Video-based learners’ observed attention estimates for lecture learning gain evaluation

Burnik, Urban; Zaletelj, Janez; Košir, Andrej

doi:10.1007/s11042-017-5259-8

Cited by 10 publications

(12 citation statements)

References 29 publications

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“…These results corroborate with previous studies that evaluated knowledge improvement after a 1‐h lecture of dental trauma in permanent dentition 17‐19 . It is relevant to note that students' concentration varies over time, and long classes with minimal engagement of learners tend to negatively affect their attention and learning outcomes 2 . Consistent with this perspective, the methodological care taken with the elaboration of a lecture with sufficient time for theorical exposure and discussions of clinical cases may have contributed to our results by gathering students' attention despite its inherent fluctuation.…”

Section: Discussionsupporting

confidence: 90%

Mobile application as a learning tool for improving dental students' knowledge regarding dental trauma

Lins

Pugliesi

Lima

et al. 2021

Eur J Dental Education

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Introduction Current evidence suggests an important knowledge gain of health sciences' students and professionals with mobile devices (m‐learning). This study aimed to verify whether different strategies of teaching (traditional lecture classroom, m‐learning and association of both methods) would present greater efficacy in knowledge improvement regarding dental trauma in primary dentition. Materials and Methods An action research study using a participatory approach was conducted with 36 undergraduate Brazilian dental students. A structured questionnaire composed of six clinical cases of traumatic injuries was initially applied to all students (pre‐test). Participants were then randomly distributed into three experimental groups: TC—students who had access exclusively to traditional lecture classroom; APP—access exclusively to the mobile application; and TC‐APP—access to both approaches. The traditional and technological methods had evidence‐based content and visual resources of the subject. After interventions, all students responded to the same previous questionnaire (post‐test). Wilcoxon and Kruskal‐Wallis tests were performed for statistical analysis. Results All tested groups obtained statistically significant improvement between their pre‐test and post‐test scores, for both diagnosis and treatment of dental trauma (p < .005). Nonetheless, no intergroup differences were observed amongst the scores of pre‐ and post‐test for diagnosis (p = .159) and treatment (p = .206). Conclusion All teaching approaches applied obtained similar knowledge improvement of undergraduate dental students regarding traumatic injuries in primary teeth.

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Section: Discussionsupporting

confidence: 90%

Mobile application as a learning tool for improving dental students' knowledge regarding dental trauma

Lins

Pugliesi

Lima

et al. 2021

Eur J Dental Education

View full text Add to dashboard Cite

show abstract

“…Existing works used a Kinect device to capture multiple students present in a classroom. k-nearest neighbor, decision trees, support vector machines, haar cascades, and CNNs (AlexNet) were used to classify the students' behavioral patterns [15], [29], [31], [49]. The capturing range of Kinect was low when applied to large classrooms, and the techniques used were not robust enough to classify the students' expressions in the wild.…”

Section: ) Students' Engagement Analysis In Classroomsmentioning

confidence: 99%

Unobtrusive Behavioral Analysis of Students in Classroom Environment Using Non-Verbal Cues

Ashwin

Guddeti

2019

IEEE Access

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Pervasive intelligent learning environments can be made more personalized by adapting the teaching strategies according to the students' emotional and behavioral engagements. The students' engagement analysis helps to foster those emotions and behavioral patterns that are beneficial to learning, thus improving the effectiveness of the teaching-learning process. Unobtrusive student engagement analysis is performed using the students' non-verbal cues such as facial expressions, hand gestures, and body postures. Though there exist several techniques for classifying the engagement of a single student present in a single image frame, there are limited works on the students' engagement analysis in a classroom environment. In this paper, we propose a convolutional neural network architecture for unobtrusive students' engagement analysis using non-verbal cues. The proposed architecture is trained and tested on faces, hand gestures and body postures in the wild of more than 350 students present in a classroom environment, with each test image containing multiple students in a single image frame. The data annotation is performed using the gold standard study, and the annotators reliably agree with Cohen's κ = 0.43. We obtained 71% accuracy for the students' engagement level classification. Further, a pre-test/post-test analysis was performed, and it was observed that there is a positive correlation between the students' engagement and their test performance.INDEX TERMS Affective computing, affect sensing and analysis, behavioral patterns, classroom data in the wild, multimodal analysis, student engagement.

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“…The wide application value and academic influence of facial recognition have made it develop by leaps and bounds, and it has maintained a high research interest in various fields for many years. As one of the biological features, the human face can intuitively and effectively reflect the differences between individuals and provide effective identification information [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data

Zhang

2022

Occupational Therapy International

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At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. There are not many researches on the application of artificial intelligence-based neural network facial expression recognition technology in practical teaching. With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. Method. A Hadoop cluster consisting of 3 nodes is built on the Linux system, and the environment required for Opencv execution is compiled for each node, which provides support for subsequent parallel optimization, feature extraction, feature fusion, and recognition of student facial images. The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. The color features, texture features, and Sift features of students’ facial images and common distractors were analyzed. A parallel extraction framework of student facial image features is designed, and based on this, the student facial image feature extraction under Hadoop platform is implemented. This paper proposes a dynamic sequential facial expression recognition method that combines shallow and deep features with an attention mechanism. The relative position of facial landmarks and local area texture features based on FACS represent shallow-level features. At the same time, the structure of ALexNet is improved to extract the deep features of sequence images to express high-level semantic features. The effectiveness of the facial expression recognition system is improved by introducing three attention mechanisms: self-attention, weight-attention, and convolutional attention. Results/Discussion. Through the analysis of the teaching effect, we found that when teachers can obtain the correct student’s academic mood, they can intervene on the students’ positive academic mood. The purpose of the intervention is to improve the positive academic emotions of students. After the students receive the intervention, their academic emotions are also improved and are positively correlated with their academic performance. Through the analysis of teaching effect, the research can achieve the predetermined goal. From the specific teaching effect of this study, it is concluded that in classroom teaching, teachers should devote energy to intervene in students’ positive academic emotions, in order to improve students’ positive academic emotions, which will improve students’ academic performance and teaching.

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Video-based learners’ observed attention estimates for lecture learning gain evaluation

Cited by 10 publications

References 29 publications

Mobile application as a learning tool for improving dental students' knowledge regarding dental trauma

Mobile application as a learning tool for improving dental students' knowledge regarding dental trauma

Unobtrusive Behavioral Analysis of Students in Classroom Environment Using Non-Verbal Cues

Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data

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