Attention In Classroom Research Articles

The Effectiveness of Using the Geometer’s Sketchpad to Enhance TVET College Level 4 Students’ Understanding of Circle Geometry

The integration of technology in the instructional process has been at the centre of attention in mathematics classrooms since the outbreak of the novel coronavirus (Covid-19) which was declared a global pandemic on 11 March 2020. This article reports on a study that used a quasi-experimental design to investigate the effectiveness of using the Geometer’s Sketchpad (GSP) to enhance Technical Vocational Education and Training (TVET) college Level 4 students’ understanding of circle geometry. Using quantitative research methods, random sampling was used to select a sample of 70 participants from a population of 133 TVET college Level 4 mathematics students. The participants were randomly divided into an experimental and a control group. This article hinges on Lev Vygotsky’s concept of the zone of proximal development (ZPD) for learning as a potential solution in enhancing circle geometry instructional practices. Pre-test and post-test instruments consisting of 10 multiple choice questions and 10 problem-solving questions on tangents, chords and proofs were used to gather the data. The data was then analysed using inferential statistics in which the SPSS version 27 determined the statistically significant difference between the experimental and control groups. The findings indicated a significant increase in the experimental group’s understanding of properties of tangents using the GSP. Further, the questionnaire participants revealed that using the GSP enabled an engagement based on the principles of cooperative and collaborative learning. Therefore, the researchers recommend the use of the GSP within the TVET college sector, among students with limited circle geometry knowledge who intend to further their studies in geometry-related courses.

Lisdexamfetamine in combination with guanfacine as an effective treatment in the management of behavioral disturbances in patients with Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). Case report

IntroductionWe often find it challenging to manage hyperactivity, low frustration tolerance and angry outbursts in patients with ASD and comorbid ADHD. Fewer drugs are approved for these disorders and these patients are more likely to develop adverse effects.ObjectivesThe aim of this case is to show how the combination of lisdexamfetamine together with guanfacine has very positive effects on anger outbursts and boundary heteroaggressiveness in patients with ASD and ADHD.MethodsCase report and literature reviewResultsThis is a 14-year-old minor admitted to the psychiatric unit after physical aggression against his family due to anger after removal of video games, requiring police intervention. He has been diagnosed since he was 11 years old with ADHD and Autism Spectrum Disorder. He was being treated with methylphenidate 54 mg and aripiprazole 10 mg. Since the beginning of the admission, the following pharmacological adjustment has been made: Methylphenidate is substituted by lisdexamfetamine up to 50 mg per day. Guanfacine has been started up to 4 mg per day and the dose of aripiprazole has been maintained. The patient had no adverse effects with adequate tolerance without sedation, hyporexia or hypotension. With this adjustment, improvement was found in the levels of restlessness and hyperactivity. The patient expressed a subjective improvement in the levels of restlessness and with a notable improvement in attention in the hospital classroom. An improvement in emotional regulation was also observed, with more tolerance to the imposition of limits, without an explosion of anger in the face of any rule during admissionConclusionsThe management of hyperactivity and episodes of low frustration tolerance in patients with ASD and ADHD is complex. Many studies point out the time-limited use of some antipsychotics such as risperidone or aripiprazole. This work aims to show guanfacine in combination with lisdexamfetamine as an excellent combination for the management of agitation and rage explosion in these patients. In addition, the profile of adverse effects at metabolic level is much better than that of atypical antipsychotics.Reference-Extended-Release Guanfacine for Hyperactivity in Children With Autism Spectrum Disorder. Lawrence Scahill et al. Am J Psychiatry. 2015 Dec.Disclosure of InterestNone Declared

Analyzing students' attention by gaze tracking and object detection in classroom teaching

PurposeAttention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.Design/methodology/approachGiven a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.FindingsA large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.Originality/valueThe contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.

QUESTION-FORMATTED IN CLASSROOM INTERACTION TO ATTRACT PRIMARY SCHOOL STUDENT ATTENTION IN ONLINE CLASSROOM

Online learning is increasingly used around the world due to covid-19. Therefore, this research aims to investigate on conversation analysis that focuses on how the teacher’s question which can engage students’ interaction in classroom. It is known that it is very complex in engaging students ‘in online class, especially first grade primary students. While prior studies regarding online class interaction has not been scrutinized in detail regarding how teacher’s question and students’ responses. Therefore, this study eager to analyzes the teacher and students’ conversation in online class. This study employed qualitative method. The data collected from a series of classroom zoom video. In addition, the data analysis has three stages. First, reduction the utterance, it is based on the question formatted types. Second, coding the data and analyzed the data based on question formatted types. The third is concluding in order to find in the questions-formatted types in online classroom. The types are preserve the sequential implication, slightly alter the sequential implications, following student’s question, and deviant case. The result illustrates in this research is slightly later the sequential implications as the only type which occurred in first grade primary school online classes. It occurred because in online classes it is very demanding to engage student’s attention in online classroom. It is therefore teacher gave questions and pointed directly to students. Consequently, this study implicates on how the pedagogical insight in teaching online learning.

Random Interval Tracking System for Virtual Learning

With the advent of computer technology, E-Learning has become an increasingly popular learning approach in higher Education institutions. Due to the outbreak of COVID-19 pandemic that demands substantial modification in the teaching-learning process across the globe. However, the student’s attendance management in virtual classroom is quite difficult. In this paper I introduce a smart intelligent virtual classroom, which include ML based face recognition system. Further, it can generate dedicated attendance reports, pinpointing students’ attention during virtual learning at random time intervals. Moreover, the novel random interval tracking system can also prevent the dropping out of participants from the virtual classroom. The distinctive feature of randomness in Random Interval Tracking System (RITS) ensures that student’s attention and engagement in virtual classroom are enhanced.

Attention assessment based on multi‐view classroom behaviour recognition

AbstractIn recent years, artificial intelligence has been applied in many fields, and education has attracted more and more attention. More and more behaviour detection and recognition algorithms are applied in the field of education. Students' attention in class is the key to improving the quality of teaching, and classroom behavior is a direct manifestation of students' attention. In view of the problem that the accuracy of students' classroom behavior recognition is generally low, we apply deep learning to multi‐view behavior detection, which can detect and recognize behaviors from different perspectives, to evaluate students' classroom attention. First, an improved detection model based on YOLOv5 is proposed, which improves the CBL module throughout the entire network to optimize the model and uses SIoU as the loss function to improve the convergence speed of the prediction box. Second, a quantitative evaluation standard for students' classroom attention is established and then training and verification are conducted by collecting multi‐view classroom datasets. Finally, the environment variation in the training model phase is increased to make the model have better generalization ability. Experiments demonstrate that our method can effectively identify and detect students' behaviours in the classroom from different angles, and it has good robustness and feature extraction capabilities.

Non-Verbal Interaction and Students’ Visual Engagement in Mathematics and English classes

Background: The interactions in the classroom are of particular interest to the teaching and learning processes. Objectives: This study examines nonverbal interaction in mathematics classrooms, and how different modes of nonverbal behaviour, contributed to the engagement in lessons. Design: A quantitative study. Setting and Participants: 30 randomly selected students wore mini camera-mounted eyeglasses in their mathematics and English lessons. Approximately 45 hours of video recording were made from these cameras (from a first-person’s perspective) to analyse and compare the nonverbal interaction in mathematics and English lessons. Data collection and analysis: In Google Images, we objectively searched and statistically analysed frames in which the class teachers appeared within the students’ visual field. Results: The results show that how students are visually engaged with the teacher depends on a set of proxemics. Differences were found related to visual attention both regarding the subject matter and the different proxemics of the student in relation to the teacher, pointing out that students are more visually involved with the teachers’ instructions when at a proxemic of 1.20 to 3.70 meters. Furthermore, we report differences between boys and girls and how they are visually engaged in their mathematics classrooms. Conclusions: Finally, we report how teachers pointing gestures can serve as a tool to recapture student’s visual attention in mathematics classrooms.

Analysis of Students' Role Perceptions and their Tendencies in Classroom Education Based on Visual Inspection.

This paper presents an in-depth study and analysis of students' role perceptions and their tendencies in classroom education using a visual inspection approach. A multi example learning student engagement assessment method based on a one-dimensional convolutional neural network is proposed. Based on the conceptual composition of student engagement, head posture, eye gaze, and eye-opening and closing states and the most used facial movement units are used as visual features. For feature extraction, the proposed view of relative change features, based on the video features extracted from the Open Face toolset, the standard deviation of the distance between adjacent multiple frames relative to the center point of the three visual features is used as the relative change features of the video. This results in the phenomenon that students are highly motivated in the early stage and significantly increase the rate of absenteeism in the later stage. With the development of information technology injecting new vitality into educational innovation, many researchers have introduced computer vision and image processing technology into students' online learning activities, and understand students' current learning situation by analyzing students' learning status. There are relatively few studies in this area in classroom teaching. Considering the low relative position correlation of the features in the examples, the examples are analyzed using a one-dimensional convolutional neural network to obtain the example-level student engagement, and a multi-example pooling layer is used to infer the student engagement in the video from the example-level student engagement. Finally, the experimental method is used to apply the student classroom attention evaluation detection system to actual classroom teaching activities, and the effectiveness and accuracy of the design of the student classroom attention evaluation detection system are investigated in depth through specific applications and example analysis, and the accuracy of the method of this paper is further verified by communicating feedback with teachers and students in the form of interviews.

Impact of Different Styles of Online Course Videos on Students' Attention During the COVID-19 Pandemic.

BackgroundThe COVID-19 pandemic interfered with normal campus life, resulting in the need for the course to be conducted in an ideal online format. The purpose of this study is to analyze the impact of different styles of online political course videos on students' attention during the COVID-19 pandemic.MethodsFour college students participated in this small sample study. They were required to conduct two sessions of the experiment, in which they were required to watch three different styles of course videos in each session. While watching the videos, their EEG signals were acquired. For the acquired EEG signals, the sample entropy (SampEn) features were extracted. On the other hand, Mayer's theories of multimedia technology provide guidance for teachers' online courses to enhance students' attention levels. The results of EEG signals analysis and Mayer's theories of multimedia technology were combined to compare and analyze the effects of three styles of instructional videos.ResultsBased on comparisons of the SampEn and Mayer's theories of multimedia technology analysis, the results suggest that online instruction in a style where the instructor and content appear on the screen at the same time and the instructor points out the location of the content as it is explained is more likely to elicit higher levels of students' attention.ConclusionsDuring the COVID-19 pandemic, online instructional methods have an impact on students' classroom attention. It is essential for teachers to design online instructional methods based on students' classroom attention levels and some multimedia instructional techniques to improve students' learning efficiency.

Classroom Design and Children's Attention Allocation: Beyond the Laboratory and into the Classroom

AbstractPrior laboratory research suggests the visual environment can be a source of distraction for children, reducing attention to instructional tasks and learning outcomes. However, systematic research examining how the visual environment relates to attention in genuine classrooms is rare. In addition, it is unknown what specific aspects of the environment pose a challenge for attention regulation. This observational study aims to (1) provide a nuanced examination of specific elements of the classroom visual environment (e.g., visual noise, display quantity, color variability) by analyzing panoramic classroom photographs (N = 58) and (2) investigate whether specific visual environment elements are related to children's rates of on‐task behavior. Results indicate on‐task behavior was lower in classrooms containing greater quantities of visual noise and color variability, and in classrooms with either relatively small or large amounts of displays (controlling for observation session, school type, student gender, grade‐level, and instructional format). Implications for creating more optimal visual learning environments are discussed.

Learning with simulated virtual classmates: Effects of social-related configurations on students’ visual attention and learning experiences in an immersive virtual reality classroom

Immersive virtual reality (IVR) provides great potential to experimentally investigate effects of peers on student learning in class and to strategically deploy virtual peer learners to improve learning. The present study examined how three social-related classroom configurations (i.e., students' position in the classroom, visualization style of virtual avatars, and virtual classmates' performance-related behavior) affect students' visual attention toward information presented in the IVR classroom using a large-scale eye-tracking data set of N = 274 sixth graders. ANOVA results showed that the IVR configurations were systematically associated with differences in learners' visual attention on classmates or the instructional content and their overall gaze distribution in the IVR classroom (Cohen's d ranging from 0.28 to 2.04 for different IVR configurations and gaze features). Gaze-based attention on classmates was negatively related to students' interest in the IVR lesson (d = 0.28); specifically, the more boys were among the observed peers, the lower students' situational self-concept (d = 0.24). In turn, gaze-based attention on the instructional content was positively related to students' performance after the IVR lesson (d = 0.26). Implications for the future use of IVR classrooms in educational research and practice are discussed.

Exploration on the Skills of Attracting Students' Attention in Primary School Mathematics Classroom

Exploration on the Skills of Attracting Students' Attention in Primary School Mathematics Classroom

EEG Data Quality in Real-World Settings: Examining Neural Correlates of Attention in School-Aged Children.

Advances in mobile electroencephalography (EEG) technology have made it possible to examine covert cognitive processes in real-world settings such as student attention in the classroom. Here, we outline research using wired and wireless EEG technology to examine attention in elementary school children across increasingly naturalistic paradigms in schools, ranging from a lab-based paradigm where children met one-on-one with an experimenter in a field laboratory to mobile EEG testing conducted in the same school during semi-naturalistic classroom lessons. Despite an increase of data loss with the classroom-based paradigm, we demonstrate that it is feasible to collect quality data in classroom settings with young children. We also provide a test case for how robust EEG signals, such as alpha oscillations, can be used to identify measurable differences in covert processes like attention in classrooms. We end with pragmatic suggestions for researchers interested in employing naturalistic EEG methods in real-world, multisensory contexts.

Visual attention in mathematics classroom: use of eye-glass cameras

Visual attention in mathematics classroom: use of eye-glass cameras

Visual attention in mathematics classroom: use of eye-glass cameras

Visual attention in mathematics classroom: use of eye-glass cameras

ENHANCING THE MOTIVATION AND LEARNING PERFORMANCE IN AN ONLINE CLASSROOM WITH THE USE OF NEUROMARKETING

In recent years, the newly emerging discipline of neuromarketing, which employs brain (emotions and behaviour) research in an organisational context, has grown in prominence in academic and practice literature. With the increasing growth of online teaching, COVID-19 left no option for higher education institutions to go online. As a result, students who attend an online course are more prone to lose focus and attention, resulting in poor academic performance. Therefore, the primary purpose of this study is to observe the learner's behaviour while making use of an online learning platform. This study presents neuromarketing to enhance students' learning performance and motivation in an online classroom. Using a web camera, we used facial coding and eye-tracking techniques to study students' attention, motivation, and interest in an online classroom. In collaboration with Oxford Business College's marketing team, the Institute for Neuromarketing distributed video links via email, a student representative from Oxford Business College, the WhatsApp group, and a newsletter developed explicitly for that purpose to 297 students over the course of five days. To ensure the research was both realistic and feasible, the instructors in the videos were different, and students were randomly allocated to one video link lasting 90 seconds (n=142) and a second one lasting 10 minutes (n=155). An online platform for self-service called Tobii Sticky was used to measure facial coding and eye-tracking. During the 90-second online lecture, participants' gaze behaviour was tracked overtime to gather data on their attention distribution, and emotions were evaluated using facial coding. In contrast, the 10-minute film looked at emotional involvement. The findings show that students lose their listening focus when no supporting visual material or virtual board is used, even during a brief presentation. Furthermore, when they are exposed to a single shareable piece of content for longer than 5.24 minutes, their motivation and mood decline; however, when new shareable material or a class activity is introduced, their motivation and mood rise.
 
 JEL: I20; I21
 
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Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom

The partial differential equation learning model is applied to another high-level visual-processing problem: face recognition. A novel feature selection method based on partial differential equation learning model is proposed. The extracted features are invariant to rotation and translation and more robust to illumination changes. In the evaluation of students’ concentration in class, this paper firstly uses the face detection algorithm in face recognition technology to detect the face and intercept the expression data, and calculates the rise rate. Then, the improved model of concentration analysis and evaluation of a college Chinese class is used to recognize facial expression, and the corresponding weight is given to calculate the expression score. Finally, the head-up rate calculated at the same time is multiplied by the expression score as the final concentration score. Through the experiment and analysis of the experimental results in the actual classroom, the corresponding conclusions are drawn and teaching suggestions are provided for teachers. For each face, a large neighborhood set is firstly selected by the k -nearest neighbor method, and then, the sparse representation of sample points in the neighborhood is obtained, which effectively combines the locality of k -nearest neighbor and the robustness of sparse representation. In the sparse preserving nonnegative block alignment algorithm, a discriminant partial optimization model is constructed by using sparse reconstruction coefficients to describe local geometry and weighted distance to describe class separability. The two algorithms obtain good clustering and recognition results in various cases of real and simulated occlusion, which shows the effectiveness and robustness of the algorithm. In order to verify the reliability of the model, this paper verified the model through in-class practice tests, teachers’ questions, and interviews with students and teachers. The results show that the proposed joint evaluation method based on expression and head-up rate has high accuracy and reliability.

Comparing the characteristics of EFL students’ multimodal composing and traditional monomodal writing: The case of a reading-to-write task

Although digital multimodal composing (DMC) is receiving increasing attention in language classrooms, the extent to which it contributes to students’ writing practices is controversial. In order to understand the affordances of DMC compared to traditional monomodal writing in school contexts, it is pertinent to compare DMC and traditional writing using academic integrated-skills tasks. The current study aims to investigate the relationship between the quality of Korean high school students’ multimodal composing and that of the same students’ traditional monomodal writing, as well as content and language alignment. Thirty-one Korean high school students carried out a summary-reflection task through DMC and traditional monomodal writing. After reading a short fable by Aesop, students summarized and reflected on the text. While students used only one mode in traditional writing (i.e. English text), they utilized multiple modes in DMC (e.g. pictures, movies). Students’ task outcomes were scored using analytic rubrics, and texts were coded in terms of the content and linguistic features students retrieved from the text (i.e. alignment) and their degree of reflection. The results showed that there were no statistically significant differences in the quality, content and language alignment, or amount of reflection in writing outcomes between students’ DMC and traditional monomodal writing.

Sustained attention in the elementary school classroom

In this 10 minute video, the challenges of fostering sustained attention the elementary school classroom are discussed and practical classroom approaches are presented to support student growth and development.

Teachers’ Focus of Attention in First-grade Classrooms: Exploring Teachers Experiencing Less and More Stress Using Mobile Eye-tracking

ABSTRACT This study investigated teachers’ focus of attention and stress in first-grade classrooms. Teachers’ (n = 53) focus of attention was recorded in fall and spring with a mobile eye-tracking device, and the teachers reported stress via questionnaires. Correlation analysis was used to examine association between teacher stress (exhaustion, cynicism, and inadequacy) and focus of attention. Then, one teacher reporting more stress and one reporting less stress were selected for a case study to examine variations in their focus of attention. The results showed positive associations between teachers’ perceived inadequacy and overall focus of attention (whole eye-tracking recording) both in fall and spring. Teachers’ focus of attention during specific activity settings of management/routines and transitions correlated positively with all three stress domains in fall. In addition, a positive association was also found between teacher inadequacy and focus of attention during teacher-directed large group activity setting.