System for real-time academic stress analysis using advanced data analytics and machine learning to generate insights

Abstract

A system for academic stress analysis and the generation of actionable insights. The system comprises a module 102 for creating and distributing customisable surveys to groups of students; a module 10

Description

TECHNICAL FIELD

[001] The present invention generally relates to the field of data analytics and machine learning. More specifically, relates to a system and a method for real-time academic stress analysis, utilizing data analytics to generate actionable insights to assist in improving student well-being, performance, and academic outcome. BACKGROUND

[002] Academic stress is a prevalent issue among students, adversely affecting their mental health, academic performance, retention, and overall well-being. Traditional methods used by institutions primarily rely on periodic surveys, teacher observations, manual feedback, or post-event evaluation to measure and address academic stress.

[003] A major limitation of existing systems is their inability to analyze stress levels in real time. Most stress management approaches rely on periodic assessments, which provide a snapshot of a student's emotional state at a given time but fail to capture dynamic changes in stress levels. The conventional approach fails to provide real-time, personalized insights needed to address the varying stress levels of students. Consequently, these systems cannot offer timely, actionable interventions, leading to missed opportunities for early intervention.

[004] Traditional stress assessment systems are inefficient, relying on manual processes and multiple tools, which results in delays in taking action. Dropout rates linked to stress and mental health issues contribute to a significant economic burden, with 8% of GDP spent on mental health care. Students who drop out earn 25% less than graduates, and UK institutions risk losing £1.6bn annually in student fees due to dropouts. The lack of real-time data processing further complicates stress management. While some systems collect stress-related data, they lack the capability to analyze it immediately or correlate it with other factors, leading to delayed feedback that is less effective in proactively managing stress.

[005] Existing solutions are fragmented and fail to offer seamless integration or real-time insights. These systems operate reactively, addressing mental health challenges only after they have significantly affected students. Moreover, they lack mechanisms to predict and prevent dropouts through early intervention.

[006] Furthermore, the conventional system also lacks customization. Generic surveys orone-size-fitsall solutions often fail to address the unique needs of students from diverse academic backgrounds. This lack of personalization makes it difficult to provide accurate and relevant solutions, leaving students with limited options for managing their stress.

[007] With the advent of data analytics, there exists a significant opportunity to leverage various realtime data sources to analyze academic stress as it manifests, thereby enabling early intervention, personalized support, and improved academic performance. The present invention, therefore, provides a system that overcomes these limitations by providing a scalable, cloud-based solutions that integrate data collection, analysis, and Generative Al-powered insights. OBJECTIVES OF THE INVENTION

[008] Some of the objects of the invention are as follows:

[009] An object of the present invention is to provide a system for real-time academic stress analysis and provide actionable insight using machine learning and Artificial Intelligence.

[010] Another object of the present invention is to provide a system that enables the customization of surveys with predefined constructs and Likert scale questions to collect data from students, ensuring personalized stress analysis.

[011] Another object of the present invention is to provide a system that collects survey responses via QR codes or direct links, making it accessible through both mobile and desktop devices for greater convenience and flexibility.

[012] Another object of the present invention is to integrate an in-built analytics system that identifies key factors contributing to academic stress, enabling more accurate data interpretation and insights.

[013] Another object of the present invention is to implement a machine learning module that categorizes stress levels (i.e., low, medium, and high) based on survey responses, allowing for real-time assessments and timely interventions.

[014] Another object of the present invention is to provide a generative Al component that generates actionable insights and personalized interventions based on survey results and assessed stress levels.

[015] Another object of the present invention is to provide a system that ensures GDPR compliance by incorporating secure mechanisms for anonymized or identified data collection and processing, thus safeguarding student privacy.

[016] Another object of the present invention is to offer cloud-based hosting with scalable infrastructure to ensure real-time performance, providing accessibility from any device, at any time. SUMMARY OF THE INVENTION

[017] The present invention addresses the need for an Al-driven cloud-based system that provides realtime academic stress analysis and actionable insights for students. The system uses a customizable survey framework to create and distribute surveys with predefined constructs and questions to the students. The data from the students is collected in real-time by collecting responses to the survey using QR codes or direct links accessible through a mobile device or a desktop device. The system comprises an in-built analytics engine based on machine learning that pre-process the data and analyzes exploratory key factors to identify key stress factors. The machine learning algorithm categorizes the stress level of students into high, medium, and low levels. A generative Al is integrated into the system to generate actionable insights based on the survey responses, providing students with timely and relevant recommendations.

[018] According to a first aspect of the invention, the system includes a survey module that allows academic institutions to create and distribute customized surveys using predefined constructs and Likert scale-based questions. The system is designed to analyze stress levels in real time, offering a seamless solution that integrates data from various sources to track the evolving needs of each student.

[019] In one embodiment of the invention, the system utilizes machine learning algorithms to detect patterns in student stress levels, correlating responses with external factors such as academic workload, personal life, and environmental triggers. The system automatically categorizes students into different stress levels and generates executable recommendations using generative Al. These recommendations can include academic, wellness, and time management recommendations to alleviate stress and improve well-being.

[020] The system also ensures compliance with data protection regulations by implementing GDPR compliant mechanisms for secure and anonymized data collection. Additionally, the system offers cloud based hosting with dedicated, scalable infrastructure, ensuring real-time performance and accessibility for users.

[021] In another aspect of the invention, the system provides a comprehensive and user-friendly interface for students, faculty, and academic advisors, allowing them to access real-time stress data and reports. The interface ensures that relevant stakeholders can collaborate effectively and address academic stress proactively, improving overall student well-being and academic success.

[022] The system integrates advanced Al techniques and cloud-based technologies to provide a comprehensive solution to the ongoing problem of academic stress management. It empowers academic institutions to manage student well-being in a personalized and real-time manner, improving academic performance and reducing mental health risks such as burnout and dropout.

[023] In an embodiment of the present invention, Generative Al (which includes technologies like Generative Adversarial Networks (GANs) or transformer models) represents an advanced field of artificial intelligence that is capable of generating new, personalized content based on learned data patterns. Unlike traditional Al systems, which are focused on classification or prediction tasks, Generative Al creates actionable insights or content by synthesizing information tailored to the unique needs of each student. In the context of academic stress management, this technology enables the system to generate customized, dynamic interventions that adjust as students' needs change over time.

[024] Through the use of Generative Al, the system generates real-time, individualized recommendations and action plans aimed at mitigating stress. These interventions could range from suggesting stressrelieving activities such as relaxation exercises or time management strategies, to proposing personalized academic assistance or counseling services. This dynamic intervention process is designed to continuously evolve, ensuring that the system’s responses align with the fluctuating stress levels and needs of each student. BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

[025] The accompanying drawings illustrate the best mode for carrying out the invention as presently contemplated and set forth hereinafter. The present invention may be more clearly understood from a consideration of the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings wherein like reference letters and numerals indicate the corresponding parts in various figures in the accompanying drawings, and in which:

[026] FIG. 1 shows a configuration of a cloud-based Al-driven system for real-time academic stress analysis and actionable insights, in accordance with an embodiment of the present invention.

[027] FIG. 2 illustrates a cloud-based architecture for deploying the system for real-time stress analysis, securely and efficiently, in accordance with an embodiment of the present invention.

[028] FIG. 3 is a block diagram showing a process flow in the system for real-time academic stress analysis, in accordance with an embodiment of the present invention.

[029] FIG. 4 is a block diagram showing survey management in the system for real-time academic stress analysis, in accordance with an embodiment of the present invention.

[030] FIG. 5 illustrates a survey distribution process used in the system for real-time academic stress analysis, in accordance with an embodiment of the present invention.

[031] FIG. 6 illustrates an Exploratory Factor Analysis framework integrated into the system for realtime academic stress analysis, in accordance with an embodiment of the present invention.

[032] FIG. 7 shows a configuration of a machine learning pipeline implemented by the system for realtime academic stress analysis, in accordance with an embodiment of the present invention.

[033] FIG. 8 illustrates a workflow for stress analysis and reporting using an interactive system that integrates advanced analytics and generative Al capabilities, in accordance with an embodiment of the present invention.

[034] FIG. 9 illustrates a workflow for data visualization and report distribution using an interactive system that integrates advanced analytics and generative Al capabilities, in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[035] Embodiments of the present invention disclosure will be described more fully hereinafter with reference to the accompanying drawings in which like numerals represent like elements throughout the figures, and in which example embodiments are shown.

[036] The detailed description and the accompanying drawings illustrate the specific exemplary embodiments by which the disclosure may be practiced. These embodiments are described in detail to enable those skilled in the art to practice the invention illustrated in the disclosure. It is to be understood that other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the present disclosure. The following detailed description is therefore not to be taken in a limiting sense, and the scope of the present invention disclosure is defined by the appended claims. Embodiments of the claims may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.

[037] As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, article, or apparatus that comprises a list of elements is not necessarily limited only to those elements but may include other elements not expressly listed or inherent to such a process, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

[038] Additionally, any examples or illustrations given herein are not to be regarded as restrictions on, limits to, or express definitions of, any term or terms with which they are utilized. Instead, these examples or illustrations are to be regarded as being described with respect to one particular embodiment and as illustrative only. Those of ordinary skill in the art will appreciate that any term or terms with which these examples or illustrations are utilized will encompass other embodiments which may or may not be given therewith or elsewhere in the specification and all such embodiments are intended to be included within the scope of that term or terms. Language designating such non-limiting examples and illustrations includes, but is not limited to: “for example,” “for instance,” “e.g.,” “in one embodiment”.

[039] In an embodiment of the present invention, the system is a cloud-based, Al-driven platform for realtime assessment of academic stress among students. The system is built on an integrated framework comprising a customizable survey tool, a real-time data collection interface, advanced analytics, machine learning algorithms, and generative Al components. These elements collectively enable the collection, processing, and analysis of stress-related data, providing personalized, actionable insights to address the stress levels of the students. The system's real-time assessment contributes to improved well-being, academic performance, and mental health outcomes.

[040] The system integrates multiple components working together to offer a comprehensive approach to stress management. Central to the system is a customizable survey tool that enables academic institutions to create tailored surveys. The real-time data collection interface gathers stress-related data, allowing the system to track fluctuations in stress levels as they occur.

[041] The system employs advanced analytics, machine learning algorithms, and generative Al components to process and analyze the collected data. These technologies interpret survey responses and other stress-related inputs, generating actionable interventions to address individual stress levels.

[042] FIG. 1 shows a configuration of a cloud-based Al-driven system for real-time academic stress analysis and actionable insights, in accordance with an embodiment of the present invention.

[043] The customizable survey engine 102 is key to optimizing system functionality, allowing institutions to adjust surveys to meet their specific requirements. It supports various survey types, including questionnaire-based surveys, ideal for collecting broad quantitative data on academic stress across student populations.

[044] The system also supports interview-based surveys for qualitative data collection, allowing students to provide detailed feedback on individual stress experiences. This dual approach enables institutions to gather both generalizable data and in-depth insights, enhancing the comprehensiveness of academic stress analysis.

[045] The real-time data collection module 104 gathers survey responses dynamically, ensuring immediate processing and secure transmission of data to subsequent components.

[046] The in-built analytics module 106 processes the collected data, generating visualizations, statistical insights, and reports. It identifies trends and patterns to enhance the interpretability of survey results.

[047] Machine learning models 108 analyze both historical and real-time data, predicting outcomes, identifying anomalies, and providing optimization recommendations to improve system efficiency and accuracy over time.

[048] Generative Al integration module 110 enables functionalities such as automatic question generation, summarization of responses, and adaptive survey workflows based on participant interactions.

[049] The system also includes a GDPR compliance module 112 to ensure that all data handling, storage, and processing adhere to legal and ethical standards, safeguarding participants' privacy and security.

[050] The system supports multiple survey modes, including online, mobile, offline, and embedded surveys, ensuring accessibility and flexibility for institutions and students. Online surveys can be accessed via a web platform, while mobile surveys allow students to participate through smartphones. Offline surveys can be completed without an internet connection, and embedded surveys integrate directly into existing academic platforms, such as Learning Management Systems (LMS).

[051] The system allows for survey audience customization, enabling the targeting of specific demographic groups or student profiles. This segmentation ensures that the collected data is relevant to the specific needs of the targeted group, facilitating tailored interventions for students experiencing academic stress.

[052] The cloud-based platform serves as the central data repository, facilitating seamless integration and instant availability of survey responses for processing and analysis. The cloud infrastructure supports scalability, allowing the system to manage large volumes of data from diverse student populations across multiple institutions.

[053] The real-time analytics engine processes incoming survey responses continuously, identifying stress factors by cross-referencing responses with contextual data such as academic performance, behavioral factors, and lifestyle elements. This integration provides a holistic view of each student's mental well-being.

[054] Machine learning algorithms assess the stress levels of students by analyzing survey data, and categorizing stress into low, medium, or high levels. High-stress students are flagged for immediate intervention, such as counseling referrals or academic workload adjustments.

[055] The system also generates comprehensive reports on student stress trends across various demographic categories, helping institutions identify areas that may require targeted interventions or additional resources. These reports support informed decision-making and optimize student support services, improving overall student success and retention.

[056] The system is configured to offer multiple methods for the distribution of surveys, including but not limited to QR-code-based and Link-Based Distribution, as well as other options. The QR-Code Distribution generates a unique QR code that students may scan to directly access the survey, providing a seamless and efficient means of access in both physical and digital environments. The Link-Based Distribution sends personalized survey links through channels such as email, text, or digital platforms, thereby enabling institutions to track participation rates and target specific student demographics. Additional methods include direct email invitations, integration with Learning Management Systems (LMS), mobile application distribution, and physical forms containing QR codes or URLs. These diverse distribution methods ensure effective reach, thereby promoting increased participation and gathering valuable stress-related data.

[057] Once completed, the system moves on to the collection and secure storage of survey data. Data is gathered with explicit consent from students, in full compliance with GDPR guidelines to protect privacy and maintain confidentiality. The secure storage infrastructure prevents unauthorized access, ensuring that sensitive student data remains protected throughout the entire process.

[058] The collected data is processed through the system’s integrated data analytics and Al modeling module. Advanced machine learning algorithms are utilized to analyze survey responses and identify key stress factors impacting students. This analysis culminates in the generation of detailed stress-related reports, which are subsequently used to generate actionable insights for academic institutions. These insights enable the creation of tailored recommendations and interventions aimed at addressing identified stress factors, such as adjusting academic workloads or offering wellness programs, thus enhancing student well-being.

[059] The in-built analytics engine processes and analyzes the collected student data in real-time, evaluating various data types, including but not limited to survey responses, academic performance metrics, behavioral patterns, and lifestyle data. This comprehensive evaluation provides an overview of each student’s stress levels. The engine facilitates continuous, automated stress assessment without requiring manual intervention, thereby ensuring the timely identification of students who may require support.

[060] The system continuously captures incoming data from multiple sources, including real-time survey responses and academic performance data, to detect fluctuations in stress levels. This real-time data analysis ensures that the system can promptly identify students experiencing elevated stress levels, allowing for immediate intervention. For instance, the system may flag students with high stress levels and trigger the delivery of relevant resources such as counseling referrals or academic workload adjustments, thus preventing the escalation of stress.

[061] The analytics engine utilizes advanced machine learning algorithms to process and interpret the data, recognizing patterns and correlations within the data to categorize stress levels as low, medium, or high. This categorization enables the prioritization of interventions, ensuring that students experiencing high stress levels receive timely, personalized support.

[062] The system generates personalized recommendations for students based on the analyzed data, including but not limited to suggestions for stress-relief techniques, time management strategies, and mental health resources. These personalized interventions are tailored to each student’s unique stress profile, to reduce stress and enhance overall academic performance and well-being.

[063] The system operates in real-time, and as students provide updated feedback, the system reanalyzes the data and adjusts its recommendations and interventions accordingly, in real-time, and as students provide updated feedback, the system reanalyzes the data and adjusts its recommendations and interventions accordingly. This ongoing analysis ensures that the system remains responsive to the evolving needs of students, delivering dynamic and timely interventions.

[064] The analytics engine generates actionable insights for academic institutions by aggregating and analyzing stress data across different student demographics. This aggregated data provides institutional decision-makers with insights into broader stress patterns, enabling them to identify trends and target interventions where needed.

[065] In one embodiment, the system integrates machine learning (ML) models as a core component for analyzing and interpreting the data collected from students. These ML models are specifically designed to identify patterns, correlations, and trends related to student stress levels, academic performance, and lifestyle factors. The system utilizes these models to automatically categorize students based on stress levels and predict potential outcomes, thereby facilitating more accurate and efficient interventions.

[066] In another embodiment, the machine learning models used in the system are trained on historical data, including survey responses, academic performance metrics (e.g., grades, assignment deadlines), and behavioral factors (e.g., study hours, extracurricular activity participation). The system processes this data to generate predictive models that can forecast future student stress levels. These models provide valuable insights into the factors that contribute to stress, allowing the system to predict which students are most at risk for heightened stress in the future.

[067] In a further embodiment, the machine learning models classify stress levels into distinct categories, such as low, medium, or high, based on survey responses and other data inputs. This classification allows the system to prioritize intervention efforts, ensuring that students experiencing high levels of stress receive prompt, tailored support. For example, students categorized as experiencing high stress may be automatically referred to counseling services, while those with medium stress levels may be provided with proactive wellness resources, such as relaxation techniques or academic workload adjustments.

[068] In yet another embodiment, the machine learning models continuously learn and adapt over time. As new survey responses and behavioral data are collected, the models refine their predictions, improving their ability to assess stress levels accurately. This continuous learning enhances the system's ability to provide up-to-date recommendations and interventions, ensuring that it remains responsive to evolving patterns of behavior and environmental factors.

[069] In a further embodiment, the machine learning models perform sentiment analysis on open-ended responses collected through interview-based surveys. This analysis allows the system to detect subtle emotional cues and identify stress-related concerns that may not be captured by structured survey questions. Incorporating sentiment analysis into the system enables a more nuanced understanding of students' emotional states, improving the overall accuracy of stress assessments.

[070] In yet another embodiment, the machine learning models can segment students based on demographic and behavioral factors, such as academic program, year group, or personal preferences. This segmentation allows the system to tailor interventions to specific student profiles, ensuring that the support provided is relevant not only to the student’s stress level but also to their unique circumstances and needs.

[071] Additionally, the machine learning models enable the system to evaluate the effectiveness of stress reduction interventions. By comparing pre- and post-intervention data, the system assesses whether the implemented interventions resulted in a reduction of student stress levels. This data-driven feedback loop allows for the continuous refinement of intervention strategies, ensuring the use of the most effective approaches to support student well-being.

[072] In one embodiment of the present invention, the system integrates Generative Al as a key technology to provide personalized, actionable insights to students experiencing stress. The Generative Al component leverages advanced machine learning techniques and natural language processing (NLP) to generate tailored recommendations, interventions, and feedback based on the stress-related data collected from students through surveys and other data streams.

[073] In another embodiment, the Generative Al system operates by analyzing inputs from various data streams, such as survey responses, academic performance, lifestyle factors, and behavioral data. The Al component uses this data to construct personalized recommendations that help address the underlying causes of stress. These recommendations may include academic accommodations (e.g., deadline extensions, workload redistribution), wellness activities (e.g., mindfulness exercises, stress management programs), or mental health support options (e.g., counseling services, peer support networks).

[074] In a further embodiment, the Generative Al system continuously learns from new data as it is collected from students. As students submit new survey responses or provide updates on their stress levels, the Al system refines its understanding of their stress patterns and adapts its recommendations accordingly. This continuous learning process ensures that the system can deliver the most accurate and up-to-date interventions based on the evolving needs of each student. The ability to adapt in real-time enhances the effectiveness of the system in mitigating student stress.

[075] In yet another embodiment, the Generative Al system is designed to produce natural language outputs that are easy for students to understand and engage with. When providing personalized recommendations or interventions, the Al generates human-readable text, offering clear and supportive guidance that students can easily follow. These outputs may include step-by-step instructions, encouraging messages, or tailored action plans, all of which are specifically designed to help students manage their stress effectively.

[076] In a further embodiment, the Generative Al component incorporates NLP capabilities to evaluate the sentiment and emotional tone of open-ended responses provided by students through interview based surveys. By performing sentiment analysis, the system can assess the emotional states of students and generate responses that acknowledge their feelings and provide relevant suggestions. This ability to understand and respond empathetically to students' emotions ensures that the interventions are not only personalized but also sensitive to the student's psychological needs.

[077] In yet another embodiment, the Generative Al system offers a feedback loop, wherein it generates follow-up interventions based on the effectiveness of previous recommendations. After a student implements a suggested intervention, the system analyzes the outcomes through subsequent survey responses or behavioral data. If the stress levels persist or fluctuate, the system generates new, adjusted recommendations that are more suited to the student's current situation. This iterative feedback mechanism ensures that the system’s interventions remain relevant and effective throughout the student’s academic journey.

[078] Additionally, the Generative Al integration enables the system to provide proactive support. Rather than merely reacting to stress signals after they are detected, the system can predict potential stressors based on students' historical data, including academic workloads, personal schedules, and past stress patterns. The Al can then preemptively suggest stress-reducing strategies, such as workload adjustments or wellness activities, before the student experiences significant stress. This predictive approach further enhances the system’s ability to support students in managing their academic stress before it reaches critical levels.

[079] In another embodiment, the Generative Al system can be customized by academic institutions to align with specific student wellness goals. Institutions can modify the underlying Al models to prioritize certain aspects of student well-being (e.g., academic success, mental health, physical activity) based on institutional priorities. This customization ensures that the system’s interventions are not only personalized to the student but also tailored to the institutional context, aligning with broader strategies for student support and success.

[080] FIG. 2 illustrates a cloud-based architecture for deploying the system for real-time stress analysis, securely and efficiently, in accordance with an embodiment of the present invention. The architecture spans across AWS cloud 202 and Azure cloud. The entire architecture in AWS resides within a Virtual Private Cloud, providing an isolated network. A public subnet layer comprises an Application Load Balancer 204 that provides security by allowing inbound traffic only on Port 443. The Application Load Balancer 204 accepts incoming traffic over HTTPS and distributes traffic to multiple web applications deployed in the public subnet. The web applications are hosted within the public subnet layer and are likely auto-scaled to handle varying traffic loads. A Redis server 206 is hosted in the private subnet to store cache or session data and communicates only with the web application in the public subnet on Port 6379. The Redis server 206 is securely isolated from direct external access.

[081] The repository of the system is stored in SQL database 208, which is a managed database service provided by Azure. The SQL database 208 accepts connections only from the web application instances (IPs) in the AWS cloud 202. This ensures a secure link between the AWS-hosted apps and the Azure hosted database.

[082] The architecture is designed to meet enterprise-grade scalability, security, and GDPR compliance by leveraging advanced features in cloud infrastructure. The use of the Application Load Balancer (ALB) ensures efficient traffic distribution, while auto-scaling web applications dynamically adjust to varying traffic loads, enabling seamless performance even during high-demand periods.

[083] The public-facing ports are limited to port 443 (HTTPS), ensuring secure communication, while internal services like Redis (port 6379) are only accessible within trusted networks, minimizing potential attack surfaces. Sensitive application components, including databases and critical services, are deployed within private subnets, enhancing security by isolating them from direct internet exposure.

[084] The architecture ensures that data remains within the European Union, aligning with data residency regulations and providing assurance for GDPR compliance.

[085] To protect data integrity and confidentiality, all sensitive data is encrypted both in transit (via SSL / TLS) and at rest (using industry-standard encryption mechanisms), ensuring it remains secure throughout its lifecycle.

[086] IP-based access control is implemented for Azure SQL Database, restricting access to only trusted IP addresses, which adds a layer of protection against unauthorized access.

[087] FIG. 3 is a block diagram showing a process flow in the system for real-time academic stress analysis, in accordance with an embodiment of the present invention. The platform of the system uses a client-based access control model tailored for educational institutions like schools, colleges, and universities. The platform provides a login system 302 for a client administrator to access the platform. The administrator has access to the specific features within the platform, and the activities are limited by a predefined response quota that restricts the number of survey responses collected by the administrator. In the survey platform 304, the client administrator is able to create and distribute surveys. The surveys are tailored to specific institution groups, such as students, staff, and teachers ensuring relevance for the targeted audience. The client administrator is able to create surveys for various purposes, including course feedback, faculty evaluation, and student satisfaction. The survey can be customized to meet the needs of each institution. In Distribution Management 306, the administrator is able to set several key settings, such as maximum response limit, email collection toggle, and stress level display option. The platform provides QR codes and survey links to make accessing surveys simple and convenient for participants.

[088] In the response management 308 feature of the platform, responses can be tracked at granular levels, such as by departments, classes, or groups, enabling detailed insights into different segments of the institution. The platform uses Exploratory Factor Analysis Module 310 and Machine Learning Module 312 to uncover hidden patterns and relationships in survey responses. In the generative Al and reports step 314, the platform generates automated reports featuring sentiment analysis results and other actionable insights. The reports help administrators to make informed decisions based on survey data. In step 316, Al-powered sentiment analysis is applied to gauge the overall mood or perception of different groups. The sentiment analysis is integrated into the final report. In the next step 318, the reports are displayed on a visual dashboard. The visual dashboard displays present key metrics visually, such as response rates per department, survey completion status, sentiment trend across groups, comparative analysis between departments, etc. The reports provide insight into how actively departments are participating, the progress of ongoing surveys, and changes in mood or perception over time for students, staff, and teachers, highlighting differences in response or satisfaction levels across departments.

[089] In a specific use case, in a university, an administrator is able to use the platform to create multiple surveys for different purposes, such as a teaching evaluation survey for students to provide feedback on instructors, a work satisfaction survey for staff to assess job satisfaction, a research feedback survey for faculty to understand their research-related needs. The administrator is able to view response rates and quota usage while reviewing Al-generated reports to gain targeted insights. These reports reveal department-level patterns, sentiment trends, and areas for improvement, helping the institution make data-driven decisions. The process is streamlined and enhances survey management, data analysis, and decision-making efficiency for educational institutions.

[090] FIG. 4 is a block diagram showing survey management in the system for real-time academic stress analysis, in accordance with an embodiment of the present invention. The Survey Management 402 includes a survey creation 404, a survey clone 406, and a survey translation 408 features. Each survey begins with a participation sheet that provides essential information, ensuring transparency and compliance. The participation sheet contains information on the survey purpose and objectives, the organization’s contact information, data usage policies, consent form with GDPR compliance, expected completion time, and confidentiality statement. Each survey also has an end sheet that includes a summary or closing information for participants. The survey creation 404 also performs survey chain setup. The survey chaining enables automatic redirection to follow-up surveys upon completion of the current one. This is useful for conducting multi-stage surveys, where initial responses inform subsequent questions.

[091] The survey creation enables the creation of a structured questionnaire with multiple blocks: a demographic block 410, a Likert scale block 412, and an open-ended block 414. The Demographic Block 410 captures respondent profiles through questions about age, education, location, and other attributes. The Demographic Block 410 helps segment responses for more targeted analysis. The Likert scale block 412 contains multiple rating questions on a 5 to 7-point rating scale for measuring attitude, opinion, or level of agreement. For each survey, 15 to 20 number of the Likert scale blocks 412 are allowed, making it highly customizable for detailed research. The open-ended block 414 includes free-text questions to gather detailed feedback, opinions, or qualitative insights from respondents.

[092] The Survey clone 406 allows administrators to duplicate an existing survey for reuse, saving time and effort. Survey Translation 408 facilitates multi-lingual surveys by translating content into supported languages like Spanish, Mandarin, Chinese, Hindi, Arabic, French, and German.

[093] Each of the survey blocks: Demographic block 410, Likert scale block 412, and open-ended block 414 connects to a word export wizard 416 and Excel export wizard 418, while maintaining the original question structure. This feature makes the survey data accessible in multiple formats.

[094] The survey management 402 streamlines survey creation with a structured block for diverse question types. This enhances respondent trust and participation through clear communication and GDPR compliance. The survey translation provides flexibility to cater to multilingual audiences and conduct complex, multi-stage surveys. The survey management allows easy data export for further processing and reporting.

[095] FIG. 5 illustrates a survey distribution process used in the system for real-time academic stress analysis, in accordance with an embodiment of the present invention. The Survey Distribution 502 involves several features and settings to ensure efficient and flexible survey deployment while maintaining participant privacy and data control. The distribution settings 504 include setting maximum response limit 506, email collection toggle 508, and stress level display option 510. The maximum response limit 506 allows the administrator to set a cap on the total number of responses allowed for a survey. This helps control the number of participants and ensures that the survey stays within predefined response quotas. The email collection toggle 508 allows administrators to enable or disable email collection. When enabled, the platform tracks respondent identities via email, which is helpful for followups or personalized reporting. When disabled, respondents can remain anonymous, enhancing privacy. The stress level display option 510 allows administrators to choose to display a “stress level” indicator to participants during the survey. This feature raises participant awareness of their emotional state as they respond, potentially improving the quality of feedback.

[096] The distribution tools 512 enable the platform to distribute surveys to the participant. The platform provides QR codes and survey links to make accessing surveys simple and convenient for participants. The distribution tolls 512 are designed for easy sharing via email, posters, or online platforms.

[097] The survey distribution 502 also comprises response management 514. The response management enables the platform to track response submissions. The system consider both complete and incomplete responses, offering insights into survey progress and participation rates. The administrators can download raw response data for advanced analysis or integration with external tools. Specific responses may be deleted if necessary, such as duplicate entries or inappropriate submissions, ensuring data accuracy and relevance.

[098] The survey distribution setting can be configured at the pre-distribution or mid-distribution stage. In the pre-distribution stage, all settings such as maximum response limits, email collection, and stress level visibility can be configured before survey distribution. In mid-distribution adjustment, based on survey requirements or privacy preferences, settings can be modified even after the survey is live.

[099] FIG. 6 illustrates an Exploratory Factor Analysis framework integrated into the system for realtime academic stress analysis, in accordance with an embodiment of the present invention. The Exploratory Factor Analysis (EFA) 602 is used to identify and refine the underlying structure of survey data. This approach ensures that the final survey is both reliable and valid by focusing on meaningful constructs and removing unnecessary items. The Exploratory Factor Analysis 602 uses Principal Component Analysis (PCA) with Varimax Rotation. The PCA is used to reduce the dimensionality of survey data by identifying patterns and grouping related variables. It simplifies large sets of variables into a smaller number of components (factors) that explain the maximum variance in the data. The varimax rotation technique is applied to make the factor structure clearer and easier to interpret. It redistributes the variance across factors, ensuring that each variable has a higher association (loading) with a single factor while reducing overlap with others. Variables are evaluated based on their factor loadings (correlation with a factor): Below 0.3 (Variables are weakly associated with the factor and are usually excluded), between 0.3 and 0.7 (Variables are retained as they meaningfully contribute to the factor).

[0100] In data validation 604, the survey data is carefully examined for inconsistencies, missing values, and outliers to ensure its integrity. In variable analysis 606, Variables are analyzed to determine their importance and relevance to the constructs. In Factor Construction 608, the remaining variables are grouped into factors, each representing an underlying construct (e.g., satisfaction, engagement). These constructs explain the relationships between variables in the survey. In Factor Identification 610, the factors are identified. In unimportant item filtering 612, the items that do not significantly contribute to the identified factors are removed. This ensures that only relevant and meaningful questions are retained.

[0101] In construct redefinition 614, the factors and their associated constructs are re-evaluated to ensure they align with the survey’s objectives. The constructs are redefined to reflect a more accurate and refined understanding of the underlying data structure.

[0102] In new survey creation 616, a new version of the survey is created, containing only the validated and relevant questions. The new survey is created with refined constructs and filtered questions. The refined constructs ensure that the survey is more focused, reliable, and effective. The optimized survey has higher reliability, improved construct validity, and enhanced future data collection. The refined constructs reduce ambiguity and improve consistency in responses. The survey captures the true essence of the constructs. The streamlined survey is more efficient and better suited for collecting meaningful insights.

[0103] In an embodiment of the present invention, the system uses a machine learning (ML) pipeline designed for efficient stress level classification based on survey responses. The pipeline leverages a modern GPU server for fast processing and incorporates robust clustering and validation mechanisms to analyze stress patterns, based on the response data collected from the students. The machine learning pipeline employs a K-means clustering algorithm that runs on GPU, ensuring high computational efficiency, especially for large datasets to classify stress levels (High, Moderate, Low) in survey responses using an unsupervised machine learning algorithm. The K-means algorithm operates without predefined labels, grouping survey responses based on similarity in stress patterns across each block. Each response is assigned to a cluster based on its proximity to cluster centroids.

[0104] To ensure the distinctness of clusters, the pipeline calculates silhouette coefficients. A silhouette score measures how similar a data point is to its cluster compared to others. Higher scores indicate welldefined, non-overlapping clusters. Clusters are labeled as High, Moderate, or Low-stress levels based on the characteristics of their centroids (average value of responses within a cluster). This provides an interpretable classification of stress patterns.

[0105] The pipeline generates a structured results table and includes: GUID (Globally Unique Identifier), Block Name, Cluster Assignment, Mean Stress Value, and Silhouette Score. The result table provides a detailed, actionable summary of the analysis and enables the identification of stress patterns across different survey blocks.

[0106] The machine learning algorithm allows administrators to identify high-stress groups or patterns specific to certain demographics, tasks, or environments. Block-wise analysis allows granular detection of stress triggers, enabling targeted interventions. The GPU-accelerated clustering significantly reduces processing time, making the pipeline suitable for large-scale surveys with thousands of responses. The system can be integrated into existing survey platforms and adapted for various use cases like workplace stress, mental health studies, or student well-being assessments.

[0107] FIG. 7 shows a configuration of a machine learning pipeline implemented by the system for real time academic stress analysis, in accordance with an embodiment of the present invention. FIG. 7 shows a machine learning workflow for classifying stress levels using survey response data. It demonstrates the flow of data and processes from the initial response collection to the final stress level classification.

[0108] The machine learning pipeline comprises a Web App server 702 which serves as an interface where survey responses are collected from participants. Once responses are submitted, they are sent as response data to a GPU server 704 for analysis. The GPU server 704 processes data using FastAPI, a high-performance web framework that enables efficient communication between the Web App and the Machine Learning Model. The GPU accelerates the processing of computationally intensive tasks, making the pipeline faster and more scalable.

[0109] The Block wise Data Analysis 706 involves analyzing the survey response block by block, where each block represents a section of the survey (e.g., Demographics, Workload, or Stress Levels). This step ensures granular analysis of patterns within specific sections of the survey.

[0110] Silhouette Analysis 708 is used to evaluate the quality of the clustering performed on the data. The Silhouette Analysis 708 measures how well each data point fits within its cluster compared to other clusters. Higher silhouette scores indicate more distinct and meaningful clusters.

[0111] In Cluster Validation 710, the clusters formed during the analysis are validated for consistency and reliability. This ensures that the groups (clusters) represent meaningful patterns in the data.

[0112] In Stress Level Classification 712, once clusters are validated, they are labeled as one of three stress levels: High Stress 714, Moderate Stress 716, and Low Stress 718. The classification is based on the cluster centroids (average stress level of the responses in each cluster). High Stress indicates significant stress levels, Moderate Stress represents moderate or manageable stress and Low Stress reflects minimal or no stress levels.

[0113] FIG. 8 illustrates a workflow for stress analysis and reporting using an interactive system that integrates advanced analytics and generative Al capabilities, in accordance with an embodiment of the present invention. The process begins with an Interactive dashboard 802 that acts as the user interface for managing data and monitoring progress. The Interactive Dashboard 802 allows users to upload survey data, view results, and customize analysis options. In Generative Al Reports 804, the system automatically generates comprehensive reports using Al-driven insights. The reports include detailed findings derived from stress-related survey data. The Executive Summary 806 provides a high-level overview of the key outcomes from the stress analysis. The Executive Summary 806 summarizes critical findings for quick comprehension by stakeholders.

[0114] The Stress Factors Analysis 808 identifies and evaluates the primary contributors to stress levels based on the survey responses. It prioritizes stress factors for targeted intervention.

[0115] The Recommendations section 810 suggests specific strategies or actions to address the identified stress factors. The recommendation offers evidence-based recommendations for stress mitigation.

[0116] The Sentiment Analysis 812 analyzes the tone and emotion behind the responses, categorizing them as positive or negative. The Sentiment Analysis 812 enhances understanding of participants’ emotional states.

[0117] The Sentiment Scoring (0-10) 814 assigns numerical scores to sentiments, with separate evaluations for: Positive Sentiments (Reflecting optimistic or stress-free responses) and Negative Sentiments (Indicating concern, dissatisfaction, or high stress).

[0118] The Open-Ended Analysis 816 examines free-text survey responses to extract detailed insights. It identifies themes, keywords, and unique stress contributors.

[0119] The Top Stress Reasons 818 aggregates and ranks the most frequently mentioned or impactful causes of stress.

[0120] The Coping Mechanisms 820 suggests practical methods or tools for alleviating stress, tailored to the identified reasons.

[0121] FIG. 9 illustrates a workflow for data visualization and report distribution using an interactive system that integrates advanced analytics and generative Al capabilities, in accordance with an embodiment of the present invention. The process begins with Visualization Components 902 by selecting appropriate visualization tools or components to represent the data effectively. The next step, Word Cloud Visualization 904 creates a graphical representation of text data, with word sizes corresponding to their frequency or importance. The word Cloud Visualization 904 is ideal for analyzing open-ended survey responses to highlight key themes. The results are displayed in the form of Pie charts 906, stacked bar charts 908, and heat maps 910.

[0122] The Pie chart 906 displays data distribution in proportional slices, useful for showing percentages or categorical comparisons. The Stacked Bar Charts 908 represents multiple data series stacked on top of each other, enabling comparison across categories and subcategories. The Heat Maps 910 provides a color-coded representation of data, allowing quick identification of patterns, trends, or outliers.

[0123] The Report Generation 912 step compiles the visualizations and analytical insights into a structured report format.

[0124] The Final Document Compilation 914 step combines the generated report with other necessary content, ensuring it is complete and well-organized for presentation.

[0125] In Annexures Attachment step 916, supplementary documents or appendices, such as raw data summaries, detailed tables, or extended analyses are added.

[0126] Distribution Options 918 offers flexible methods for sharing the report, such as Email distribution 920 or Download report 922. The Email Distribution sends the report directly to stakeholders via email. Download Report provides an option to download the report locally for offline access.

[0127] This visualization and distribution pipeline ensures that data insights are effectively communicated through intuitive graphics and structured documentation. It also provides flexibility in report sharing, making it suitable for various stakeholders and scenarios. The workflow emphasizes clarity, accessibility, and user convenience in delivering analytical findings.

[0128] In an embodiment of the present invention, the system is designed with GDPR compliance at its core to ensure that the personal data of students is handled securely, transparently, and lawfully throughout the entire data collection and processing cycle. The system takes into consideration all aspects of GDPR, ensuring that the academic stress analysis and management functionalities do not compromise student privacy or violate any data protection rights.

[0129] To adhere to GDPR, the system includes several mechanisms that prioritize data privacy and security. First, the system ensures explicit consent is obtained from each student before any personal data is collected. Upon initiating the survey or interacting with the system, students are presented with clear, concise information about how their data will be used and the potential risks involved. The system only collects the data that is necessary for analyzing academic stress and providing relevant interventions, thereby adhering to the data minimization principle. Students have the option to provide their consent by opting in voluntarily, and the consent is recorded within the system to ensure transparency and accountability.

[0130] Furthermore, following GDPR requirements, the system includes a data anonymization process. Personal identifiers, such as names and contact details, are separated from the survey responses and behavioral data, reducing the potential for misuse or unauthorized access. Any data that could potentially identify an individual is stored separately from the stress-related data, ensuring that the analysis and interventions are based on anonymized data. The anonymized data can then be used to generate insights and predictions while ensuring the protection of individual privacy.

[0131] The system also enables students to exercise their rights under GDPR, including the right to access, rectify, and erase their personal data. Students have the ability to request a copy of the data that has been collected about them, along with a detailed explanation of how the data is being used. Should any inaccuracies be found in the data, students can request corrections, ensuring that the system operates on accurate and up-to-date information. Moreover, students have the right to request the erasure of their data, which the system can accommodate by deleting the relevant records from the database upon request, in compliance with the right to be forgotten.

[0132] In another embodiment, the system includes secure data storage and transmission protocols to protect personal data from unauthorized access or breaches. Data is encrypted both during transmission and when stored in the cloud-based platform, utilizing the latest encryption standards to ensure that any personal data is safeguarded. The system regularly undergoes security audits to ensure that it meets the highest standards of data protection and is compliant with both GDPR and other relevant privacy regulations.

[0133] Additionally, the system maintains a clear data retention policy, which ensures that personal data is stored only for as long as necessary to achieve the purposes for which it was collected. Once the data is no longer required, it is securely deleted, as per GDPR guidelines on data retention and the principle of storage limitation.

[0134] In yet another embodiment, the system provides data breach notification procedures. If a breach of personal data is detected, the system is designed to promptly inform the relevant supervisory authority and affected individuals within the stipulated 72-hour period, as per GDPR requirements. This ensures that any potential data breaches are handled swiftly and transparently, minimizing any potential harm to students' privacy.

[0135] Various modifications to these embodiments are apparent to those skilled in the art, from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the embodiments shown along with the accompanying drawings but is to provide the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims.

Claims

1. A system for academic stress analysis and generating actionable insights, the system comprising:A customizable survey framework module configured to create and distribute one or more surveys to group of students.A data collection module configured to collect responses to the one or more survey in real-time.An analytics module configured to pre-process the responses, performing exploratory factor analysis and identifying key stressors.A machine learning module configured to analyse the responses to predict outcomes, identify anomalies, and to classify stress levels.A generative Al integration module configured to generate actionable insights based on the collected response.

2. The system of claim 1, wherein the system is a GDPR-compliant data management system ensuring secure and flexible data collection.

3. The system of claim 1, wherein the customizable survey framework module is configured to define constructs and Likert-scale questions tailored to academic stress factors.

4. The system of claim 1, wherein the system collects response to the one or more surveys in real-time via QR codes or unique links, ensuring accessibility across devices.

5. A method for academic stress analysis and generating actionable insights, the method comprising:Providing a platform to create and distribute one or more surveys to a group of students.Setting one or more key settings by the administrator for distributing the one or more surveys.Collecting a response of the group of students corresponding to the one or more surveys.Performing Exploratory Factor Analysis and Machine Learning to uncover hidden patterns and relationships in the response to one or more surveys.Categorising stress level based on the response to one or more surveys.Generating an automated report featuring sentiment analysis results and a set of actionable insights.

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