Classroom Attendance Research Articles

THE EFFECT OF MIXED TEACHING MODE BASED ON NETWORK OPEN COURSE AND INTELLIGENT TEACHING PLATFORM ON ALLEVIATING STUDENTS' PSYCHOLOGICAL ANXIETY

THE EFFECT OF MIXED TEACHING MODE BASED ON NETWORK OPEN COURSE AND INTELLIGENT TEACHING PLATFORM ON ALLEVIATING STUDENTS' PSYCHOLOGICAL ANXIETY

CONTINUIDAD EDUCATIVA DURANTE LA PANDEMIA EN ARGENTINA POLÍTICAS, PEDAGOGÍAS Y PLATAFORMAS

The COVID-19 pandemic has placed the urgent need to sustain the pedagogical bond without classroom attendance on the world educational policy agenda. The analysis presented in this article attempts to shed light on the response to educational continuity in Argentina during the COVID-19 pandemic addressing three dimensions: policies, pedagogical strategies and technopedagogical trends involved. The article concludes that most of the experiences were polarised in two strategies, being one the reproduction of face-to-face attendance by videoconference and the other, mostly, tasks by email or WhatsApp. The multiple digital divides and the political controversy over "attendance" left little room for the stability of online or hybrid forms of teaching.

College Smart Classroom Attendance Management System Based on Internet of Things.

Since entering the information age, educational informatization reform has become the inevitable trend of the development of colleges and universities. The traditional education management methods, especially the classroom attendance methods, not only need to rely on a large number of manpower for data collection and analysis but also cannot dynamically monitor students' attendance and low efficiency. The development of Internet of things technology provides technical support for the informatization reform of education management in colleges and universities and makes the classroom attendance management in colleges and universities have a new development direction. In this study, a college smart classroom attendance management system based on RFID technology and face recognition technology is constructed under the architecture of the Internet of things, and the corresponding simulation experiments are carried out. The experimental results show that the smart classroom attendance management system based on RFID technology can accurately identify the absence and substitution of students and has the advantages of fast response and low cost. However, its recognition is easily affected by obstructions, which requires students to place identification cards uniformly. The smart classroom attendance management system based on face recognition technology can accurately record and identify the situation of students entering and leaving the classroom and identify the situations of being late and leaving early, absenteeism, and substitute classes. The experimental results are basically consistent with the sample results, and the error rate is low. However, the system is easily affected by environmental light, students' sitting posture, expression, and other factors, so it cannot be recognized. Generally speaking, both can meet the needs of classroom attendance in colleges and universities and have high accuracy and efficiency.

Weekly Quizzes Reinforce Student Learning Outcomes and Performance in Biomedical Sciences in-course Assessments

Studies have highlighted the benefits of frequent quizzing in class. Frequent quizzing can promote more student attendance, engagement, practice and review, and achievement. Conversely, the opponents of frequent quizzing suggest that too frequent testing might hinder learning by frustrating anxious students and inhibiting larger units of instructional material. Notably, most studies have used degree examinations to evaluate the impact of quizzes on student learning and performance, yet little is known about whether quizzes can reinforce student performance in the in-course assessments (ICAs) despite ICA importance in student learning. The present study aimed to test the hypothesis that administration of weekly MCQ quizzes can enhance the leaning outcomes and performance of biomedical science students in assessment methods such as essay and oral presentation that can directly measure and provide information about student learning. It was therefore limited to in-course assessments. We found that the performance of the weekly quiz student group is remarkably better than that of the control student group in both the essay and oral presentation ICAs, which are two measures and indicators of student learning, suggesting improved student learning outcomes and performance after administrating weekly MCQ quizzes that also promoted student attendance in classrooms. The findings of this research study have implications for students, teachers, and curriculum designers in higher education.

Comparative Analysis of Facial Recognition Models to Record Classroom attendance with one click

Human face recognition is the hottest topic in this era due to its importance in identity authentication and security checking. It has grown a momentous spot among all biometric-based systems. It can be used for verification and scrutiny to prove the identity of a person and sense individuals, singly. Great success has been achieved recently on face recognition comparison by using different models. But in this research, the HOG (Histogram Oriented Gradient) and Vgg (Visual Geometry Group) algorithms are selected as these algorithms are common and still widely accessible on the internet, and these are usually used out of the box for various applications. In this paper, we examine the accuracy and time efficiency between the HOG (Histogram Oriented Gradient) and machine learning model. Also, this paper conferred these pre-trained models and outlines which results in high-tech performance without the need for any prevailing hardware. Their features are observed and the result of each model is estimated. Their comparative analysis of the output of both of these models with the execution time and the number of repetitions of the practical algorithms is offered. It also constructed two simple applications for face recognition-based algorithms. By using these application testing data is taken from the live stream/camera in the form of image and compared through training images stored in the faces database and their time and accuracy are noted during this process and their performance is evaluated based on time and accuracy.

Effects of armed conflicts on teaching and learning: Perspectives of secondary school teachers in Cameroon

The ongoing armed conflict in Cameroon has resulted in thousands of attacks on schools, yet education cannot wait for conflict to stop. This study sought to determine the effects of the ongoing armed conflict on classroom activities in operational schools in affected areas in Cameroon. Using a mixed method sequential exploratory design, qualitative data were collected from 19 teachers through open-ended questions and analysed using thematic analysis. Qualitative findings were used to design a questionnaire, administered to 652 teachers, and analysed using multiple response and the chi-square and equality of proportion test. Findings presented indicate that attendance, content delivery, safety, discipline, and assessment have been significantly hampered. However, classroom attendance was most significantly (p = .001) affected, disrupting teaching and evaluation with differing effects found across locations and school types. It was concluded that teaching and learning activities are ineffective and may result in further damaging effects-requiring more practical actions on school sites.

Rapid Marking Attendance with Face Recognition

Marking attendance (MA) of students in the classroom and exam halls is not only a burdensome task for the instructors, but it is also time consuming. There is a growing need for efficient and autonomous techniques in AM. This article introduces an attendance system based on face recognition. The developed method detects the students exploiting their faces present in live camera images or in a given image through the Eigen Face Recognizer algorithm. After then, students are recognized and their attendance information recorded in an offline database. HAAR algorithm is used as a classifier in recognition process. In the experimental studies, it has been observed that the face recognition system works with an average accuracy of 79.31% in the real classroom environment. The obtained results showed that the designed system is promising for automatic authentication and marking attendance in classroom and exam sessions. It has been also shown that with the proposed system, marking, authentication and recording works can be completed in a much shorter time and with higher accuracy.

Student Attendance Analysis and Statistics Platform based on Capture Recognition Technology

With the development of face recognition technology and HD camera, it is possible to use face recognition to realize the classroom attendance statistics. The traditional way of classroom attendance statics needs teachers to roll call according to the list of students, but face recognition can not only save the time of class, but also lighten the burden of statistics attendance of school. This paper realizes the face recognition system of attendance analysis and statistics platform, and it needs cameras and a main computer. In software, under the development environment of VS2017, it relies on OPENCV, arcsoft face recognition SDK and My -SQL database realizes a real-time video stream face recognition system with C++ programming. The system captures the student face by the camera installed in the classroom, then using SDK extracts the face feature and compares with the feature in the database. When the compare value is over the set value, the system will output the corresponding students' information and completion time to the TXT file.

Web-based University Classroom Attendance System Based on Deep Learning Face Recognition

Web-based University Classroom Attendance System Based on Deep Learning Face Recognition

Online Classroom Attendance Marking System Using Face Recognition, Python, Computer Vision, and Digital Image Processing

Abstract: The face is the identity of someone. The tactic to appear out this physical feature has seen an exquisite change since the advent of the image processing method. The attendance is taken in every school, college and library. The regular method for attendance is teachers calling student name & marking attendance. Nowadays, AI has been highly explored for computer vision applications[1]. So, we use the concept of neural network in Face – recognition for automatic attendance marking systems. In this project, we perform the face recognition and face detection algorithms, to produce the computer systems the ability to find and recognize human faces fast and precisely in live videos so that the systems can be used in marking attendance. Keywords: Face detection, Python, Computer Vision, Attendance marking system, Image processing

Classroom Attendance Scale Development and Validation Study

Classroom Attendance Scale (CAS) is prepared with 14 items. Prepared CAS consists of items with likert type. Sample of the study consists of 318 college students who study at education faculty at science education and primary education programs along with students who work at engineering faculty. Principal axis factoring with orthogonal rotation (varimax) is used for exploratory factor analysis. Factor eigenvalues are obtained and corrected-item total correlations were analyzed. Items which did not serve the purpose of scale were omitted from CAS. Thus, analyses with same procedure were reconducted several times until reaching to a final version of the CAS. A confirmatory factor analysis with Maximum Likelihood is applied to a different sample (N=229) and CAS is approved by most common fit indices values. Total sample of the study consists of 547 participants and finalized CAS consists of 8 items and scale’s Cronbach’s alpha value is .923

A New Multiface Target Detection Algorithm for Students in Class Based on Bayesian Optimized YOLOv3 Model

Deep learning theory is widely used in face recognition. Combined with the needs of classroom attendance and students’ learning status monitoring, this article analyzes the YOLO (You Only Look Once) face recognition algorithms based on regression method. Aiming at the problem of small target missing detection in the YOLOv3 network structure, an improved YOLOv3 algorithm based on Bayesian optimization is proposed. The algorithm uses deep separable convolution instead of conventional convolution to improve the Darknet-53 basic network, and it reduces the amount of calculation and parameters of the network. A multiscale feature pyramid is built, and an attention guidance module is designed to strengthen multiscale fusion, detecting different sizes of targets. The loss function is improved to solve the imbalance of positive and negative sample distribution and the imbalance between simple samples and difficult samples. The Bayesian function is adopted to optimize the classifier and improve the classification efficiency and accuracy, ensuring the accuracy of small target detection. Five groups of comparative experiments are carried out on public COCO and VOC2012 datasets and self-built datasets. The experimental results show that the proposed improved YOLOv3 model can effectively improve the detection accuracy of multiple faces and small targets. Compared with the traditional YOLOv3 model, the mean mAP of the target is improved by more than 1.2%.

Interaction Between Teacher and Students in Virtual Classroom

Several school and colleges of Nepal have adopted online and virtual teaching instead of physical class during the pandemic situation of covid-19. The main objective of this action research is to find the interaction between teachers and students in virtual classroom. 161 students have been taken as sample from different grades 5,8,9 and 12 from the school of Gandaki Province. During the virtual class, students neither responded the teacher nor involved in the classroom interaction. After the regular treatment for couple of months, in comparison to the initial phase the remarkable improvements were seen in the student’s participation, attendance, assignments and classroom interactions in the virtual classroom.

RELATION OF EXAMINATION PERFORMANCE AND CLASS ATTENDANCE OF MBBS STUDENTS IN PHARMACOLOGY - A RETROSPECTIVE STUDY

Introduction: Students attendance is considered as an important factor in the academic performance of medical students. Student attendance is an integral part of professional development and, from a regulatory perspective, considered evidence of professionalism.Aim of the undergraduate medical education is to produce competent doctors with adequate medical knowledge, affective attitude for the patients and proper clinical skills for practice Medical education demands high attendance for good understanding and grasps over the subject Aim: To study the relationship between student attendance and their performance in theory examination. Study design: A retrospective study was conducted among Second year MBBS pharmacology students. Student classroom (theory) attendance was compared with their marks secured in the internal assessment conducted by the pharmacology department. Materials and method: The second year MBBS students who attended pharmacology internal assessment were included in the study. The attendance of a total of 145 students was compared with their internal assessment marks. Statistical analysis was performed using ANOVA. Results: Among 145 students more number of students attendance lied between 61-70 percent.Only 2 of them got more than 90 percent attendance, in this one got below 50 percent marks and6 of them got less than 50 percent in both marks and attendance. Discussion: This study clearly demonstrated that the higher the percentage of attendance lesser is the chance of failure in theory internal assessment (P value is 0.002). It shows attending the theory lectures is significant to perform in the examinations.

A Review on Online Classroom Attendance Marking System Using Face Recognition, Python, Computer Vision, and Digital Image Processing

Abstract: The face is that the identity of someone. The tactic to appear out this physical feature has seen an exquisite change since the advent of the image processing method. Attendance is monitored in every school, college and library. The regular method for attendance is for teachers to call student name & mark attendance. Nowadays, AI has been explored for computer vision-related applications. So, we use the neural network concept in Face recognition for automatically attendance marking systems. This project will perform the face recognition and face detection algorithms, to generate the computer systems strength of acquiring and recognizing human faces fast, accurately, and precisely in live streams so that the systems can be used in the marking attendance

A Three-Dimensional Anisotropic Diffusion Equation-Based Video Recognition Model for Classroom Concentration Evaluation in English Language Teaching

In this paper, a three-dimensional anisotropic diffusion equation is used to conduct an in-depth study and analysis of students’ concentration in video recognition in English teaching classrooms. A multifeature fusion face live detection method based on diffusion model extracts Diffusion Kernel (DK) features and depth features from diffusion-processed face images, respectively. DK features provide a nonlinear description of the correlation between successive face images and express face image sequences in the temporal dimension; depth features are extracted by a pretrained depth neural network model that can express the complex nonlinear mapping relationships of images and reflect the more abstract implicit information inside face images. To improve the effectiveness of the face image features, the extracted DK features and depth features are fused using a multicore learning method to obtain the best combination and the corresponding weights. The two features complement each other, and the fused features are more discriminative, which provides a strong basis for the live determination of face images. Experiments show that the method has excellent performance and can effectively discriminate the live nature of faces in images and resist forged face attacks. Based on the above face detection and expression recognition algorithms, the classroom concentration analysis system based on expression recognition is designed to achieve real-time acquisition and processing of classroom images, complete student classroom attendance records using face detection and face recognition methods, and analyze students’ concentration from the face integrity and facial expression of students facing the blackboard by combining face detection and expression recognition to visualize and display students’ classroom data for teachers, students, and parents with more data support and help.

School-Based Feeding Program in Ilocos Sur: Impacts on Health and Educational Outcomes

The Department of Education created a conditional food transfer program to improve the health and school performance of Filipino children. This descriptive correlational study focused on the impact of the School-Based Feeding Program (SBFP) of public elementary school beneficiaries in the Schools Division of Ilocos Sur for School Year 2019-2020. It involved 625 school heads, teachers, representatives of partner organizations, and parents from the top 11 districts with the most number of severely wasted beneficiaries. The findings revealed that partner organizations highly supported the feeding program in terms of financial and technical resources. SBFP is very highly implemented through the effective utilization of its operational guidelines and well-managed strategies of its eligible and complementary activities. Moreover, it facilitates the rehabilitation of the nutritional status of the beneficiaries, contributes to high classroom attendance, and improves their health and nutritional values and behaviors. In addition, the feeding program is also effective in the attainment of high school performance with decreased dropout rate, increased promotion rate, and reduced repetition rate for the last three years. The respondents raised the following concerns: the incapacity of parents to consistently attend meal preparation and cooking, presence of repeat beneficiaries, delayed budget release, extra effort in buying commodities with receipts, and SBFP Core groups not always following the usual cycle menu. Therefore, the researcher crafted an intervention plan to improve the implementation and impact of the feeding program.

Dental students' perceptions and academic performance in a preclinical fixed prosthodontics course delivered remotely during the COVID-19 pandemic

Introduction: The quarantine, due to the COVID-19 pandemic, forced dental schools globally to change to distant learning methods which include viewing previously recorded lectures or attending online virtual classes. Aims: The aim of this study was to assess the impact of three methods of teaching: Online interactive virtual classes, recorded lectures, and real-life classroom lectures on the academic performance of dental students. The second aim was to evaluate the students' perceptions of the two remote teaching methods as a substitute for traditional classroom lectures. Materials and Methods: The cross-sectional study was conducted with the second-year dental students at the College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, during the spring semester of 2020. The last six lectures of a Preclinical Fixed Prosthodontics course were delivered remotely due to the COVID-19 pandemic. The students' performance in these lectures was compared to their performance in the first nine lectures of the course delivered traditionally before the quarantine period. An online survey was distributed to the students at the end of the course with questions about their perceptions regarding the different teaching methods used to deliver the didactic component of the course. Results and Discussion: The performance of 104 students (56 males and 48 females) was assessed through the number of correct responses for each teaching method. The percentage of students who responded correctly to all the questions was 44% for the classroom lecture format, 75% in the interactive virtual class format, and 78% in the recorded lecture format. The response rate to the online survey was 87.5%. The majority of the students (78%) indicated that the interactive virtual classes were an effective learning method. The perception about viewing recorded material was also positive as 83% of the students agreed that it is an adequate learning method when the classroom lectures were canceled. Nevertheless, only 43% indicated that recorded material could be used as a substitute for classroom attendance. Conclusion: The students' performance in the lectures taught remotely in interactive virtual classes or by viewing recorded lectures was both effective and comparable to traditional classroom lectures. The dental students were generally satisfied with the remote online education.

Dental Students and Faculty Perceptions of Teaching Methods: Traditional Classes, Online Virtual Classes, and Recorded Lectures

Background: Rapid advancement in technology has provided alternatives to traditional classroom teaching. Such instructional methods have gained increasing importance during the COVID-19 pandemic when physical classroom attendance was not possible. The study evaluated faculty’s and students’ perceptions concerning the online virtual classes and recorded lectures as compared to traditional classes delivered at the College of Dentistry, King Saud bin Abdulaziz University for Health Sciences. Materials and Methods: Surveys were developed and distributed to 34 faculty members and 186 students. Perceptions about virtual classes, recorded lectures, physical attendance, the effectiveness of different teaching methods, and overall experience were evaluated. Descriptive statistics were presented using frequencies and percentages. The Chi-square test compared the students’ and the faculty members’ responses. The level of significance was set at α =0.05. Results: Thirty-one faculty members and 149 dental students participated, and the overall response rates were 91.2% and 80.1%, respectively. While there was a general agreement on the usefulness of making recorded lectures available, a statistically significant difference (p<0.001) was found between students’ and faculty members’ views on making classroom-lecture attendance optional (67.1% of students and 12.9% of the faculty agreed/strongly agreed). Statistically significant differences (p<0.001) were found between the students and faculty members concerning the effectiveness of recorded lectures and attending online virtual classes as an alternative to classroom attendance. Conclusion: Overall, students were more accepting of technology than faculty members as a substitute for traditional classroom teaching. For a more efficient and satisfactory learning experience, both teaching methods should be considered in a blended-learning module.

DOES DENTAL STUDENTS' ATTENDANCE IN CLASSROOM LECTURES DEPEND ON THE MODE OF ATTENDANCE TRACKING?

Purpose: The necessity to attend classroom lectures is a disputable topic among dental schools globally. Since there is an ongoing debate on different aspects of this problem in literature, the purpose of this study was to compare students’ attitudes toward classroom attendance and investigate if stricter attendance tracking methods could lead to better classroom attendance at two dental schools utilising different modes of tracking students’ attendance. Method: This was an observational, cross-sectional survey distributed among dental students enrolled at King Abdul-Aziz University (KAU) and King Saud University (KSU) in Saudi Arabia. The survey included questions on demographics, average travel time, student's attitudes toward classroom lectures and common reasons for absenteeism. Collected data were analysed and summarised as frequencies and percentages and then compared using the Chi-square test for statistical significance. Findings: The study involved 678 participants from KAU and 475 participants from KSU. In general, there was a significant difference in students’ attendance between both schools in which 26.8% of KAU dental students skipped 5 or more lectures/month compared to 11.5% of students at KSU. Among the factors affecting classroom lecture attendance, commuting time was a major one reported by students (44.8% of students at KSU and 51.4% at KAU needed 30-60 min to reach their schools). The availability of lectures through online resources and the necessity to study for exams were additional factors reported by students of both schools. Implications for research and practices: Based on the current data, the school’s method to track students’ attendance may have a role in the pattern of classroom absenteeism.