Examining Constructivist Learning in Context and in the Moment

• In Event: Making Constructivist Learning Feasible: Exploring Convergence of Virtual Reality and Learning Analytics for Future Research Directions

Abstract

While research in virtual reality (VR) and learning analytics has advanced our understanding about how students engage and learn in technology supported constructivist learning, the investigations have primarily been situated in contexts where students’ learning is mediated through technological tools and platforms – here I refer as “online learning”. However, students do also spend a great amount of time learning in preparation for a VR session or in reflection after a VR session. These activities can happen in a classroom, at home, in the library, or at a coffee shop where learning is not necessarily mediated through technology – here I refer as “offline learning”. How do we capture analytics data in offline learning settings to provide a fine-grained and nuanced understanding about students’ engagement? Indeed, analytics about “online learning” and “offline learning” complement each other, and together may provide a more comprehensive picture about students’ learning experiences in the modern education environment.

Experience-sampling method (ESM) is a technique used to collect data when people are in an everyday context so that the data reflect what is happening in that moment (Larson & Csikszentmihalyi, 1983). Combined with the affordances of mobile technologies and the proliferation of mobile phones, ESM has become a powerful method that has similar features as learning analytics yet can be applied to both online and offline learning settings.

Modern ESM has several unique characteristics. First, ESM captures learning behavior, thoughts, and feelings in the moment and within the context of learning. Compared to traditional widely used self-report methods in educational research that are based on projections or retrospections, ESM’s in-situ approach of data collection provides the needed proximity of time and space to improve its ecological validity as research instrumentation (Xie et al., 2019). Second, ESM applies an intensive longitudinal design. It typically asks participants to respond to short surveys at an intensive frequency and for a longer period. This intensive longitudinal design has four sampling strategies, including random sampling (sampling at random), fixed sampling (sampling at fixed interval or fixed schedule), event-based sampling (sampling at events of interest), and context-aware sampling (sampling at contexts of interest). Examples of ESM studies will be presented and discussed in the symposium session (e.g., Manwaring et al., 2017; Schmidt et al., 2018; Xie et al., 2019). In addition, with sensor technologies built in mobile devices, ESM collects multimodal data, including psychological data through self-report, physical data, physiological data and contextual data. In this presentation, I will further contrast and compare ESM with traditional self-report research and learning analytics methods.

Taking the affordances of ESM, researchers may combine it with learning analytics to examine students’ engagement in advanced learning environments such as VR or constructivist learning activities such as project-based learning. Using a combination of these approaches will take the benefits from each approach in advancing our understanding of how people learn.

Author

• Kui Xie, Michigan State University
  Presenting Author