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Student open feedback is an essential element to improve the teaching service. Comprehending the feedback collected daily may not be possible especially in a large classroom. There is needed an automated system that processes feedback and helps to recommend focused, precise points to the teacher stating the positives and negatives of a class. Also, the feedback texts are neither going to be grammatically correct nor going to consist only of English. Hence, an automated feedback processing system is essential that processes the mixed-language language text that provides crisp clear insights to the teachers, thus making effective student–teacher interaction.

This research is designed to analyse daily feedback from the students in grammarless English-Tamil mixed feedback and creates a dashboard that displays concise keywords regarding positive and negative aspects of the class. An ML-based system architecture is proposed for processing English-Tamil mixed grammarless feedback texts and validates the same with an experimental prototype and compares the results with other state-of-the-art models. This prototype classifies the text into different categories and provides the concise view with topic modelling techniques. This system is useful in progressive improvement of teaching learning process, subsequently leading to better teaching learning environment.

The proposed web-based architecture is validated with a prototype by comparing the results with other state-of-the-art models. The accuracy of the results is higher (>90%) in the proposed architecture than other models (<60%). The created teacher dashboard is highly recommendable and provides day-to-day recommendation for finetuning teaching and learning process. The web-based dashboard created for teachers enables them to interpret the student feedback with much ease due to the Machine learning algorithms used in implementing the web-based solution.

This system is designed to help the teachers to improve themselves in the teaching learning process with the feedback. The proposed system is a prototype that is initially tested with sample feedback texts obtained in sessions in postgraduate classrooms. The implementation of the prototype and analysis of teacher and student experience are presented as the immediate scope of this research work. This helps the teachers to get an overall view on the best teaching practices and what to improve. This work currently uses Bidirectional Encoder Representations from Transformers (BERT) uncased and in the increase of native language text the system may work with BERT multilingual.

This prototype will be implemented as a web-mobile based application. Students can submit their daily feedback through a mobile app, while teachers will access a dashboard that presents a concise overview generated by the proposed system architecture. The dashboard will also provide trend analysis, highlighting positive and negative aspects of the sessions. The system's effectiveness will be evaluated through a qualitative study, incorporating feedback from teachers and insights from students. This evaluation will help teachers gain a comprehensive understanding of the most effective teaching practices and areas needing improvement, thereby enhancing the teaching-learning process. The web-mobile application aims to Streamline the feedback process, making it easy for students to share their thoughts and for teachers to receive actionable insights. This study offers a clear and concise summary of student feedback and trend analysis from which the teachers can quickly identify patterns and make necessary adjustments to their teaching methods. Ultimately, this approach will foster a more responsive and effective educational environment, supporting continuous improvement and better student–teacher interactions. Further, the proposed system requires lesser technical knowledge and can be used by anyone.

A literature review has identified a critical need for a feedback processing system that functions at short intervals. Such a system is essential for providing teachers with concise, periodic summaries of students' open-ended feedback, which is vital for fostering continuous improvement in the teaching-learning process. The immediate processing of feedback, particularly when it contains English-mixed texts, is crucial for making timely adjustments that enhance both student performance and experience. By swiftly addressing concerns and reinforcing positive feedback, the system will improve student–teacher interactions, provide meaningful insights that contribute to progressive educational growth. This will help implement a feedback system that operates in these short intervals and allows for real-time monitoring and response to students' needs and experiences. Additionally, by highlighting areas of success, teachers can build on effective strategies and practices.

This research paper proposes a system architecture PSFAS: Progressive Student Feedback Analysis System with Multi Level Classification and Clustering that enables effective interaction between the student and the teacher with the findings from feedback and presenting an experimental prototype that can be incorporated into the regular teaching learning process, whether online or offline. It has been found from the literature review, that feedback processing is mostly done in the English language. This work proposes a system architecture that gives higher accuracy than other state-of-the art models for feedback texts having English-mix grammarless sentences.

The new ways of working (NWW), a contemporary work environment with temporal and spatial flexibilities, has become an enforced reality after the COVID-19 pandemic disrupted workplaces. However, the understanding of how it impacts employee well-being perceptions is limited. Hence, the current study aims to examine how the NWW facets, namely, time- and location-independent work, management of output, access to organizational knowledge and flexibility in working relations relate to employees' life satisfaction, mediated by psychological capital.

A cross-sectional survey was designed to collect data from 459 Indian knowledge workers. Model fit and the hypothesized relationships were tested using IBM SPSS 25, AMOS and PROCESS Macro.

All four NWW facets positively relate to psychological capital, which in turn associates with life satisfaction. Except for the facet “management of output”, the other three facets associate positively with life satisfaction before accounting for the mediator. Indirect effects of all four facets on life satisfaction via psychological capital were established. Overall, the findings establish the important mediating role of psychological capital in relating the NWW facets with life satisfaction.

By examining the previously unexplored relationships between NWW, psychological capital and life satisfaction, this study provides novel insights into the role of personal resources in maximizing the beneficial effects of the NWW practices and is highly relevant in the current context where organizations are trying to identify coping mechanisms that help employees adapt to workplace transformations.

This paper aims to confront the rising threat of deepfake videos, focusing on the limited research on deepfake detection strategies among seniors. The study thus investigates seniors’ video credibility conceptualizations and identifies their deepfake detection strategies.

This study employed semi-structured interviews with 20 seniors aged 55 to 70. Areas covered include their perceptions of video information credibility and identification strategies undertaken. Qualitative content analysis was conducted to interpret interview responses.

Seniors emphasized the importance of objectivity, trustworthiness, believability, reliability and truthfulness in terms of video credibility. Regarding strategies for assessing video credibility, seniors employed five categories: character appearance, non-human visuals, audio, personal knowledge and external sources.

This study contributes to the literature on human-oriented deepfake detection strategies by uncovering diverse methods employed by seniors. It enhances the understanding of how individuals assess video credibility in the context of deepfakes. Furthermore, this study offers practical and applicable strategies for real-world deepfake detection.