It was a simple question asked in a meeting of BC online schools.
“Are we using data to prove or to improve?”
Is it about accountability or improvement? What does the data teach us about our practice? How does it affect our future outcomes and where we focus our support and funding?
What is the real value of collecting data, and how is it best used to inform our practice?
I met two young, gifted students yesterday, interested in attending our school next year. It’s fascinating to meet 13 year old kids who aren’t just good students but passionate learners. Kids who see school as places to connect with friends and get exposed to ideas that they wouldn’t get exposed to if they stayed home. Kids who want to go to school because it’s more interesting than staying home.
It excites me to think that these kids will come to our school and part of their day will be dedicated to them perusing passion projects that they design. They aren’t just going to be taking notes, do practice questions from a textbook, or comple ‘cookie cutter’ styles projects where most of the final products look the same.
I think some kids learn despite the system they are in. These kids I met yesterday would be successful no matter what school they attend. But they deserve an opportunity to attend a school where they get to shine… Where they get to try something that can fully engage their passion for learning. Even where they can try something too big and fail, but learn that this too is a learning experience.
When I see kids with a passion for learning, I see kids that should have some autonomy over their day at school. They want to learn, let them discover, explore, and innovate. Let them follow their passions and interests. Let them own some of their own learning.
Keep the passion for learning alive.
From the Wall Street Journal:
“How ChatGPT Brought Down an Online Education Giant
Chegg’s stock is down 99%, and students looking for homework help are defecting to ChatGPT”
This is an excellent example of job loss due to AI. From the article:
“Since ChatGPT’s launch, Chegg has lost more than half a million subscribers who pay up to $19.95 a month for prewritten answers to textbook questions and on-demand help from experts. Its stock is down 99% from early 2021, erasing some $14.5 billion of market value.”
Chegg is a clear loser, but so is just about every website that offered to write essays for students. Imagine watching your profits disappear and seeing your entire business model collapse before your eyes.
This is just one example. There are many fields and jobs that either have disappeared or are going to disappear. Just think about the shift that’s already happening. People who thought they were in stable jobs, stable careers, are now realizing that they might be obsolete.
There will be new jobs, but more often than not there will be a condensing of jobs… one person where there used to be five, maybe ten people. For example, if a company had 5 writers producing daily articles for websites, they could lay off all but their best writer, who now acts as an editor for articles written by AI. Keep the person that understands the audience best, and that person ensures the AI writing is on point.
Code writing, data analysis, legal services, finance, and as mentioned above, media and marketing, these are but a handful of areas where AI is going to undermine the job market. And jobs are going to disappear, if they haven’t already. This has been something mentioned a lot, but the demise of a company like Chegg, with no vision for how they can pivot, is a perfect example of how this isn’t just a problem of the future, it’s happening right now.
Where will this lead in the next 5 years? What does the future job market look like?
Will there be new jobs? Of course! Will the job loss outpace the creation of new jobs? Very likely. And so where does that lead us?
Maybe it’s time to take a hard look at Universal Basic Income. Maybe it’s time to embrace AI and really think about how to use it in a way that helps us prosper, rather than to help us write emails and word things better. Maybe it’s time to accept that the AI infused world we live in now is going to undermine the current job market, and forever change whole industries. This isn’t some dystopian future, this is happening right now.
I love this quote, shared in a video on LinkedIn:
“It used to be about ability. And now, in a changing world, I think what we should be looking for is agility. I want to know how quickly do you change your mind? How fast are you to admit you’re wrong? Because what that means is you’re not just going to be reacting to a pandemic or to AI, you’re actually going to be anticipating those problems and seeing around corners, and then leading change as opposed to being a victim of it.” ~Adam Grant
It’s more than just anticipating problems, it’s about being agile, understanding challenges, and addressing them while they are small. It’s about understanding your strengths, and the strengths of your team… as well as weaknesses.
It’s about Agile Ability, which is why I titled this ‘Ability AND Agility’, rather than ‘Ability VERSUS Agility’. We need to embrace our failures and learn from them, recognize problems early, even predict them and be preemptive. This is so different than a culture of accountability and blame.
The desired student, employee, partner, colleague of the future will learn what they need to on the job. They’ll be exceptional because of their agility and willingness to learn, not just because of what they came to the table already knowing.
What a fantastic quote by Adam Grant,
“The hallmark of expertise is no longer how much you know. It’s how well you synthesize.
Information scarcity rewarded knowledge acquisition. Information abundance requires pattern recognition.
It’s not enough to collect facts. The future belongs to those who connect dots.”
Pattern recognition and synthesis are the path to innovation, ingenuity, and invention. The collection of knowledge is not enough. Wisdom comes from recognizing how to make connections across different fields, how to make meaning out of relationships that not everyone sees.
Artificial Intelligence can give us the knowledge we seek. It can dumb down the ideas to our level of understanding, and even teach us with relevant examples when we are stuck. More information won’t be what we seek. Instead we will seek new connections, patterns, and relationships.
The desired experts of tomorrow are probably not the siloed experts we once sought. Instead they will be information generalists who understand how to take information from different fields, identify relationships others don’t see, and synthesize information such that they can tell a story others won’t know to tell.
How are we preparing the next generation of learners for this new future? How will schools need to change to help students prepare for the future in a world of abundant and easily accessible information? It certainly won’t be by feeding them content. Instead, the future of education lies in creating challenges where students need to synthesize information and recognize connections and patterns across different fields of study.
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Related: My ‘Transforming Our Learning Metaphors’ Ignite Presentation from almost a decade ago.
William (Bill) Ferriter shared a post on LinkedIn about the struggles his daughter is having at school. While I will share a key quote from his post, I encourage you to read the full post here. Bill said,
Should we be failing students who pass unit tests and quizzes but don’t turn in practice tasks? Were those practice tasks essential as a vehicle for preparing students or assessing learning if a student can demonstrate mastery on the unit test without them? How many assignments do we really need to determine if a student is working at or above grade level? Could we use something other than zeros — think codes like INC or placeholder grades like 50s — to report on missing work? Does every student have to do every assignment?
On a more philosophical level, are we cheapening our professional credibility when we report that a student who passes most/all of our quizzes and tests has failed our class? Are grading policies with rigid consequences for missing work effective for encouraging learning? For changing behavior? Is the purpose of grades to report on student mastery of essential outcomes or to report on the ability (or lack thereof) to keep up with schoolwork?
I left Bill the following comment:
In my first year teaching a colleague (also in his first year) was experimenting with grading and asked a simple question that has stuck with me:
“Are we counting marks or marking what counts.”
(See the first half of this old post – if you go past the first half, sorry that the image links seem to be broken.)
My daughter was training 24-26 hours a week in Synchronized Swimming and missed some gym classes going to Provincials and Nationals. Despite consistently being the second fastest girl doing their weekly runs (behind a Provincial level soccer player), she was told at the end of the year she would only get a ‘B’ unless she made up a run and did a volleyball rules quiz she missed.
I share this because it exemplifies the idea of just counting marks.
To me this undermines the professionalism of teaching. It says, ‘We only care about the numbers’, and that my friend is exactly what AI can do better than us. I hope to see educators around the world thinking more deeply about what really matters to students in school. We need to stop building schools and courses like boxes students need to fit into and more like boxes made to fit students!
One of the most powerful things we can do is to steadfastly hold an opinion, receive new information… and change our minds. This is what school board member Courtney Gore did:
“Gore, the co-host of a far-right online talk show, had promised that she would be a strong Republican voice on the nonpartisan school board. Citing “small town, conservative Christian values,” she pledged to inspect educational materials for inappropriate messages about sexuality and race and remove them from every campus in the 7,700-student Granbury Independent School District, an hour southwest of Fort Worth. “Over the years our American Education System has been hijacked by Leftists looking to indoctrinate our kids into the ‘progressive’ way of thinking, and yes, they’ve tried to do this in Granbury ISD,” she wrote in a September 2021 Facebook post, two months before the election. “I cannot sit by and watch their twisted worldview infiltrate Granbury ISD.”
But after taking office and examining hundreds of pages of curriculum, Gore was shocked by what she found — and didn’t find.
The pervasive indoctrination she had railed against simply did not exist. Children were not being sexualized, and she could find no examples of critical race theory, an advanced academic concept that examines systemic racism. She’d examined curriculum related to social-emotional learning, which has come under attack by Christian conservatives who say it encourages children to question gender roles and prioritizes feelings over biblical teachings. Instead, Gore found the materials taught children “how to be a good friend, a good human.”
Gore rushed to share the news with the hard-liners who had encouraged her to run for the seat. She expected them to be as relieved and excited as she had been. But she said they were indifferent, even dismissive, because “it didn’t fit the narrative that they were trying to push.”
To learn new information that ‘doesn’t fit your narrative’, and then to change your mind and take a new stance… this is learning. This is a growth mindset. This is what we need in the world.
But if you read or listen to the full article, you’d learn that Courtney Gore’s new outlook led to threats against her and her family’s lives. It’s hard to change your mind, even harder to change the minds of people with fixed mindsets.
Fixed mindset thinking is why I spoke out before our last School Trustee election. It’s why our votes matter in every election. It’s why we need to pay attention to civic positions of power and not just provincial or federal elections. Fixed mindsets threaten learning and common sense… and can ultimately limit our rights and freedoms. At least here in Canada it’s less likely for guns and threats of violence to be the part of the consequence of fighting a fixed mindset. So we really don’t have an excuse.
There is an element of control that needs to be given up by teachers if they are truly empowering students. There has to be a willingness to accept a potential outcome that is less than ideal… An understanding that students won’t always hit the high standard you expect.
This isn’t about lowering standards or expectations, in fact, if you are empowering students you need to make your high expectations clear. Rather, this is the realization that students bite off more than they can chew (or rather can do), and then they end up scrambling to do less and still produce a good product or presentation. It’s an acceptance that a student’s vision doesn’t match yours but their outcome is still good, or (and this is the tough part for teachers) good enough. It’s about mistakes being honoured as learning opportunities rather than as something to penalize.
Empowering students doesn’t happen with outcomes that are exactlywhat the teacher envisioned and expected. Outcomes will vary. Results will be less predictable. But the learning will be rich, authentic, and far more meaningful and memorable for the students… As long as they feel empowered, and are given the space to have autonomy, lead, and learn in ways that they choose.
And while that won’t always end with results that the teacher envisioned or expected, it will always end with learners feeling like they owned their own learning. Shouldn’t that be the essence of a great learning experience?
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Related: Teacher as Compass
It’s interesting how the idea that ‘learning can be fun’ has been translated into the gamification of education, which in turn has devolved into making games that are essentially about practice pages that are ‘fun and interactive’.
I think AI has the ability to change this. Learning can be less about practice questions and more about deeper learning. Instead of playing a game with progressively harder, very predictable levels, the learning could authentically go where a student is interested. Two students could start the same, entertaining journey but end up learning and achieving vastly different outcomes. Not just higher math skills, but rather practical learning. A puzzle trying to determine the wiring of some gadget could lead to teaching basic electronics and it could lead to learning about electrical engineering.
The more used approach in machine assisted learning is to have specific goals and be responsive to the learner’s ability. The more advanced approach is to have general objectives and to be responsive to the learner’s interests.
It’s not just the outcomes of these that are drastically different, it’s also the entire approach to what it means to say, ‘Learning can be fun’.
Yesterday, when I took a look at how it’s easier to make school work Google proof than it is to make school work AI proof, I said:
How do we bolster creativity and productivity with AND without the use of Artificial Intelligence?
This got me thinking about using AI effectively, and that led me to thinking about ‘content free’ learning. Before I go further, I’d like to define that term. By ‘content free’ I do NOT mean that there is no content. Rather, what I mean is learning regardless of content. That is to say, it doesn’t matter if it’s Math, English, Social Studies, Science, or any other subject, the learning is the same (or at least similar). So keeping with the Artificial Intelligence theme, here are some questions we can ask to promote creativity and productivity in any AI infused classroom or lesson:
“What questions should we ask ourselves before we ask AI?”
“What’s a better question to ask the AI?”
“How would you improve on this response?”
“What would your prompt be to create an image for this story?”
“How could we get to a more desired response faster?”
“What biases do you notice?”
“Who is our audience, and how do we let the AI know this?”
“How do we make these results more engaging for the audience?”
“If you had to argue against this AI, what are 3 points you or your partner would start with?”
In a Math class, solving a word problem, you could ask AI, “What are the ‘knowns and unknowns’ in the question?”
In a Social Studies class, looking at a historical event, you could ask AI, “What else was happening in the world during this event?” Or you could have it create narratives from different perspectives, before having a debate from the different perspectives.
In each of these cases, there can be discussion about the AI responses which are what students are developing and thinking about… and learning about. The subject matter can be vastly different but the students are asked to think metacognitively about the questions and tasks you give AI, or to do the same with the results an AI produces.
A great example of this is the Foundations of Inquiry courses we offer at Inquiry Hub. Student do projects on any topics of interest, and they are assessed on their learning regardless of the content. See the chart of Curricular Competencies and Content in the course description. As described in the Goals and Rationale:
At its heart inquiry is a process of metacognition. The purpose of this course is to bring this metacognition to the forefront AS the learning and have students demonstrate their ability to identify the various forms of inquiry – across domains and disciplines and the stages of inquiry as they move through them, experience failure and stuckness at each level. Foundations of Inquiry 10 recognizes that competence in an area of study requires factual knowledge organized around conceptual frameworks to facilitate knowledge retrieval and application. Classroom activities are designed to develop understanding through in-depth study both within and outside the required curriculum.
This delves into the idea of learning and failure, which I’ve spoke a lot about before.
In each of the examples above, we are asking students challenging questions. We are asking them to critically think about what we are asking AI; to think about how we can improve on AI responses; or, to think about how to use AI responses as a launching point to new questions and directions. The use of AI isn’t to ‘get to’ the answer but rather to get to a challenging place to stump students and force them to think critically about the questions and responses they get from AI.
And sometimes the activity will be too easy, other times too hard, but even those become learning opportunities… content free learning opportunities.
