Returns to effort: experimental evidence from an online language platform
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Returns to effort: experimental evidence from an online language platform Fulya Ersoy1 Received: 5 February 2020 / Revised: 6 November 2020 / Accepted: 8 November 2020 © Economic Science Association 2020
Abstract While distance learning has become widespread, causal estimates regarding returns to effort in technology-assisted learning environments are scarce due to high attrition rates and endogeneity of effort. In this paper, I manipulate effort by randomly assigning students different numbers of lessons in a popular online language learning platform. Using administrative data from the platform and the instrumental variables strategy, I find that completing 9 Duolingo lessons, which corresponds to approximately 60 minutes of studying, leads to a 0.057–0.095 standard deviation increase in test scores. Comparisons to the literature and back-of-the-envelope calculations suggest that distance learning can be as effective as in-person learning for college students for an introductory language course. Keywords Returns to effort · Distance learning · Manipulation of effort · Field experiment JEL Classification I23 · I26 · C93
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1068 3-020-09689-1) contains supplementary material, which is available to authorized users. * Fulya Ersoy [email protected] 1
Department of Economics, Loyola Marymount University, 1 LMU Drive, Los Angeles, CA 90045, USA
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1 Introduction There has been a growing interest in the use of educational technology (Escueta et al. forthcoming) and a steady increase in the number of students who engage in distance learning in recent years (Seaman et al. 2018).1 Although technologyassisted learning has the potential to be a transformative force in education due to its flexibility, accessibility (Goodman et al. 2019), and affordability (Deming et al. 2015), we do not know much about the effectiveness of effort in these learning environments. High attrition rates in distance learning environments2 combined with endogenous effort choices of students render measuring the causal returns to effort in these settings quite difficult. In this paper, I design an experiment in which study effort is exogenously manipulated and cleanly observed to measure the returns to effort for one of the most popular language learning platforms, Duolingo. The aim of this paper is not to evaluate the effectiveness of Duolingo, it is rather to understand whether and how study effort affects performance in a remote setting with technology-assisted learning. To measure causal returns to effort in a technology-assisted learning environment, I design an experiment. First, I recruit college students who want to learn Spanish. I assess their initial Spanish knowledge with two tests. Students take an internal test which is based on Duolingo lessons and an external test, WebCAPE. Then, students sign up for Duolingo. I randomly assign them to one of the five groups (32-lessons, 48-lessons, 64-lessons, 8
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