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By Jennifer Rose, Research Professor, and Lisa Dierker, Professor, of Wesleyan University’s Department of Psychology and instructors for the Data Analysis and Interpretation Specialization on Coursera
International Women’s Day on March 8th celebrates social, economic, cultural and political achievements of women – and also encourages action to accelerate gender parity across the globe. Follow #WomeninTech throughout this week to see different perspectives on how to close the gender gap in technology from inspiring female professors who teach on Coursera.
In an increasingly data-driven world, statistical inquiry is emerging as a critical component of almost any discipline. Rather than trying to directly create more access and interest in this area through STEM-related disciplines, our goal is a little different. We want to encourage women to enter this field in a more unconventional way, regardless of their areas of interest. There are so many brilliant, curious, and talented women in non-STEM disciplines, so how can we bring statistical inquiry beyond the world of STEM?
We followed a non-traditional path into quantitative methods and data analysis. Arguably, this path was a driving factor behind our interests and success. Instead of entering a male dominated discipline such as computer science or mathematics, we both pursued studies in psychology. Here, we were presented with a welcoming culture that allowed us to encounter and wrestle with the exciting, messy, curious, and stimulating world of statistical inquiry. We were free to explore questions that we believed really mattered. Increasing access across a range of fields, not directly tied to STEM, had a positive impact on opening up different ways for us and other women to learn quantitative methods and data analytics.
With MOOCs, our approach strives to integrate and teach quantitative methods and data analysis as tools to address important issues within a broader range of traditionally non-STEM disciplines. We want to present other avenues to attract women who might otherwise show less interest, or who might even question their ability to learn these skills. In our Data Analysis and Interpretation Specialization, we want to encourage women from multiple disciplines to seek out these skills. We ask learners to identify research questions that interest them, pursue answers to their questions and to tell their story over the course of the Specialization.
In teaching this way, we’ve seen that personal interest excites and empowers learners in a way that traditional methods of teaching quantitative methods do not. It is our hope that this sense of empowerment will serve to inspire women (in both STEM and non-STEM fields) to continue to apply and expand their data analytic skills, and to give them the confidence they need to tackle the challenges of any field, including the traditionally male-dominated STEM areas.
Other blog posts in Coursera’s #WomeninTech series:
International Women’s Day: Our Weeklong Reflections on Women in Tech — Daphne Koller, Coursera
How can we encourage more women to go into Computer Science? — Colleen van Lent, University of Michigan
The ‘Pipeline Problem’ is Leaky — Christine Alvarado and Mia Minnes, University of Californa, San Diego
Continue reading...
International Women’s Day on March 8th celebrates social, economic, cultural and political achievements of women – and also encourages action to accelerate gender parity across the globe. Follow #WomeninTech throughout this week to see different perspectives on how to close the gender gap in technology from inspiring female professors who teach on Coursera.
In an increasingly data-driven world, statistical inquiry is emerging as a critical component of almost any discipline. Rather than trying to directly create more access and interest in this area through STEM-related disciplines, our goal is a little different. We want to encourage women to enter this field in a more unconventional way, regardless of their areas of interest. There are so many brilliant, curious, and talented women in non-STEM disciplines, so how can we bring statistical inquiry beyond the world of STEM?
We followed a non-traditional path into quantitative methods and data analysis. Arguably, this path was a driving factor behind our interests and success. Instead of entering a male dominated discipline such as computer science or mathematics, we both pursued studies in psychology. Here, we were presented with a welcoming culture that allowed us to encounter and wrestle with the exciting, messy, curious, and stimulating world of statistical inquiry. We were free to explore questions that we believed really mattered. Increasing access across a range of fields, not directly tied to STEM, had a positive impact on opening up different ways for us and other women to learn quantitative methods and data analytics.
With MOOCs, our approach strives to integrate and teach quantitative methods and data analysis as tools to address important issues within a broader range of traditionally non-STEM disciplines. We want to present other avenues to attract women who might otherwise show less interest, or who might even question their ability to learn these skills. In our Data Analysis and Interpretation Specialization, we want to encourage women from multiple disciplines to seek out these skills. We ask learners to identify research questions that interest them, pursue answers to their questions and to tell their story over the course of the Specialization.
In teaching this way, we’ve seen that personal interest excites and empowers learners in a way that traditional methods of teaching quantitative methods do not. It is our hope that this sense of empowerment will serve to inspire women (in both STEM and non-STEM fields) to continue to apply and expand their data analytic skills, and to give them the confidence they need to tackle the challenges of any field, including the traditionally male-dominated STEM areas.
Other blog posts in Coursera’s #WomeninTech series:
International Women’s Day: Our Weeklong Reflections on Women in Tech — Daphne Koller, Coursera
How can we encourage more women to go into Computer Science? — Colleen van Lent, University of Michigan
The ‘Pipeline Problem’ is Leaky — Christine Alvarado and Mia Minnes, University of Californa, San Diego
Continue reading...
