Exploring Student Attention Tracking (SAT): a New, Integrated Behavioral Coding Protocol for Student Attention

• In Event: Measuring Attention and Engagement in Real-world Settings

Abstract

Attention is a cognitive process that cannot be directly observed. In educational settings, teachers rely on student behaviors to assess attention, which can guide instructional choices. This may have greater impact on the experiences of children with lower self-regulation skills or ADHD, who may exhibit behaviors associated with lower attention in class. Given the importance of understanding children’s attention for academic and social outcomes (Ahmed et al., 2021; Duncan et al., 2007), behavioral coding has been employed by clinical, cognitive, and educational researchers. In a separate line of work, neuroscientists have investigated the neural correlates of attention using EEG. Combining behavioral and neuroscientific methods may help elucidate the complex relations between neural processes associated with attention and children’s behavior. However, there exists a need for behavioral measures that capture – on a continuous scale – the full range of behaviors related to attention/inattention.

Here, we share a new coding protocol that draws on multidisciplinary perspectives. Specifically, the Student Attention Tracking (SAT) protocol integrates behavioral cues of gaze, fidgets, and active/passive engagement. Additionally, unlike most coding that use discontinuous measurement of behaviors (e.g., partial interval, momentary time sampling), SAT involves coding of a continuous measurement (e.g., frequency, duration) to capture fluctuations in children’s behaviors.

SAT is built upon three main literatures: classroom observation with children with ADHD (Rapport et al., 2009), a framework of systematic behavioral observations of students in schools (Shapiro, 2004), and investigations of fidgeting (Farley et al., 2013). Attentional behaviors were categorized as either actively engaged (e.g., answering teacher’s questions) or passively engaged (e.g., listening to the video). Behaviors associated with inattention included head movement, appendage movement, body movement, micro fidgeting (small and repetitive movements), off-task verbal (e.g., humming), and off-task passive (e.g., turning gaze away from the learning materials). See Table 1 for codes’ definitions, examples, and source literatures.

Here, we report on the use of SAT to quantify children’s (in)attentive behaviors in semi-naturalistic settings. Children (aged 6-10) with and without a diagnosis of ADHD were recruited to participate in a set of science learning activities conducted by a trained researcher in-person and online. Two cameras captured both the child’s face and the side of their full body. Preliminary data coded from recordings has illustrated SAT’s utility for quantifying the nuances in children’s behaviors. For example, results indicated that children changed attentional behavior for an average of 170 times (SD=43) in the entire learning session. Additional information will be presented regarding the use of this new coding scheme and reliability metrics. We will also describe our efforts to characterize behaviors of children with and without ADHD as well as applications of SAT for understanding the links between children’s behaviors and related neural processes.

Authors

• Xinyi Zoe Mao, University of Virginia - Charlottesville
  Presenting Author

• Jennie K Grammer, University of Virginia - Charlottesville

• Agatha Lenartowicz, University of California, Los Angeles