Power BI Case Studies for MCA Students

Case Study 01

 Title: Workplace Stress Across Different Industries

Problem Statement:

Workplace stress has become a common issue across many industries due to factors such as high workload, poor work–life balance, long working hours, and limited control over job roles. These stress factors affect employee performance, job satisfaction, and overall well-being. However, organizations often lack clear, data-based insights into which industries experience higher stress levels and what factors contribute most. This case study focuses on analyzing and comparing workplace stress across industries using data visualization and predictive analysis for better decision-making.

Objective:

To analyze workplace stress levels across different industries, identify major contributing factors, compare stress patterns, and use analytical tools to predict employee stress levels for improved organizational planning.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:
Employees from multiple industries, including:

• Education: school teachers, college faculty, and academic administrators

• Information Technology: software engineers, project managers, and IT support staff

• Healthcare: doctors, nurses, and hospital administrative staff

• Banking and Finance: bank officers, financial analysts, and support staff

• Manufacturing: production supervisors, operators, and engineers

• Service Sector: BPO, retail, and hospitality employees

Case Study 02

Title: Electric Vehicle Adoption in Urban Areas

Problem Statement:

Electric Vehicles (EVs) are considered an eco-friendly alternative to traditional fuel-based vehicles. Despite environmental benefits and government support, EV adoption in urban areas remains limited due to factors such as high purchase cost, insufficient charging infrastructure, range anxiety, and low consumer awareness. At the same time, factors like fuel savings, incentives, environmental concern, and technological improvements create strong future potential. This case study focuses on analyzing both challenges and opportunities related to EV adoption using data-driven insights and Power BI dashboards.

Objective:

To analyze the problems and prospects of electric vehicle adoption in urban areas and evaluate the factors influencing consumer adoption decisions using interactive data visualization.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Private vehicle owners in urban areas, including car and two-wheeler owners

• Potential EV buyers who are considering switching from conventional vehicles

• Fleet managers of ride-sharing services and corporate vehicle fleets

• Government and municipal officials involved in urban transport planning and EV policies

• Charging infrastructure providers and EV dealerships

• 
  

Case Study 03

Title: Effectiveness of a New Teaching Method

Problem Statement:

Educational institutions are adopting new and innovative teaching methods to enhance student learning and academic performance. However, the effectiveness of these methods may differ based on factors such as gender, classroom environment, and individual study habits. Conventional evaluation approaches often provide limited insights and do not support prediction of outcomes. This case study focuses on a data-driven evaluation of a new teaching method using visualization and analytical techniques to understand performance variations and predict academic results.

Objective:

To evaluate the impact of a new teaching method on student academic performance, compare results across different student groups, analyze the influence of study hours, and use analytical tools to visualize trends and predict exam outcomes.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Undergraduate students across different courses (BCA, BBA, BA, BSc, etc.)

• Postgraduate students (MCA, MBA, MSc, etc.)

• Faculty members implementing the new teaching method

• Academic administrators monitoring student performance and classroom outcomes

Case Study 04

Title: Customer Satisfaction and Purchase Behavior in Online Shopping

Problem Statement:

The rapid expansion of online shopping platforms has increased competition among e-commerce companies, making customer satisfaction a critical success factor. Although large volumes of transaction data are available, many organizations lack integrated insights derived from customer perception and behavior data. Factors such as product quality, pricing, delivery experience, customer service, and return policies significantly influence satisfaction and repeat purchases. This case study focuses on analyzing customer satisfaction and purchase behavior using data visualization and predictive analytics for informed business decisions.

Objective:

To study customer satisfaction and purchase behavior on online shopping platforms, identify key influencing factors, analyze repeat purchase intentions, and use analytical tools to visualize trends and predict customer behavior.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Online shoppers across different age groups (students, working professionals, homemakers)

• Consumers purchasing from major e-commerce platforms such as Amazon, Flipkart, Myntra, and others

• Frequent and occasional buyers to capture diverse purchase behavior

• Respondents across urban and semi-urban areas to reflect varying perceptions and accessibility

This ensures the study collects diverse consumer feedback to analyze satisfaction, purchase patterns, and repeat buying behavior effectively.

Case Study 05

Title: Social Media Usage and Academic Performance of College Students

Problem Statement:

Social media platforms have become an essential part of college students’ everyday life for communication, entertainment, and learning. While moderate use can support educational activities, excessive social media usage may reduce study time, concentration, and overall academic performance. Many existing studies provide limited analytical depth and lack integrated visualization and prediction. This case study aims to analyze the effect of social media usage on academic performance using primary data, visual analytics, and predictive techniques.

Objective:

To study social media usage patterns among college students, analyze its impact on academic performance, examine the role of study hours, and use analytical tools to visualize trends and predict academic outcomes.

Respondents for Primary Data Collection:

• Undergraduate students (BCA, BBA, BA, BSc, etc.)

• Postgraduate students (MCA, MBA, MSc, etc.)

Case Study 06

Title: Digital Payment Adoption and User Trust

Problem Statement:

Digital payment systems have significantly changed the way individuals conduct financial transactions by offering convenience, speed, and accessibility. However, users continue to face challenges such as security concerns, fear of fraud, privacy issues, and reduced control over spending due to easy payment mechanisms. At the same time, factors like ease of use, incentives, transaction transparency, and financial inclusion support continued adoption. This case study focuses on analyzing both the challenges and opportunities influencing digital payment adoption and user trust through data-driven analysis and visualization.

Objective:

To study the problems and prospects of digital payment adoption, analyze the role of user trust and security perception, examine spending behavior, and use analytical tools to visualize patterns and predict transaction frequency.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Digital payment users across various age groups (students, working professionals, homemakers)

• Users of multiple payment platforms such as UPI, mobile wallets (Paytm, Google Pay, PhonePe), net banking, and credit/debit cards

• Frequent and occasional users to capture diverse usage behavior

• Urban and semi-urban users to account for differences in access, awareness, and adoption levels

Case Study 07

Title: Employee Productivity in Hybrid Work Environments

Problem Statement:

Hybrid work environments, combining remote and onsite work arrangements, are increasingly adopted across industries. While hybrid work offers flexibility, reduced commuting time, and improved work–life balance, it also creates challenges such as communication gaps, reduced team cohesion, unclear work boundaries, and difficulty in monitoring employee performance. Organizations need data-driven insights to understand how different work modes influence employee productivity and well-being. This case study focuses on analyzing employee productivity in hybrid work environments using visualization and analytical techniques.

Objective:

To compare employee productivity across hybrid, remote, and onsite work modes, examine the impact of work mode on work–life balance, analyze communication effectiveness, identify key problems and prospects of hybrid work, visualize productivity patterns, and predict employee productivity using workplace and behavioral factors.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Employees working in hybrid work mode

• Employees working in fully remote work mode

• Employees working in fully onsite work mode

• Respondents from IT, education, finance, healthcare administration, and service sectors

Case Study 08

Title: Cybersecurity Awareness and Online Safety Behavior

Problem Statement:

With the increasing use of the internet for education, work, banking, and social networking, cyber threats such as phishing, identity theft, malware attacks, and data breaches have become common. Despite frequent interaction with digital platforms, many users continue to follow unsafe online practices due to limited cybersecurity awareness and low risk perception. Understanding how awareness and past cyber experiences influence online safety behavior requires a data-driven approach. This case study focuses on assessing cybersecurity awareness, risk perception, and online safety behavior using visualization and analytical techniques.

Objective:

To assess cybersecurity awareness among internet users, analyze online safety behavior and password practices, examine the influence of awareness and past cyber incidents on risk perception, visualize cybersecurity patterns, and predict safe online behavior using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• College students (UG and PG)

• Working professionals across various industries

Case Study 09

Title: Consumer Awareness and Purchase Intention towards Sustainable Products

Problem Statement:

Increasing environmental concerns and climate change awareness have led to the availability of sustainable and eco-friendly products in urban markets. However, despite growing awareness, adoption of these products remains inconsistent due to factors such as higher prices, limited trust in environmental claims, and insufficient product information. Organizations and policymakers require data-driven insights to understand both the barriers and motivating factors influencing sustainable consumption. This case study focuses on analyzing consumer awareness, perception, and purchase intention towards sustainable products using visualization and analytical techniques.

Objective:

To assess environmental awareness among urban consumers, analyze perception towards sustainable products, study the influence of awareness, price sensitivity, and brand trust on purchase intention, visualize consumer behavior patterns, and predict adoption of sustainable products using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Urban consumers purchasing FMCG products

• Consumers purchasing apparel and lifestyle products

• Consumers purchasing household and personal care products

• Working professionals, homemakers, and students

Case Study 10

Title: Student Satisfaction and Learning Outcomes in Online Learning Platforms

Problem Statement:

The widespread use of online learning platforms has significantly transformed the education system by providing flexible and accessible learning opportunities. However, differences in platform usability, content quality, and instructor interaction strongly influence student satisfaction, engagement, and course completion. Many learners experience challenges such as low motivation, limited interaction, and usability issues. Institutions require data-driven insights to understand both the challenges and benefits of online learning platforms. This case study focuses on evaluating student satisfaction and learning outcomes using analytical and visualization techniques.

Objective:

To evaluate student satisfaction with online learning platforms, analyze the impact of content quality, usability, and instructor interaction on learning outcomes, identify key problems and prospects, visualize learning patterns, and predict satisfaction and course completion using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Undergraduate students

• Postgraduate students

• Professional certification learners

Case Study 11

Title: Healthcare Service Quality, Patient Satisfaction, and Loyalty

Problem Statement:

Healthcare organizations are increasingly emphasizing patient-centered care to enhance service quality and satisfaction. However, challenges such as long waiting times, inconsistent staff behavior, communication gaps, and perceived treatment quality issues continue to influence patient experiences. Even when clinical care is adequate, poor service quality can reduce patient trust, loyalty, and revisit intention. Healthcare administrators require data-driven insights to understand service-related problems and improvement opportunities. This case study focuses on evaluating healthcare service quality, patient satisfaction, and loyalty using analytical and visualization techniques.

Objective:

To assess patient perception of healthcare service quality, analyze the influence of waiting time, staff behavior, and treatment quality on patient satisfaction, identify service-related problems and prospects, visualize service quality patterns, and predict patient satisfaction and loyalty using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Patients visiting government hospitals

• Patients visiting private hospitals

• Patients visiting clinics and diagnostic centers

• OPD patients, in-patients, and follow-up patients

Case Study 12

Title: Brand Loyalty and Switching Behavior among Smartphone Users

Problem Statement:

The smartphone market is highly competitive, with frequent technological upgrades, aggressive pricing, and rapid product launches. Despite significant investments in innovation and marketing, maintaining long-term brand loyalty remains challenging. Consumers often switch smartphone brands due to factors such as price sensitivity, perceived superiority of features, declining brand trust, or better alternatives. Organizations require data-driven insights to understand both the drivers of brand loyalty and the factors leading to brand switching. This case study focuses on analyzing brand loyalty and switching behavior among smartphone users using analytical and visualization techniques.

Objective:

To identify factors influencing brand loyalty among smartphone users, analyze the role of brand trust, product features, and price sensitivity in switching behavior, identify key problems and prospects in sustaining loyalty, visualize brand-wise loyalty patterns, and predict customer loyalty and switching probability using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Smartphone users across different age groups

• Users of brands such as Apple, Samsung, OnePlus, Xiaomi, Realme, Vivo, etc.

• Students, working professionals, and business users

Case Study 13

Title: Public Transport Service Quality and Commuter Satisfaction in Pune City

Problem Statement:

Public transport systems such as buses, metro rail, and suburban trains are essential for urban mobility and sustainable development. Despite ongoing investments in infrastructure, commuters frequently experience issues related to punctuality, safety, overcrowding, and comfort, which affect satisfaction and regular usage. Transport authorities require data-driven insights to understand service quality gaps and improvement opportunities. This case study focuses on evaluating public transport service quality, commuter satisfaction, and usage behavior in Pune City using analytical and visualization techniques.

Objective:

To evaluate commuter perception of public transport service quality, analyze the influence of punctuality, safety, and comfort on satisfaction and usage behavior, identify key service-related problems and prospects, visualize service quality trends, and predict commuter satisfaction and usage frequency using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Daily commuters using public buses

• Daily commuters using metro rail

• Daily commuters using suburban/local trains

• Students, working professionals, and daily wage commuters

Case Study 14

Title: Mobile App Usability and User Retention

Problem Statement:

The rapid expansion of mobile applications across domains such as e-commerce, fintech, education, and entertainment has increased competition among app providers. While user acquisition through promotions is common, retaining users remains a significant challenge. Issues such as poor usability, slow performance, complex navigation, and unsatisfactory user experience often result in high uninstall and churn rates. Organizations need data-driven insights to understand usability-related problems and opportunities for improvement. This case study focuses on analyzing mobile app usability and its impact on user retention and engagement using analytical and visualization techniques.

Objective:

To evaluate user perception of mobile app usability, analyze the influence of navigation, performance, and overall user experience on retention behavior, identify key usability-related problems and prospects, visualize usability and retention patterns, and predict user retention intention using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Mobile app users of e-commerce applications

• Users of fintech and payment applications

• Users of education and learning applications

• Users of entertainment and social media applications

• Students, working professionals, and business users

Case Study 15

Title: Short-Video (Reels) Consumption and Student Health

Problem Statement:

The increasing popularity of short-video platforms such as Instagram Reels, YouTube Shorts, and Snapchat Spotlight has significantly influenced students’ digital consumption habits. While short-video content provides entertainment, relaxation, and creative expression, excessive and uncontrolled usage has raised concerns related to mental well-being and physical health. Problems such as sleep disturbances, reduced physical activity, anxiety, attention issues, and digital addiction are increasingly observed among students. Educational institutions and policymakers require data-driven insights to understand both the risks and potential benefits associated with short-video consumption. This case study focuses on analyzing the impact of Reels consumption on students’ mental and physical health using analytical and visualization techniques.

Objective:

To analyze short-video consumption patterns among students, examine their impact on mental health, physical activity, and sleep quality, identify problematic and controlled usage behaviors, visualize health-related patterns, and predict mental and physical health risk levels using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Undergraduate students

• Postgraduate students

• Professional course students

Platforms Used by Respondents:

• Instagram Reels

• YouTube Shorts

• Snapchat Spotlight

Case Study 16

Title: Impact of Generative AI Platforms on Students’ Academic Performance and Well-being

Problem Statement:

The increasing use of generative AI platforms such as ChatGPT and Google Gemini has significantly changed the way students approach learning, assignments, examinations, and project work. While these tools provide quick access to information, support content creation, and enhance learning efficiency, excessive dependence on AI may reduce critical thinking, self-learning ability, and originality. Additionally, prolonged screen time and overreliance on digital tools may contribute to stress, anxiety, reduced motivation, and physical health concerns. Educational institutions require data-driven insights to understand both the benefits and risks of generative AI usage among students. This case study focuses on analyzing the impact of generative AI platforms on students’ academic performance, mental health, and overall well-being using analytical and visualization techniques.

Objective:

To examine students’ usage patterns of generative AI platforms for academic activities, analyze their impact on academic performance and learning outcomes, assess effects on mental and physical well-being, explore students’ perceptions of AI as a learning tool, visualize usage and impact patterns, and derive predictive insights related to academic performance and well-being.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Undergraduate students

• Postgraduate students

• Professional course students

• Students actively using generative AI platforms such as ChatGPT, Google Gemini, and similar tools

Case Study 17

Title: Use of Generative AI Platforms by Academicians and Researchers

Problem Statement:

The emergence of generative AI platforms such as ChatGPT and Google Gemini has significantly influenced academic research and scholarly activities. Academicians and researchers increasingly use these tools for literature review, idea generation, drafting manuscripts, and supporting data interpretation. While AI platforms enhance research efficiency and productivity, they also raise concerns related to ethical authorship, accuracy of generated content, intellectual dependency, and potential bias in research outcomes. Institutions and researchers require data-driven insights to understand both the opportunities and challenges associated with AI-assisted research. This case study focuses on examining how generative AI platforms are used in academic research and their impact on scholarly productivity.

Objective:

To assess the usage of generative AI platforms by academicians in research activities, analyze their impact on research productivity and efficiency, identify ethical and accuracy-related challenges, examine perceptions and acceptance of AI-assisted tools, and suggest responsible practices for integrating AI in academic research.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Academicians and faculty members

• Research scholars (PhD and postdoctoral researchers)

• Independent researchers using generative AI platforms

Case Study 18

Title: Brand Craze, Peer Influence, and Financial Decision-Making among Students

Problem Statement:

In recent years, premium footwear brands such as Nike, Adidas, Puma, and similar labels have become highly popular among students. Ownership of branded footwear is often associated with social status, identity, and peer acceptance rather than functional need. Despite limited financial capacity, many students purchase high-priced branded shoes due to peer pressure, social comparison, social media influence, and fear of social exclusion. This creates a clear gap between students’ financial affordability and their actual purchasing behavior, leading to financial stress and irrational consumption patterns. This case study focuses on understanding the social, psychological, and financial factors influencing students’ brand-driven purchasing decisions using a data-driven analytical approach.

Objective:

To examine brand consciousness among students, analyze the influence of peers, social comparison, and social media on footwear purchase decisions, assess the financial strain caused by premium brand purchases, evaluate the relationship between brand usage and social acceptance or self-esteem, and visualize consumption patterns using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Undergraduate students

• Postgraduate students

• Students purchasing or aspiring to purchase premium footwear brands

Case Study 19

Title: Celebrity Influence, Fashion Trends, and Financial Pressure among Youth

Problem Statement:

Fashion and personal style have become important aspects of youth identity, strongly influenced by celebrities from films, sports, social media, and advertising. Celebrity endorsements and branded fashion trends create aspirational value and encourage imitation among youth. Despite having limited financial resources, many young individuals follow celebrity-inspired fashion, leading to impulsive purchases, unplanned spending, and financial stress. Peer influence and aggressive brand marketing further weaken rational financial decision-making. This case study focuses on analyzing how celebrity influence and fashion trends affect youth spending behavior and contribute to financial pressure using a data-driven analytical approach.

Objective:

To analyze the extent of celebrity influence on youth fashion preferences, examine the role of brands in promoting celebrity-inspired trends, assess the impact of fashion influence on spending behavior, study financial pressure arising from fashion-related consumption, and evaluate youth awareness and management of personal finances using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Youth and college students

• Young working professionals

• Individuals actively following celebrity fashion trends

Case Study 20

Title: Students’ Daily Expenses and Pocket Money Utilization

Problem Statement:

Financial independence among students is largely influenced by pocket money, allowances, and limited income sources. Daily expenses such as food, transportation, mobile recharges, entertainment, and discretionary purchases reflect students’ spending behavior and financial discipline. However, most students do not systematically track their daily expenses, resulting in impulsive spending, poor budgeting, and financial stress. The lack of data-driven monitoring prevents students from understanding their consumption patterns and planning future financial needs. This case study focuses on analyzing and predicting students’ daily expenses and pocket money utilization using analytical and visualization techniques.

Objective:

To track students’ daily expenses over a defined period, analyze pocket money utilization across different expense categories, identify impulsive spending patterns, examine the relationship between pocket money and spending discipline, visualize expense trends, and predict future spending behavior using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Undergraduate students

• Postgraduate students

• Students receiving regular pocket money or allowances

Case Study 21

Title: Multidimensional Responsibilities and Well-Being of Working Women

Problem Statement:

Working women often manage multiple roles simultaneously, including professional responsibilities, household management, caregiving for children and elders, and fulfilling family expectations. Despite increased participation of women in the workforce, domestic and caregiving responsibilities remain unevenly distributed. This cumulative burden frequently results in time scarcity, mental stress, physical exhaustion, reduced job satisfaction, and poor work–life balance. There is limited data-driven research that examines these interconnected responsibilities and their impact on women’s mental health, physical well-being, and time management in an integrated manner. This case study focuses on analyzing these dimensions using analytical and visualization techniques to support informed policy and organizational decisions.

Objective:

To examine the distribution of household and caregiving responsibilities among working women, analyze the impact of multiple roles on mental and physical health, evaluate time management practices, study the role of workplace support in achieving work–life balance, and visualize well-being patterns using analytical tools.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Working women across sectors such as education, IT, healthcare, finance, and services

• Married and unmarried working women

• Women with and without caregiving responsibilities

Case Study 22

Title: Physical Fitness and Academic Performance of Students

Problem Statement:

Despite awareness of the benefits of physical fitness, many students fail to engage in regular physical activity, resulting in fatigue, stress, poor concentration, and reduced academic performance. There is limited empirical evidence linking fitness practices with health, mental freshness, and academic outcomes.

Objective:

To examine students’ physical fitness practices, analyze their impact on health and freshness, study the relationship with academic performance, compare practices across gender and age groups, and identify effects among physically inactive students.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• School, undergraduate, and postgraduate students

• Both physically active and inactive students

• Students from various academic disciplines

Case Study 23

Title: Physical Fitness and Work Performance of Working Professionals

Problem Statement:

Many working professionals neglect physical fitness due to time constraints, resulting in reduced productivity, mental exhaustion, and health issues. There is limited data-driven research analyzing how fitness practices influence job performance, freshness, and well-being across age and gender groups.

Objective:

To assess adoption of physical fitness practices, analyze their impact on health and mental freshness, study the relationship with work performance, compare practices across gender and age, and evaluate outcomes among inactive professionals.

Data to Collect & Analysis Goals:

(Already provided)

Respondents for Primary Data Collection:

• Working professionals across industries

• Both physically active and inactive professionals

• Employees of different age groups and genders

Case Study 24

Title: Real-Time Student Attendance Analytics Using Power BI

Problem Statement:

Student attendance is a critical indicator of engagement, learning continuity, and academic performance. Traditional attendance systems are often manual, fragmented, and updated infrequently, making it difficult for faculty and administrators to monitor attendance trends effectively. Delayed or inaccurate attendance data prevents timely interventions for at-risk students, reduces accountability, and limits the ability to link attendance with academic performance. There is a need for a real-time, automated, and dashboard-driven system that can dynamically track attendance, identify chronic absenteeism, and provide actionable insights to support informed decision-making in higher education institutions.

Objective:

To analyze real-time student attendance patterns, identify chronic absenteeism and engagement gaps, automate attendance reporting, and assess the relationship between attendance and academic performance using Power BI.

Data to Collect & Analysis Goals:

(Already provided)

Respondents / Data Source:

• Institutional student attendance records

• Faculty attendance inputs (LMS/ERP-integrated)

Case Study 25

Title: Automated Lecture Feedback and Teaching Effectiveness Analysis

Problem Statement:

Student feedback is a vital source of information for improving teaching quality and learning experiences. However, manual collection and analysis of lecture feedback is time-consuming, prone to errors, and often delays actionable improvements. Traditional methods make it difficult to track trends, identify strengths and weaknesses of faculty, or benchmark teaching effectiveness. There is a need for a data-driven, automated system that can efficiently analyze feedback, visualize patterns, and support real-time decision-making to enhance teaching quality.

Objective:

To automate lecture feedback analysis, identify teaching effectiveness indicators, and enable continuous teaching improvement using Power BI.

Data to Collect & Analysis Goals:

(Already provided)

Respondents:

• Student lecture feedback (primary data)

Case Study 26

Title: Learner Type Identification and Personalized Learning Analytics

Problem Statement:

Students exhibit diverse learning preferences, including visual, auditory, reading/writing, and kinesthetic styles. Traditional teaching methods often adopt a uniform approach, which may not address individual learning needs, leading to reduced engagement, comprehension, and academic performance. Without learner analytics, educators cannot effectively identify different learner types or tailor teaching strategies to maximize learning outcomes. There is a need for a data-driven approach that classifies learner types, analyzes performance variations, and enables personalized learning interventions using Power BI.

Objective:

To classify learner types, analyze performance differences across learner categories, and support personalized learning strategies using Power BI analytics.

Data to Collect & Analysis Goals:

(Already provided)

Respondents / Data Source:

• Students across different courses and academic levels

• Data on learning preferences, performance scores, and engagement metrics

Case Study 26

Title: Learner Type Identification and Personalized Learning Analytics

Problem Statement:

Students exhibit diverse learning preferences, including visual, auditory, reading/writing, and kinesthetic styles. Traditional teaching methods often adopt a uniform approach, which may not address individual learning needs, leading to reduced engagement, comprehension, and academic performance. Without learner analytics, educators cannot effectively identify different learner types or tailor teaching strategies to maximize learning outcomes. There is a need for a data-driven approach that classifies learner types, analyzes performance variations, and enables personalized learning interventions using Power BI.

Objective:

To classify learner types, analyze performance differences across learner categories, and support personalized learning strategies using Power BI analytics.

Data to Collect & Analysis Goals:

(Already provided)

Respondents / Data Source:

• Students across different courses and academic levels

• Data on learning preferences, performance scores, and engagement metrics

Case Study 27

Title: NAAC Criterion Data Analysis and Accreditation Automation Using Power BI

Problem Statement:

NAAC accreditation requires comprehensive, multi-year data collection across seven criteria, including curriculum, teaching–learning processes, research, infrastructure, student support, governance, and innovations. Manual data compilation is time-consuming, prone to errors, and often fragmented across departments, leading to delays in reporting, difficulty in maintaining compliance, and challenges in continuous quality improvement. Institutions lack a centralized system to track criterion-wise performance, generate audit-ready reports, and identify areas for strategic enhancement. There is a need for an automated, data-driven solution using Power BI to streamline NAAC data analysis, improve accuracy, and support timely accreditation processes.

Objective:

To automate NAAC criterion-wise data analysis, ensure data accuracy and compliance, and support continuous quality improvement using Power BI.

Data to Collect & Analysis Goals:

(Already provided)

Respondents / Data Source:

• Institutional records across all NAAC criteria

• Faculty, administrative staff, and departmental data inputs

Case Study 28

Title: Student Result Analysis and Predictive Academic Performance Automation

Problem Statement:

Student result analysis is essential for monitoring academic progress, identifying at-risk students, and supporting timely interventions. Traditional manual methods of analyzing results are time-consuming, prone to errors, and lack predictive capabilities. Institutions struggle to scale result analysis across multiple courses, semesters, and departments, limiting their ability to provide data-driven academic counseling and plan effective learning interventions. There is a need for an automated, analytics-driven system using Power BI to process results efficiently, visualize performance trends, and predict academic risk and success patterns for informed decision-making.

Objective:

To automate student result analysis, predict academic risk and success patterns, and support data-driven academic counseling using Power BI.

Data to Collect & Analysis Goals:

(Already provided)

Respondents / Data Source:

• Student academic records across courses and semesters

• Examination scores, grade point averages, and assessment metrics

Case Study 29

Title: Data-Driven Human Resource Management and Employee Performance

Problem Statement:

Human Resource (HR) departments play a crucial role in managing talent, enhancing employee engagement, and driving organizational performance. Traditional HR practices often rely on intuition, fragmented records, and historical data, lacking predictive and analytical capabilities. This can result in inefficient talent utilization, delayed interventions, low employee engagement, and higher attrition rates. With the increasing complexity of the workforce, there is a critical need for data-driven HR analytics using tools like Power BI to monitor performance, predict risks, optimize engagement strategies, and support informed decision-making.

Objective:

To examine the adoption of HR analytics, analyze its impact on employee performance and engagement, assess Power BI’s effectiveness in HR decision-making, and evaluate the influence of analytics on employee retention.

Data to Collect & Analysis Goals:

(Already provided)

Respondents / Data Source:

• Employee performance records

• HR databases on engagement, appraisal, and retention

• Surveys and feedback from employees across departments

Case Study 30

Title: Automation of Faculty Appraisal and Promotion under UGC Career Advancement Scheme (CAS) Using Power BI

Problem Statement:

Higher Education Institutions (HEIs) in India follow UGC guidelines for faculty appraisal and promotion under the Career Advancement Scheme (CAS). CAS evaluates faculty performance across teaching, research, academic contributions, and institutional responsibilities using Academic Performance Indicators (API). Traditionally, CAS documentation and API score calculation are manual, time-consuming, prone to errors, and lack transparency. Institutions face challenges such as inconsistent API calculations, difficulty in real-time monitoring of faculty performance, delays in appraisal and promotion processes, problems mapping CAS data to NAAC criteria, and absence of predictive insights for career progression. There is a critical need for an automated, transparent, and scalable CAS–API analytics framework using Power BI to enhance efficiency, accuracy, and data-driven decision-making.

Objectives:

The objectives of the study are to analyze faculty performance under UGC CAS using API scores, design dynamic dashboards in Power BI for automated CAS appraisal, evaluate faculty eligibility and readiness for promotion, identify gaps in teaching, research, and academic contributions, support NAAC criterion-wise data analysis and reporting, and apply predictive analytics for forecasting promotion eligibility and performance trends.

Respondents / Data Source:

Faculty CAS records, API scores, departmental and institutional academic contributions, and IQAC/administrative documentation.

Data to Collect & Analysis Goals:

(Already provided)