Bullie was the most caring person in the group. Whenever someone else was ill, she would sit with her and comfort her. Bram and Wezel had the strongest friendship. There was just the two of them after their friends had died, and they did everything together: eating, sleeping, pottering about like an old married couple. They also took up nest-building and created beautiful flower-shaped nests for sleeping in.

Bullie, Bram, and Wezel were three of the 25 ex-laboratory mice with whom I lived between 2020 and 2023. These mice were part of a pilot project for the adoption of small laboratory animals—mice and rats—set up by a coalition of Dutch animal advocacy organizations and Utrecht University, in the Netherlands. 

I am a philosopher who writes about the languages and political agency of nonhuman animals. When I adopted these mice, I looked forward to interacting with them and perhaps becoming friends. The mice soon made clear that they were not interested in that. Whenever I put my hand in their house, they looked at it with disgust and tried to bury it. I decided to play music for them, because they did like my voice. When I played songs for them on the guitar and ukulele, I watched them. Through spending time with them, I learned that mice are not the kind of beings that most humans think they are.

Mice are used in experiments because they are small and cheap, because they make lots of babies quickly, and because humans see them as simplistic, mechanistic, and replaceable. In the Netherlands, where I am based, around 200,000 mice are used in experiments every year, and about as many are bred but not used. Nearly all are killed. In the United States, an estimated 10 to 111 million mice are used each year; rodents are not covered by the federal Animal Welfare Act, so exact counts are not made. Yet even though humans use so many mice, we know surprisingly little about them.

This is because the experiments are focused on humans. Laboratory mice are rarely studied to learn about mouse cognition, emotions, or social life for their own sake. Instead they are used to create knowledge about human bodies, minds, and disease. Apart from the many problems of extrapolating from mice to humans, those findings don’t always give us a clear picture of mice. For example, mice are frequently scared of male scientists, which may confound observations. The conditions under which mice live also distort insights. Apart from their captivity and boredom, they are often cold.

In order to understand these mice who came to live with me better, I looked for studies about the social lives of laboratory mice: about their characters, friendships, communities, the ways in which they play. I did not find any. There are plenty of studies on capacities like “emotional contagion,” or behaviors like “affiliative grooming”—but those studies paint an incomplete picture because they only focus on one aspect of mouse life, and set out to map species characteristics instead of looking at individuals and social relations. Of course, lab mice generally don’t live long enough to build a community, and scientists usually view them as objects of a study instead of subjects with a life that matters most to them. 

I learned that mice are not the kind of beings that most humans think they are.

I then looked for studies about the social lives of mice more generally. Again I did not find what I was looking for. There are studies about mouse communication, sex lives, and social structure, but no long-term studies of their communities as there are for, say, chimpanzees or corvids. I decided to write about mice myself.

I am not a biologist or ethologist, and as a philosopher I am quite critical of human-centered ways of getting to know other animals. I turned to the work of ethologists like Barbara Smuts, who lived with a troop of baboons, and Len Howard, who opened her English countryside home to the birds living outside because she wanted to get to know them. They learned to see other animals by attending to them over long periods of time and on their own terms, approaching them as beings who had their own perspectives on life. I decided I would try to do the same with laboratory mice.

In order to find out more about the relationships between the mice and to understand their practices I first needed to learn who was who. The first group I adopted consisted of 10 female mice. Each had brown hair. After a few days I began to recognize the differences between them. I made a list of their names and physical characteristics. Some were bigger, like Grote Muis (Big Mouse) and Kleinoor (Small Ear). Other mice were smaller, like Kleine Muis (Little Mouse) and Kraaloog (Beady Eye). Witoog (White Eye) had a white ring around one of her eyes, and through describing them in this way I learned to see who was who. After a while I also began to see differences in their characters. Flankie was always the first to try out new foods and houses. Vachtje was a bit of a loner. Kleinoor and Grote Muis were calm, Kraaloog was feisty and fast.

Slowly I began to understand their forms of expression. They made a chk-chk sound when they explored, which expressed a kind of content curiosity. They squeaked loudly while being groomed. When they wanted to be groomed, they had a special movement—holding half of their face flat to the floor—that they used as invitation. Holding their tails straight up meant excitement. Mice who liked each other often sat with the tips of their tails wrapped together. The mice also had many greetings, like kissing one another on the mouth, intertwining their tails’ tips, and aligning their posture with that of another.

Over time I also began to see how meaningful some of their habits and practices were. The first time I clearly saw this was when Vachtje could not run in the exercise wheel anymore because of her tumor. (Many of the mice developed tumors, and not much health care was available for them. This is ironic, given how much we know about their bodies and how much cancer drug research involves mice.) The mice liked running in the wheel; when someone else was already running, they sat next to the wheel and gauged its speed with their hands before jumping in. When Vachtje could not make the jump anymore, she still sat beside the wheel and moved it with her hands.

The mice also changed over time. A simple example is grooming. In the first days after a new group arrived, I only saw them groom themselves, but never one another. They usually began mutual grooming after a week or so—quite long in mouse time—in the shoeboxes in which they slept. As time went on, they became more comfortable grooming out in the open, and they especially liked to use the roof of the sleeping shoebox for this.

When they got older, they further developed their practices of care. The first year with each group was easiest: all mice were healthy and happy. But after about a year, they began to develop illnesses—mostly tumors, but they also had strokes. Whenever a mouse was ill, others took care of her. They would sit next to her to support her with their body while eating; healthy mice slept next to mice who were unwell, perhaps to keep them warm. I twice witnessed the mice form a circle around a mouse who was ill. A scientist might write about this as their acquiring potentially useful information about their environment. To me it looked like a circle of care.

Vachtje was a bit of a loner. Kleinoor and Grote Muis were calm, Kraaloog was feisty and fast.

They also began taking care of their dead companions. Whenever a mouse died, I left her body in the mouse house, so that the others could understand she was dead and say goodbye. I saw the same response in all three groups. With the first dead mouse, they ignored the body and went on with their business. When the second and third of their companions died, it frightened them. They were shy and stayed in their sleeping houses more than usual. With the fourth death, they began taking care of the body. They first greeted the dead mouse; then groomed her in the same way they groomed their living companions; and then dragged the body to a corner, where they buried the mouse with nesting material and hay. One New Year’s Eve I missed Wolkje (Little Cloud) and had to take all the boxes and wheels out of their house before I found her body, completely sealed in with pieces of paper in one of the small play houses.

Of course, I did not just look at the mice. They also looked back and tried to figure out what kind of being I was. Over time we formed habits, like the music ritual, and I shared my food with them. When they were old, they could roam freely in the room where their house was located and were not scared of me, so our contact grew. But they were still mostly interested in one another and I did not form strong relationships with them. This changed with Spokie.

Spokie was the last mouse of the last group. After her friends died, she was too old to match to new mice. She had never been very social and I was glad that it was her who remained, and not someone who was more attached to the other mice and would miss them. Still, I worried she felt alone, and I often sang or spoke to her. She’d then come out of her house to greet me. We also went on trips through my house: I brought her to different rooms and she enjoyed exploring new spaces. She was not fast anymore, and this was in summer, so we also went into the garden, where she could roam through the grass while I sat next to her. At first she seemed indifferent to my presence, but as time passed she stayed close and sat with me. One day she invited me to groom her. She held one side of her face down to the floor and looked at me with the other eye. I recognized this posture from when the mice invited other mice to groom them. So I began to stroke her head and ears, very softly, because that was what she wanted.

In one of her books about living with songbirds, Len Howard writes that she cannot make general statements about what kind of animal a great tit is. They all have their own personalities, similar to humans. Howard does not mean that birds do not have common characteristics; rather, she wants to draw attention to the fact that they are individuals, and that defining them in species-general ways risks obscuring their individuality. 

The same can be said of mice. Flankie, Bullie, Spokie, and the others all had their own characters and personalities. At the same time, it is clear that the existing image of mice in human societies and science, as simple, mechanistic, replaceable beings, is entirely wrong—so I do want to make general statements to counter this. The mice who lived with me were deeply social beings, and care was central to their lives. They continued learning throughout life, and kept developing their projects and relationships with others. 

All animals, I think, know more or less the same about what matters in life: community, love, care, death. Seeing how the mice lived their lives—not as a biologist or neuroscientist, but as a philosopher and fellow creature to the mice—taught me about life itself. Because their lives are so short, I also witnessed the cycle of life 25 times. First you are young and look for your place in life; then you are strong and work on your projects; then you slow down; and finally you become part of all there is before you were born. The mice showed me how life and death are always entwined, and how thin the thread is that separates them. In the end, we are all vulnerable beings who are here only briefly. The best thing we can do is take good care of one another.

The post Living with Lab Mice appeared first on Nautilus.

In my recent articles, White Boards versus Chromebooks and Are Digital Tools Cultivating Thinking? I talk about using digital interactives as thinking tasks. Everyday, teachers assign a multitude of tasks, and not all inspire thinking. The same is true for digital interactives. A digital interactive is a form of media where the user can engage with content which impacts the experience. There are so many digital interactives for teachers to choose from, it is important to be able to determine whether a digital interactive truly inspires thinking.

In Peter Liljedahl’s book, Building Thinking Classrooms in Mathematics, he describes scripted thinking tasks.

• Begin by asking a question about prior knowledge.

• Then ask a question that is an extension of that prior knowledge.

• Then ask students to do something without telling them what to do.

I might also add that a digital interactive inspires thinking when students are required to do something, an action, and think about the relationship between that action and its consequence.

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Sometimes, I assign a single interactive or several interactives put together to form a digital lesson. In the digital lesson, Adding Integers Splat! there are many interactive slides. Let’s focus on a few slides with interactives that are meant to draw out students’ thinking about adding integers.

Begin by asking a question about prior knowledge.

On this slide students use their prior knowledge of two numbers that together make six. They may not choose correctly on the first try and that is okay. The interactive uses arrows and a boot as feedback to let the students determine if they were successful or not. Students use the feedback to adjust their answer, making a connection between the interactive and addition by writing a number sentence. This process allows all students to engage with the interactive and the math in an interesting way.

Then ask a question that is an extension of that prior knowledge.

The next two slides extend thinking about what happens when one of the numbers is negative. Students are new to adding negative numbers. They have seen that a negative number can be represented by an arrow pointing left on the number line which may help them choose the numbers. At this point students should start to make connections about the directions of the arrows and that adding positive and negative numbers acts a lot like subtraction.

Then ask students to do something without telling them what to do.

Finally, students try to find many different ways to add two numbers together to get the same result. By doing this, students start to develop their own understanding of what happens when they add two integers, which they formalize on the next slide.

After students have explained their thinking, the teacher has the opportunity to use students’ answers to drive a discussion about the algorithm for adding integers. Meaning, the students will tell the teacher how to add integers instead of the teacher telling the students. There is power in having students make their own connections between a math model and the algorithm, showing that these digital interactives inspired thinking.

Now that students know the algorithm for adding integers, the number line interactive is no longer needed and phased out as students practice their new knowledge. I think it is important to point out that the digital interactives did not show students how to add integers and did check their answers by stating correctness. All feedback was interpretive and allowed students to decide the path to a correct result.

In this article, Mitchel Resnick, a professor at MIT, says that for technology to be an effective teaching and learning tool it must have three qualities.

1. It has a low floor

2. It has wide walls

3. It has a high ceiling

In the next interactive, students choose numbers to input into a function machine that generates output by applying an unknown function. Students are tasked with identifying the function.

Low floor.

Every student can engage with the interactive. Placing numbers into the function machine is exciting for students. They like seeing the numbers go in and come out.

Wide walls.

Usually, when students are learning about writing function equations they are given the input and output in the form of a table, graph, or context. The interactive is an interesting and different way to present the information. There is no guide for students on how to navigate the problem so they are allowed to choose their own path to explore the math.

High ceiling

Students choose how to organize the input and output and look for patterns. Some students make tables and find the first and second differences or notice that the output numbers are perfect squares. Students may choose to graph the relationship and notice that the graph is a parabola or think about what they know about quadratic equations.

Not all digital interactives inspire thinking. While it is easy for all students to engage with this interactive, it tells the user why the relation is or isn’t a function, taking away the opportunity for students to think.

To make this interactive inspire thinking, I would change the feedback to only tell the students if the relation is a function or not. Then after several examples, students should be able to develop a conjecture about the definition of a function and test out their thoughts. I would then lead a discussion about the students’ thoughts and formalize a definition as a class.

Here are a few places I look to find digital interactives:

Desmos Classroom

GeoGebra (Be careful there are also many non-thinking tasks as I pointed out.)

Tim Brzezinski

Steve Phelps

SolveMe puzzles

David Poras

Good luck!! I hope both you and your students enjoy thinking with digital interactives!

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Today a bitter dispute about the nature of biology is underway. A simple bag of beans may be what tips the balance

- by Zachary B Hancock

Read at Aeon

In a very real sense, the Internet, this marvelous worldwide digital communications network that you’re using right now, was created because one man was annoyed at having too many computer terminals in his office.

The year was 1966. Robert Taylor was the director of the Advanced Research Projects Agency’s Information Processing Techniques Office. The agency was created in 1958 by President Eisenhower in response to the launch of Sputnik. So Taylor was in the Pentagon, a great place for acronyms like ARPA and IPTO. He had three massive terminals crammed into a room next to his office. Each one was connected to a different mainframe computer. They all worked slightly differently, and it was frustrating to remember multiple procedures to log in and retrieve information.

Author’s re-creation of Bob Taylor’s office with three teletypes. Credit: Rama & Musée Bolo (Wikipedia/Creative Commons), steve lodefink (Wikipedia/Creative Commons), The Computer Museum @ System Source

In those days, computers took up entire rooms, and users accessed them through teletype terminals—electric typewriters hooked up to either a serial cable or a modem and a phone line. ARPA was funding multiple research projects across the United States, but users of these different systems had no way to share their resources with each other. Wouldn’t it be great if there was a network that connected all these computers?

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The 74 published an op-ed this week, penned by an AI industry consultant, invoking Sputnik and claiming that "AI education is the new space race." I'm going to set aside its problematic call for "AI literacy" for another day; and although it's really the main gist of the author's argument, I'm not going to engage too deeply either with its xenophobia. It's not the Soviets who pose the threat (LOL?), but the Chinese apparently; but the gist of the argument is similar to the one made in 1957: the American education system must invest in rigorous science and math education for the sake of national security. Or at least invest in "AI literacy." (Also LOL.)

The author points to the National Defense Education Act, which was passed in response to Sputnik and marked an unprecedented injection of federal dollars into the US school system; and she argues we need that again, but for AI. So I guess I'm sidestepping too how utterly tone-deaf this whole article is considering the Trump Administration is actively trying to dismantle the Department of Education and axe funding for all sorts of scientific research initiatives in and adjacent to K-12 schools and universities.

The passage of the NDEA, as I argue in Teaching Machines, was a boon for the nascent education technology industry as it generated an incredible amount of interest and funding from the government and from philanthropies for education R&D, for all sorts of content, curricula, and gadgetry. And as I've said before, Sputnik was, in its original instantiation, already "the Sputnik moment for AI," and the passage of the NDEA initiated a great deal of interest and investment in AI in general and in education – that is, in intelligent tutoring systems.

But to talk about the changes in education in postwar America solely as a response to Sputnik, solely as a boon for math and science education, and then to call for changes to education today so that we can echo that very "arms race" isn't just bad history or bad policy or bad metaphor – although the latter should give you a clue that it is, indeed, bad: a "weaponized" vision of education is not a virtuous or just vision of education, folks.

As Jim Wynter Porter argues in a 2018 article in Multiethnica, one of the "underexamined aspect of NDEA legislation was its incentives for curricular stratification by 'ability.' About 50% of its total spending was for increased 'intelligence' testing," used to group and track students based on "their natural, individual differences." [emphasis mine]

Wynter Porter argues that the passage of the NDEA – and specifically its support for intelligence testing – must be viewed alongside that other momentous governmental intervention of the 1950s: Brown v Board of Education, the Supreme Court decision that ended de jure school segregation. The discourse around the NDEA's passage, he argues, "worked to justify durable structural changes that remade or maintained divisions by 'race' (and so 'race' itself) via racialized tracking." That is, while race-based segregation was ostensibly dismantled post-Brown, much of it was then rebuilt through tracking and through discrimination based on "individual ability" – ability as decreed by intelligence testing, a practice with its own racist history.

Tracking students into different classes and into different schools – dividing students into "bright, medium, and dull" as Wynter Porter puts it, into Honors Math or AP History – has come and gone and come again, has been rebranded and technologized in the decades since the Cold War.

That individualization and "intelligence" work hand-in-hand should be no surprise to those familiar with education technology – its practices, its history – and the ways in which ed-tech has, on one hand, promised a technologically-enabled meritocracy, serving all students at the level and pace they need, while at the very same time re-inscribing educational inequalities through an algorithmic sleight-of-hand that never has to mention race or racism. (And by re-inscribing educational inequalities, I should add, by extracting data from students and schools and dollars from communities that might better be used to build human capacity rather than buy gadgets and software.)

"The new psychometric individualism could still accomplish the work of 'race'," Wynter Porter contends, "not only because of the neo-hereditarian principles around which it was structured, but also because 'race' was embedded latently in the tests, in the structures of schooling itself, and indeed in the broader knowledge production practices and social matrix that surrounded them."

"Individualized learning," particularly when enacted through technologies that defy scrutiny and accountability through algorithmic decision-making, echo the legacy of "separate but equal." Individualization surely has great appeal – it is a core tenet of American ideology, after all; and most teachers today who embrace it do not see themselves as reinforcing discriminatory practices. But in the school and in the classroom, individualization operates through the establishment of a hierarchy based on "ability," often determined by standardized testing – a way to avoid saying "IQ" perhaps while continuing to practice ranking based on "intelligence."

This ranking is at the heart of artificial intelligence too – not simply the rating and ranking of machine over human but the ranking and rating and sorting of humans.

AI determines access to content and to opportunity and – this is crucial – to human support. AI technologies offer a less valuable, less human educational experience through the ranking and rating of "intelligence."

In 1693, Christiaan Huygens was struggling to learn the new calculus developed by his former student Gottfried Leibniz. He wrote to Leibniz asking for “any important problems where they should be used, so that this give me desire to study them”. Ever since, ‘when will I ever use this?’ is a common refrain, especially among engineering students — right?

A study published in 2020 had found engineering students preferred pure problems without context, but we weren’t sure — it turns out defining when a problem is and isn’t placed in context isn’t as easy as we thought. We wrote some questions that were either just ‘solve this equation’ or were dressed up with an engineering context, and asked students what they preferred and why.

We found pretty split preferences between contextual and non-contextual problems, and learned a lot about why different students prefer different sorts of problems and how they solve them (the quotes in the title give a flavour of this). The resulting article has just been published in Teaching Mathematics and its Applications. Check it out!

On the value of context in engineering mathematics problems: students’ perspectives by Patrick Johnson, Peter Rowlett and Alexandra Shukie.