Dear Isabel. 

Thank you for inquiring about our study. For this particular study all participants were girls aged 8 through 14. They were 6 teams in total, where 5 of the 6 groups consisted of 3 girl-participants and one group had just 2 participants. All six teams participated on the one day event at the same time (about a 7 1/2 hour day). 

Negative attributions are not all necessarily bad, in fact there is an inherent amount of frustration when learning new tasks. The yellow team, for example, presented the largest number of negative internal attributions. Coincidentally this was also one of the teams that completed the least number of robotic challenges. Although we can only conjecture at this point, it appears that the  perception of internalized stereotypes in girls might hinder motivation and willingness to learn tasks in areas where members of the opposite gender are supposed to excel. The data appears to indicate that for those teams with the lowest number of internal negative attributions (dark gray and light blue) there was a higher level of motivation for excelling at robotic tasks. It is important to highlight that both the dark gray and light blue teams performed the greatest number of successful robotic challenges. 

I think it is very hard to act upon a task when we have an internalized perception that we are not going to do very well.

Our results are premilinary, but it does raise questions as to how educators can counter the effects of gender-based internalized stereotypes. 

Great to see more hard evidence documenting the reality of “stereotype threats” . My own experience in engineering is that there are still many disbelievers – i.e. those that don’t succeed just aren’t motivated enought etc.

Hmmm…it’s hard to understand how knowledgeable people fail to understand the impact of culture and society on the development of self, yet, there it is. We just need to keep doing our work Tamara!

This is very important work—making sense of internalized stereotypes in the STEM space. Could you say a little more about how the workshop was advertised or how the girls and boys were recruited to participate? Also, where was the event conducted? in school? in an informal learning environment? I am also interested in learning how you decided to place girls in the groups (random or based on their survey responses). I am thinking about the impact of peer pressure in the age group and how it can “sway” attitudes and behaviors. I am also interested in any plans you have to use the data to help teachers understand the dynamics of learning and gender differences that may be operating in STEM.

HI Vivian – Thanks for your questions. This was a one-day robotics workshop for girls (only) ages 8-14. A total of 17 girls participated in 6 teams. The girls were recruited from area schools – the teams were made up of girls from the same school, but, they were not, necessarily friend groups. The technology teachers from the participating schools helped us recruit participants. This would classify as an informal learning setting – the workshop took place in Holyoke, MA at the Massachusetts Green High Performance Computing Center (http://www.mghpcc.org/). We are currently working on another, related paper that looks at group dynamics and how those dynamics play out in terms of opportunities to learn within the group. The results from both of these, related, investigations should yield recommendations for teachers and curriculum developers.

Were your groups mixed age? The experience of being 8 is very different than that of 13—I wonder about the social dynamic (13 year olds influence youngers, and can dominate; some studies have indicated that previously confident girls show a dramatic shift following puberty—and in theory social/peer pressure, etc.) Did you do any analysis/find any age differences?

No, we have not done that. Actually, only one group had younger girls (an 8 y.o. and a 9 y.o.). these two were sisters and were paired with a friend who was 10 y.o. It was hard for the 8 y.o. to stay engaged. Really, it was beyond her. That said, I have met 8 y.o. girls who can and do stick with this type of robotics activity. ideally the age range would be 12-13 y.o., which were the majority of the participants. However, this workshop has traditionally been open to ages 8 – 12, so we stuck with the “Girls Connect” model. They were our partners.

Yes, Tamara, I do think that makes a difference. We had some serious contingents of self-identified “geeky” girls. It is also interesting to see how some of that played out within groups vis-a-vis control of devices, direction of activities, etc.

I’d be interested to hear about this with an “age” filter as well—the 13 year old girls I know are acutely more interested in validation from their male counterparts than 8 year olds—and almost all “youngers” are influenced by older students

Sophie – we had a prior set of data that included an all-boy group and a mixed group from two six grade science classrooms. The data sets were different in that the prior data sets were collected over a 12-day period – in 90 minute blocks. Our one day event consisted of a total of about 4 hours of active building and programming (with the rest of the time spent on instruction, lunch, breaks and the final activity where the girls showed off what their bots could do). As to the mixed group, one girl was fairly negative and the other pretty positive. The positive one worked hard to build the confidence of the negative one, that was a very interesting dynamic. I do think that boys have the stereotype that they should be better at robots than girls, and so we have seen such dynamics (in terms of assertions to dominance) in the mixed groups. At some point, it is the personality of the girl that dictates her acquiescing or resisting that narrative from male members of the group. We wanted to see what happens in all-girl groups – do the stereotypes show up in a (seemingly) more equitable environment. Of course, as Lisa is pointing out, there were other factors that came into play that created some level of hierarchy. So, we are working on unpacking that, as well.

Hello Emily:

This is a great question. After a few attempts at parsing the data, we realized that by having a lexicon that in only including utterances with negative attributions we missed other utterances that could possibly indicate a negative salience. We found that there were times where just one word did not determine the saliency but rather the entire sentence (context) As a result we expanded our lexicon to include words related to confidence as well as other indicators such as “we are not good at this” and “smart”.

Once our analysis tool flagged an utterance, we manually checked the entire sentence and categorized based on investigators consensus.

In the specific case of the word “ smart", we found that there were a few cases where it was used to compare the level of confidence between participants. So for example: “The girl from the light blue team is very smart” – The previous utterance by itself does not indicate a negative attribution, in fact it is positive. However, when used to compare one’s confidence with another participant it might represent feelings of inadequacy. It is important to look at the entire utterance and its context. Our lexicon purposely did not include words such as “not”, because after a few trials we realized that we had a large number of false positives.

I also wonder about the results from your different data sources (observational video vs. survey) Did the two data sets validate each other ? or did they reveal differences? Often we researchers learn the most where there are mismatches in the data…

Hello Tamara,

The robotic tasks were based on the First Lego League 2011 “Food Factor” set of missions. Here you can access the Wikipedia page for the specific challenge: https://en.wikipedia.org/wiki/Food_Factor

In addition, here is the actual document describing all missions included in the challenge:
http://www.firstlegoleague.org/sites/default/fi...

Each year the First Lego League creates a game table setup accompanied by a series of missions that need to be completed for points.

Participants need to program their robots in order to complete missions such as: retrieval or placement of objects, and trigger mechanisms or props within the game table. Each team has the same amount of time to successfully perform as many missions as possible. Points are awarded (or deducted) based on how the robot performs each challenge.

Normally teams would have months to prepare, for our research we did training and performance all in one day.

The entire day was structured as follows:
- Robot construction (Lego Mindstorms)
- Programming instruction
- Build + experimentation
- Lunch
- Build + experimentation
- Cookie break
- Build + experimentation
- Competition
- Awards

Ah, that’s worth knowing about. And, yes, the careful work is very important and very time consuming. We are currently working on developing computational means for exploring these data – in hopes that we can find a way to still dig deep, but to do it with a large data set and with certain aspects of the analysis being automated. Stay tuned on that one!

Good points, Tamara. Each of us has our own stereotypes about what “robotics” is or means so the examples would be great to examine for their inherent “nature.”

Tamara and Vivian – see Ricardo’s reply above to learn more about the robotics challenges students undertook and how our workshop day was structured.

Hi Barbara – Artbotics should be more cooperative – so, it might change the results for sure. That is one of the things I don’t love about the FIRST lego league (Girls Connect is an intro to FLL – we collected data at a Girls Connect event). I do think that the competition aspect may have a negative effect on participation or how one feels about small failures in development. This would be a good next step – looking at how competition may or may not be related to affect or the triggering of internalized stereotypes.

That is a really interesting question. Our group also does arts based robotics projects (Arts & Bots). It would be interesting to see how girls’ attitudes and stereotypes compare across different types of activities. I wonder, for instance, about what types of activities are most encouraging for students with varying levels of existing interest in STEM. Some start with very little interest in STEM or negative opinions of their abilities. Others are interested enough to sign up for a robotics program. How might the type of activity they participate in influence their beliefs about their own STEM abilities?