CHAPTER 5

EARLY WARNİNG SYSTEMS FOR IDENTİFYİNG AT-RİSK

LEARNERS İN INDİA: A QUALİTATİVE STUDY

Sreelogna Dutta Banerjee

& Jayanta Mete

Research Scholar

Former Professor & Dean

Department of Education,

Faculty of Education, University of Kalyani, Kalyani, West Bengal, India

sreelognadutta@gmail.com, ORCID – 0009-0006-7585-7182

jayanta_135@yahoo.co.in, ORCID – 0000-0002-9409-2983

Abstract

The issue of studying at risk learners who may fail academically, become disengaged, drop out, or delayed development

is of a vital concern in the Indian education system. Even with the enrolment advancement, a high number of students

are still exposed to structural, academic, socio-economic, and psychological barriers that influence retention and

educational achievement. Here, Early Warning Systems (EWS) have become a pioneering international set of

strategies that can be used to proactively recognize vulnerable students and learners prior to the irreversible

consequences of educational failure. This paper discusses the idea, importance, relevance and issues of Early Warning

Systems in detecting at-risk learners in India. The research methodology is the qualitative review-based approach

and relies on the official policy reports, books, and journals. The paper tells about recent school dropout in India,

the applicability of EWS according to the National Education Policy (NEP) 2020, the opportunities of learning

analytics, attendance data, academic records, and behavioural indicators to create an Indian model of learner support.

The results indicate that India is in dire need of institutionalized early identification systems, particularly in school

and higher education environments that are characterized by digital inequality, first-generation students, and diverse

educational preparedness. The paper concludes that EWS can be a significant educational facilitating tool in India

provided it is adopted ethically, contextually and with robust human intervention systems and not as merely

technological surveillance tools.

Keywords:

Early Warning Systems, At-Risk Learners, India, Dropout, Learning Analytics, Student

Retention, NEP 2020, Educational Data Mining.

Introduction

India is home to one of the largest and most complex education systems in the world,

encompassing millions of learners across school, college, and university levels. Over the past two

decades, substantial progress has been made in expanding access to education through policy

reforms, infrastructural development, and increased public investment in universal education [10–

12]. However, access alone does not ensure meaningful participation, academic progression, or

successful completion. A significant proportion of learners continue to face challenges such as

poor academic performance, disengagement, irregular attendance, examination failure, and

eventual dropout. This persistent gap between enrolment and educational success remains one of

the most pressing concerns within the Indian education system [16].

Recent national data highlights both progress and ongoing challenges. According to the

Economic Survey 2024–25, dropout rates stand at 1.9% at the primary level, 5.2% at the upper

primary level, and 14.1% at the secondary level, indicating a sharp increase as students advance

through the system [11]. Similarly, UDISE+ data for 2024–25 reports a decline in dropout rates—

from 3.7% to 2.3% at the preparatory level, 5.2% to 3.5% at the middle level, and 10.9% to 8.2%

at the secondary level—suggesting improvement, yet continued vulnerability at higher levels of

schooling [12]. These figures reveal a critical pattern: while access has improved, retention and

engagement remain fragile, particularly during transitional stages marked by academic pressure,

socio-economic constraints, and reduced motivation.

Graph -1 Represent Comparative Analysis of Student Dropout Rates by Educational

Stage

Source: Economic Survey 2024–25

In this context, the need for proactive and data-driven interventions becomes evident. Early

Warning Systems (EWS) have emerged as a strategic response to identify and support at-risk

learners before they disengage or drop out. EWS are structured, evidence-based frameworks that

utilize indicators such as attendance records, academic performance, behavioural patterns, and

Preparatory Middle Secondary

Earlier (%)

3,70% 5,20% 10,90%

Recent (%)

2,30% 3,50% 8,20%

3,70%

5,20%

10,90%

2,30%

3,50%

8,20%

0,00%

2,00%

4,00%

6,00%

8,00%

10,00%

12,00%

Earlier (%) Recent (%)

socio-economic background to detect early signs of risk [5]. Unlike traditional reactive approaches,

these systems enable preventive interventions through timely identification and targeted support

mechanisms [6,14].

The application of learning analytics and educational data mining has significantly strengthened

the effectiveness of EWS. Studies demonstrate that predictive models using machine learning

algorithms can accurately identify students at risk by analysing patterns in digital engagement,

assessment performance, and participation [8,13,15]. Research in online and blended learning

environments further confirms that EWS can enhance retention rates by enabling personalized

interventions and continuous monitoring [6,7]. Moreover, systematic reviews highlight that

learning analytics plays a crucial role in reducing dropout rates and improving academic outcomes

in higher education [4,17].

The relevance of EWS in India has become even more pronounced in the post-pandemic

context. The COVID-19 crisis exposed deep structural inequalities, including the digital divide,

learning loss, disrupted routines, and increased psychological stress among students [16]. These

challenges have intensified the need for robust monitoring systems that can track learner progress

and provide timely academic and emotional support. The National Education Policy (NEP) 2020

further reinforces this need by emphasizing equity, inclusion, flexibility, and reduced dropout rates,

thereby creating a strong policy foundation for the integration of EWS in educational institutions

[10].

Globally, early warning frameworks have been successfully implemented across various

domains, including finance, disaster management, and environmental risk assessment,

demonstrating their adaptability and effectiveness in predictive modelling [1,2,3]. Their application

in education represents a natural extension of these approaches, where data-driven insights can

inform decision-making and improve institutional responsiveness [18]. In the Indian context,

integrating EWS within the education system can significantly enhance learner retention, promote

inclusive education, and align with national development goals.

Thus, the adoption of Early Warning Systems represents a critical shift from reactive to

preventive educational practices. By leveraging data, technology, and policy support, EWS can play

a transformative role in addressing the enrolment–achievement gap and ensuring that access to

education translates into meaningful learning outcomes and long-term success [9].

Review of Related Literature

The concept of early identification of at-risk learners before formal academic failure had

received considerable attention within the domains of learning analytics, educational data mining,

and student retention research. International scholarship consistently demonstrated that students

exhibited measurable early warning indicators—such as absenteeism, low digital engagement, poor

assessment performance, delayed submissions, and reduced participation—well before actual

failure or dropout occurred [13,14,17]. These observable patterns provided a strong empirical

foundation for the development of Early Warning Systems (EWS) aimed at timely intervention

and learner support.

A seminal study by Akçapınar et al. had highlighted the role of learning analytics in identifying

at-risk students through digital traces of learner behaviour in online environments [5]. Their

findings suggested that analysing real-time engagement data enabled institutions to detect potential

academic risk during the learning process rather than after final assessments. Importantly, the study

emphasized that predictive accuracy improved significantly when multiple indicators—academic,

behavioural, and interactional—were combined instead of being used in isolation [5]. This

multidimensional approach enhanced the reliability of early identification models and strengthened

risk detection frameworks.

Similarly, Bañeres et al. had developed an EWS model in online higher education and

demonstrated that predictive analytics could effectively identify students at risk of disengagement

or academic failure [6]. However, they argued that prediction alone was insufficient and that the

true pedagogical value of EWS lay in enabling timely, personalized, and context-sensitive

interventions. This perspective reinforced the idea that EWS functioned not merely as diagnostic

tools but as mechanisms for proactive academic support and student retention improvement [6].

Further strengthening this argument, de Oliveira et al. conducted a systematic review and

concluded that learning analytics contributed significantly to dropout prevention when institutions

actively used learner data to support student persistence rather than simply record outcomes [4].

Likewise, Queiroga et al. found that early identification allowed institutions to design targeted

interventions, thereby improving student retention and academic continuity [14]. These studies

collectively underlined the importance of integrating academic performance with behavioural and

participation-based indicators to enhance the effectiveness of EWS frameworks.

Recent advancements in machine learning further expanded the scope of early detection

models. Techniques such as decision trees, logistic regression, support vector machines, random

forests, and neural networks had been increasingly applied to predict student risk profiles [8,15].

Research indicated that combining static variables (such as socio-economic background) with

dynamic variables (such as weekly engagement patterns) significantly improved predictive

accuracy, particularly when analysed longitudinally rather than at a single point in time [8]. This

dynamic modelling approach enabled continuous monitoring and adaptive intervention strategies

within educational systems.

In the Indian context, large-scale empirical research on EWS remained limited; however,

existing policy documents and educational studies strongly indicated the need for such systems.

Key challenges such as irregular attendance, foundational learning gaps, socio-economic

disparities, and transitional vulnerabilities between educational stages were consistently reported

[11,12,16]. Evidence suggested that while enrolment rates appeared high, actual classroom

engagement and participation varied significantly, making attendance and engagement critical

indicators of student risk [16].

The policy environment in India further supported the conceptual relevance of EWS. The

National Education Policy (NEP) 2020 emphasized equity, inclusion, flexible learning pathways,

and reduced dropout rates, all of which aligned closely with early identification and intervention

frameworks [10]. Although EWS had not yet been systematically institutionalized across Indian

education systems, its theoretical and policy foundations had become increasingly evident.

Objectives of the Study

The present paper is guided by the following objectives:

1. To find out the concept and significance of Early Warning Systems for

identifying at-risk learners.

2. To study the relevance of Early Warning Systems in the Indian educational

context.

3. To find out the major indicators that can be used to detect at-risk learners in

India.

4. To explore the role of educational data, learning analytics, and institutional

intervention in building an Early Warning System.

5. To find out the educational implications, opportunities, and challenges of

implementing EWS in India.

Methodology

The current research uses a qualitative research approach as a review. It is conceptual,

interpretive in nature and it does not contain collection of primary field data. Rather the paper

relies on secondary data and academic analysis.

Nature of the Study

This article is a qualitative journal-type review paper that aims at the conceptual, policy and

practical relevance of the Early Warning Systems to identify at risk learners in India.

Sources of Data

The present study is grounded in a comprehensive and systematic review of diverse categories

of academic and policy-oriented sources, ensuring both theoretical depth and empirical relevance.

A significant portion of the literature has been drawn from peer-reviewed journal articles available

through academic databases such as Google Scholar. These studies provide rigorous, evidence-

based insights into learning analytics, educational data mining, student retention, and Early

Warning Systems (EWS). Foundational works in this domain highlight the role of predictive

analytics, behavioural indicators, and digital engagement in identifying at-risk learners and

improving educational outcomes [5,13,17]. Such peer-reviewed contributions form the backbone

of the analytical framework employed in this research.

In addition to journal articles, scholarly papers accessed through reputed journal have been

extensively utilized to capture emerging trends and recent developments in the field. These sources

include contemporary studies on machine learning applications in education, predictive modelling

of student performance, and the integration of artificial intelligence in early detection systems

[8,15]. Publications often provide access to preprints and ongoing research, thereby offering timely

and relevant perspectives that complement traditional peer-reviewed literature.

Official reports and publications by the Ministry of Education, Government of India, constitute

another critical category of sources. Documents such as the National Education Policy (NEP)

2020 and the Economic Survey 2024–25 provide authoritative insights into the policy landscape,

educational priorities, and systemic challenges within the Indian context [10,11]. These reports are

essential for understanding the structural dimensions of education, including access, equity,

inclusion, and dropout trends, which directly inform the need for Early Warning Systems.

National education databases, particularly UDISE+ (Unified District Information System for

Education) and AISHE (All India Survey on Higher Education), serve as vital sources of

quantitative data. These databases offer large-scale, longitudinal data on enrolment, attendance,

dropout rates, and institutional performance across different levels of education [12]. The use of

such datasets enhances the empirical grounding of the study by providing measurable indicators

of learner vulnerability and educational disparities.

Furthermore, international reports and studies focusing on student retention, dropout

prevention, and learning analytics have been incorporated to provide a global perspective. Reports

by organizations such as UNESCO and UNICEF, along with comparative studies in educational

technology, highlight best practices and conceptual frameworks for implementing EWS in diverse

educational settings [16]. These global insights help situate the Indian experience within a broader

international discourse on sustainable and inclusive education.

Policy documents, particularly the National Education Policy (NEP) 2020, play a central role

in shaping the conceptual foundation of this study. NEP 2020 emphasizes learner-centric

approaches, flexible progression pathways, reduced dropout rates, and the integration of

technology in education, all of which align closely with the principles of Early Warning Systems

[10]. The policy’s focus on equity, inclusion, and holistic development further reinforces the

relevance of EWS as a tool for achieving sustainable educational outcomes.

The integration of peer-reviewed research, scholarly publications, official reports, national

databases, and international policy frameworks ensures a multidimensional approach to the study.

This diverse source base not only strengthens the validity and reliability of the research but also

enables a comprehensive understanding of the need, scope, and implementation of Early Warning

Systems in the Indian educational context [4,6,14].

Method of Analysis

The present study employs a qualitative synthesis approach grounded in thematic analysis to

examine the collected literature on Early Warning Systems (EWS) and at-risk learners. This

methodological choice is particularly suitable for an emerging research area like EWS in India,

where conceptual clarity, contextual interpretation, and policy relevance are more critical than

purely statistical generalization [8,16]. The gathered information has been systematically analysed

across key thematic categories to provide a comprehensive understanding of the subject.

The first theme focuses on the theoretical understanding of at-risk learners. Existing research

in learning analytics and educational data mining suggests that at-risk status is not an abrupt

condition but a gradual process characterized by observable indicators such as declining academic

performance, absenteeism, low engagement, and socio-economic vulnerability [13,15].

Foundational models of learning analytics further emphasize the importance of tracking learner

behaviour and interaction patterns to understand risk trajectories over time [7]. These theoretical

insights establish the basis for early identification frameworks.

The second theme explores the role of EWS in learner support. Studies demonstrate that

EWS function as structured, data-driven systems designed to identify students at risk and enable

timely intervention [5,6]. Importantly, the effectiveness of these systems depends on the

integration of predictive analytics with meaningful pedagogical responses, such as mentoring,

academic support, and personalized feedback [6,17]. Research also highlights that early

identification combined with intervention significantly improves retention and academic outcomes

[14].

The third thematic category addresses indicators of educational risk. Empirical evidence

shows that combining multiple indicators—academic performance, attendance, participation, and

digital engagement—enhances predictive accuracy [5,15]. Advanced machine learning models,

including ensemble learning techniques, have further improved the capacity to detect at-risk

students by integrating both static and dynamic variables [9,20]. These approaches enable

continuous monitoring and adaptive intervention, making EWS more effective and responsive.

The fourth theme examines dropout trends and participation patterns in India. National

reports such as the Economic Survey 2024–25 and UDISE+ data highlight persistent challenges

in student retention, particularly at higher levels of schooling [11,12]. Despite improvements in

enrolment, disparities in participation and completion remain significant due to socio-economic

inequalities and learning gaps [16]. These findings underscore the urgent need for systematic

mechanisms to identify and support vulnerable learners.

The fifth theme considers the implementation possibilities of EWS in Indian institutions.

Although large-scale institutional adoption is still limited, policy frameworks such as the National

Education Policy (NEP) 2020 emphasize equity, inclusion, and technology-enabled learning,

aligning closely with the principles of EWS [10]. Additionally, studies from other domains, such

as disaster management and environmental risk prediction, demonstrate the adaptability and

effectiveness of early warning frameworks in complex systems [1,2,19]. These insights suggest

strong potential for the integration of EWS within Indian education.

The qualitative synthesis approach enables a multidimensional analysis by integrating

theoretical, empirical, and policy perspectives. Given the developmental stage of EWS in India,

this method provides a robust framework for understanding its relevance, challenges, and future

potential beyond mere statistical description [4,8,16].

Understanding At-Risk Learners in India

The concept of the at-risk learner has gained increasing attention in contemporary educational

research, particularly within the domains of learning analytics, student retention, and educational

equity. An at-risk learner is not necessarily one who has already failed, but one who shows early

signs of vulnerability that may lead to academic underachievement, disengagement, or eventual

dropout if timely intervention is not provided [5,8,14]. This conceptual shift from outcome-based

identification to process-based monitoring is crucial for developing responsive and preventive

educational systems.

In the Indian context, the condition of being “at risk” is deeply embedded in structural and

socio-economic inequalities. Unlike purely performance-based interpretations, research suggests

that educational vulnerability often arises from a combination of contextual and behavioural

factors rather than individual incapacity [10,11,12]. Poverty and financial stress, for instance,

significantly affect students’ ability to access resources, maintain continuity in education, and

remain motivated [16,17]. Similarly, weak foundational learning—particularly in literacy and

numeracy—creates long-term academic difficulties that accumulate over time, especially during

transitions from primary to secondary education [11,16].

First-generation learners and students from rural or remote areas face additional barriers due

to limited academic support at home and inadequate institutional infrastructure [12,16]. These

challenges are further intensified by limited digital access, which became highly visible during and

after the COVID-19 pandemic, highlighting disparities in participation in online learning

environments [17,18]. Language barriers also play a critical role, as students studying in non-native

or unfamiliar mediums often struggle to engage meaningfully with curriculum content [10].

Gendered expectations, commuting burdens, and family caregiving responsibilities

disproportionately affect certain groups, particularly girls, influencing attendance, participation,

and continuity in education [16,17].

Beyond structural factors, psychological and emotional dimensions such as stress, anxiety, and

lack of self-efficacy contribute significantly to learner vulnerability. These aspects are often

invisible in formal records but strongly influence engagement and performance [8,18]. Research in

learning analytics demonstrates that early indicators such as absenteeism, low participation in

digital platforms, delayed assignment submission, and reduced interaction with teachers are strong

predictors of future academic risk [5,6,15]. Romero et al. emphasized that patterns of online

engagement can effectively predict student performance long before final assessments occur [15].

Importantly, the Indian educational landscape presents a unique challenge where risk is often

“silent.” A student may remain officially enrolled but gradually disengage from the learning process

through irregular attendance, minimal participation, and declining motivation [11,12]. Such

students may not immediately appear in dropout statistics, yet they represent a significant portion

of the vulnerable population. Therefore, relying solely on examination outcomes provides an

incomplete understanding of educational risk.

This underscores the importance of Early Warning Systems (EWS) in education. EWS

frameworks emphasize continuous monitoring of multiple indicators—academic, behavioural, and

socio-economic—to identify emerging vulnerabilities rather than reacting to failure after it occurs

[5,6,13]. Studies have shown that combining multiple indicators improves predictive accuracy and

allows institutions to design timely and targeted interventions [5,9]. Moreover, machine learning

approaches further enhance the ability to track dynamic patterns of student engagement over time,

making early identification more precise and actionable [9,20].

Identifying at-risk learners in India requires a multidimensional and context-sensitive approach

that goes beyond traditional examination metrics. Early Warning Systems offer a transformative

framework by focusing on patterns of vulnerability, enabling institutions to shift from reactive to

preventive educational practices. Such systems are essential for promoting equity, improving

retention, and ensuring that all learners are provided with meaningful opportunities to succeed

[5,16,17].

Why India Needs Early Warning Systems

The need for Early Warning Systems (EWS) in India emerges from multiple structural,

pedagogical, and policy-related realities that continue to shape the educational landscape. Despite

significant expansion in access to education, challenges related to retention, engagement, and

progression remain persistent across different levels of schooling and higher education [10,11,16].

EWS provides a systematic and data-driven approach to identifying learners at risk before

academic failure or dropout occurs, thereby enabling timely intervention and support.

One of the most critical concerns is the persistence of dropout and progression loss. Although

India has made notable progress in reducing dropout rates over the years, recent national statistics

indicate that the risk of dropout increases significantly at higher levels of education, particularly

during the transition from upper primary to secondary stages [11,12]. This trend reflects the

growing academic pressure, socio-economic constraints, and disengagement that learners

experience as they move through the system. Learning analytics research suggests that such risks

can be anticipated through early behavioural and academic indicators, allowing institutions to

intervene before disengagement becomes irreversible [5,14,15].

Another major concern is the fragility of attendance and engagement. In many Indian

educational settings, enrolment figures do not accurately reflect actual participation. Studies have

shown that even in contexts with near-universal enrolment, regular attendance and active

engagement remain inconsistent [16]. Indicators such as absenteeism, reduced classroom

interaction, and low participation in digital learning environments are strong predictors of

educational vulnerability [5,6]. EWS frameworks emphasize the importance of tracking these

behavioural patterns over time, as they often precede academic decline and eventual dropout

[13,15].

The post-pandemic educational scenario has further intensified the need for EWS. The

COVID-19 pandemic exposed and widened existing inequalities in digital access, learning

continuity, and student support systems. Many learners returned to formal education with

disrupted study habits, reduced motivation, and weakened academic confidence [16,17]. Research

on digital learning environments highlights that gaps in engagement and participation can persist

even after students re-enter classrooms, making it essential to identify and support those who

struggle to reintegrate [18,20]. EWS can play a crucial role in detecting such patterns of

disengagement and facilitating targeted remedial measures.

The relevance of EWS is also closely aligned with the vision of the National Education Policy

(NEP) 2020. The policy emphasizes learner-centric education, inclusivity, flexibility in curricular

progression, and reduction of dropout rates [10]. Achieving these goals requires robust

mechanisms for continuous monitoring and early identification of learner vulnerability. EWS

supports this policy direction by providing institutions with tools to track academic performance,

attendance, and behavioural indicators in a systematic manner, thereby enabling proactive

educational planning and intervention [11,12].

Furthermore, EWS contributes to strengthening institutional accountability. Traditionally,

student failure in India has often been attributed to individual shortcomings rather than systemic

issues. However, contemporary research in educational data mining and learning analytics

emphasizes that institutions share responsibility for identifying and addressing learner challenges

[13,18]. When early warning signs such as declining performance or absenteeism are ignored, the

system itself contributes to student exclusion. EWS shifts the focus from reactive assessment to

proactive support, encouraging institutions to take responsibility for student success and retention

[6,9].

The implementation of Early Warning Systems in India is not merely a technological innovation

but an educational necessity. By addressing issues of dropout, engagement, post-pandemic

disruption, policy alignment, and institutional accountability, EWS offers a comprehensive

framework for improving educational outcomes. It enables a transition from a reactive to a

preventive model of education, ensuring that learners receive timely support and opportunities for

sustained academic success [5,16,17].

Core Components of an Early Warning System

A functional Early Warning System usually includes four interconnected components:

Data Collection

Recent School Dropout Rates in India (Official UDISE+/Government Sources)

Table No. 1: represent the dropout data in different levels of Indian Schools

Academic

Year

Primary /

Preparatory

(%)

Upper

Primary /

Middle (%)

Secondary (%)

Source

2020–21 NA 3.0 12.7 UDISE+/Rajya

Sabha government

release

2021–22 NA 3.0 12.7 UDISE+/Rajya

Sabha government

release

2022–23 8.7

(Preparatory,

NEP structure)

8.1

(Middle)

13.8 UDISE+ /

Ministry of Education

2023–24 1.9 (Primary)

/ 3.7

(Preparatory)

5.2 (Upper

Primary /

Middle)

14.1 (Secondary)

/ 10.9 (NEP stage)

Economic Survey /

UDISE+

2024–25 0.3 (Primary)

/ 2.3

(Preparatory)

3.5 11.5 (school-level

release) / 8.2 (NEP

stage release)

UDISE+ / PIB

Source: UDISE+/Rajya Sabha government release, UDISE+ / Ministry of Education,

Economic Survey / UDISE+

Graph-2 Represent Trends in Student Dropout Rates Across Educational Stages in

India (2020–25)

Source: UDISE+/Rajya Sabha government release, UDISE+ / Ministry of Education,

Economic Survey / UDISE+

Graph-3 Using School-Level (Primary–Upper Primary–Secondary)

2020–21 2021–22 2022–23 2023–24 2024–25

Preparatory (%)

0 0 8,7 3,7 2,3

Middle (%)

3 3 8,1 5,2 3,5

Secondary (%)

12,7 12,7 13,8 10,9 8,2

0 0

8,7

3,7

2,3

3 3

8,1

5,2

3,5

12,7 12,7

13,8

10,9

8,2

Preparatory (%) Middle (%) Secondary (%)

2020–21 2021–22 2022–23 2023–24 2024–25

Primary (%)

0 0 0 1,9 0,3

Upper Primary (%)

3 3 8,1 5,2 3,5

Secondary (%)

12,7 12,7 13,8 14,1 11,5

0 0 0

1,9

0,3

3 3

8,1

5,2

3,5

12,7 12,7

13,8

14,1

11,5

Primary (%) Upper Primary (%) Secondary (%)

Source: UDISE+/Rajya Sabha government release, UDISE+ / Ministry of Education,

Economic Survey / UDISE+

Risk Identification:

The effectiveness of an Early Warning System (EWS) in education depends significantly on its

ability to accurately identify, communicate, and respond to learner risk through a structured and

evidence-based framework. The process of risk identification is central to this system and involves

the systematic use of academic, behavioural, and contextual data to detect early signs of

vulnerability among learners [5,13,14]. In practice, risk identification may occur through

predefined thresholds such as attendance below 75%, cumulative risk scoring models, teacher-

generated alerts, statistical analyses, or advanced machine learning-based forecasting techniques

[9,20]. These approaches allow institutions to move beyond subjective judgment toward data-

informed decision-making.

Research in learning analytics strongly supports the predictive potential of early behavioural

indicators. Akçapınar et al. demonstrated that learner engagement patterns at the beginning of a

course—such as login frequency, participation, and assignment submission—can effectively

predict end-of-course outcomes [5]. This finding highlights that institutions need not wait until

final examinations to recognize academic risk. Instead, early-stage data can provide actionable

insights, enable timely identification of vulnerable learners and reduce the likelihood of late

intervention [14,15].

Once risk is identified, the next critical stage is alert generation. An effective EWS must ensure

that relevant stakeholders are informed in a timely and structured manner. These stakeholders

typically include class teachers, mentors, counsellors, academic advisors, department heads, and,

in school contexts, parents or guardians [16,17]. In some models, learners themselves are also

notified, encouraging self-regulation and responsibility in the learning process. The purpose of

alert generation is not merely to communicate risk but to initiate a coordinated institutional

response aimed at learner support [6,18].

Intervention represents the most crucial and impactful component of the EWS framework.

Without meaningful and timely intervention, risk identification remains only a diagnostic exercise.

Bañeres et al. emphasized that predictive systems achieve educational value only when they are

directly linked to targeted and personalized interventions [6]. These interventions may take

multiple forms depending on the nature and severity of the risk. Academic mentoring and remedial

teaching are commonly used to address learning gaps, while attendance counselling and parental

engagement help improve participation and accountability [11,12]. Peer support systems can

enhance motivation and belongingness, whereas digital access assistance is essential in contexts

affected by technological inequality [17,18]. Additionally, emotional and psychological support,

including counselling referrals, plays a vital role in addressing non-academic barriers to learning

[8,18]. Flexible academic planning, such as adjusted timelines or alternative learning pathways,

further supports learners facing complex challenges [10].

The identification of at-risk learners in India requires particular attention to context-specific

indicators. A robust EWS must integrate both academic performance metrics and structural

dimensions of disadvantage [10,11]. Indicators such as irregular attendance, declining academic

performance, and low classroom or digital participation remain fundamental [5,15]. However, in

the Indian context, these must be complemented by factors such as socio-economic background,

first-generation learner status, rural or remote location, language barriers, and access to digital

resources [11,12,16]. National and international reports emphasize that such multidimensional

indicators are essential for understanding the complex nature of learner vulnerability and ensuring

equitable intervention strategies [16,17].

Moreover, advancements in educational data mining and artificial intelligence have enhanced

the capacity of EWS to analyse both static and dynamic variables over time. Techniques such as

decision trees, regression models, and ensemble learning approaches enable more accurate

prediction of student attrition and disengagement [9,13,20]. These technologies allow institutions

to continuously monitor patterns of behaviour rather than relying on one-time assessments,

thereby improving the precision and effectiveness of risk identification.

In conclusion, risk identification, alert generation, and intervention form an interconnected

framework that determines the success of Early Warning Systems in education. In the Indian

context, the integration of diverse indicators and context-sensitive strategies is essential for

addressing educational vulnerability. By combining data-driven insights with human-centered

intervention, EWS offers a powerful approach to promoting student retention, engagement, and

overall educational equity [5,16,17].

Identifying At-Risk Learners

A comprehensive Early Warning System (EWS) in education depends on the identification of

multiple interrelated indicators that together reflect the academic, behavioural, and socio-

economic conditions of learners. In the Indian context, where educational risk is often gradual and

multidimensional, these indicators must go beyond examination outcomes and incorporate

patterns of engagement, participation, and contextual vulnerability [10–12,16]. Attendance is

widely recognized as one of the most powerful early indicators of risk. Persistent or irregular

absence is often associated with disengagement, family-related stress, health concerns, or socio-

economic pressures. National datasets and policy analyses show that even where enrolment is high,

actual participation may remain inconsistent, making attendance a critical signal of emerging

vulnerability [11,12,16].

Academic performance is another key dimension, but contemporary research emphasizes the

importance of identifying trends rather than isolated instances of low achievement. A gradual

decline in internal assessments, class tests, and semester evaluations often reflects deeper learning

gaps or disengagement, which may not be visible through a single examination score [5,14,15].

Similarly, assignment and task completion patterns provide strong predictive insights. Learners

who consistently miss assignments, fail to participate in project work, or show irregular coursework

engagement often exhibit early signs of academic withdrawal, which can later translate into failure

or dropout [13–15].

With the expansion of blended and digital learning environments, digital participation has

emerged as a crucial indicator. Patterns such as low frequency of learning management system

access, minimal interaction with digital content, lack of participation in online discussions, and

irregular attendance in virtual classes have been shown to correlate strongly with academic risk.

Studies in learning analytics demonstrate that digital behaviour, particularly in the early stages of a

course, can effectively predict long-term outcomes, thereby enabling early intervention [5,8,18].

This is particularly relevant in the post-pandemic Indian context, where disparities in access to

digital infrastructure continue to influence learning continuity and engagement [16–18].

In addition to academic and behavioural indicators, socio-economic and contextual factors play

a decisive role in shaping learner risk in India. Conditions such as being a first-generation learner,

financial instability, lack of access to digital devices or internet connectivity, long commuting

distances, migration-related disruptions, family responsibilities, and language transition challenges

significantly affect student participation and progression [10–12,16,17]. These factors highlight

that educational vulnerability is not merely an individual issue but is deeply embedded in broader

structural inequalities.

Equally important are behavioural and psychosocial indicators, which are often not formally

recorded but are critical for early identification. Signs such as sudden withdrawal from

participation, reduced classroom interaction, visible stress, low confidence, and repeated requests

for deadline extensions may indicate underlying emotional or psychological challenges. Research

in educational technology and learner analytics underscores the importance of integrating such

qualitative observations with quantitative data to achieve a more accurate understanding of student

risk [8,18].

Therefore, an effective EWS in India must adopt a holistic and integrated approach that

combines measurable indicators with teacher observations and contextual awareness. Advances in

machine learning and predictive analytics further enhance the ability to track both static and

dynamic variables over time, improving the precision of risk identification and enabling timely

interventions [9,13,20]. By recognizing patterns of vulnerability early and responding proactively,

institutions can shift from a reactive to a preventive model of education, thereby improving

retention, equity, and overall learning outcomes [5,16,17].

9. Early Warning Systems in School Education in India

Early Warning Systems (EWS) hold significant potential in strengthening school education in

India, particularly in addressing persistent challenges such as chronic absenteeism, poor

foundational learning, transition-related risks, gendered discontinuity, and silent disengagement

among learners. These issues are deeply embedded in the structure of Indian schooling and require

systematic, preventive mechanisms rather than reactive responses [10–12,16]. Chronic

absenteeism, for instance, is widely recognized as an early indicator of disengagement and is often

linked with socio-economic pressures, household responsibilities, or lack of academic motivation.

Research suggests that irregular attendance, when tracked consistently, can help institutions

identify vulnerable learners long before they formally drop out [11,16].

Poor foundational learning is another critical concern, especially at the primary and upper-

primary levels, where gaps in literacy and numeracy can accumulate over time and hinder

progression. When such gaps remain unaddressed, learners face increasing difficulty in coping with

higher-level curricula, leading to frustration, low confidence, and eventual withdrawal [11,12].

EWS can assist in identifying these learners early by monitoring assessment patterns and classroom

participation, thereby enabling timely remedial interventions [5,14].

Transition stages, particularly from upper-primary to secondary education, represent a high-risk

period for many students in India. During this phase, learners encounter increased academic

demands, examination pressure, social expectations, and financial constraints. Official statistics

consistently show that dropout rates are significantly higher at the secondary level, indicating that

many students struggle to sustain engagement as they progress through the system [11,12]. EWS

can play a crucial role in detecting early warning signs during these transitions, allowing schools to

provide targeted academic and emotional support [16,17].

Gendered discontinuity further complicates the issue, as social norms, safety concerns, and

domestic responsibilities disproportionately affect girls’ participation and continuity in education.

These factors often remain invisible in formal data but contribute significantly to dropout and

irregular attendance [16,17]. Similarly, silent disengagement—where students remain enrolled but

gradually withdraw from active participation—poses a serious challenge. Such learners may attend

classes irregularly, fail to complete assignments, and show minimal interaction, yet may not

immediately appear in dropout statistics [11,12]. EWS helps in identifying these patterns through

continuous monitoring of engagement indicators [5,15].

Importantly, the implementation of EWS in Indian schools does not necessarily require

advanced artificial intelligence or complex technological infrastructure. Research in educational

data systems indicates that even low-cost, school-based dashboards can be highly effective when

used consistently [13,18]. Indicators such as attendance records, periodic test performance, teacher

observations, homework completion, and parental follow-up provide sufficient data to identify

early signs of risk [11,12]. When these indicators are systematically tracked and reviewed, they

enable schools to take timely and informed action.

Such systems are particularly valuable in government schools, where a large proportion of

students come from socio-economically disadvantaged backgrounds. In these settings, EWS can

support coordinated efforts among teachers, school management committees, counsellors, and

parents to address learner needs holistically [16,17]. Studies emphasize that the effectiveness of

EWS depends not only on data collection but also on institutional responsiveness and collaborative

intervention strategies [6,9].

In conclusion, EWS offers a practical and scalable solution for addressing key educational

challenges in Indian school education. By focusing on early identification and timely intervention,

even simple monitoring systems can significantly improve retention, engagement, and learning

outcomes. This approach aligns with broader policy goals of equity, inclusion, and reduced

dropout, reinforcing the need for widespread adoption of EWS in Indian schools [10,16,17]

Early Warning Systems in Higher Education in India

Early Warning Systems (EWS) are increasingly necessary in higher education in India,

particularly at the undergraduate level particularly in rural area, where students often encounter

multiple adjustment challenges that affect their academic progression and retention. The transition

from school to college represents a significant shift in learning environment, expectations, and

responsibilities. Many students struggle with English-medium instruction, independent learning

demands, unfamiliar disciplinary content, weak academic self-regulation, and limited access to

structured mentoring support [10–12,16]. These challenges are especially pronounced among first-

generation learners and those from diverse socio-economic backgrounds, making the first year of

undergraduate education a critical period of vulnerability.

In many cases, students do not immediately express these difficulties but instead experience

what can be described as “silent disengagement.” They may continue attending classes irregularly,

fail to actively participate, or gradually fall behind in coursework until academic backlogs

accumulate or attendance drops significantly [11,12]. Research in learning analytics indicates that

such behavioural patterns—low participation, irregular attendance, and incomplete coursework—

are strong predictors of future academic risk and dropout [5,14,15]. Without systematic

mechanisms for early identification, institutions often respond only after failure has occurred,

reducing the effectiveness of remedial efforts.

Although India does not yet maintain a comprehensive, standardized dropout tracking system

in higher education comparable to school-level databases, national frameworks such as the All-

India Survey on Higher Education (AISHE) provide valuable insights into enrollment,

progression, and participation trends [11]. These data highlight the scale, diversity, and complexity

of the higher education system, where student needs vary widely across regions, disciplines, and

institutional types. With the introduction of recent reforms such as the Academic Bank of Credits,

multiple entry and exit options, and flexible degree pathways, the structure of higher education has

become more dynamic and student-cantered [10]. While these reforms enhance flexibility and

access, they also increase the need for continuous learner tracking and timely academic support,

as students may move in and out of the system at different stages.

In this evolving context, EWS can serve as a critical institutional tool for supporting student

success. Indicators used in higher education EWS typically include low attendance, non-

submission of assignments, poor internal assessment performance, reduced participation in

Learning Management Systems (LMS), repeated backlog accumulation, low credit completion

rates, and minimal engagement with mentoring systems [5,13,20]. These indicators reflect both

academic and behavioural dimensions of student engagement and can be monitored over time to

detect emerging patterns of risk. Studies have shown that combining multiple indicators enhances

predictive accuracy and allows institutions to design targeted interventions [5,9].

Digital participation, in particular, has become an important dimension of risk identification in

higher education. Patterns such as infrequent LMS access, low engagement with online resources,

and limited interaction in virtual discussions provide early signals of disengagement, especially in

blended and online learning environments [8,18]. Similarly, the accumulation of academic backlogs

and low credit completion rates are strong indicators of progression challenges, which, if

unaddressed, may lead to dropout or delayed graduation [14,15].

EWS also plays a vital role in strengthening mentoring and student support systems within

higher education institutions. Regular monitoring of student engagement allows mentors, faculty

members, and academic advisors to identify at-risk learners and provide timely guidance,

counselling, and academic assistance [6,9]. This shifts the institutional approach from reactive

problem-solving to proactive support, fostering a more inclusive and responsive learning

environment.

The implementation of Early Warning Systems in Indian higher education is essential for

addressing the complex challenges faced by undergraduate students. By integrating academic,

behavioural, and digital indicators, EWS enables institutions to identify vulnerability early and

provide targeted interventions. This not only improves student retention and progression but also

supports the broader goals of flexibility, inclusivity, and learner-cantered education envisioned in

contemporary policy reforms [10,16,17].

Role of Learning Analytics and Educational Data Mining

The increasing prominence of learning analytics has significantly strengthened the development

and application of Early Warning Systems (EWS) in education, particularly in identifying at-risk

learners through data-driven insights. Learning analytics refers to the systematic collection,

measurement, analysis, and interpretation of learner data to optimize learning processes and

educational environments [7,13]. In digital and blended learning contexts, students continuously

generate behavioural traces such as login frequency, clicks, resource access, participation in

discussions, quiz attempts, and content viewing patterns. These digital footprints provide valuable

insights not only into academic performance but also into patterns of engagement and

disengagement, which are crucial for early risk detection [14,15].

Research has consistently demonstrated the predictive potential of such behavioural data.

Akçapınar et al. showed that interaction data collected early in a course can effectively identify

students at risk before final assessments, enabling timely intervention [5]. Similarly, Bañeres et al.

highlighted that artificial intelligence–driven systems can successfully detect disengaged learners in

online higher education settings, provided these systems are coupled with appropriate pedagogical

responses [6]. These findings are further supported by systematic reviews indicating that learning

analytics can play a critical role in reducing dropout rates when institutions actively use data to

support student persistence rather than merely record outcomes [8]. Advanced machine learning

techniques, including ensemble models and predictive algorithms, have also been found to

enhance the accuracy of identifying at-risk students by combining both static and dynamic

variables [9,20].

However, the effectiveness of EWS should not be equated solely with technological

sophistication. In the Indian context, where institutional diversity and resource constraints are

significant, even basic analytics based on attendance trends, assessment performance, participation

frequency, and submission patterns can be highly effective in identifying vulnerable learners [10–

12]. Studies in broader early warning applications, including environmental and financial systems,

also suggest that the success of such systems depends more on timely interpretation and response

than on complexity alone [1,2,4]. Moreover, predictive analytics frameworks designed for online

learners emphasize that the integration of early warning signals with structured intervention

strategies is essential for meaningful educational outcomes [3].

Policy frameworks and international reports further reinforce the importance of data-informed

educational practices. The National Education Policy 2020 emphasizes learner-centric approaches,

inclusion, and reduced dropout, aligning closely with the principles of EWS [10]. Reports by

UNESCO and UNICEF also advocate for systematic monitoring mechanisms to identify and

support at-risk learners, particularly in contexts marked by inequality and disruption [16,17].

Additionally, research on artificial intelligence in higher education highlights the growing role of

intelligent systems in enhancing student support, though it also calls for careful integration with

pedagogical practices [18].

Ultimately, the value of Early Warning Systems lies not in their technological advancement but

in their educational responsiveness. Whether through advanced machine learning models or simple

institutional dashboards, the primary goal remains the same: to detect early signs of learner

vulnerability and enable timely, meaningful intervention. In this sense, EWS represents a shift from

reactive to proactive education systems, where the focus is on prevention, inclusion, and sustained

learner engagement across diverse educational contexts [19].

Findings and Discussion

The review of literature, policy frameworks, and recent educational data highlights several

interrelated findings regarding Early Warning Systems (EWS) in education, particularly in the

Indian context.

India has a strong need for early identification of learner risk

Despite significant improvements in enrolment, national data continue to indicate challenges

in retention, especially at the secondary level, as reflected in UDISE+ statistics and national

education overviews published by the Ministry of Education, Government of India (12, 11). This

suggests that India’s current education system remains largely reactive rather than preventive in

addressing student dropout and disengagement. International research also emphasizes that early

detection mechanisms are critical to improving learner persistence and educational outcomes (17,

8).

At-risk status in India is multidimensional

The literature consistently demonstrates that student vulnerability cannot be explained solely by

academic performance. Instead, it is shaped by a combination of attendance patterns, socio-

economic constraints, language barriers, digital divide, and psychosocial factors (8, 14, 6). Studies

in learning analytics and educational data mining further confirm that at-risk identification

requires integrating multiple indicators rather than relying on a single metric (13, 7). This

multidimensionality aligns with broader global findings in predictive education models and risk

classification systems (9, 20).

Existing educational systems often identify risk too late

In many institutions, students are classified as “at risk” only after academic failure, backlog

accumulation, or prolonged absenteeism. This reflects a post-failure identification model rather

than a preventive intervention framework (6, 14). Literature on dropout prevention in higher

education highlights that delayed identification significantly reduces the effectiveness of remedial

interventions (8, 15). UNICEF (17) similarly emphasizes that early detection is essential to

preventing permanent disengagement from education systems.

Early Warning Systems can be built with both high-tech and low-tech models

Contrary to the assumption that EWS requires advanced artificial intelligence infrastructure,

research demonstrates that even simple indicators such as attendance records, classroom

participation, and continuous internal assessment can form effective early warning mechanisms (6,

19). While machine learning and ensemble models enhance predictive accuracy (9, 20),

foundational systems based on structured observation remain highly relevant in resource-

constrained educational contexts like India.

Prediction without intervention is educationally incomplete

Finding across studies is that predictive analytics alone is insufficient unless linked with

structured interventions. Bañeres et al. (6) explicitly argue that EWS must function as both a

detection and response mechanism. Similarly, literature on learning analytics highlights that

actionable insights must be translated into mentoring, counselling, and remedial academic support

to improve student outcomes (8, 5). Without intervention pathways, predictive systems lose

educational value.

NEP 2020 creates a favourable policy environment

The National Education Policy 2020 strongly supports inclusive, flexible, and student-centred

learning pathways, which align closely with the philosophy of EWS (10). Policy documents,

including the Economic Survey and UDISE+ reports, further reinforce the need to reduce

dropout rates and improve retention through systemic reforms (11, 12). This policy environment

provides a strong institutional foundation for integrating EWS into Indian schools and higher

education systems.

Challenges in Implementing EWS in India

Despite the strong theoretical and policy support for Early Warning Systems (EWS) in

education, their implementation in India faces multiple structural, pedagogical, and ethical

challenges. These challenges must be understood in relation to institutional capacity, digital equity,

and intervention readiness.

Data fragmentation

One of the most significant barriers is the fragmentation of student data across institutions.

Attendance, assessment, behavioural records, and participation data are often stored in separate,

non-integrated systems. This lack of interoperability makes it difficult to develop a unified learner

profile for predictive analysis. Literature on learning analytics consistently emphasizes that

effective EWS requires integrated data ecosystems and standardized data pipelines (7, 13, 8). In

the absence of such integration, predictive accuracy and system reliability are significantly reduced

(6, 14).

Unequal digital access

Another major challenge is the digital divide, particularly in rural and economically

disadvantaged contexts. Over-reliance on digital behaviour indicators (such as LMS logins or

online participation) may misclassify students who lack access to digital infrastructure as “at risk,”

even when their academic engagement is adequate in offline settings (16, 17). Studies on

educational inequality highlight that digital exclusion can distort predictive analytics and reinforce

existing inequities if not carefully contextualized (8, 11).

Teacher workload and capacity constraints

The success of EWS depends heavily on teachers’ ability to interpret dashboards and respond

to risk signals. However, in many Indian schools and colleges, teachers already face high

workloads, limiting their capacity to engage with additional analytical tools. Research indicates

that without adequate training and institutional support, learning analytics tools remain

underutilized or misinterpreted (5, 18). Effective implementation therefore requires professional

development and simplification of dashboard interfaces (6, 9).

Risk of labelling and stigma

A critical ethical concern is the potential for labelling students as “at risk,” which may

unintentionally lead to stigma and reduced teacher expectations. Studies in educational psychology

warn that predictive categorization without sensitive communication strategies can negatively

affect student identity and motivation (17, 14). UNICEF (17) emphasizes that early warning

mechanisms must prioritize supportive language and confidentiality to avoid reinforcing

marginalization.

Weak intervention infrastructure

Perhaps the most significant implementation barrier is the lack of robust intervention systems.

While EWS can identify vulnerable learners, many institutions lack sufficient counsellors, mentors,

or structured remedial programs to act on these alerts (6, 8). Literature consistently highlights that

predictive systems without intervention pathways fail to produce meaningful educational

improvement (5, 15). This gap between detection and response remains a major limitation in

developing countries’ education systems (12, 19).

Raching the Ultimate Process of Context-Specific EWS Implementation

IMAGE -1 Repesent the Contextual Early Warning System Model (CEWSM)

Source: Developed by Investigator

The proposed model operates as a dynamic and iterative system that moves from identification

to intervention and finally to continuous refinement. The ultimate goal is not merely early detection

of at-risk learners, but the creation of a responsive, inclusive, and sustainable educational support

system.

The process begins with Needs Assessment, where institutions identify their local realities,

including socio-economic conditions, digital access, language diversity, and institutional capacity.

This ensures that the system is grounded in context rather than borrowed from external models.

This is followed by Indicator Selection, where relevant and measurable indicators such as

attendance, academic performance, engagement levels, and socio-emotional signals are chosen.

The accuracy of the system depends heavily on selecting indicators that reflect real student

vulnerabilities.

In the Data Collection phase, institutions systematically gather student data through both digital

and manual means. This stage emphasizes regularity, reliability, and inclusivity of data, ensuring

that no student group is overlooked.

The next stage, Risk Analysis, transforms raw data into meaningful insights. Through simple

analytics or advanced models, patterns of disengagement and vulnerability are identified early,

allowing institutions to shift from reactive to proactive approaches.

Once risks are identified, Alert and Reporting mechanisms ensure that relevant stakeholders—

teachers, mentors, counsellors, and parents—are informed in a timely manner. This creates a

shared responsibility framework within the institution.

The most critical stage is Intervention Strategies, where targeted support such as remedial

teaching, mentoring, counselling, or financial and digital assistance is provided. The effectiveness

of the entire model depends on the quality and timeliness of these interventions.

Subsequently, Monitoring and Feedback tracks student progress after intervention, assessing

whether the support has improved engagement and performance. This ensures accountability and

evidence-based decision-making.

Finally, the process culminates in Review and Adaptation, where the system is continuously

refined based on outcomes, challenges, and feedback. This transforms the model into a self-

improving ecosystem.

Educational Implications and Recommendations

Based on the present analysis of literature, policy frameworks, and educational data, several

recommendations emerge for strengthening Early Warning Systems (EWS) in India in a more

practical, ethical, and policy-aligned manner. A key recommendation is that institutions should

begin EWS implementation with simple and readily available indicators such as attendance records,

internal assessment scores, assignment completion rates, and classroom participation. These

indicators are cost-effective, easy to collect, and highly relevant in identifying early signs of

academic disengagement. Existing research in learning analytics and educational data mining

supports the view that even basic data sources, when systematically organized, can provide reliable

signals for identifying at-risk learners without requiring advanced technological infrastructure [3,

13, 15]. This makes the approach particularly suitable for diverse Indian educational settings where

digital maturity varies widely.

Another essential recommendation is the integration of EWS with structured mentoring

systems. The effectiveness of any early warning mechanism depends not only on detection but

also on timely human intervention. Therefore, every risk signal generated by the system should

automatically trigger a mentor–mentee response loop, ensuring that students receive personalised

academic guidance, emotional support, and remedial assistance. Literature consistently highlights

that predictive models alone are insufficient unless they are directly linked to actionable

interventions that improve student outcomes [5, 8]. In this sense, teachers and mentors become

central agents in transforming data insights into meaningful educational support rather than

passive observers of risk dashboards [6, 15].

A further recommendation is the inclusion of socio-economic sensitivity within EWS

interpretation frameworks. Student risk must not be understood purely as an individual academic

issue but as a reflection of broader structural inequalities. Factors such as poverty, first-generation

learning status, language barriers, and limited access to digital resources significantly shape student

engagement and performance. Research shows that without contextual interpretation, data-driven

systems may unintentionally misclassify disadvantaged learners as academically weak, thereby

reinforcing inequality rather than reducing it [11, 16]. Therefore, EWS must incorporate socio-

economic context as a core analytical dimension to ensure fairness and accuracy in decision-

making [8, 17].

It is also important that EWS implementation is aligned with the National Education Policy

2020 and institutional quality assurance frameworks. NEP 2020 strongly emphasizes equity,

retention, flexibility, and student support, which directly correspond to the objectives of early

warning systems [10]. Integrating EWS into institutional monitoring processes, quality audits, and

student progression systems can ensure long-term sustainability and policy coherence. National

reports such as the Economic Survey and UDISE+ also reinforce the importance of improving

retention and reducing dropout rates through systematic and data-informed interventions [12, 20].

In addition, protecting privacy and dignity must remain a central ethical requirement in any

EWS framework. The use of student data must be governed by clear principles of consent,

confidentiality, and responsible data handling. International literature on learning analytics

emphasizes that ethical safeguards are essential to maintain trust and prevent misuse of predictive

systems [16, 18]. Students should never be stigmatized or labelled negatively based on algorithmic

predictions, as this can harm motivation and self-perception [17].

To operationalize these recommendations, this study proposes an integrated “Contextual Early

Intervention System Model (CEISM)”. The model consists of four interconnected layers: data

collection (simple indicators from academic and behavioural records), risk identification (rule-

based or basic analytic scoring), contextual interpretation (teacher and institutional review

considering socio-economic background), and intervention response (mentor-led support,

counselling, and remedial action). The model ensures that prediction is always followed by human

judgment and structured support, making EWS both practical and ethically grounded.

Early Warning Systems in India must evolve as inclusive, low-cost, and intervention-driven

frameworks. When grounded in simple indicators, human mentorship, socio-economic sensitivity,

policy alignment, and ethical safeguards, and supported by models such as CEISM, EWS can

transform from a monitoring tool into a comprehensive student support ecosystem that promotes

equity, retention, and educational success [10, 11, 16].

Conclusion

Early Warning Systems (EWS) represent a fundamental shift in educational thinking, moving

away from reactive approaches that manage student failure after it occurs toward proactive systems

that identify and support learners before failure happens. This transformation is especially

significant in the Indian education system, where student vulnerability often develops gradually

and remains unnoticed until it manifests as poor performance, absenteeism, or dropout. Research

in learning analytics and educational data mining consistently emphasizes that early detection of

risk factors is crucial for improving student retention and academic success [3, 13, 15]. In this

context, EWS provides a structured mechanism to identify subtle patterns of disengagement that

traditional evaluation systems may overlook.

In India, the need for such systems is particularly urgent due to persistent challenges at the

secondary education level, where dropout rates and irregular attendance remain significant

concerns. National datasets such as UDISE+ and policy analyses from the Ministry of Education

highlight continued disparities in retention and progression across regions and socio-economic

groups [11, 12]. Additionally, issues such as digital inequality, language diversity, and uneven access

to learning resources further contribute to educational vulnerability. International research also

supports the view that student risk is multi-causal and evolves over time rather than resulting from

a single academic failure event [8, 16]. Therefore, EWS becomes an essential tool for identifying

cumulative risk patterns rather than isolated performance gaps.

The literature further demonstrates that Early Warning Systems are not merely technological

tools but integrated educational frameworks that combine data analytics with institutional response

mechanisms. Studies in learning analytics highlight that predictive systems alone have limited value

unless they are connected to timely interventions such as mentoring, counselling, and academic

support [5, 6]. This reinforces the idea that EWS is not only about identifying at-risk students but

also about ensuring that institutions are prepared to respond effectively and empathetically.

Without such intervention structures, EWS risks becoming a diagnostic system without

educational impact [8, 15].

In the Indian context, EWS also reflects a broader pedagogical and institutional responsibility.

The National Education Policy 2020 emphasizes equity, inclusion, and student support as core

principles of educational reform, which align closely with the objectives of early warning systems

[10]. Integrating EWS into institutional quality assurance frameworks and student progression

monitoring systems can therefore strengthen accountability while improving learner outcomes.

Furthermore, national reports such as the Economic Survey and UDISE+ underline the

importance of reducing dropout rates and improving learning continuity through systematic

reforms [12, 20].

Importantly, the true value of EWS lies not in its ability to classify students but in its capacity

to enable timely, sensitive, and context-aware responses. Research consistently warns against the

risk of labelling students in ways that may lead to stigma or reduced expectations, highlighting the

need for ethical safeguards in data use and interpretation [16, 17]. EWS must therefore be

implemented with strong attention to privacy, dignity, and socio-economic context to ensure that

it supports rather than disadvantages vulnerable learners.

Ultimately, when implemented thoughtfully, Early Warning Systems can help Indian

educational institutions transition toward a more equitable and responsive model of education.

Such a system would ensure that vulnerable learners are identified early, supported appropriately,

and provided with opportunities to succeed before they reach a point of academic failure. In doing

so, EWS transforms education from a reactive system of correction into a proactive system of

continuous support and inclusion, aligning both with global best practices and India’s evolving

educational priorities [10, 11, 16].

100

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