my slow cooker

How a special appliance has saved me both time and sanity

I want to thank one special appliance
Whose dedication and trusty alliance
Have been a time saver for a busy grad mom.
You snuck into my kitchen with quiet aplomb,
Arriving, in a box, some years ago—
Black and sleek. How was I to know
That you would save me countless hours?
Minimizing meal prep with your heating powers.
And you save me, too, from decision fatigue!
Other kitchen gadgets just aren't in your league.
So, on Sunday mornings, that was our routine!
Chopping veggies, carrots, and sometimes green beans.
Toss in some lentils, barley, or peas!
We varied by week: soup or Chinese?
Chili, orange chicken, sometimes a stew,
Rice with beans; often barbecue.
By evening, the apartment always smelled great.
My spouse and I filled up our plates.
And leftovers! Man, were those our goal!
We dished them straight into jars and bowls.
Dinners for a week—for two, no less!
No need to prep or make a mess.
Your 6-quart volume held just enough
To keep us fed when nights were rough.
'Cause let's face it. Grad school's no joke.
You're stressed and tired and sometimes you're broke.
Between classes, field studies, and paper writing;
Managing undergrads, coding, and citing...
A grad student's work never feels done...
(Even if I think some of mine is fun!)
So when I'm at home at the end of the day,
When I want to sleep and my kid wants to play,
Finding that dinner is ready! Already! It's nice.
Microwave a bowl and eat in a trice.
So as I reflect on what helped me through grad school,
I'd say you, dear slow cooker, were a most useful tool.
Food fuels the brain and the body too...
So I wanted to say: Dear slow cooker, thank you.

This article originally appeared on the MIT Graduate Student Blog, May 2019


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Why is having kids, moving out of the city, and following an unusual path a waste?

Randy, Elian at 8 months (sporting his lab t-shirt!, and I

"She's worried you'll waste your degree."

My friend (let's call her Anna) relays this message to me as coming from another friend, but I can tell from her tone of voice that she's clearly worrying about the same potential waste. That makes the question doubly irritating. As if pretending to be merely the messenger could disguise the passive-aggressive way of questioning my life decisions. Decisions which, I might add, I'm pretty darn happy with.

The primary decisions in question are these:

First, I had a baby in grad school. I'm growing another tiny human now, in fact—I gave my defense talk while 6 month pregnant! Evidently, instead of seeing this as a badass feat of time management and life balance, Anna took it as ultra-clear proof that childbearing, not science, is my ultimate goal in life, since the two clearly aren't compatible. As if there aren't amazing examples to the contrary, like two of my committee members, who are inspiring women with three kids apiece.

Second, while finishing my last semester of writing, I moved to a town that Anna has frequently referred to as "the middle of nowhere," despite it having a regional population in the 200,000's, as well as a branch of a state university. Maybe she thinks "middle of nowhere" really refers to how far you are from a large number of appropriately ethnic restaurants? Being out west, up in the skinny part of Idaho with the abundance of beautiful clear lakes, pine-filled mountainsides, and a peaceful pace of life has been wonderful. Less stressful. It's a nice place for writing, and a nice place for families.

And then, there's the somewhat non-traditional plan for my post-MIT life. It's not perfectly mapped out, but it will certainly involve my husband and I homeschooling/unschooling our kids, coming up with flexible work arrangements so we can travel more and spend more time with family, and having a high degree of independence. My husband's current software-as-a-service company is a good start. We have some other ideas, too—after all, leaving MIT and Boston doesn't mean I'm leaving research or a creative, intellectual life.

Given those decisions, well, of course! Getting a degree is a waste! If my life plan does not follow the norm, if it does not include seeking out a high-paying industry job in a big city or a prestigious professorship at an R1 school while placing my kids in daycare and coercive schooling for upwards of 14000 hours, then of course, I'm wasting my degree.

But isn't a big part of the point of grad school learning? Learning about project management. Developing writing skills. Doing independent research. Asking interesting questions. Pursuing ideas. Managing time, balancing multiple commitments, and being involved in many activities I care about. Whether or not I then use those skills to pursue any of the most common paths out of grad school isn't the point. What I learned will still serve me well in future endeavors—writing papers and essays, consulting, hiking in the mountains, self-funding our startups, blogging, gardening, reading philosophy, advocating for self-directed education, or spending time with the people who really matter to me.

The implicit assumption Anna had that "wasting my degree" is even possible is, frankly, an insult. She identifies as a feminist. Isn't feminism supposed to be about empowering and supporting women in making life choices that are right for them?

Grad school was one step that was right for me. Having kids I actually spent time with, moving out of the city, pursuing whatever creative, intellectual, maternal, or domestic activities I happen to want to do next...? Also right for me. Sorry to disappoint, Anna.

This article originally appeared on the MIT Graduate Student Blog, March 2019


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A poem to celebrate my year

2018: A year defined by a PhD,
A study, analyses, and a writing spree.
A kid who’s growing; a family, moving.
Always learning, ever improving.

In January, I was glued to a laptop,
Programming robots and testing nonstop.
I recorded dialogue; recruited schools;
Prepped assessments; built software tools.

snow-covered front steps of a house

February is a wild, snowy blur
Of consent forms, paperwork, and red and blue fur.
Kids signed up!
The robot was ready!
All this made me happy, since progress was steady.

the robot tega's face

As March snow melted, the study began!
I drove to schools and followed my plan.
Eight sessions each, plus pre and post;
The robot was keeping the kids engrossed.

In April, one kid, who wasn’t too shy,
Told me he was “actually part robot, so I can fly!”
(Tega, our robot, it’s worth pointing out,
Just talks, and sits, and looks about.)

By May, I was glad if the robots didn’t break,
But why oh why did I choose this headache?
Long-term studies will be my demise
Why oh why do I do this, you guys?

Oh wait, it’s June, long-term studies are the best!
Look, I have data, totally worth being stressed!
Learning with robots over time—this is nice!
Awesome research, look: data! Worth the price.

sunny blue couer d alene lake

In July, let’s mix it up and buy a home,
Way out west where there’s space to roam,
More lakes, more space, and bonus, it’s cheap!
Less traffic, more mountains; more yard upkeep.

In a haze of boxes and packing tape,
The month of August and ggplot graphs take shape.
Let’s leave the humidity and Boston’s heat:
Analyze data; start writing; retreat.

light coming through leaves

September is data, papers, and writing.
And writing, revising, and then some rewriting.
I find getting three great professors to be
In the same place at the same time isn’t all that easy.

yellow leaves on a maple tree

I like watching the colored October leaves from my chair.
They dance and they spin, red and yellow in the air.
Oh wait, I’m still writing. I need a new graph…
Add to this chapter; fix that paragraph….

me hugging little Elian in front of evergreens

My baby is two! He’s as tall as a table!
He’s finally stopped trying to eat all our cables!
I’m still writing. Time to start my talk prep.
Defense Day is looming on the doorstep.

my PhD committee and me, post-defense!

Now here’s a day that I’ll remember!
Dissertation defense on the 12th of December.
Crazy year it’s been, that and then some…
But hey: Dr. Jackie, here I come!

This article originally appeared on www.media.mit.edu, January 2019


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silhouette of a person standing arms outstretched in front of a sunset

So where do you get good ideas?

Even at MIT, good ideas don't grow on trees.

Instead, I've found that good ideas have two ingredients: preparation and practice.

1. Preparation. The act of acquiring new knowledge and ideas. The foundation on which my good ideas will be built.

2. Practice. Generate lots of ideas. Engage with ideas in new ways. Think about what's next, what could be changed, what can be improved, how things work, what might happen if, implications, extrapolations.

Here's my method.

Preparation

I read outside my field, especially non-fiction. This gives me new information and new perspectives.

For example, I picked up Vera Johnson-Steiner's book Notebooks of the Mind, which was a nice qualitative discussion of creativity. I read Cal Newport's Deep Work, which changed how I approach my work time. Peter Gray's book on self-directed education, Free to Learn, is personally relevant, and discussed a lot of education research about how children learn (including anthropology work about hunter-gatherer tribes!), which influenced how I approach my research on kids, robots, and learning. I've read books on laughter, mutual causality and systems theory, the differences between ancient Chinese and Western medicine, the impact of socioeconomic status and race on language and society, the psychophysiology of stress, and many more.

I read papers in my field. I read the "future work" sections in papers I like. These sections are full of researchers' ideas that didn't quite make it into the current project, ways to extend their work, and ways to improve their work.

I try to have a regular academic reading group. Success has varied. With my lab group, some years we've managed to meet weekly! Some years, we're lucky if meet once a month, if at all. Right now, I'm also in a reading group organized around the broad topic of learning; we've read papers recently on the connections between Piaget and Vygotsky, Bandura's intrinsic motivation theory, and how stress affects learning.

We take turns choosing papers to read, which means I often read papers I may not otherwise have picked up. Some are highly relevant to my work, and some, not so much. One question I always try to ask is "How could I apply the ideas in this paper to my work?" That is, what can I learn from this paper? Having this question in mind helps me ground what I'm reading in what I already know.

Practice

Notebooks: I have one. Several, actually (along with some text files and unsent email drafts).  I jot down ideas regularly: thoughts on whatever I'm reading about, interesting things I notice about the world, how concepts connect back to other things I've learned. I review these notes periodically. I look for patterns. When deliberating dissertation topics, I noticed themes in what I highlighted in my notes, which helped me narrow in on what really interested me. I've developed new research ideas and come up with ways of building on my previous work.

Spend time thinking, processing, summarizing, planning, and synthesizing. For me, this often overlaps with "notebook time", in that I do a lot of this thinking and planning on paper. I find writing time (such as working on a paper) is also synthesizing time. The process of writing coherent paragraphs about a topic means I'm clarifying and summarizing my understanding of the topic at the same time. The important thing, however you do it, is to not only accumulate knowledge but also process what you've learned. I find it important to spend connecting ideas and deepening my understanding of how different pieces of knowledge fit together.

Use class projects as an opportunity to explore random ideas. I've benefited from the MIT Media Lab's project-heavy class structure, since there's ample space to try out new things, no long-term vision or research agenda required. In my final project for an Affective Computing class, I tested a hypothesis about the impact of introducing a social robot in a particular way might have on people's social judgements of the robot. I've also made light-up balls that change color in response to accelerometer data (we called them glorbs, and created life-size paper robot silhouettes to ask questions about the "aliveness" of robots.

Other people in my lab have, perhaps, gone in wackier directions—for example, two students did a project about enhancing creativity during early stages of sleep, which involved getting people to fall asleep wearing an EEG cap, and having a robot wake them up with questions every time they started to get comfortably dreamy.

I talk to people. For example, in my lab group, we used to all walk downstairs to get tea or coffee from the 3rd floor kitchen at least twice a day. We'd troop back up to the lab, steaming mugs in hand, and stand around throwing ideas off the wall for half an hour before getting back to work. We discuss some serious stuff, like the ethics of child-robot interaction, as well as random stuff, like ceiling robots that could unobtrusively steal leftover food from other people's meetings.

I also try to talk to people from outside my lab and outside my field. Hearing from people who see things from a different perspective or who need me to explain things in a different way can be incredibly helpful for gaining new insights and seeing things from a different point of view.

In all these conversations, notebooks, and classes, I try to keep asking, "And then what?" If my hypotheses are supported, what next? If I'm wrong about something, what are the implications? Where are the opportunities? What might happen if?

That's where my good ideas come from. Preparation and practice.

This article originally appeared on the MIT Graduate Student Blog, June 2018


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Exploring how the relational features of robots impact children's engagement and learning

One challenge I've faced in my research is assessment. That's because some of the stuff I'd like to measure is hard to measure—namely, kids' relationships with robots.

a child puts her arm around a fluffy red and blue robot and grins

During one study, the Tega robot asked kids to take a photo with it so it could remember them. We gave each kid a copy of their photo at the end of the study as a keepsake.

I study kids, learning, and how we can use social robots to help kids learn. The social robots I've worked with are fluffy, animated characters that are more akin to Disney sidekicks than to vacuum cleaners—Tega, and its predecessor, DragonBot. Both robots use Android phones to display an animated face; they squash and stretch as they move; they can playback sounds and respond to a variety of sensors.

In my work so far, I've found evidence that the social behaviors of the robot—such as its nonverbal behavior (e.g., gaze and posture), social contingency (e.g., performing the right social behaviors at the right times), and expressivity (such as using a very expressive voice versus a flat/boring one)—significantly impact how much kids learn, how engaged they are in the learning activities, and how credible they think the robot is.

I've also seen kids treat the robot as something kind of like a friend. As I've talked about before, kids treat the robot as something in between a pet, a tutor, and a technology. They show many social behaviors with robots—hugging, talking, tickling, giving presents, sharing stories, inviting to picnics—and they also show understanding that the robot can turn off and needs battery power to turn back on. In some of our studies, we've asked kids questions about the properties of the robot: Can it think? Can it break? Does it feel tickles? Kids' answers show that they understand that robot is a technological, human-made entity, but also that it shares properties with animate agents.

In many of our studies, we've deliberately tried to situate the robot as a peer. After all, one key way that children learn is through observing, cooperating with, and being in conflict with their peers. Putting the cute, fluffy robot in a peer-like role seemed natural. And over the past six years, I've seen kids mirror robots' behaviors and language use, learning from them the same way they learn from peers.

I began to wonder about the impact of the relational features of the robot on children's engagement and learning: that is, the stuff about the robot that influences children's relationships with the robot. These relational features include the social behaviors we have been investigating, as well as others: mirroring, entrainment, personalization, change over time in response to the interaction, references to a shared narrative, and more. Some teachers I've talked to have said that it's their relationship with their students that really matters in helping kids learn—what if the same was true with robots?

My hunch—one I'm exploring in my dissertation right now via a 12-week study at Boston-area schools—is that yes: kids' relationships with the robot do matter for learning.

But how do you measure that?

I dug into the literature. As it turns out, psychologists have observed and interviewed children, their parents, and their teachers about kids' peer relationships and friendship quality. There are also scales and questionnaires for assessing adults' relationships, personal space, empathy, and closeness to others.

I ran into two main problems. First, all of the work with kids involved assumptions about peer interactions that didn't hold with the robot. For example, several observation-based methodologies assumed that kids would be freely associating with other kids in a classroom. Frequency of contact and exclusivity were two variables they coded for (higher frequency and more exclusive contact meant the kids were more likely to be friends). Nope: Due to the setup of our experimental studies, kids only had the option of doing a fairly structured activity with the robot once a week, at specific times of the day.

The next problem was that all the work with adults assumed that the experimental subjects would be able to read. As you might imagine, five-year-olds aren't prime candidates for filling out written questionnaires full of "how do you feel about X, Y, or Z on a 1-5 scale." These kids are still working on language comprehension and self-reflection skills.

I found a lot of inspiration, though, including several gems that I thought could be adapted to work with my target age group of 4–6 year-olds. I ended up with an assortment of assessments that tap into a variety of methodologies: questions, interviews, activities, and observations.

three drawings of a robot, with the one on the left frowning, the middle one looking neutral, and the one on the right looking happy

We showed pictures of the robot to help kids choose an initial answer when asking some interview questions. These pictures were shown for the question, 'Let's pretend the robot didn't have any friends. Would the robot not mind or would the robot feel sad?'

We ask kids questions about how they think robots feel, trying to understand their perceptions of the robot as a social, relational agent. For example, one question was, "Does the robot really like you, or is the robot just pretending?" Another was, "Let's pretend the robot didn't have any friends. Would the robot not mind or would the robot feel sad?" For each question, we also ask kids to explain their answer, and whether they would feel the same way. This can reveal a lot about what criteria they use to determine whether the robot has social, relational qualities, such as having feelings, actions the robot takes, consequences of actions, or moral rules. For example, one boy thought the robot really liked him "because I'm nice" (i.e., because of the child's attributes), while another girl said the robot liked her "because I told her a story" (i.e., because of actions the child took).

seven cards, each with a picture of a pair of increasingly overlapping circles on it

The set of circles used in our adapted Inclusion of Other in the Self task.

Some of these questions used pictorial response options, such as our adaptation of the Inclusion of Other in the Self scale. In this scale, kids are shown seven pairs of increasingly overlapping circles, and asked to point to the pair of circles that best shows their relationship with someone. We ask not only about the robot, but also about kids' parents, pets, best friends, and a bad guy in the movies. This lets us see how kids rate the robot in relation to other characters in their lives.

a girl sits at a table with paper and pictures of different robots and things

This girl is doing the Robot Sorting Task, in which she decides how much like a person each entity is and places each picture in an appropriate place along the line.

Another activity we created asks kids to sort a set of pictures of various entities along a line—entities such as a frog, a cat, a baby, a robot from a movie (like Baymax, WALL-e, or R2D2), a mechanical robot arm, Tega, and a computer. The line is anchored on one end with a picture of a human adult, and on the other with a picture of a table. We want to see not only where kids put Tega in relation to the other entities, but also what kids say as they sort them. Their explanations of why they place each entity where they do can reveal what qualities they consider important for being like a person: The ability to move? Talk? Think? Feel?

In the behavioral assessments, the robot or experimenter does something, and we observe what kids do in response. For example, when kids played with the robot, we had the robot disclose personal information, such as skills it was good or bad at, or how it felt about its appearance: "Did you know, I think I'm good at telling stories because I try hard to tell nice stories. I also think my blue fluffy hair is cool." Then the robot prompted for information disclosure in return. Because people tend to disclosure more information, and more personal or sensitive information, to people to whom they feel closer, we listened to see whether kids disclosed anything to the robot: "I'm good at reading," "I can ride a bike," "My teacher says I'm bad at listening."

a fluffy red and blue tega robot with stickers stuck to its tummy

Tega sports several stickers given to it by one child.

Another activity looked at conflict and kids' tendency to share (like they might with another child). The experimenter holds out a handful of stickers and tells the child and robot that they can each have one. The child is allowed to pick a sticker first. The robot says, "Hey! I want that sticker!" We observe to see if the child says anything or spontaneously offers up their sticker to the robot. (Don't worry: If the child does give the robot the sticker, the experimenter fishes a duplicate sticker out of her pocket for the child.)

Using this variety of assessments—rather than using only questions or only observations—can give us more insight into how kids think and feel. We can see if what kids say aligns with what kids do. We can get at the same concepts and questions from multiple angles, which may give us a more accurate picture of kids' relationships and conceptualizations.

Through the process of searching for assessments I need, discovering nothing quite right existed, and creating new ways of capturing kids' behaviors, feelings, and thoughts, the importance of assessment really hit home. Measurement and assessment is one of the most important things I do in research. I could ask any number of questions, hypothesize any number of outcomes, but without performing an experiment and actually measuring something relevant to my questions, I would get no answers.

We've just published a conference paper on our first pilot study validating four of these assessments. The assessments were able to capture differences in children's relationships with a social robot, as expected, as well as how their relationships change over time. If you study relationships with young kids (or simply want to learn more), check it out!

This article originally appeared on the MIT Media Lab website, May 2018

Acknowledgments

The research I talk about in this post was only possible with help from multiple collaborators, most notably Cynthia Breazeal, Hae Won Park, Randi Williams, and Paul Harris.

This research was supported by a MIT Media Lab Learning Innovation Fellowship and by the National Science Foundation. Any opinions, findings and conclusions, or recommendations expressed in this article are those of the authors and do not represent the views of the NSF.


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