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Citation: Córdova-Esparza, D.-M.;
Terven, J.; Romero-González, J.-A.;
Córdova-Esparza, K.-E.; López-
Martínez, R.-E.; García-Ramírez, T.;
Chaparro-Sánchez, R. Predicting and
Preventing School Dropout with
Business Intelligence: Insights from a
Systematic Review. Information 2025,
16, 326. https://doi.org/10.3390/
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Systematic Review
Predicting and Preventing School Dropout with Business
Intelligence: Insights from a Systematic Review
Diana-Margarita Córdova-Esparza 1, Juan Terven 2, Julio-Alejandro Romero-González 1,
Karen-Edith Córdova-Esparza 3,* , Rocio-Edith López-Martínez 1, Teresa García-Ramírez 1
and Ricardo Chaparro-Sánchez 1
1Facultad de Informática, Universidad Autónoma de Querétaro, Av. de las Ciencias S/N,
Queretaro 76230, Mexico; diana.cordova@uaq.mx (D.-M.C.-E.); julio.romero@uaq.mx (J.-A.R.-G.);
rocio.edith.lopez@uaq.mx (R.-E.L.-M.); teregar@uaq.mx (T.G.-R.); rchapa@uaq.mx (R.C.-S.)
2CICATA-Unidad Querétaro, Instituto Politécnico Nacional, Cerro Blanco 141, Col. Colinas del Cimatario,
Queretaro 76090, Mexico; jrtervens@ipn.mx
3Facultad de Filosofía, Universidad Autónoma de Querétaro, Av. 16 de Septiembre No. 57, Centro Histórico,
Queretaro 76000, Mexico
*Correspondence: karen.cordova@uaq.mx
Abstract: School dropout in higher education remains a significant global challenge with
profound socioeconomic consequences. To address this complex issue, educational in-
stitutions increasingly rely on business intelligence (BI) and related predictive analytics,
such as machine learning and data mining techniques. This systematic review critically
examines the application of BI and predictive analytics for analyzing and preventing stu-
dent dropout, synthesizing evidence from 230 studies published globally between 1996
and 2025. We collected literature from the Google Scholar and Scopus databases using a
comprehensive search strategy, incorporating keywords such as “business intelligence”,
“machine learning”, and “big data”. The results highlight a wide range of predictive tools
and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms
like decision trees, Random Forest, and logistic regression, demonstrating effectiveness
in identifying dropout patterns and at-risk students. Common predictive variables in-
cluded personal, socioeconomic, academic, institutional, and engagement-related factors,
reflecting dropout’s multifaceted nature. Critical challenges identified include data privacy
regulations (e.g., GDPR and FERPA), limited data integration capabilities, interpretability
of advanced models, ethical considerations, and educators’ capacity to leverage BI effec-
tively. Despite these challenges, BI applications significantly enhance institutions’ ability
to predict dropout accurately and implement timely, targeted interventions. This review
emphasizes the need for ongoing research on integrating ethical AI-driven analytics and
scaling BI solutions across diverse educational contexts to reduce dropout rates effectively
and sustainably.
Keywords: business intelligence; school dropout; machine learning
1. Introduction
In today’s information-driven society, educational institutions face the challenge of
efficiently transforming large volumes of data into actionable insights to address critical
problems, such as school dropout. School dropout, particularly in higher education, is a
complex phenomenon with severe implications at the individual, social, and economic lev-
els. Factors contributing to dropout range from socioeconomic circumstances and academic
Information 2025,16, 326 https://doi.org/10.3390/info16040326
Information 2025,16, 326 2 of 30
performance to institutional policies and emotional support [
1
,
2
]. Despite considerable
efforts to mitigate dropout, many institutions still struggle due to inadequate predictive
tools, delayed interventions, and fragmented data management practices.
In this context, business intelligence (BI) has emerged as a powerful approach to
support data-driven decision-making, offering institutions strategic tools for analyzing data
to identify patterns, trends, and risks proactively [
3
]. Originally defined as “concepts and
methods to improve business decisions through fact-based support systems” [
3
], BI now
encompasses a wide range of technologies and methodologies, including data warehousing,
online analytical processing (OLAP), predictive analytics, and data visualization. These
capabilities enable organizations not only to understand historical data but also to anticipate
future trends and issues, thus facilitating informed and timely decisions [4,5].
Educational institutions adopting BI tools can significantly improve their ability to
predict and prevent dropout by leveraging predictive models and comprehensive data
analyses. Specifically, BI allows for the integration of diverse data sources—from academic
records and socio-demographic indicators to real-time student engagement data—into
cohesive analytical frameworks that help identify at-risk students early and precisely. This
integration supports the development of targeted interventions and strategic resource
allocation to effectively reduce dropout rates.
Nevertheless, despite growing interest and promising results reported in various
studies, the application of BI tools for dropout prediction in education remains fragmented.
Many institutions struggle to identify best practices, appropriate methodologies, and robust
predictive models suitable for their contexts. Furthermore, the existing literature is often
dispersed, lacking comprehensive syntheses or clear guidance on effectively implementing
BI approaches for dropout prevention.
Addressing these gaps, this systematic review aims to consolidate and critically
analyze the existing literature on the application of BI and related data-driven ap-
proaches—specifically machine learning and big data analytics—in predicting and pre-
venting school dropout in higher education. The study seeks to clarify how these tools
have been implemented, identify the most effective strategies, and highlight ongoing chal-
lenges and opportunities for future research. By providing a clearer and more gradual
transition from general BI concepts to specific applications in dropout analysis, this re-
view contributes to enhancing institutional capacity to reduce dropout rates and improve
educational outcomes globally.
Business Intelligence in the Educational Sector
Business intelligence (BI) provides educational institutions with critical tools and
methodologies for the continuous analysis of institutional data, allowing for the proactive
identification of trends, opportunities, and risks [
6
]. By effectively transforming large
volumes of diverse data into actionable insights, BI supports better decision making in
various educational operations.
The key benefits of BI for educational institutions include creating a continuous cycle
of improvement in which data are analyzed to inform decision-making, subsequently gen-
erating new data for ongoing analysis [
3
]. BI also enables comprehensive, historical views
of institutional performance through robust metrics such as Key Performance Indicators
(KPIs) and Key Goal Indicators (KGIs). Additionally, BI tools provide detailed yet intuitive
access to updated data, facilitating swift and effective decision-making.
Within educational contexts, implementing BI technologies—such as data warehous-
ing, online analytical processing (OLAP), visual analytics, predictive analytics, dashboards,
and data integration through Extract–Transform–Load (ETL) processes—significantly im-
Information 2025,16, 326 3 of 30
proves institutional responsiveness and efficiency [
3
,
7
]. Specifically, as illustrated in
Figure 1, these technologies empower institutions to:
•
Analyze Student Data: Evaluate academic performance, attendance, and progression
metrics to proactively identify students at risk, enabling targeted instructional and
support interventions.
•
Monitor Institutional Performance: Track strategic indicators, including graduation
rates, student satisfaction, and employment outcomes, to pinpoint strengths and
weaknesses and develop evidence-based improvement strategies.
•
Personalize Learning Experiences: Tailor educational content and approaches based
on students’ learning patterns, preferences, and challenges, thereby enhancing educa-
tional effectiveness and student engagement.
•
Optimize Resource Utilization: Analyze the allocation and effectiveness of educa-
tional resources such as staff, materials, and technology to maximize educational
impact and institutional efficiency.
•
Evaluate Educational Programs: Assess program performance comprehensively, iden-
tifying successful initiatives and areas needing improvement, thus guiding informed
decisions about curricular and operational adjustments.
This systematic review specifically explores how educational institutions leverage BI
and related predictive analytics tools, such as machine learning and big data, to address
school dropout. By examining global case studies, the review identifies successful strategies,
highlights implementation challenges, and outlines opportunities for future research and
practice aimed at reducing dropout rates and improving educational outcomes.
Aplications of BI in the
educational context
Resource
optimization
Personalization
of learning
Figure 1. Business intelligence applications in the educational environment.
Information 2025,16, 326 4 of 30
2. Materials and Methods
This study was conducted using a systematic literature review approach, following
the guidelines proposed by the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) framework [
8
]. The PRISMA methodology was selected as it
ensures transparency, replicability, and rigor in the identification, selection, and analysis
of the relevant literature. This systematic approach allows us to comprehensively address
our research questions regarding the use of business intelligence and machine learning
methods for predicting and preventing school dropout in higher education.
To develop this study, we followed the four stages proposed by Grijalva et al. [
9
]
shown in Figure 2.
Identification of study
field and sources Planning
Conducting the
search Selection
Management and
cleaning of results Extraction
Discussion and
conclusions of
findings
Execution
Figure 2. Stages followed to conduct the systematic review based on [9].
The following are detailed descriptions of each stage.
2.1. Planning
In this stage, this study addresses the following research questions, which serve as
a guide.
• Has business intelligence allowed for the prediction and prevention of school dropouts?
•
What are the methodologies and tools for business intelligence that educational insti-
tutions have used in their management processes?
•
What have been the main contributions of business intelligence to the issue of
school dropout?
Information Retrieval
To perform the analysis, we selected two academic databases: Google Scholar and
Scopus. Google Scholar was chosen due to its extensive indexing of a wide range of
academic outputs, including journal articles, conference proceedings, preprints, theses,
dissertations, and patents. This broad scope ensured comprehensive coverage of diverse
and emerging literature related to business intelligence, machine learning, and predictive
analytics in education. Scopus was selected as a complementary source because of its
specialized focus on peer-reviewed literature, providing high-quality academic articles and
ensuring methodological rigor in our systematic review process.
Information 2025,16, 326 5 of 30
The search criteria included scientific articles, conference proceedings, book chapters,
and theses containing the keywords presented in Table 1. These databases together offered
the most balanced and thorough representation of relevant scholarly work, aligning well
with the objectives and scope of this review.
Table 1. Search strings used for retrieving articles and theses from Google Scholar and Scopus databases.
Academic Database Keywords
Google Scholar
((“school dropout”) AND (“higher education”) AND (“busi-
ness intelligence” OR “artificial intelligence” OR “machine
learning”)):AB
Scopus
ALL ( ( ( “school dropout” ) AND ( “higher education” ) AND
( “business intelligence” OR “artificial intelligence” OR “ma-
chine learning” ) ) )
2.2. Data Selection
Articles were selected for this systematic review based on strict inclusion and exclusion
criteria shown in Table 2. If a study met the following criteria, it was included: (1) Business
intelligence-related tools were used for dropout prediction; and (2) the data were from
real-world case studies. The excluded articles were those that did not have a clear BI
implementation framework, were solely theoretical in focus, or were not published in
English or Spanish. This approach helped prevent the review from including studies with
little or no practical relevance to dropout prevention.
Table 2. Inclusion and exclusion criteria for article selection.
Inclusion Criteria Exclusion Criteria
Articles published in English or Spanish
Articles published in languages other than
English or Spanish
Studies explicitly using Business Intelligence
(BI), machine learning, or data mining tools
for dropout prediction and prevention
Studies without clear use or implementa-
tion details of BI, machine learning, or data
mining tools
Empirical studies based on real-world ed-
ucational data
Purely theoretical studies or conceptual pa-
pers without empirical validation
Peer-reviewed journal articles, conference
proceedings, book chapters, and validated
academic theses
Non-peer-reviewed sources, short commu-
nications, editorials, abstracts, or opinion
pieces
2.3. Information Extraction
The methodology used for this systematic review is based on the PRISMA State-
ment [
8
] (shown in Figure 3), which aims to identify and analyze business intelligence
methodologies that have been used as strategies to analyze school dropout.
Information 2025,16, 326 6 of 30
Identification of studies via database and academic search engines
Identification
Records identified from:
Scopus (n=625)
Google Scholar (n=966)
(n=1591)
ScreeningEligibilityInclusion
Records screened
(n=532)
Records excluded after
reading the abstract and the
title
(n=407)
Records assessed for
eligibility
(n=230)
Studies included in review
(n=230)
Articles after removing
duplicates (n=939)
Revising the inclusion and
(n=302)
exclusion criteria
Figure 3. Process of selecting scientific articles for review.
3. Results
This section integrates and summarizes the main findings from the literature on
business intelligence and data-driven approaches to predict and prevent student dropout
in higher education. The discussion is organized according to the distribution of research
by region (Section 3.1), publication trends over time (Section 3.2), data sources used in
the analyzed studies (Section 3.3), the machine learning methods employed (Section 3.4),
software tools and platforms utilized (Section 3.5), the general types of research approaches
(Section 3.6), the main application objectives of these studies (Section 3.7), and the reported
challenges (Section 3.8). Each subsection integrates insights from all the references we
identified, highlighting how each study contributes to understanding the school dropout
phenomenon and how business intelligence can address it.
3.1. Global Distribution of Research
A large portion of research on student dropout prediction and prevention using busi-
ness intelligence originates from countries such as the United States, the United Kingdom,
Spain, Brazil, and other regions in Latin America (e.g., Mexico, Colombia, Chile) [
10
],
reflecting a growing global interest in these techniques. For example, multiple studies
conducted in the United States explore how analytics can identify at-risk students [
11
–
24
],
Information 2025,16, 326 7 of 30
while in Latin America, Brazil has a large body of work focusing on machine learning
models for prediction of dropout [
25
–
38
] and also from other works [
39
–
50
] that reflect
widespread investigations in the region. The focus on Latin America is further emphasized
by customized solutions such as the ADHE Dashboard [
51
], developed in Colombia to help
academic program administrators better visualize the dropout phenomenon, or the multi-
department study in Peru [
52
], which employed machine learning and survival analysis to
predict dropout times in various engineering faculties. Additional examples include recent
Ecuador-based investigations [
53
,
54
], where machine learning aids in the identification of
at-risk students in STEM institutions. Other Latin American countries with documented
efforts include Chile, where logistic regression and other machine learning models have
been tested using public datasets [
10
]. Increasingly, higher education institutions in Mexico
are using predictive analytics to identify at-risk students in large-scale data from multiple
groups [50,55–61].
There is also a notable concentration of publications from Asia, such as those from
Saudi Arabia [
62
–
67
], India [
68
–
74
], China [
75
–
77
], Taiwan [
78
], Malaysia [
79
], Thailand [
80
],
and Japan [
81
], in conjunction with a wider collection of works that demonstrate a growing
interest in Asian contexts [
82
–
89
]. For example, Patel et al. [
73
] focuses on a newly cre-
ated Gujarat dataset (EduDropX) to analyze dropout based on demographic and cultural
variables, achieving highly accurate regression-based predictions. In contrast, the work
presented in [
74
] analyzes Kaggle-based data in India to compare random forest and naive
Bayes for a balanced dropout classification. Tsai et al. [
78
] present a case study in Taiwan
exploring statistical and deep learning for precision education, while [
81
] leverage LMS log
data in Japan for early detection of at-risk students. Meanwhile, Villegas et al. [
54
] offer
insight into improving student retention in higher education settings through a sustainable
approach, reflecting how institutions around the world seek more holistic frameworks for
retention, resource optimization, and ethical AI use.
Research in African contexts, though less frequent, is present with Ghana and Tan-
zania as prime examples [
90
–
92
], along with Nigeria [
93
]. Likewise, references from
European contexts abound, such as Germany [
94
–
99
], Finland [
100
], Spain [
101
–
104
],
Portugal [
105
–
107
], and Italy [
108
–
110
], each discussing methods to track and forecast stu-
dent dropout. The Netherlands is represented by [
111
], which documents a field experiment
on early warning systems and the effect of improving individual student counseling. Other
publications reflect cross-continental collaborations and meta-analyses [
8
,
112
–
116
]. Africa-
based or Africa-related studies also include references from Uganda [
117
] and data-mining
applications in Ghana [118].
An early examination of academic support and monitoring in Latin American contexts
also emerges in [
119
], highlighting how new technologies (artificial intelligence, smart
monitors) can significantly improve student retention but require careful integration within
local cultures. Furthermore, some universities have begun focusing on designing actionable
methods that target specific course pathways, as outlined in [
120
]. Overall, the scope of
publications underscores that the worldwide spread of learning analytics and data-driven
insights is not limited to a single region, but rather is a universal pursuit to reduce student
dropout rates.
The global distribution of research highlighted above is visually represented in
Figure 4,
which provides an overview of the geographic concentration of publications
focusing on student dropout prediction and prevention through business intelligence and
data-driven methods. The map illustrates the number of studies per country, with color
intensity corresponding to publication frequency. Countries such as the United States,
Brazil, Spain, and the United Kingdom show particularly high research activity, reflecting
robust academic and institutional interest. Additionally, notable concentrations in regions
Information 2025,16, 326 8 of 30
like Latin America, Asia, and Europe underscore the worldwide adoption and adaptation
of these predictive analytics techniques in diverse educational contexts.
Figure 4. Global distribution of research papers focused on using business intelligence and data-
driven techniques for predicting student dropout in higher education. Color intensity indicates the
number of publications per country.
3.2. Temporal Distribution of Research
Figure 5highlights how interest in dropout prediction has expanded considerably
since earlier works of the late 1990s, such as those by Witten [
121
] and Shin [
122
], and
by Dietterich [
123
]. Although earlier approaches were more exploratory, the prolifera-
tion of computing power, big data, and machine learning frameworks has led to more
sophisticated predictive models. Classic works in the early 2000s demonstrated the fea-
sibility of machine learning for dropout or at-risk detection [
124
–
127
], while mid-2010s
studies investigated large-scale analytics in more depth [
128
–
130
]. From 2019 onward,
the growth in publications has been markedly accelerated, reflecting a significant increase
in the adoption of advanced data-driven solutions and the urgency to reduce dropout
rates [105,108,115,131–135].
Recent studies (2020–2025) exhibit a wide methodological range, including multi-
phase predictive modeling [
107
] and novel data pipelines [
136
], while also addressing
interpretability challenges [
120
,
137
]. Works such as [
138
] explore recommender systems
to guide students toward suitable courses and prevent attrition; similarly, Alturki & Al-
turki [
65
] emphasizes how the increasing adoption of educational data mining from around
2015 onward promoted new lines of inquiry focused on predicting academic achievement
and identifying at-risk students. The need for very early detection has propelled investiga-
tions into multi-semester data usage [
72
,
139
], while deep learning or neural approaches
have gained attention in 2020 publications [
84
,
95
]. For example, Tsai et al. [
78
] shows
how the combination of big data with deep neural networks can yield 77% precision for
identifying dropouts in Taiwan. Similarly, Paul [
140
] describes how student participation
factors are essential for reliable predictions using supervised and unsupervised methods.
Furthermore, Villegas et al. [
54
] details recent explorations of integrating AI-based
retention strategies into broader institutional sustainability and quality frameworks. The
works in [
44
,
141
,
142
] represent intermediate phases in which educational institutions have
increasingly sought predictive analytics to curb dropout, although with fewer data in-
tegration capabilities compared to today. In the very recent period (2020–2025), many
works [
12
,
48
,
57
,
82
,
86
,
143
,
144
] explore or refine deep learning approaches, advanced clus-
Information 2025,16, 326 9 of 30
tering, or creative feature engineering. Studies such as [
145
] propose probabilistic machine
learning pipelines that quantify uncertainty, helping instructors with structured interven-
tions throughout the semester. Meanwhile, the works from [
66
,
146
] demonstrate the value
of early-stage predictions in online or hybrid contexts, allowing for timely intervention to
keep students engaged.
Figure 5. Temporal distribution of research papers published on predicting student dropout in higher
education using business intelligence and machine learning techniques (1996–2025). The number of
publications demonstrates rapid growth in interest, particularly from 2019 onward.
Several 2023–2025 publications reflect the growing interest in specialized approaches.
For example, Mosia [
145
] uses probabilistic logistic regression in multiple stages to iden-
tify at-risk students at different times, while [
89
] focuses on MOOC dropout modeling
with XGBoost. Suaprae et al. [
80
] discuss an intelligent consulting system equipped
with cognitive technology for student counseling, and [
93
] refines predictive modeling in
Nigeria by applying improved data pre-processing in multi-year records. Furthermore,
Demartini et al. [
147
] shows how AI-driven dashboards can catalyze acceptance and usage
of learning analytics in primary and secondary education, although the insights apply
equally to higher education. Awedh and Mueen [
67
] present a new hybrid approach
(LR-KNN) and point to the future need to bridge interpretability with advanced analytics.
These contributions highlight how the literature continues to evolve, pushing toward more
adaptive, context-driven, and multi-source methodologies.
3.3. Data Sources in Dropout Prediction
The literature confirms that an accurate prediction of dropout is often based on a com-
bination of institutional and external data. Studies repeatedly highlight internal academic
records (grades, assessments, GPA) and demographic information as fundamental charac-
teristics [
9
,
46
,
148
–
151
]. Some authors, such as [
64
,
90
,
152
–
154
], incorporate data extracted
from virtual learning environments (VLEs), learning management systems (LMSs) [
81
,
146
],
Information 2025,16, 326 10 of 30
or MOOC platforms [
89
]. Others emphasize less common input features; for example, the
clustering of digital traces from e-learning platforms [
46
], the combination of continuous
and categorical data through specialized clustering-then-classification methodologies [
133
],
or the exploration of textual variables with advanced NLP [155,156].
Longitudinal data from multi-semester course performance have been proven critical
in many contexts, including recent work such as [
139
], where the course completion
data for each semester improve predictive accuracy. Similarly, Martins et al. [
107
] uses
academic, social–demographic, and macroeconomic information collected at different
phases of a student’s first year to detect dropout risk as early as possible. Additional
works incorporate structured questionnaires to measure intangible factors associated with
dropout, such as CPQ-based approaches in [
103
], while [
157
] emphasizes the importance
of thorough data cleaning, labeling, and transformation steps in building robust predictive
pipelines. Jaiswal et al. [
158
] highlights how mining large educational datasets can unmask
hidden relationships that predict dropout, particularly in contexts where institutional data
accumulate every year.
In many Latin American institutions, official government or institutional datasets
are central for analyses [
10
,
51
,
52
], combining socio-demographic, socio-economic, and
academic indicators to identify when a student becomes at risk of leaving. Elsewhere,
Vidal et al. [
53
] shows how such data can reveal motivational or attributional patterns,
especially relevant in STEM contexts. In the United States, Jain [
22
] merges high school and
freshman college data for early warning systems, while [
23
] leverages Canvas engagement
logs. The works such as [
79
] in Malaysia or [
93
] in Nigeria illustrate how data from mul-
tiple enrollment groups combined with pre-processing in Weka yield accurate predictive
models of student attrition. Demartini et al. [
147
] adapt analytics to a broader educational
domain, analyzing data streams from secondary schools and exploring how AI-based
dashboards benefit teachers. Meanwhile, Guarda [
61
] discusses how identifying the per-
sonal and institutional reasons for dropout requires cross-sectional and time series data for
robust classification.
Socio-economic variables remain consistently identified as crucial predictors of
dropout. Multiple references [
42
,
96
,
127
,
159
–
162
] and more recent works [
62
,
67
,
117
] stress
that parental support, financial conditions, and scholarship data can carry strong predictive
power. De Jesus [
163
] uses fuzzy logic to prescribe specific interventions based on a set
of risk factors that include family environment, personal motivation, and socioeconomic
background. Sani et al. [
93
] and Adnan et al. [
146
] confirm that thorough pre-processing
and feature selection further augment the value of such data, thus enhancing predictive
performance. Finally, specialized data warehousing frameworks that unify multiple data
streams appear in [
49
,
164
,
165
], ensuring that advanced machine learning models receive
high-fidelity inputs. While data availability expands, the works from [
157
,
166
] remind us
of the critical need for systematic data cleaning and transformation to combat noisy or
incomplete institutional records.
Figure 6summarizes the range of data sources used in dropout prediction studies. It
shows that internal databases, academic records, and socio-demographic data are the most
frequently utilized sources in the literature employing business intelligence and machine
learning. This emphasizes the critical role of comprehensive data integration in developing
accurate predictive models.
Information 2025,16, 326 11 of 30
Figure 6. Data sources utilized in studies predicting student dropout in higher education using
business intelligence and machine learning techniques. Internal institutional databases, socio-
demographic data, and academic performance data are the most frequently employed data sources
across the reviewed literature.
3.4. Machine Learning Methods Used
Figure 7illustrates the algorithms most commonly employed in the prediction
of student dropout. Decision trees remain quite popular [
24
,
35
,
68
,
71
,
112
,
159
,
167
–
173
],
mainly due to their interpretability and ease of implementation. Random Forest and
Gradient Boosting Machines (e.g., XGBoost, LightGBM, CatBoost) have gained traction
for their robust performance [
46
,
47
,
82
,
89
,
107
,
109
,
118
,
131
,
133
,
143
,
174
–
183
], including in
MOOC dropout research. Logistic regression remains a consistent baseline for inter-
pretability [
68
,
69
,
72
,
79
,
87
,
96
,
145
,
152
,
162
,
184
–
186
], and some authors integrate stepwise
or probabilistic refinements [145].
Ensemble methods and hybrid approaches often emerge as top performers, exem-
plified in [
43
,
66
,
114
,
132
,
150
,
175
,
181
,
187
–
189
]. Deep learning frameworks (feedforward
networks, CNNs, LSTMs) have gained traction [
71
,
78
,
84
,
87
,
95
,
152
,
154
,
160
,
190
], and ap-
proaches like [
67
] show potential for hybrid classification (LR-KNN) or fuzzy logic-based
systems [
44
,
153
,
163
]. Adnan et al. [
146
] proposes a staged approach to identify at-risk
students at 0%, 20%, 40%, 60%, 80%, and 100% of the course timeline, utilizing random
forest models to capture changing levels of engagement. Meanwhile, Sani et al. [
93
] applies
Random Forest and decision trees with improved data pre-processing, surpassing 79%
accuracy in student attrition forecasting.
Less common techniques such as Fuzzy-ARTMAP networks [
44
], Bayesian classi-
fiers [
23
,
56
,
191
,
192
], and cluster-then-classify strategies [
46
,
133
] complement the more tra-
ditional approaches, especially when institutions seek to handle unstructured or noisy data.
Kondo et al. [
81
] demonstrate how straightforward logistic regression and decision trees
benefit from LMS log data for early warnings, while [
80
] deploy an intelligent consulting
Information 2025,16, 326 12 of 30
system integrated with cognitive technology. An increasing emphasis on explainability also
appears, with XAI methods (e.g., LIME, SHAP) helping to clarify model decisions [
131
,
137
].
As these tools evolve, a shared trend is to balance predictive performance with transparency
for institutional acceptance [120,170].
Figure 7. Frequency of machine learning methods employed in predicting student dropout in
higher education.
3.5. Software Tools and Platforms
Regarding specific software tools, the works analyzed indicate frequent usage of open
source data mining platforms such as Weka [
79
,
93
,
193
–
195
], KNIME [
196
], Orange, R, and
Python-based libraries (scikit-learn,TensorFlow,PyTorch) [
36
,
143
,
154
,
160
,
197
,
198
]. Commercial
platforms like IBM SPSS Modeler and SAS Enterprise Miner appear in large institutional or multi-
campus projects [
199
–
201
], while Power BI or Tableau are commonly cited for data visualization
and reporting to decision-makers [
202
–
204
]. In some cases, specialized data warehousing or
star schema designs facilitate data extraction and transformation [164,205–207].
LMS and MOOC platforms also serve as crucial data sources. The works of [
81
,
89
]
use online log data to train predictive models and implement early warning systems.
Novel or hybrid frameworks appear in references such as [
157
], which offers an end-
to-end pipeline with data augmentation, labeling, and feature engineering. In [
147
], a
cloud-based AI dashboard is implemented to enhance teacher acceptance of learning
analytics, especially in primary and secondary schools, but with clear parallels to higher
education usage. Suaprae et al. [
80
] emphasize integrating machine learning into intelligent
consulting systems, demonstrating how advanced analytics can be wrapped into user-
friendly interfaces for counselors. In general, researchers stress solutions that are accessible
and flexible enough to accommodate diverse institutional data, ensuring robust and scalable
dropout prevention initiatives.
The diverse ecosystem of software tools and platforms employed in the research on
dropout prediction discussed above is visually summarized in Figure 8. This waffle plot
illustrates the proportional usage of various tools across reviewed studies. Open-source
data mining platforms such as Weka,KNIME, R, and Python-based libraries are prominently
featured, along with commercial solutions like IBM SPSS Modeler,SAS Enterprise Miner,
Power BI, and Tableau. The visualization highlights the widespread adoption of versatile
Information 2025,16, 326 13 of 30
software tools, emphasizing both the flexibility and accessibility required by institutions
implementing effective dropout prediction and prevention systems.
Figure 8. Waffle plot illustrating the proportion of software tools used in studies predicting student
dropout in higher education.
3.6. Types of Research Approaches
Figure 9shows how most studies employ quantitative empirical methods that feature
some variant of supervised machine learning in institutional data. Many of these are
cross-sectional analyses, although a growing number of researchers adopt longitudinal ap-
proaches to capture changes in student performance over
time [42,79,102,128,203,208,209].
Intervention-based pilot implementations of early warning systems are increasingly com-
mon [
34
,
66
,
131
,
210
–
213
]. Here, data dashboards are tested in real courses or programs,
with interventions triggered once high-risk profiles emerge [
81
,
146
]. The work of De Jesus
et al. [
163
] proposes a fuzzy logic-based prescriptive analytics system that predicts both
dropout risk and suggests targeted interventions. Similarly, [
80
] integrates predictive ana-
lytics into an intelligent counseling system, guided by cognitive technology to decrease
mid-exits.
Studies such as [
113
] test specific interventions through randomized or quasi-
experimental designs, while meta-analyses such as [
112
,
114
,
116
] evaluate multiple predic-
tive modeling approaches. Other authors focus on design-based research [
58
,
135
,
136
,
214
],
focusing on iterative improvements in early warning dashboards. Works such as [
52
,
93
]
employ survival analysis or multi-year classification to unravel dropout trajectories. Some
efforts combine numeric data with interviews or surveys to validate the root causes of
dropout behavior [21,61,191,215].
New lines of research incorporate novel analytics and AI. For example, Demartini
et al. [
147
] addresses K–12 education, but highlights a multidimensional approach to bridging
analytics and stakeholder acceptance, relevant to higher education. Vidal et al. [
53
] employ
neural networks and a robust set of motivational variables in Ecuador, demonstrating how
psychosocial factors can affect the success of STEM courses. In [
67
], the authors propose a
layered approach that integrates pre-processing, clustering (canopy + Gaussian flow opti-
mizer), and a hybrid LR-KNN model to identify at-risk students at King Abdulaziz University.
Information 2025,16, 326 14 of 30
References like [
157
] detail data pre-processing pipelines (CRISP-DM, star schemas) that serve
as the backbone for predictive tasks. Finally, large-scale or multi-institutional explorations,
such as [
104
,
190
], underscore the importance of shared data standards to refine predictions
across varying contexts.
Figure 9. Treemap representing the distribution of research types among studies on predicting
student dropout in higher education using business intelligence and machine learning techniques.
3.7. Application Objectives of the Studies
Whereas early work on dropout analysis often aimed simply to identify at-risk
students, recent publications indicate broader objectives of data usage. Figure 10
illustrates how early detection and intervention design remain the most common
goals [46,83,148,181,185,216].
Many studies propose frameworks for timely or even real-
time interventions, focusing on academic counseling, customized feedback, or improved
course design [
24
,
63
,
80
,
132
,
142
,
153
,
163
,
217
–
219
]. Another line of research focuses on build-
ing recommendation systems or advising platforms (e.g., personalized learning path-
ways [133,216] or data-driven academic counseling tools [93,108,220]).
In addition, a portion of the literature contemplates how predictive insights can guide
broader institutional policy. For example, works in [
159
,
221
,
222
] detail using analytics to
reorient curriculum design and resource allocation. Several authors highlight the use of AI-
based predictive models to reduce inequality and strengthen equity in education, especially
for disadvantaged groups [
53
,
62
,
82
,
117
,
223
]. Another dimension lies in designing advanced
dashboards or holistic monitoring solutions that unify multiple data streams into a coherent
framework [
132
,
147
,
206
,
224
–
226
], supporting administrators in real-time decision-making.
Integrating the concept of “digital portraits” from social media and university records
has also been explored [
227
] to provide a 360-degree overview of students’ academic and
extracurricular lives.
Several works address how performance analytics can be leveraged by institutional
leaders to formulate macro-level policies. Martins et al. [
107
] study whether students
are likely to drop out, delay, or complete on time, while [
66
] focuses on improving reten-
tion rates for students at risk by combining LSTM with other machine learning classifiers.
Sharma and Yadav [
72
] help first-year engineering students through a streamlined predictor
set for timely interventions, while Adnan et al. [
146
] explicitly tailors random forest models
to identify at-risk students at multiple junctures across a course. MOOC-based contexts also
Information 2025,16, 326 15 of 30
arise: Patel and Amin [
89
] show how advanced gradient enhancement promotes retention
through early detection, facilitating more personalized online learning. Integration of insti-
tutional sustainability goals into references such as [
54
] reveals a growing acknowledgment
that dropout reduction aligns with resource optimization, mental health considerations,
and institutional longevity.
In general, these specialized objectives converge on the broad aim to reduce dropout,
improve academic performance, and optimize routes in a data-driven, equitable manner.
Local contexts, from Taiwan [
78
] to Nigeria [
93
] to Ecuador [
53
], demonstrate that region-
specific features (cultural, socioeconomic, infrastructural) must be integrated into models
to maximize impact. Consequently, the typical workflow is shifting from predictive to
prescriptive as exemplified by [
163
], which merges fuzzy logic with interventions to address
the multifaceted challenges of student attrition.
Figure 10. Distribution of application objectives among studies predicting student dropout in higher
education using business intelligence and machine learning techniques.
3.8. Challenges Reported
Several recurring challenges in the use of BI and machine learning for predicting
dropout appear in the literature, such as data privacy, data quality, interpretability, resource
limitations, and ethical concerns, as depicted in Figure 11.
Figure 11. Challenges reported in studies focused on predicting student dropout in higher education
using business intelligence and machine learning techniques.
Information 2025,16, 326 16 of 30
3.8.1. Data Privacy and Security
Data privacy remains a critical concern, especially regarding sensitive student informa-
tion. Studies such as [
12
,
228
] emphasize risks associated with bias and privacy violations
arising from inappropriate use or sharing of student data. Transparent data governance
frameworks are recommended by [
20
,
185
] to mitigate these risks. Cross-platform and
cross-institutional data sharing raise additional privacy complexities, especially in contexts
involving emerging AI-driven analytics [
83
,
156
,
190
]. Maintaining adherence to robust ethi-
cal standards and institutional policies, as highlighted by [
54
], is essential for responsible
and secure data use.
3.8.2. Data Quality and Integration
Integrating data from diverse sources often poses significant challenges due to in-
consistencies, missing values, or variations in data formats [
155
,
166
]. The literature ad-
vocates for adopting robust data management practices, including structured pipelines,
the CRISP-DM methodology, star schema designs, and advanced data warehousing ap-
proaches [
49
,
51
,
164
,
205
,
229
,
230
]. Additionally, longitudinal data present difficulties in
maintaining consistency over extended periods, requiring special attention to shifting defi-
nitions and evolving institutional standards [
72
,
79
,
139
]. Work such as [
73
] emphasizes the
need to generate region-specific datasets, while [
74
,
157
] emphasizes systematic strategies
to handle class imbalances, data cleaning, and labeling to improve model robustness.
3.8.3. Model Interpretability
The increasing sophistication of machine learning models, such as deep neural net-
works and ensemble methods, often results in reduced transparency, commonly referred
to as the black box problem [
95
,
160
,
170
,
185
]. This lack of interpretability can undermine
stakeholder trust and limit practical adoption. To address these issues, researchers em-
phasize the development of explainable AI (XAI) frameworks such as LIME or SHAP,
which provide insights into model decisions [
131
,
133
,
231
,
232
]. Additionally, probabilistic
approaches and Bayesian models are recommended to integrate uncertainty explicitly, thus
enhancing interpretability without sacrificing predictive performance [
23
,
145
]. Studies such
as [
120
,
137
] further highlight the importance of obtaining causal insights and actionable
interventions from predictive analytics.
3.8.4. Resource Limitations and Expertise
A recurring barrier to effective implementation of BI in higher education is the lack of
technical expertise and the necessary infrastructure [
114
,
167
,
191
]. Institutions often face
constraints related to limited financial resources, insufficient technological infrastructure,
and staff training gaps [
166
,
233
]. These limitations are particularly pronounced in devel-
oping countries where unstable internet connectivity, inadequate hardware, and minimal
technical support restrict the deployment of sophisticated predictive systems [
91
–
93
]. Ad-
dressing these challenges requires comprehensive capacity building initiatives, targeted
training programs, and the creation of user-friendly analytical tools to ensure successful
adoption and sustained implementation [80,119].
3.8.5. Ethical Concerns and Bias
The risk of perpetuating existing biases through predictive analytics is widely noted,
raising significant ethical concerns [20,83,108,143,185]. There is a critical need for fairness-
aware modeling practices to ensure that dropout prediction tools do not disproportionately
disadvantage marginalized or vulnerable groups [
12
,
14
,
234
]. Researchers advocate for
interpretive frameworks that explicitly account for demographic, socioeconomic, and
Information 2025,16, 326 17 of 30
cultural contexts to prevent unintended discrimination [
98
,
117
]. Moreover, careful vali-
dation practices and inclusive model design are essential to mitigate inadvertent biases,
especially in diverse educational settings such as MOOCs or digitally mediated learning
environments [67,89].
4. Discussion
The results of this systematic analysis highlight the rapidly expanding global interest
in using business intelligence (BI) and data-driven approaches to predict and prevent
student dropout in higher education. Taken together, these findings emphasize how
diverse regions, institutional contexts, and research methods converge on a common goal:
to reduce attrition rates by leveraging advanced analytics. Below, we discuss key insights
and their implications for educational practice, policy, and research.
4.1. Global and Temporal Trends
The geographical scope of the reviewed research demonstrates that dropout prediction
efforts are widespread in North America, Europe, Latin America, and Asia, with a growing
presence in Africa. This global engagement highlights the universal challenge of student
attrition, while also revealing significant context-specific nuances. Nations such as the
United States, Brazil, Spain, and the United Kingdom stand out for their high volume of
studies, although work from other regions continues to grow. At the same time, the marked
increase in publications from 2019 onward suggests that the urgent need to improve student
success, combined with greater data availability and computational capacity, has fueled
more robust and sophisticated modeling techniques.
Despite varying resource constraints across regions, the common denominator is a
shared objective: transitioning from traditional, often manual tracking systems to proactive,
data-driven strategies that inform timely interventions. This widespread interest points to
the potential for cross-regional knowledge exchange, especially as more mature educational
systems refine their approaches and emerging systems adopt best practices. Moreover, the
methodological evolution observed over the last decade—from relatively simple classifica-
tion models to deep learning and explainable AI—reflects heightened demand for better
predictive power and interpretability.
4.2. Data Sources and Methods
A variety of data sources have been employed to predict dropout, with the most
prominent being institutional academic records (e.g., grades, GPA) and socio-demographic
variables (e.g., socioeconomic status, parental support). Additionally, learning manage-
ment system (LMS) logs, MOOC platform data, and longitudinal performance records are
increasingly integrated to capture student engagement and changes in risk over time. This
combination of data highlights the multifactorial nature of dropout, in which academic,
social, psychological, and economic factors intersect to influence students’ decisions to
abandon their studies.
From a methodological point of view, machine learning approaches show significant
heterogeneity. Decision trees and logistic regression are consistently used because of their
interpretability, while ensemble methods (e.g., Random Forest, XGBoost) and deep learning
models have gained popularity for their ability to handle complex, high-dimensional data.
However, the shift toward more advanced models often comes at the cost of reduced
transparency, leading to a growing emphasis on explainable artificial intelligence (XAI)
methods. Such approaches aim to balance predictive accuracy with stakeholder inter-
pretability, particularly as institutional acceptance and ethical use hinge on clear, justifiable
decision-making processes.
Information 2025,16, 326 18 of 30
4.3. Research Approaches and Application Objectives
Quantitative empirical analyses predominate, often featuring supervised learning to
classify or predict at-risk students. Yet, a notable subset of studies incorporates intervention-
based designs, in which predictive models guide real-time or near-real-time actions. These
interventions—ranging from personalized counseling to curricular adjustments—are often
delivered through user-friendly dashboards or integrated advising platforms. By embed-
ding predictive results into day-to-day academic operations, these studies demonstrate the
feasibility of moving beyond retrospective analytics and toward proactive student support.
The reviewed research also reveals an expansion of objectives. While early detection
of at-risk students remains central, many institutions now aim to use dropout risk data
to inform broader policy decisions. For example, predictive insights help drive course
redesign, equitable resource distribution, and even institutional strategies to improve
mental health and social engagement. Some universities align these efforts with long-term
sustainability and inclusion goals, recognizing that dropout prevention intersects with
ethics, student well-being, and institutional reputation.
4.4. Challenges and Limitations
Educational institutions often face technical challenges due to educators’ limited
familiarity with BI tools and predictive analytics. To effectively overcome these barriers,
institutions should invest in targeted professional development initiatives, including hands-
on workshops, certifications in data analytics, and training programs tailored specifically
to educational contexts. Collaborative efforts with industry experts or partnerships with
institutions experienced in data-driven practices can also facilitate knowledge transfer
and skill enhancement. Additionally, implementing user-friendly, intuitive BI dashboards
designed explicitly for educators can significantly reduce barriers to effective use, ultimately
improving the practical impact of BI tools on dropout prevention strategies.
Data privacy remains a significant challenge in implementing BI and predictive an-
alytics in education, particularly given stringent legal frameworks such as the General
Data Protection Regulation (GDPR) in the European Union and the Family Educational
Rights and Privacy Act (FERPA) in the United States. GDPR sets strict standards for
collecting, processing, and managing personal data, emphasizing transparency, consent,
and accountability, while FERPA specifically protects the privacy of student educational
records, limiting how institutions can use and disclose data. These regulations require
educational institutions to establish robust data governance practices and infrastructure to
ensure compliance, which poses considerable resource challenges, especially for smaller or
under-resourced institutions.
Data quality and integration also pose substantial difficulties. Heterogeneous data
systems, missing values, and inconsistent recording practices can hinder model accuracy,
underscoring the need for standardized data warehousing solutions and sophisticated
pre-processing workflows.
Another limitation involves the interpretability of advanced predictive methods. Al-
though sophisticated algorithms, including deep learning and ensemble models, offer
high predictive performance, their inherent complexity often limits transparency. This
so-called “black-box” issue can undermine institutional stakeholders’ trust, necessitating
greater emphasis on explainable AI methodologies that provide clarity and confidence in
predictive outputs.
Additionally, successful BI implementation hinges upon adequate training and skill
development among educators and administrators. Even the most effective predictive
analytics systems require skilled users who can interpret and translate data insights into
Information 2025,16, 326 19 of 30
actionable interventions. Without sufficient training and ongoing professional development,
educational institutions may struggle to realize the full potential of BI technologies.
Finally, ethical concerns remain significant. Predictive models trained on historical
data risk reinforcing existing biases, inadvertently disadvantaging vulnerable student
populations. Ensuring fairness and equity requires rigorous validation, ongoing monitoring,
and transparent modeling practices, highlighting the ethical responsibilities of institutions
employing predictive analytics.
4.5. Future Directions
Several avenues for further work emerge from these findings. First, efforts to integrate
multi-modal data—encompassing academic records, LMS logs, and psychosocial indica-
tors—should continue, as richer datasets promise more robust risk assessments. Second,
bridging the gap between prediction and intervention calls for interdisciplinary research,
uniting data scientists, educators, and policy experts to design actionable strategies. Third,
scaling up cross-institutional collaborations can accelerate learning from diverse contexts,
particularly if researchers embrace open science principles that encourage data and method-
ology sharing. Lastly, developing and validating fairness-aware machine learning models
is critical to ensuring that dropout prevention strategies support, rather than stigmatize,
underrepresented or marginalized student groups.
5. Conclusions
The collective evidence from these studies points to the growing sophistication and
relevance of business intelligence and machine learning methods in addressing student
dropout in higher education. Institutions worldwide are leveraging increasingly varied
data sources and advanced analytical tools, supported by user-centered dashboards and
interventions that target students most in need. However, persistent challenges, such
as data quality, privacy, interpretability, and ethical risk, underscore that successful im-
plementation requires robust governance and community involvement. In the future,
collaborative and context-specific research will play a key role in unlocking the full po-
tential of data-driven dropout prevention, ultimately contributing to more inclusive and
effective educational systems.
Author Contributions: Conceptualization, D.-M.C.-E.; methodology D.-M.C.-E., J.T., J.-A.R.-G. and
K.-E.C.-E.; formal analysis, D.-M.C.-E., J.T., J.-A.R.-G. and K.-E.C.-E.; investigation, D.-M.C.-E., J.T. and
K.-E.C.-E.; resources D.-M.C.-E., J.T., J.-A.R.-G., K.-E.C.-E., R.-E.L.-M. and R.C.-S.; writing—original
draft preparation, D.-M.C.-E., J.T., J.-A.R.-G. and K.-E.C.-E.; writing—review and editing, D.-M.C.-E.,
J.-A.R.-G., J.T., K.-E.C.-E., R.-E.L.-M., T.G.-R. and R.C.-S.; supervision, D.-M.C.-E., K.-E.C.-E., T.G-R.
and J.T.; project administration, D.-M.C.-E., J.T. and K.-E.C.-E. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: No new data were created or analyzed in this study.
Acknowledgments: We thank the Secretaría de Ciencia, Humanidades, Tecnología e Innovación
(SECIHTI) for its support through the National System of Researchers (SNII).
Conflicts of Interest: The authors declare no conflicts of interest.
Information 2025,16, 326 20 of 30
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