The pandemic has changed what we seek to learn
Online learning is a deep and rich field of research, tools, and ideas. The use of the term ‘online learning’ is not universal and can describe different concepts (Moore et al. 2011). It can refer to learning entirely online or more broadly the use of online tools and virtual spaces to engage and support learning (Wallace 2003). The pandemic has caused many shifts including behavioral responses to interest in online learning, teaching, and information technology. Some of these changes are driven by necessity, including emergency delivery of teaching content online but also may stem from fear of infection. Further, interest in online learning can shift because of emotional drivers; to explore this idea, we can turn to data on human web searches (i.e., google trends). Predicting human behaviour using web searches is a well-established field of research in consumer studies (Preis et al. 2013) and other disciplines (Nuti et al. 2014), and the evidence suggests that volume of searches provides a useful predictive guide to the near future in behaviour (Goel et al. 2010). This tool estimates the relative interest (or trends) of internet users globally by comparing the relative frequencies of search queries between different time periods, allowing us to infer change in human priorities. There are numerous examples of this approach, and current examples have involved using Google Trends data to examine indicators of private consumption (Vosen and Schmidt 2011), predicted cryptocurrency market values (Kristoufek 2013), trading behavior in financial markets (Preis et al. 2013), human health and healthcare (Heerfordt and Heerfordt 2020, Nuti et al. 2014), and–more pertinent to this discussion–shifts in consumer choices during the pandemic (Claudia et al. 2020). Specifically, the Google Trends tool provides data and trend analyses by key terms through time. While the overall predictive accuracy of Google Trends has been challenged by periods of unusually high search prevalences (i.e. ephemeral spikes; Butler 2013), recent developments have improved its predictive accuracy (Kandula and Shaman 2019). Our primary aim here was to compare the dynamics of relative interests in different types of information during the pandemic globally using Google Trends (Choi and Varian 2012). Understanding that limitations exist with Google Trends, we saw an opportunity to frame the concepts developed in this special issue within this larger, global conversation and engagement with information to understand how we fit in to the ecology and evolution of the pandemic but also how our learning environment must change in response.
To do this, we first examined the relative interests in topics related to risk (“death”), resources (“money”), and reproduction (“love”), three currencies we often value in natural systems in our fields. Google trends indicated dramatic shifts in relative interest between these topics at the onset of the COVID-19 pandemic. Google searches between the same January-June period in 2019 and then again in 2020 showed that our relative interests in reproduction (google search global topics related to “love”), risks (google search global topics related to “death”), and resources (google search global topics related to “money”) dramatically shifted in 2020 tracking increases in confirmed infections (https://github.com/maacevedo/covid19gtrends). While in 2019, topics of “love” were the most frequently searched of these three, in 2020, with an increasing number of infections, “love” was replaced by topics related to “death” (Figure 1). A shift in relative importance of reproduction and risk of mortality can be expected based on ecological and evolutionary theory on the effects of predation risk (Lima and Dill 1990) and risk of infection (Weinstein et al. 2018), but we found it interesting that this pattern seems to have emerged in global patterns of online searches related to the pandemic. These changes may stem from the desire to learn about COVID-19 so that one can better calculate the risk it poses and determine the appropriate behavioral response–especially given that other authors have attributed to the pandemic a heightened level of fear in people (Ahorsu et al. 2020, Lin 2020). In essence, risks from COVID-19 seem to have, at least temporarily, changed the relative importance of the information we seek to learn. Changes in density and behavior (Lima and Dill 1990, Brown et al 1999) in response to fear of predation have been common in ecological literature. However, demonstrating that individuals of any species fundamentally change how they value different types of information is difficult to measure (Clinchy et al. 2011). Understanding these patterns and processes in humans can inform our understanding of the role emotion can play in natural communities (Clinchy et al. 2011, Frey et al. 2014). These data provide solid evidence that understanding how humans interact with their environment and other organisms may provide us with rare opportunities to identify ecological and evolutionary mechanisms not easily measured in other organisms.