the process, I’ve taken the opportunity to update things that were or have broken (for example, the link to the Arbella Speech that I used in the first iteration of the Source Analysis Activity has since become defunct). More significantly, I edited some of the language to apply more clearly to the Canvas environment– students no longer need either so many or so vague instructions about how to turn in their answers if the activity is embedded within an assignment, for example; the activity also no longer needs to collect their name to associate with their answers, but it does still need to instruct students on how to collect and turn in their answers into a format that can be delivered to the instructor by Canvas. 

One of the beautiful things about distributing your activity via an iframe in an LMS is that it works well on mobile– even the process of copying and pasting my answers into the text box was relatively straightforward when testing this on my iPhone.

 

I hope this inspires you to try this out in your own courses, even if only on an unpublished demo page. If you need a Canvas space to experiment in, you can create a Free-for-Teacher account. 

If you have questions or get stuck at any of these steps, feel free to reach out in comments, or schedule a quick chat with me via my Calendly.