Showing Is Knowing: The Potential and Challenges of Using Neurocognitive Measures of Implicit Learning in the Classroom

Abstract

The value of neurocognitive measures to study memory, attention, cognition, and learning is well established. However, the vast majority of work using these tools is performed in tightly controlled lab experiments using simple lab stimuli. This article looks at the viability of using multimodal neurocognitive instruments to measure implicit knowledge in real‐world learning contexts. We focus on some of the most promising neurocognitive tools for this purpose, including eye‐tracking, electroencephalography (EEG), and functional near infra‐red spectroscopy (fNIRS). The specific challenges and potential of each tool are considered for use within learning contexts. These tools may be of particular importance to student populations that typically underperform on traditional learning assessments, including students with disabilities, English language learners, and students from low socioeconomic status backgrounds, among others. This review concludes with recommendations to the field for further work required to bring objective measures of implicit knowledge to real world learning contexts.

Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis

“…Technical challenges also arise when applying neurophysiology in learning and training, as collection, synchronization, and analysis of large amounts of data from multi-channel data streams are required (Dahlstrom-Hakki et al, 2019). Technological advancements are making these requirements increasingly easier, yet overcoming these technical challenges as much as possible is time-consuming as things such as time desynchronization between multiple devices and delays between the device and scripts occur frequently (Dahlstrom-Hakki et al, 2019).…”

“…Technical challenges also arise when applying neurophysiology in learning and training, as collection, synchronization, and analysis of large amounts of data from multi-channel data streams are required (Dahlstrom-Hakki et al, 2019). Technological advancements are making these requirements increasingly easier, yet overcoming these technical challenges as much as possible is time-consuming as things such as time desynchronization between multiple devices and delays between the device and scripts occur frequently (Dahlstrom-Hakki et al, 2019). Sufficient attention and time should be given to personnel training and device maintenance and extra time should also be allocated for experimental preparations and procedures during testing to ensure that data of high quality is collected (Kivikangas et al, 2011).…”

“…Again, the authors concluded that neurophysiology is promising for the assessment of learning. Although most work on neurophysiology in learning is conducted in the controlled environment of the lab, research has also demonstrated neurophysiological measures can be successfully applied for examining learning in real-life scenarios such as in a classroom (Ko et al, 2017;Poulsen et al, 2017;Bevilacqua et al, 2019;Dahlstrom-Hakki et al, 2019).…”

Brain-to-Brain Synchrony in the STEM Classroom

“…Using these methods, we can better explore the cognitive processing that is taking place during learning, rather than just before and after learning (Mayer, 2017). Further, these methods can potentially measure implicit processes that learners are unaware of or unable to report accurately, such as lapses of attention (Dahlstrom-Hakki et al, 2019). Finally, neuroscientific data can be used to explore individual differences that mediate learning and predict how students would benefit from different pedagogies (Gabrieli, 2016;Mayer, 2017).…”

Cyberlearning Special Issue: Exploring the Potential of Technologies to Bridge Understanding Across Neuroscience, Cyberlearning, and Education

“…The review article by Dahlstrom‐Hakki, Asbell‐Clarke, and Rowe () provides an overview of implicit learning and neurocognitive tools, including eye tracking, electroencephalography (EEG), functional near‐infrared spectroscopy, haptic sensors, and emotion recognition software that can be used to measure implicit learning in natural settings. Specific challenges as well as recommendations for lines of research are presented to move the field forward in terms of bringing objective measures of implicit knowledge to real‐world learning contexts.…”