PNAS, 2020  

The confidence gap predicts the gender pay gap among STEM graduates

Adina D. Sterling, Marissa E. Thompson, Shiya Wang, Abisola Kusimo, Shannon Gilmartin, and Sheri Sheppard

How is self-confidence related to the gender wage gap in STEM?

Is there a gender pay gap among graduates in some science, technology, engineering, and math (STEM) fields? Women and men have near-identical human capital at college exit, but cultural beliefs about men as more fit for STEM professions than women may lead to self-beliefs that affect pay. We hypothesized that women and men would be paid differently upon college exit, and that gender gaps in self-beliefs about one’s abilities, or self-efficacy, would correspond to this difference. Using data from a three-wave longitudinal study of graduates of engineering programs from 2015–2017, we find a confidence gap that aligns with a gender pay gap. Overall, these findings point to the role that cultural beliefs play in creating gender disparities among STEM degree-holders.

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Journal of Higher Education, 2021  

Ambiguous credentials: How learners use and make sense of massively open online courses

Krystal Laryea, Kathy Mirzaei, Andreas Paepcke & Mitchell Stevens

What good is a MOOC?

As low-status academic offerings purveyed by high-status institutions, massively open online courses (MOOCs) are ambiguous credentials. In interviews with 60 people who devoted substantial time to at least one MOOC between 2014-2017, we find that people use MOOCs to build skills for application at work and home, build relationships, navigate life transitions, and enhance formal presentations of self.

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Course reviews reveal gender differences and other scientific insight about the students who submit them

David Lang, Youjie Chin, Andreas Paepcke, and Mitchell L. Stevens

What's in a course review?

College and university students submit millions of course reviews each year, yet these instruments are only rarely leveraged for scientific inquiry. This paper examines 11,255 reviews submitted to computer science courses to illustrate how such inquiry might proceed.

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Hamilton Project / Brookings, 2020  

Building tomorrow’s workforce today: Twin proposals for the future of learning, opportunity and work

Richard Arum & Mitchell L. Stevens

A federal policy proposal to assist adult learners in the wake of the pandemic

The US federal government has serially called upon colleges and universities to assist the nation in moments of national crisis. This brief outlines an ambitious plan to enlist the postsecondary sector in helping millions of Americans get back to work in the wake of the pandemic and equip them for ongoing prosperity in a highly dynamic economy.

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AERA Open, 2021  

Studying undergraduate course consideration at scale

Sorathan Chaturapruek, Tobias Dalberg, Rene F. Kizilcec, Marissa E. Thompson, Sonia Giebel, Monique Harrison, Ramesh Johari, and Mitchell L. Stevens

What role does course consideration play in college students' pathways?

Using digital trace data to observe this process at scale at a private research university, together with qualitative student interviews, we provide a novel empirical study of course consideration as part of the course selection process.

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Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 2018  

OARS: Exploring instructor analytics for online learning

Jonathan Bassen, Iris Howley, Ethan Fast, John Mitchell, and Candace Thille

How can learning analytics systems improve online education?

Our study suggests new design goals for learning analytics systems, the importance of real-time analytics to many instructors, and the value of flexibility in data selection and aggregation for an instructor when working with an analytics system.

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Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020  

Reinforcement learning for the adaptive scheduling of educational activities

Jonathan Bassen, Bharathan Balaji, Michael Schaarschmidt, Candace Thille, Jay Painter, Dawn Zimmaro, Alex Games, Ethan Fast, and John C. Mitchell

Can reinforcement models improve learning?

We show that a Reinforcement Learning (RL) model produces better learning gains using fewer educational activities than a linear assignment condition, and produces similar learning gains to a self-directed condition using fewer educational activities and with lower dropout rates.

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InsideHigherEd, 2015  

What we’ve learned from MOOCs

editorial by Candace Thille, John Mitchell and Mitchell Stevens

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The New York Times 2020  

What is a college education in the time of coronavirus?

an editorial by Richard Arum and Mitchell L. Stevens

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Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020  

AI and holistic review: Informing human reading in college admissions

AJ Alvero, Noah Arthurs, anthony lising antonio, Benjamin W. Domingue, Ben Gebre-Medhin, Sonia Giebel, and Mitchell L. Stevens

Can AI improve holistic review?

We use a variety of text classification algorithms on a large corpus of college admissions essays to model the extent and ways that AI might inform human evaluation.

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