Or Feedback as a Learning Activity
So, it’s that time of the year again – revision and wrap-up time. Time to review (check out my 12 Five-Minute Vocabulary Revision Activities if you’re looking for some ideas) and make those final preparations for whatever your curriculum calls for to check learners’ progress and catapult them to the next level. Today’s post will be useful for those teaching writing, particularly essay writing. But even if that’s not your focus, you might still find a couple of ideas worth experimenting with (I hope!).
‘Feedback is of little or no value unless learners learn from it.’
Much has been said about AI and its potential impact on learning (the search for the Holy Grail in the educator community; it never ends). I won’t reiterate it all here, but one common argument for introducing AI into classrooms is that it’s capable of giving personalised feedback, especially on written assignments, way more detailed compared to the one that teachers have the time to give. The process seems pretty straightforward: the student writes a paper, the AI checks it, and delivers super detailed feedback (yay, lots of beautiful words in no time!). It might even be programmed to throw in a little you-can-do-it-doo-doo-doo motivation boost (to the tune of Baby Shark, or whatever catchy jingle you prefer). And that’s all good, except for one thing: no matter how long or nuanced the feedback is, whether it’s human- or AI-generated, whether a student learns from it is another matter entirely.
In my experience, detailed feedback on essays is worthwhile only for highly motivated students with strong autonomy. For everyone else, it often goes straight to the bin (rubbish or recycle, depending on the medium) without so much as a glance. Pass or non-pass, that is the question! Curtain falls.
That’s not to say we shouldn’t give feedback, far from it, but we do need to be realistic in our expectations, including those we place on AI-assisted teaching and learning, and plan accordingly. As Philip Kerr, author of the Cambridge paper on feedback, puts it: ‘Learning from feedback cannot be forced: the teacher’s task is to try to create the right conditions for learning to take place.’
Feedback as a Learning Activity
I’ve tried many techniques – coded feedback, colour coded, individual, peer and whole-class, but somewhat the most effective one was turning feedback into a learning activity itself, when feedback becomes the input for learning, guiding student-driven output and encouraging multiple iterations. I’ve already shared some ideas and activities on the blog (see Making Learning Visible). Today, I’m adding a new one that I hope will speak volumes (😉) – podcast-style feedback.
Preparation
First, you’ll need an essay with various excerpts from students’ writing. I usually create one by combining bits and pieces from the group’s essays, especially those that show recurring mistakes or patterns, and tweaking the wording slightly. That way, the text won’t be an exact copy of what they wrote but they will recognise the issues and relate to them.
Next, we’ll need to create a podcast with feedback on the essay. Thanks to AI, this has become a relatively simple task. For our purposes, tools like Notebook LM or Podcastle work particularly well. ElevenLabs also offers a podcast studio, but it requires a paid subscription to access it.
I used Notebook LM to create the podcast below. The process is easy, though it doesn’t allow for script editing. What you can do, though, is customise it in quite a bit of detail by telling it exactly what you want included. The podcast I was making was meant to support an activity focused on analysing an essay using the success criteria – Content, Structure, and Accuracy – I included these three areas in the prompt. I also added the areas with my comments in the source text to guide the audio overview.
You can also guide the model to highlight specific points. In the worst-case scenario, if the podcast doesn’t include the key elements you need, you can upload a description of those points as part of the source input and ask the podcast fairy😉 to quote them line by line. (I used this trick to create a podcast on Socks and Cognitive Biases, and it worked really well.)
Notebook LM works best for intermediate+ students. You can simplify the language using a combo of the source input and customization prompt, but the hosts will still speak quite fast. You can slow down the audio, but it may affect quality. As a quick workaround, you can turn the audio into a video with captions to support comprehension. The easiest (and fastest) way is to use Clipchamp Video Editor from Microsoft. It generates AI captions with good quality, so you’ll hardly need to spend much time editing.
You can play around with the customization function in Notebook LM to turn the podcast into a more targeted activity. It all depends on the focus of your feedback. If you’re focusing on accuracy, for example, you can create a podcast that covers only common mistakes or challenges. In that case, use the chat to list the errors, identify patterns, and then save that as a source (using ‘Convert to Source’) to generate your audio overview.
All in all, the whole process takes about an hour, including video editing. With a template and perfectionism switched off, you’re down to 20 minutes, or just a couple if you choose to use the audio overview as is.
In the Classroom
Step 1. Split students into small teams or pairs. Start by discussing the essay success criteria together: e.g., Content, Structure, and Accuracy. Then, hand out the essay and ask them to review it using the criteria you’ve discussed. To make the process easier, have them assign points for each area. For example, under Structure, you might use the following scale: 3 points – Super clear. All the parts are there and everything flows. 2 points – Mostly clear, but a few bits could use more detail. 1 point – Oops, we got lost along the way.
Then, ask them to add evidence or examples from the essay to support their scores – this goes in the first column. In the last (‘moving forward’) column, have them suggest how the score could be improved – practical pointers or corrections or tips for making the writing better.
Once they’ve done that, play the podcast and ask them to add any new points to their table as they listen.
Wrap up with a group discussion, and for homework, ask students to use the feedforward pointers they came up with in class to review their own essays and make any necessary changes.
Time for iterations.
References and Tools:
Kerr, P. (2020). Giving feedback to language learners. Part of the Cambridge papers in ELT series. [pdf] Cambridge: Cambridge University Press
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