Understanding the complex biology of the brain, is no easy task for researchers. The study of neuroscience and brain activity during learning requires advanced imaging technologies such as functional magnetic resonance imagine (fMRI) or electroencephalography (EEG) devices (Janssen et, al., 2021). Traditionally, these technologies restricted imaging to laboratory settings. One of the challenges for the study of learning and  neuroscience is in capturing brain images or activity in natural learning settings. This limitation has meant that studies have failed to capture brain activity in the more complex environment of a classroom and has resulted in a lack of generalizability of the findings in many studies (Janssen et al., 2021).

Stangl et al. (2023) discuss the potential of newer mobile imaging technologies to improve the study of the brain in real-world environments from both a scientific perspective, but also a clinical one (p. 351). These technologies are relatively new, and therefore it will take time before the research has an impact on the field. Stang el al. (2023) argue for continued study in the laboratory as well so that real world and lab studies can continue to inform each other.

The short video (2 minutes) here from the University of South Australia is optional. It discusses how researchers are trying to use mobile EEG devices to get a real-world understanding of the brain.

Confounding variables in experimental design are much harder to control for in real world experiments. "For example, in natural social settings, the behaviour and cognitive processes of a research participant might be influenced by luminance, sounds, odours, distracting environmental elements, movements of the participant and that of other people, complex emotional and interpersonal factors, and the behaviour of others" (Stangl et al., 2023, p. 357). New methods and mathematical models in study are therefore needed, opening a possibility for future ventures in these areas. Additionally, Stagle et al. (2023) discuss potentials for collection of multimodal data and applying computational approaches to reduce the noisiness (confounds) in the study.

Mobile EEG wearable devices are a significant advancement for researchers. However, commercial interests are also marketing versions of these systems to businesses and schools to monitor the attention of students or employees for the purposes of promoting more productivity (Janssen et al., 2021). This is concerning from a privacy perspective, but there are also ongoing questions about the efficacy of interpreting EEG signals to determine attention of users (Janseen et al., 2021). 

Another interesting area is in neuro-adaptive technologies and claims by commercial interests to provide tools and methods to enhance brain activity. That is, where artificial intelligence, learning analytics and EEG data are utilized to provide a personalized adaptive learning environment. Williams (2019) points out that there is little evidence that tracking neural signals can translate into meaningful data for use in adaptive learning environments. Although, as we will explore in our section on futures in this field, there are some promising approaches to adaptive learning environments.