An Exploration and Critical Examination of How “Intelligent Classroom Technologies” Can Improve Specific Uses of Direct Student Behavior Observation Methods

Abstract

Within classrooms, psychologists and teachers use direct behavior observation methods, systematic behavior observations (SBOs) and direct behavior ratings (DBRs), to gather information about students' behaviors for the purposes of making decisions related to diagnosis and classroom management or behavioral feedback respectively. Observers use SBOs to document predetermined target behaviors exhibited by one or a few target students with standardized parameters. Teachers use DBRs to track and manage their students' behaviors, most frequently recording data on all of their students' desirable and undesirable behavior throughout class while teaching. While SBOs and DBRs are ecologically valid, they are limited by observers' attentional limits and implicit biases, and DBRs are uniquely limited by teacher resources as they balance teaching with recording behavioral data about many students. Intelligent classroom technologies collect a high volume of data with object reliance on algorithms as opposed to observational judgment, which makes them well suited to address limitations specifically associated with the use of data derived from DBRs. Intelligent classroom technologies include eye gaze tracking methods that document students' visual fixations and wearable or within‐desk devices that monitor students' movements and/or vocalizations. These devices have the potential to record myriad behaviors of interest and transmit data in vivo for immediate responses from teachers with high efficiency and reduced reliance on human judgment and error. However, they may be associated with ethnical and/or privacy issues, which are discussed along with suggestions for addressing these challenges.

Technology Revolution in Learning: Building the Future of Education

“…The research sample consisted of 15 education students who were purposively selected, considering criteria such as semester level, education major, and level of understanding of technology. The data collection process involved a pre-survey questionnaire to gain an initial understanding of respondents' perceptions prior to the intervention, direct observation of technology use in learning, and in-depth interviews to explore experiences and views in detail (Woolverton & Pollastri, 2021).…”

Context-Aware Classrooms as Places for an Automated Analysis of Instructional Events

“…Their goal is simply derived from computerbased learning analytics systems that gather and compute students logs to deliver raw feedback. These classrooms are based upon the idea that a large number of students can be scrutinized by the CAC (a teacher cannot pay attention to all the students' problems at the same time), and that the teacher can derive an overall management strategy from this log data [8].…”

“…The results show that the classifier more accurately predicts engagement when previous teachers' behavior is accounted in the model and bound to students' own behavior. In that way, ecological CACs can be the place to implement a more thorough and direct behavior rating [8], and also be the place for testing more elaborated pedagogical practices, like active learning [35].…”

“…In that way, they can help overcome human observers' attentional limits and some biases in the observation of instructional situations [8].…”

Multimodal Perception and Statistical Modeling of Pedagogical Classroom Events Using a Privacy-safe Non-individual Approach

“…As part of their research on teacher-student interaction, Dessus et al [3] investigated the relationships between the distribution and lability of teachers' attention frequency and the general classroom climate they promote. Woolverton et al and Kaur et al present a survey of literature about direct student classroom behavior observation methods [9,17]. To analyze the interrelationships between attendees of the class, a lot of layers should be taken into account.…”