A COMPREHENSIVE STUDY FOR IDENTIFICATION OF FAST AND SLOW LEARNERS USING MACHINE LEARNING APPROACH

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072
© 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 72
A COMPREHENSIVE STUDY FOR IDENTIFICATION OF FAST AND SLOW
LEARNERS USING MACHINE LEARNING APPROACH
Shrawani Rayalkar1, Chirag Nere2, Dr. Hrushikesh Kulkarni2*
1School of Data science,2 School of Mechatronics Engineering, Symbiosis Skills and Professional University, Pune
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Abstract - This research paper presents a detailed
investigation into the identification of fast and slow learners
in an academic setting through the implementation of a
machine learning-based ensemble model. The proposed
ensemble model utilizes a training dataset to generate rules,
subsequently applied to a testing dataset for predicting
academic performance.
The goal is to have a better understanding of how students
learn and identify the settings in which they learn to
improve educational outcomes and to gain insights into and
explain educational phenomena. The biggest challenge is to
improve the quality of the educational processes so as to
enhance student’s performance. Thus, it is crucial to set new
strategies and plans for a better management of the current
processes. It is concerned with developing methods for
exploring the unique types of data that come from
educational environments. The paper uses trains various
machine learning model using real time data to find the
prediction and accuracy of the models and hence finds out
which works accurately and hence helping in academic
actions can be taken for each student accordingly
Experimental results, conducted using a dataset from a
computer science department, demonstrate the model's
efficacy with an accuracy of 90.83%. The ensemble model
not only provides valuable insights for instructors to
scrutinize and enhance student performance but also aids
learners in self-assessment and corrective actions. The study
concludes with potential extensions, suggesting the
development of a recommender system for course selection
based on performance predictions and incorporating
indirect factors affecting academic performance. The
integration of predictive analytics in the evolving landscape
of educational technology is also discussed, highlighting its
potential in assisting students through personalized
recommendations based on their predicted performance.
Key Words: Slow learners, fast learners, Education
performance, machine learning
1. INTRODUCTION
Educational data has become an important resource in
this modern time, contributing much to the welfare and
growth of the society. Educational system is becoming
more competitive because of the number of schools and
institutions which are growing rapidly. The educational
schools are focusing more on improving various aspects
and one important factor among them is quality learning.
A fast learner is someone who gains the skills of being
a strategic thinker and a good listener and applies them to
learning quickly. A fast learner not only learns things at
small interval of time but also gains enough knowledge
than a normal person.A slow learner work on tasks more
slowly, have poor memory and difficulties understanding
concepts and subject taught to them.
Prediction of student’s performance is challenging
task as it depends on many factors such as grades, class
performance, demographic data and emotional features. It
is important for the teachers to forecast the future
performance of a student based on his past performances,
identifying weak students at an early stage so that
additional material and special attention can be facilitated
to avoid the risk of failure [1].
Besides this, various statutory and regulatory
bodies such as National Assessment and Accreditation
Council (NAAC) and the National Accreditation Board
(NBA) which are accrediting the higher education
institutions also stress the identification of the learning
levels of the students and accordingly steer the teaching–
learning process for them. If the weak learners are
identified at the start of the semester, the respective
subject teachers can plan their academic activity for a
better understanding of the subject matter by such
students, and their results improve as they go to higher
semesters. To improve the students’ performance by
tailored teaching–learning activities for the slow and fast
learners is the key outcome of this research [3].
2. Materials and Methods
The methodology for this research involved the
identification of fast and slow learners in a college
environment to facilitate the adaptation of academic
teaching methods for personalized learning. The study
utilized a dataset comprising records of approximately
1000 students, encompassing their performance marks in
three subjects and the corresponding number of study
hours per day. Five distinct machine learning models,
namely Logistic Regression, Decision Tree, Random
Forest, Support Vector Machine (SVM), and K-Nearest
Neighbors (KNN), were employed to classify students into

fast, slow, and average learners based on their academic
achievements and study habits. The models underwent
training and evaluation processes, with performance
metrics such as accuracy, precision, recall, and F1 score
assessed to determine their effectiveness. The results
revealed that SVM exhibited the highest accuracy, while
Logistic Regression showed the lowest. The findings from
this comprehensive methodology aim to inform
educational institutions about potential modifications in
teaching approaches to cater to the diverse learning paces
of students, thereby contributing to a more effective and
inclusive educational environment.
A literature review was presented using a broad array
of data about students and courses collected by
institutions and learning analytics to improve student's
success and retention [5]. Academic analytics measure,
collect, decipher, report, effectively share data, and
identify student strengths and weaknesses [6].
3. Results and discussion
The research evaluated five machine learning models,
each with its unique characteristics and applicability. The
Support Vector Machine (SVM) emerged as the most
accurate model, achieving an accuracy of 97%. The
Logistic Regression model demonstrated an accuracy of
86%, while the Decision Tree, Random Forest, and KNN
models showed accuracies of 90%, 93%, and 87%,
respectively as mentioned in Table 1. The study provides a
detailed breakdown of precision, recall, and F1-score for
each model, offering insights into their performance
across different learner categories.
Logistic Regression can be employed to classify
students into fast and slow learners based on their
academic performance, study time, and attendance. It is
effective when the relationship between input features
and the output is approximately linear. It models the
probability of an instance belonging to a particular class,
making it suitable for binary classification tasks. Logistic
Regression estimates the coefficients for each input
feature, applying a logistic function to the linear
combination of these coefficients. This maps the input
features to a probability between 0 and 1, and a threshold
is applied to make the final classification.
Decision Trees are applicable to classify students based
on their academic performance, study time, and
attendance. It can capture non-linear relationships
between features and the target variable. They partition
the feature space based on the most informative features,
creating a tree structure that facilitates classification.
Decision Trees make decisions by recursively splitting the
data based on the feature that maximally separates the
classes. The decision at each node is determined by
feature values, and the final classification is made at the
leaf nodes.
Random Forest can enhance the classification accuracy
by combining multiple Decision Trees for categorizing fast
and slow learners. It mitigates overfitting by aggregating
predictions from various Decision Trees. It introduces
randomness in the tree-building process, leading to
diverse trees and increased robustness. Random Forest
builds multiple Decision Trees on different subsets of the
data and averages their predictions. This ensemble
approach reduces overfitting and improves generalization.
SVM can be employed for binary classification of fast
and slow learners based on their academic performance
and study time. SVM is effective in high-dimensional
spaces and can handle non-linear relationships through
the kernel trick. It identifies the hyperplane that best
separates the classes. SVM seeks the hyperplane with the
maximum margin between classes. It transforms the data
into a higher-dimensional space if needed, allowing for
better separation. The support vectors are instances
closest to the decision boundary.
KNN can classify students into fast and slow learners
based on the similarity of their academic performance,
study time, and attendance. KNN is suitable for capturing
complex relationships and works well with small to
moderately-sized datasets. It classifies instances based on
the majority class of their k-nearest neighbors. KNN
calculates distances between instances in the feature
space and assigns a class based on the majority class
among the k-nearest neighbors. It's a lazy learner as it
does not build an explicit model during training.
Fig -1: Plot showing the average of 3 subjects against
the study hours where RED is slow learners, BLUE is
average learners and GREEN is fast learners.

Table 1: Accuracy of models and their classification
report for each category
The scatter plot in Figure 1. visually illustrates the
distribution of 1000 students based on study hours and
marks, highlighting predominant average and slow
learners. The classification is depicted using various
colors: red represents slow learners who spend more time
studying but achieve lower marks, green represents fast
learners who study for shorter durations but attain higher
marks, and blue represents average learners falling in
between.
Upon examination, it becomes evident that the majority of
students in the dataset are either average or slow learners.
The x-axis represents the study hours, ranging from 2 to 7
hours, while the y-axis depicts the marks achieved,
spanning from 0 to 100. This visualization allows for a
clear understanding of the distribution of students across
different learner categories, highlighting the diverse study
habits and academic outcomes within the dataset. Notably,
a substantial portion of students falls into the average
learner category, indicating a balance between study
hours and academic performance.
On the other hand, a significant number of slow learners,
characterized by extended study hours and comparatively
lower marks, are also present. Additionally, the plot
showcases a distinct group of fast learners who achieve
high marks despite investing fewer hours in studying.
Machine learning models, including SVM with 97%
accuracy, demonstrate efficacy in learner classification.
The outcomes can inform educational strategies, aiding
early identification of underperforming students.
Implementing these models in the education system
enables personalized interventions. For instance, targeted
support mechanisms for slow learners, advanced
challenges for fast learners, and tailored approaches for
average learners could optimize academic outcomes.
Integrating predictive analytics fosters a data-driven
educational environment, guiding educators to proactively
address student needs and enhance overall learning
outcomes.
Despite providing valuable insights, the study has
limitations, including a relatively small and potentially
unrepresentative sample, a focus on limited features, and
the analysis primarily considers marks in subjects and
study hours as features. Other relevant factors influencing
academic performance, such as extracurricular activities,
socio-economic background, or mental health aspects, are
not included. The exclusion of these variables could impact
the depth of the model's understanding.
The analysis primarily considers marks in subjects and
study hours as features. Other relevant factors influencing
academic performance, such as extracurricular activities,
socio-economic background, or mental health aspects, are
not included. The exclusion of these variables could impact
the depth of the model's understanding. The
representativeness of this sample in comparison to a
larger student population or different academic disciplines
could be a limitation. Variability in academic settings and
student demographics may influence the generalizability
of the results.
4. Conclusion
It is paramount to recognize and accommodate the diverse
learning profiles exhibited by students. By tailoring
educational strategies to meet the needs of both advanced
and slow learners, educational institutions can establish
an environment that not only challenges the intellectually
curious but also provides support for those encountering
educational obstacles. The proposed interventions
outlined in this comprehensive plan aim to address
individual needs and offer the essential resources required
for academic success.
Our prior discussion on the application of Support Vector
Machines (SVM) in the education system aligns seamlessly
with the call for data-driven approaches to identify fast
and slow learners. Integrating machine learning models,
as previously explored, can enhance these initiatives by
providing valuable insights derived from data patterns,
empowering educators to make well-informed decisions.

In conclusion, a holistic approach that combines targeted
educational interventions with data-driven methodologies
is vital for fostering a more inclusive, responsive, and
effective educational system.
This approach not only supports academic excellence but
also addresses the distinctive challenges faced by
students, ensuring a comprehensive and enriching
educational experience. Conclusions drawn from this work
include the identification of principles and generalizations
from the results, recognition of exceptions or challenges to
these principles, exploration of theoretical and practical
implications, and the formulation of recommendations
based on the findings. This multifaceted approach
contributes to the creation of a well-rounded educational
system that caters to the diverse needs of students,
fostering an environment conducive to both academic
achievement and personal growth.
Initiatives for Advanced Learners(SRM)
The strategies for advanced learners focus on providing
opportunities for intellectual growth and challenging their
capabilities. Offering separate assignments with
challenging problems, encouraging participation in
advanced projects, and involving research scholars in
seminars contribute to a stimulating academic
atmosphere. Additionally, initiatives such as career
guidance, participation in conferences, and exposure to
advanced legal topics through debates aim to broaden
their perspectives. Encouraging research projects and
paper publications, along with activities in clubs and
committees, helps nurture a spirit of innovation and
leadership. The provision of individual mentorship,
specialized projects, and participation in technical events
further supports their academic journey [2].
Initiatives for Slow Learners
For slow learners, the initiatives are geared towards
providing tailored support to address specific weaknesses.
Special classes, revision of courses, and group study
systems aim to reinforce foundational knowledge.
Remedial classes and individual counseling by subject
teachers offer personalized assistance, while forming
study groups facilitates peer-to-peer learning. Special
assignments and varied instructional techniques help in
developing a better understanding of difficult topics.
Communication with parents and systematic performance
updates ensure a collaborative effort in the student's
educational journey. Initiatives like NPTEL sessions,
workshops, and compensatory teaching provide additional
resources to bridge gaps in understanding. The emphasis
on self-learning materials and multiple assessments helps
in gradual improvement, while leadership activities and
regular counseling sessions aim to boost confidence and
foster positive interactions with parents [2].
3. Acknowledgment
We would like to express my sincere gratitude to all those
who contributed to the completion of this research paper.
We are also thankful to the participants who provided
valuable data for the analysis. This work would not have
been possible without the encouragement and assistance
from colleagues and friends. Your insights and feedback
have been instrumental in shaping the outcome of this
research. We are deeply appreciative of the collaborative
efforts that made this project a reality.
REFERENCES
1. Bharadi, V. A., Prasad, K. K., & Mulye, Y. G. (2023).
Classification of Slow and Fast Learners Using
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(pp. 461-472). Singapore: Springer Nature
Singapore.
2. Bharadi, V. A., Prasad, K. K., & Mulye, Y. G. (2020).
Using Deep Learning Techniques for the
Classification of Slow and Fast Learners.
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3. Bin Mat U, Buniyamin N, Arsad P. M., Kassim R.,
(2013). "An overview of using academic analytics
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4. Baepler, Paul Murdoch, Cynthia James, (2010)
"Academic Analytics and Data Mining in Higher
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5. Vital, T. P., Sangeeta, K., & Kumar, K. K. (2021).
Student classification based on cognitive abilities
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Computing and Digital Systems, 10(1), 63-75.
6. ”The institution assesses the learning levels of the
students and organies special Programmes for
advanced learners and slow learners” weblink-
https://www.gdcthannamandi.com/naacfile/Spec
ial%20Programmes%20for%20advanced%20ler
ners.pdf

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