Cognitive Analysis Based System for Effective Online Learning

Abstract

Online learning has brought about a transformative shift in the field of education worldwide. It has revolutionised traditional educational practices, offering new possibilities and opportunities for learners, educators, and institutions. Online learning has helped bridge the gap in access to education. It has reached individuals in remote areas or underserved communities who previously faced limited educational opportunities. By removing geographical barriers, online learning has made education more inclusive, ensuring that learners from all backgrounds can access quality education. Students from all corners of the world can now enrol in courses and programs offered by prestigious universities and institutions, democratising education on a global scale. Online learning has emerged as a crucial tool during the COVID-19 pandemic, allowing individuals to continue their education remotely. With the closure of physical classrooms and educational institutions, online learning has stepped in to ensure continuity in learning. It has allowed students to stay connected with their educational pursuits, despite unprecedented challenges. By using technology and digital platforms, online learning has enabled students to access educational resources, interact with teachers and peers, and participate in virtual classes. Despite the limitations imposed by the pandemic, it has provided an adaptable solution that has allowed the education system to continue functioning. Virtual classes and online courses have become the primary mode of education worldwide, with platforms like Coursera and Udemy offering free course enrolments during the pandemic. Despite the widespread adoption of online learning, it still needs to improve to ensure learners remain attentive throughout online learning sessions like in physical classroom settings. Monitoring student attentiveness accurately in online learning is difficult for teachers, as they cannot rely on direct observations about what happens in the physical classroom environment. In traditional classes, teachers can identify distracted students through observation, differentiating those who are actively engaged from those who are disengaged. But these observations are only possible in the traditional classroom environments or, in other words, face-to-face interaction scenarios are not available in the online learning environment. Students generally fail to maintain their learning level without immediate feedback or support. As a consequence of it, learners prefer to leave the online course or online class mid-way. That is why the major issue educators and policymakers face in online learning medium is high drop-out rates. Educators are striving to find ways to keep learners engaged and motivated during online learning sessions, highlighting the need to design engaging and student-centric online learning environments. To address these challenges and enhance the effectiveness of online learning, it is important to monitor, detect, and predict the cognitive state of online learners. The cognitive state refers to the mental and psychological processes that influence a learner’s engagement, attention, and overall learning performance during the learning process. By understanding the cognitive state of learners, educators can adapt their teaching strategies, provide timely feedback, and other personalised support to enhance the learning experience. Various methods are employed for this purpose. Self-reporting is one approach where learners provide feedback on their understanding and attentiveness after online course completion. However, this method can be subjective and unreliable as it is human-biased and lacks the facility to provide immediate support to the learners if they are unsatisfied. Another approach involves the analysis of learning analytics data. By tracking learner’s digital footprints, such as their interactions with online platforms, task completion rates, and time spent on activities, valuable insights can be gained. But again, this does not fully reflect the learner’s attentiveness and does not tell the instance where online learners face issues. That’s why there is a growing need to develop effective systems that can monitor the cognitive state of online learners in real-time. Therefore, this thesis presents a cognitive analysis-based system specifically designed for online learning environments. The proposed system aims to enhance learner engagement, attention, and overall learning outcomes by utilising various techniques to analyse and interpret their cognitive states. In this research, two techniques, namely facial cues and EEG signals, are employed to monitor and analyse the cognitive behaviour of learners during online learning sessions. Facial cues include facial emotions, eye tracking, and head movements, which are captured and processed in real-time to assess the learner’s cognitive states. Monitoring facial cues provides a direct means to recognise the learner’s focus during online learning, similar to the observation in a physical classroom environment. The face, eye, and head movements serve as powerful visual indicators to interpret an individual’s intentions while learner’s engaged in an online learning session. The changes in facial emotions and eye and head movements can be considered as the learner’s responses to the delivered content in the online learning environment. To capture these cues, a built-in web camera is used to obtain real-time details of the face, eye, and head. Traditional Deep CNN models for facial emotion recognition are employed, and a CNN-based model is also proposed. The publicly available benchmarked datasets are used for training these models. Additionally, an in-house collected face dataset is utilised for testing purposes. The detection of eye-blinking patterns and head movements is achieved using a landmark approach. The outcomes of these face cues are combined to predict the final cognitive state of the online learner, whether they are attentive or distracted. The performance of the system is evaluated by comparing the standard baseline deep CNN models with the proposed model for facial emotion recognition. The experimental analysis demonstrates that the proposed model outperforms traditional deep learning algorithms for facial emotion classification. Furthermore, a comparative analysis of the proposed cognitive analysis system based on facial cues is conducted to evaluate its effectiveness against state-of-the-art models. This thesis also considers EEG signals for cognitive state prediction. EEG signals are utilised to measure learner’s brainwave activities, providing valuable insights into their cognitive workload and attention levels. By using EEG-based technology, instructors/teachers can observe a learner’s cognitive load, which refers to the level of mental effort exerted during learning tasks, without disrupting the online learning process. A Bluetooth-enabled single-electrode EEG device is employed to capture the electrical signals generated by the flow of electrons across neurons during learning tasks. EEG is considered the most effective sensor for measuring cognitive load and attentiveness in the online learning environment. It identifies fluctuations in signal levels, processes the data, and reflects different levels of alertness. EEG provides unbiased information about learner’s cognitive states, making it suitable for real-time analysis. The EEG device measures the electrical activity of neurons in the brain cortex using specific electrodes and categorises the activity into different frequency bands. The brainwave signals obtained from EEG recordings are categorised into five frequency bands: Delta, Theta, Alpha, Beta, and Gamma, each associated with specific mental activities or states. Machine learning algorithms are employed to analyse the collected EEG signal data and predict the learner’s cognitive states. These algorithms are trained using a combination of publicly available datasets and the in house EEG data collected in this research. Performance metrics such as accuracy, precision, recall, and F1 score are utilised to evaluate the effectiveness of the proposed system in predicting and classifying cognitive states. Furthermore, a comparative analysis of the proposed cognitive analysis system based on EEG signals is conducted to assess its effectiveness compared to state-of-the-art models. The implemented cognitive evaluation for face cues and EGG signals are represented separately with the help of a web application. The web application for the proposed smart education system is also presented in this thesis. The Web Application comprises a range of features that are thoroughly discussed and incorporated into the system. These features are designed to enhance the overall functionality and effectiveness of the smart education system, providing a comprehensive and user-friendly platform for online learning.