A Novel Pre-Class Learning Content Approach for the Implementation of Flipped Classrooms

Salau, Ayodeji Olalekan; Eneh, Joy Nnenna; Sochima, Vincent Egoigwe; Ozue, Izuchukwu ThankGod

doi:10.14569/ijacsa.2020.0110718

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…The previous research studies [8,9,61] showed the different approaches for student engagement measurement. According to previous studies, the different approaches are face features, eyeball tracking, eye gaze, body gestures, head motions, body surface temperature, mouse movements, and keystrokes.…”

Section: Related Workmentioning

confidence: 99%

A multimodal facial cues based engagement detection system in e-learning context using deep learning approach

Gupta

Kumar

Tekchandani

2023

Multimed Tools Appl

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Due to the COVID-19 crisis, the education sector has been shifted to a virtual environment. Monitoring the engagement level and providing regular feedback during e-classes is one of the major concerns, as this facility lacks in the e-learning environment due to no physical observation of the teacher. According to present study, an engagement detection system to ensure that the students get immediate feedback during e-Learning. Our proposed engagement system analyses the student's behaviour throughout the e-Learning session. The proposed novel approach evaluates three modalities based on the student's behaviour, such as facial expression, eye blink count, and head movement, from the live video streams to predict student engagement in e-learning. The proposed system is implemented based on deep-learning approaches such as VGG-19 and ResNet-50 for facial emotion recognition and the facial landmark approach for eye-blinking and head movement detection. The results from different modalities (for which the algorithms are proposed) are combined to determine the EI (engagement index). Based on EI value, an engaged or disengaged state is predicted. The present study suggests that the proposed facial cues-based multimodal system accurately determines student engagement in real time. The experimental research achieved an accuracy of 92.58% and showed that the proposed engagement detection approach significantly outperforms the existing approaches.

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Section: Related Workmentioning

confidence: 99%

A multimodal facial cues based engagement detection system in e-learning context using deep learning approach

Gupta

Kumar

Tekchandani

2023

Multimed Tools Appl

View full text Add to dashboard Cite

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“…Previous investigations [24][25][26] have examined diverse methodologies aimed at assessing student engagement, which include analyzing facial characteristics, monitoring eyeball movements, evaluating eye gaze patterns, interpreting bodily gestures, tracking head movements, measuring body surface temperature, analyzing mouse behaviors, and observing keystrokes. Typically, these studies entailed scrutinizing stored videos and images of students within traditional classroom environments.…”

Section: Research Of Facial Expression Recognition and Learning Emoti...mentioning

confidence: 99%

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

Aly

2024

Multimed Tools Appl

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This paper presents a groundbreaking online educational platform that utilizes facial expression recognition technology to track the progress of students within the classroom environment. Through periodic image capture and facial data extraction, the platform employs ResNet50, CBAM, and TCNs for enhanced facial expression recognition. Achieving accuracies of 91.86%, 91.71%, 95.85%, and 97.08% on the RAF-DB, FER2013, CK + , and KDEF expression datasets, respectively, the proposed model surpasses the initial ResNet50 model in accuracy and detection of students' learning states. Comparative evaluations against state-of-the-art models using the RAF-DB, FER2013, CK + , and KDEF datasets underscore the significance of the results for educational institutions. By enhancing emotion recognition accuracy, improving feature relevance, capturing temporal dynamics, enabling real-time monitoring, and ensuring robustness and adaptability in online classroom environments, this approach offers valuable insights for educators to enhance teaching strategies and student outcomes. The combined capabilities of ResNet50, CBAM, and TCNs contribute uniquely to capturing dynamic changes in facial expressions over time, thereby facilitating accurate interpretation of students' emotions and engagement levels for more effective monitoring of learning behaviors in real-time.

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Design and Validation of the Flipped‐Learning Assessment Scale for Undergraduate Nursing Education

Urcola‐Pardo,

Subiron‐Valera,

Anton‐Solanas

et al. 2024

Nursing Open

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AimTo design and validate the Flipped‐Learning Assessment Scale; a tool for assessing students' experience of flipped learning.BackgroundFrequently, university students are introduced to new content during lectures. In contrast, active learning activities, such as Flipped Learning, are designed as an instructional method to engages students in the learning process.DesignCross‐sectional descriptive study.MethodsA cross‐sectional study was carried out in three phases ((1) item selection, rephrasing and translation; (2) content analysis through expert panel and (3) confirmatory factor analysis). The final version of the scale was piloted on a sufficient sample of undergraduate student nurses from three Spanish universities.ResultsA total of 455 students completed the questionnaire; 373 women and 82 men. The total Cronbach's alpha value for the complete instrument was 0.893. Cronbach alpha for each separate dimension ranged between 0.660 and 0.897. Goodness‐of‐fit values were acceptable, implying that the model was validated.ConclusionThe flipped learning approach has become increasingly popular in academic settings. Evaluating the students' flipped learning experience is important to analyse aspects such as acceptability and effectiveness of this methodology. The Flipped‐Learning Assessment Scale is a valid and reliable tool for analysing students' experience of flipped learning.ImpactFlipped learning has been a useful pedagogical model very for cultivating student skills in problem‐solving, critical thinking, teamwork and self‐active learning in nursing education. A key issues, such as student satisfaction, has been explored further before implementing this teaching and learning methodology.Patient or Public ContributionNone.

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A Novel Pre-Class Learning Content Approach for the Implementation of Flipped Classrooms

Cited by 5 publications

References 27 publications

A multimodal facial cues based engagement detection system in e-learning context using deep learning approach

A multimodal facial cues based engagement detection system in e-learning context using deep learning approach

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

Design and Validation of the Flipped‐Learning Assessment Scale for Undergraduate Nursing Education

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