Pandemic response policies' democratizing effects on online learning.

The COVID-19 pandemic has changed peoples' lives in unexpected ways, especially how they allocate their time between work and other activities. Demand for online learning has surged during a period of mass layoffs and transition to remote work and schooling. Can this uptake in online learning help close longstanding skills gaps in the US workforce in a sustainable and equitable manner? We answer this question by analyzing individual engagement data of DataCamp users between October 2019 and September 2020 (n = 277,425). Exploiting the staggered adoption of actions to mitigate the spread of COVID-19 across states, we identify the causal effect at the neighborhood level. The adoption of nonessential business closures led to a 38% increase in new users and a 6% increase in engagement among existing users. We find that these increases are proportional across higher- and lower-income neighborhoods and neighborhoods with a high or low share of Black residents. This demonstrates the potential for online platforms to democratize access to knowledge and skills that are in high demand, which supports job security and facilitates social mobility.

Scaling up behavioral science interventions in online education.

Online education is rapidly expanding in response to rising demand for higher and continuing education, but many online students struggle to achieve their educational goals. Several behavioral science interventions have shown promise in raising student persistence and completion rates in a handful of courses, but evidence of their effectiveness across diverse educational contexts is limited. In this study, we test a set of established interventions over 2.5 y, with one-quarter million students, from nearly every country, across 247 online courses offered by Harvard, the Massachusetts Institute of Technology, and Stanford. We hypothesized that the interventions would produce medium-to-large effects as in prior studies, but this is not supported by our results. Instead, using an iterative scientific process of cyclically preregistering new hypotheses in between waves of data collection, we identified individual, contextual, and temporal conditions under which the interventions benefit students. Self-regulation interventions raised student engagement in the first few weeks but not final completion rates. Value-relevance interventions raised completion rates in developing countries to close the global achievement gap, but only in courses with a global gap. We found minimal evidence that state-of-the-art machine learning methods can forecast the occurrence of a global gap or learn effective individualized intervention policies. Scaling behavioral science interventions across various online learning contexts can reduce their average effectiveness by an order-of-magnitude. However, iterative scientific investigations can uncover what works where for whom.

Eight-minute self-regulation intervention raises educational attainment at scale in individualist but not collectivist cultures.

Academic credentials open up a wealth of opportunities. However, many people drop out of educational programs, such as community college and online courses. Prior research found that a brief self-regulation strategy can improve self-discipline and academic outcomes. Could this strategy support learners at large scale? Mental contrasting with implementation intentions (MCII) involves writing about positive outcomes associated with a goal, the obstacles to achieving it, and concrete if-then plans to overcome them. The strategy was developed in Western countries (United States, Germany) and appeals to individualist tendencies, which may reduce its efficacy in collectivist cultures such as India or China. We tested this hypothesis in two randomized controlled experiments in online courses (n = 17,963). Learners in individualist cultures were 32% (first experiment) and 15% (second experiment) more likely to complete the course following the MCII intervention than a control activity. In contrast, learners in collectivist cultures were unaffected by MCII. Natural language processing of written responses revealed that MCII was effective when a learner's primary obstacle was predictable and surmountable, such as everyday work or family obligations but not a practical constraint (e.g., Internet access) or a lack of time. By revealing heterogeneity in MCII's effectiveness, this research advances theory on self-regulation and illuminates how even highly efficacious interventions may be culturally bounded in their effects.

Estimating peer effects in networks with peer encouragement designs.

Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are central to social science. Because peer effects are often confounded with homophily and common external causes, recent work has used randomized experiments to estimate effects of specific peer behaviors. These experiments have often relied on the experimenter being able to randomly modulate mechanisms by which peer behavior is transmitted to a focal individual. We describe experimental designs that instead randomly assign individuals' peers to encouragements to behaviors that directly affect those individuals. We illustrate this method with a large peer encouragement design on Facebook for estimating the effects of receiving feedback from peers on posts shared by focal individuals. We find evidence for substantial effects of receiving marginal feedback on multiple behaviors, including giving feedback to others and continued posting. These findings provide experimental evidence for the role of behaviors directed at specific individuals in the adoption and continued use of communication technologies. In comparison, observational estimates differ substantially, both underestimating and overestimating effects, suggesting that researchers and policy makers should be cautious in relying on them.

To Advance AI Use in Education, Focus on Understanding Educators.

A better understanding of educators' perspectives of emerging education technology, specifically tools that incorporate AI, is essential to unlock the full potential benefits of these innovations. While prior research has primarily emphasized technological advancements, it has overlooked the profound influence of social, psychological, and cultural factors in shaping educators' perceptions, trust, and adoption of educational technology. As increasingly powerful AI tools emerge, their design must be rooted in a deep understanding of educators' needs and perspectives. It is only with the acceptance and trust of educators that these innovative solutions can elevate learning outcomes, academic achievements, and educational equity.

Artificial intelligence in communication impacts language and social relationships.

Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI's negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions ("smart replies"), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly.

From pipelines to pathways in the study of academic progress.

Students and administrators can benefit from new analytics.

Identifying course characteristics associated with sociodemographic variation in enrollments across 159 online courses from 20 institutions.

Millions of people worldwide use online learning for post-secondary education and professional development, but participation from historically underrepresented groups remains low. Their choices to enroll in online courses can be influenced by course features that signal anticipated success and belonging, which motivates research to identify features associated with sociodemographic variation in enrollments. This pre-registered field study of 1.4 million enrollments in 159 online courses across 20 institutions identifies features that predict enrollment patterns in terms of age, gender, educational attainment, and socioeconomic status. Among forty visual and verbal features, course discipline, stated requirements, and presence of gender cues emerge as significant predictors of enrollment, while instructor skin color, linguistic style of course descriptions, prestige markers, and references to diversity do not predict who enrolls. This suggests strategic changes to how courses are presented to improve diversity and inclusion in online education.

Online education platforms scale college STEM instruction with equivalent learning outcomes at lower cost.

Meeting global demand for growing the science, technology, engineering, and mathematics (STEM) workforce requires solutions for the shortage of qualified instructors. We propose and evaluate a model for scaling up affordable access to effective STEM education through national online education platforms. These platforms allow resource-constrained higher education institutions to adopt online courses produced by the country's top universities and departments. A multisite randomized controlled trial tested this model with fully online and blended instruction modalities in Russia's online education platform. We find that online and blended instruction produce similar student learning outcomes as traditional in-person instruction at substantially lower costs. Adopting this model at scale reduces faculty compensation costs that can fund increases in STEM enrollment.

The Relationship between Virtual Self Similarity and Social Anxiety.

In virtual reality (VR), it is possible to embody avatars that are dissimilar to the physical self. We examined whether embodying a dissimilar self in VR would decrease anxiety in a public speaking situation. We report the results of an observational pilot study and two laboratory experiments. In the pilot study (N = 252), participants chose an avatar to use in a public speaking task. Trait public speaking anxiety correlated with avatar preference, such that anxious individuals preferred dissimilar self-representations. In Study 1 (N = 82), differences in anxiety during a speech in front of a virtual audience were compared among participants embodying an assigned avatar whose face was identical to their real self, an assigned avatar whose face was other than their real face, or embodied an avatar of their choice. Anxiety differences were not significant, but there was a trend for lower anxiety with the assigned dissimilar avatar compared to the avatar looking like the real self. Study 2 (N = 105) was designed to explicate that trend, and further investigated anxiety differences with an assigned self or dissimilar avatar. The assigned dissimilar avatar reduced anxiety relative to the assigned self avatar for one measure of anxiety. We discuss implications for theories of self-representation as well as for applied uses of VR to treat social anxiety.