The document describes a proposed online learning management system and analytics platform that utilizes various machine learning and deep learning techniques. Specifically, it discusses implementing gamification elements and augmented reality content to increase student engagement. It also explores using business intelligence and data mining of student data to perform learning analytics, such as predicting student performance and factors affecting achievement, in order to help educators optimize their teaching methods. A variety of classification algorithms like decision trees, random forests, support vector machines, and logistic regression are evaluated for their ability to model student grades based on demographic and academic attributes.

1. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
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Online Learning Management System and Analytics using Deep Learning
Ansuman Singh1
, Ashok Singh2
, Devendra Singh3
, Laxman Sharma4
and Dr. N K Bansode5
1
Student, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA
2
Student, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA
3
Student, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA
3
Student, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA
5
Professor, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA
1
Corresponding Author: ansumansinghtomar78387@gmail.com
ABSTRACT
During this pandemic we have seen rise in
popularity of online learning platforms. In this paper, we are
going to discuss E-Learning using analytics and deep learning
focusing on mainly four objectives which are login systems
for teachers and students, Gamification to engage learners,
AR contents to increase the involvement of learners and
learning analytics to develop competency. We will use Data
Mining and Buisness Intelligence to extract high level
knowledge from the raw data of students. To predict
engagement of students we have used several ML algorithms.
This study provides an introduction to the technology of AR
and E-Learning systems. The main focus of this paper is to
use research on augmented reality and integrate it with
Buisness Intelligence and Data Mining(DM).
Engaging student till the end of the course became
really difficult for traditional E-Learning Platform.
Therefore, Gamification in E-learning is good way to solve
this problem.
Keywords-- Business Intelligence in Education,
Classification and Regression, Decision Trees, Random
Forest, E-Learning
I. INTRODUCTION
FRAMEWORK
The main framework could be made using any of
the new technologies which provide and encourages rapid
development and clean design should be open-source and
can be easily accessible to everyone and should be fast and
rigid when deployed. The following are the main pros of
the framework:
 Ridiculously fast.
 Reassuringly secure.
 Fully loaded.
 Exceedingly scalable.
 Incredibly versatile.
Gamification in the Learning Platform
Gamification is the mechanism of giving
application some game like elements like giving badges,
stars etc. Gamification helps in increasing motivation of
learner by giving him sense of accomplishments.
AR Implementation in the Learning Platform
Augmented Reality (AR) have many advantages:
a. Doesn’t require additional hardware. So that the
default device is much sufficient to perform every
function such as reading and scanning data from
the camera device from the provided in the
device.
b. Provides a better learning process for learners as
in Augmented reality and virtual reality
operations the knowledge comes through in
holographic or as a very descriptive performance
of data.
c. Helps in long distance practical learning. As
explained in the above point and as concerning
situation of covid-19 is increasing practical
knowledge can be provided very easily.
d. Main advantage of augmented reality and virtual
reality is that it can be applied to any level of
educations regardless of any thing as it is only
platform dependent
LEARNING ANALYTICS
During Pandemic of COVID-19 teacher are
facing an a challenge to create and have faith in a system
that could let them enable a more efficient and optimized
manner of teaching. The huge chunk of data can play a
huge role there. The rise in popularity in Buisness
Intelligence and Data mining is due to Information
Technology, that lead to increase in groth of buisness and
organizational database. All the data like likelihood,
habits, and patterns contains valuable information which
helps in improving decision making and optimizing
success rate. Humans can left some important details.
Hence, this can help in automation of analysis of raw data
and extration of high level information.
BI can do a lot in education systems since there
are multiple sources of data (e.g., traditional databases,
web pages, offline accounting) and diverse interest groups
(e.g., students, teachers, administrators, or alumni) for
example there are lot of question we can answer using

2. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
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Buisness Intelligence and Data Minding like Which
students are studying most ? Who is going to attend the
future classes ? What course a student should enroll ?
What are the main reason for absent students? Prediction
of Student performance ? What factors affect student
achievement the most? In this section we are only going to
discuss last two questions. Modelling student performance
is an important tool for both educators and students since it
can help a better understanding of this phenomenon and
ultimately improve it.
Since the former contained scarce information
(i.e., only the grades and number of absences were
available), it was complemented with the latter, which
allowed the collection of several demographic, social, and
school-related attributes. The aim to provide reliable
numbers of prediction for each student to know the
performance rate. The two core classes will be modelled
under three data mining goals(or exploratory data
analysis):
1. Final Grade Distribution
2. Correlation Heatmap
3. Romantic Status
4. Alcohol Consumption
5. Parents Education Level
6. Frequency Of Going Out
7. Desire Of Higher Education
8. Urban Vs. Rural Student
and following classifications:
1. Prepare Dataset for Modelling - First we create a
data frame for classification then label encode final
grade then split into train and test.
2. Decision Tree Classifier – it is a supervised
learning algorithm to provide solution in form of a
binary tree.
3. Random Forest Classifier – It implies to use
multiple decision trees in which first we need to find
good no of estimators in data and then finding a good
hash for minimum sample Leafs.
4. Support Vector Classifier – It is applied by using
pre-loaded library of support vector machines.
5. Logistic Regression Classifier- It is applied by
imposing logistic functions on linear classification data.
6. Ada Boost Classifier – It imposes by fitting a
classifier on a original dataset and then readjusting the
incorrect proportion of weights.
II. MATERIALS, METHODS AND
DATA FRAMEWORK
Implement a cloud-based platform for
teachers/tutors to arrange schedule and student friendly
learning environment. The admin and users should be
allowed to see changes in data and to communicate.
Figure 1: use case diagram for GUI

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Figure 2: GUI connection table
*VLE – virtual learning environment
Game-based Learning
Game-based learning is in regards to making your
substance around the narrative of a game. E-Gamification
is in regards to moulding the game around scholastic
material. It utilizes the communication of the student to be
converted into a useful setting. This may furthermore
impact the conduct of the researcher though making
learning clear. The thought process behind gamification is
to urge and move understudies to be told in an incredibly
fun and involving manner.
Gamification is acquiring quality inside the
universe of learning and training because of its adequacy
in taking part by students.
We can help you to affirm the utility of carrying
out fun and healthy competition at times in your learning
programs. Our gamified learning arrangements square
measure the fittingness of games in curious and fascinating
students with regards to their particulars and getting the
hang of their necessities, and furthermore aiming the ideal
results. though student inspiration and commitment are the
driver. The primary objective is content dominance and job
performance.
Tools We Need for Providing AR Experience
Mostly, we need two things, 1) 3D Model maker
2) AR Editor tool
3D Model Maker
There are many web-based 3D Model maker
which can be used to create 3D objects. We would be
creating courses related to python learning, so 3D objects
can be of showing automation, explaining different
algorithm, functions and lot more things are which are bit
tricky to understand.
AR Editor Tool
AR development has become easy and
comfortable to them who don’t have any programming
skill, to begin with. Especially, for learning platforms, the
course designer and teacher may not have any
programming skills,
So, In our literature survey, we came across a
really cool platform to convert our 3D models to AR Web
Experience, i.e Mywebar.com
Implementation
In our Learning platform, the course will be
designed along with gamified module to give student a
sense of achievement to know how much course he has
completed and how much left. It will also give points and
badges to learners, on the bases of performance for
achievements and completing the course.
The course will be having breaking points where
the instructor can place AR models to explain the topic in
more depth and with more visual effects. Our integrated

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webAR and 3D Model maker will be used by instructor to
create AR models and place them at any specific points in
the course to be viewed by students
There will be two views on our learning platform.
1. Instructor view
2. Learner's view
There will be a AR Library which can be used by other
content creator so that same 3D models can be used in
other course. The instructor can decide what AR model to
place and where it needs to be placed, also edit it anytime,
on the other hand, students will only have view access to
AR Library.
Student Data
We have entered the data values at our own.
Dataset has 50 rows and 33 features. In which 5 features
will be dynamic, and rest will be static
File Name: student.csv
File Size: 57KB
File Format: Comma Separated Values
Table: design layout and example dataset
school;sex;age;address;famsize;Pstatus;Medu;Fedu;Mjob;Fjob;reason;guardian;traveltime;studytime;failures;schoolsup;famsup;paid;acti
vities;nursery;higher;internet;romantic;famrel;freetime;goout;Dalc;Walc;health;absences;G1;G2;G3
"GP";"F";18;"U";"GT3";"A";4;4;"at_home";"teacher";"course";"mother";2;2;0;"yes";"no";"no";"no";"yes";"yes";"no";"no";4;3;4;1;1;3;6;
"5";"6";6
"GP";"F";17;"U";"GT3";"T";1;1;"at_home";"other";"course";"father";1;2;0;"no";"yes";"no";"no";"no";"yes";"yes";"no";5;3;3;1;1;3;4;"5"
;"5";6
"GP";"F";15;"U";"LE3";"T";1;1;"at_home";"other";"other";"mother";1;2;3;"yes";"no";"yes";"no";"yes";"yes";"yes";"no";4;3;2;2;3;3;10;"
7";"8";10
"GP";"F";15;"U";"GT3";"T";4;2;"health";"services";"home";"mother";1;3;0;"no";"yes";"yes";"yes";"yes";"yes";"yes";"yes";3;2;2;1;1;5;2
;"15";"14";15
"GP";"F";16;"U";"GT3";"T";3;3;"other";"other";"home";"father";1;2;0;"no";"yes";"yes";"no";"yes";"yes";"no";"no";4;3;2;1;2;5;4;"6";"1
0";10
"GP";"M";16;"U";"LE3";"T";4;3;"services";"other";"reputation";"mother";1;2;0;"no";"yes";"yes";"yes";"yes";"yes";"yes";"no";5;4;2;1;2;
5;10;"15";"15";15
"GP";"M";16;"U";"LE3";"T";2;2;"other";"other";"home";"mother";1;2;0;"no";"no";"no";"no";"yes";"yes";"yes";"no";4;4;4;1;1;3;0;"12";"
12";11
"GP";"F";17;"U";"GT3";"A";4;4;"other";"teacher";"home";"mother";2;2;0;"yes";"yes";"no";"no";"yes";"yes";"no";"no";4;1;4;1;1;1;6;"6"
;"5";6
"GP";"M";15;"U";"LE3";"A";3;2;"services";"other";"home";"mother";1;2;0;"no";"yes";"yes";"no";"yes";"yes";"yes";"no";4;2;2;1;1;1;0;"
16";"18";19
"GP";"M";15;"U";"GT3";"T";3;4;"other";"other";"home";"mother";1;2;0;"no";"yes";"yes";"yes";"yes";"yes";"yes";"no";5;5;1;1;1;5;0;"14
";"15";15
"GP";"F";15;"U";"GT3";"T";4;4;"teacher";"health";"reputation";"mother";1;2;0;"no";"yes";"yes";"no";"yes";"yes";"yes";"no";3;3;3;1;2;2
;0;"10";"8";9
"GP";"F";15;"U";"GT3";"T";2;1;"services";"other";"reputation";"father";3;3;0;"no";"yes";"no";"yes";"yes";"yes";"yes";"no";5;2;2;1;1;4;
4;"10";"12";12
"GP";"M";15;"U";"LE3";"T";4;4;"health";"services";"course";"father";1;1;0;"no";"yes";"yes";"yes";"yes";"yes";"yes";"no";4;3;3;1;3;5;2;
"14";"14";14
"GP";"M";15;"U";"GT3";"T";4;3;"teacher";"other";"course";"mother";2;2;0;"no";"yes";"yes";"no";"yes";"yes";"yes";"no";5;4;3;1;2;3;2;"
10";"10";11
"GP";"M";15;"U";"GT3";"A";2;2;"other";"other";"home";"other";1;3;0;"no";"yes";"no";"no";"yes";"yes";"yes";"yes";4;5;2;1;1;3;0;"14";
"16";16
"GP";"F";16;"U";"GT3";"T";4;4;"health";"other";"home";"mother";1;1;0;"no";"yes";"no";"no";"yes";"yes";"yes";"no";4;4;4;1;2;2;4;"14"
;"14";14
"GP";"F";16;"U";"GT3";"T";4;4;"services";"services";"reputation";"mother";1;3;0;"no";"yes";"yes";"yes";"yes";"yes";"yes";"no";3;2;3;1
;2;2;6;"13";"14";14
"GP";"F";16;"U";"GT3";"T";3;3;"other";"other";"reputation";"mother";3;2;0;"yes";"yes";"no";"yes";"yes";"yes";"no";"no";5;3;2;1;1;4;4;
"8";"10";10
Data Mining Models
Classification is an important data mining goal.
As it requires supervised learning, where a model is
trained to a dataset made up of k ∈ {1, ..., N} examples,
each mapping an input vector (x k 1, . . ., x Ki) to a given
target yk. The classification models are often evaluated
using the Percentage of Correct Classifications (PCC). A
high PCC suggests better classifier.
III. DATA REPRESENTATION

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Table 1: The student related tags
Attribute
student’s sex - school
student’s age - sex
student’s school - age
student’s home address type - address
parent’s cohabitation status - parents_status
mother’s education - mother_education
mother’s job - mother_job
father’s education - father_education
father’s job - father_job
student’s guardian - guardian
family size - family_size
quality of family relationships - family_quality
reason to choose this school - reason
home to school travel time - commute_time
weekly study time - study_time
number of past class failures - failures
extra educational school support - school_support
family educational support - family_support
extra-curricular activities - activities
extra paid classes - paid_classes
Internet access at home - internet
attended nursery school - nursery
wants to take higher education - desire_higher_edu
with a romantic relationship - romantic
free time after school - free_time
going out with friends - go_out
weekend alcohol consumption - weekend_alcohol_usage
workday alcohol consumption - weekday_alcohol_usage
current health status - health
number of school absences - absences
first period grade - period1_score
second period grade - period2_score
final grade - final_score

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IV. RESULTS
Predictive Performance
All the models are checked over a stream of cross-
validation of 10 folds. The stages included data pre-
processing and model evaluation to achieve the best
hyperparameters and scores for the data. The experiments
with various algorithms but Logistic Regression have
proven to be successful giving the least error rate when
compared to the other classification models like random
forest and decision.
Model Model Score Cross Validation
Decision tree 0.889041 0.891720
Random forest
0.982192 0.863057
Logistic regression 0.887671 0.904459
SVC 0.932877 0.853303
Ada boost 0.867123 0.856688
Stochastic gradient descent 0.856164 0.821656
Figure 3: Graph for results of the classifiers
The valedictorian of the high school class is likely
to have this profile:
1. Is not in a romantic relationship.
2. Does not consume alcohol.
3. Living in urban area
4. Does not go out with friends frequently.
5. Have strong desire of receiving higher education.
6. Parents both received higher educational.
7. Mother is a healthcare professional.
8. Father is a teacher.

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9. Chose to attend the school based on its reputation.
10. Study more than 10 hours weekly
11. Have access to internet at homepage.
12. Is healthy.
13. No absences to classes
Gamified Learning in Our Platform
Our model is intended for addressing the degree
of development and reception of sure-rising advancements
in technology these days. we will in general keep up the
read that gamification isn't just an innovation but
furthermore a procedure that a few associations embrace
because of expanded inspiration due to it. On this side,
gamification isn't a stringently selling pattern but a
social/emotional style pattern that might be applied to
totally various fronts. In that capacity, gamification is also
a developing space of investigation and curiosity. Thus,
Gartner's model is utilised here allegorically and as a
correlation model. We will in general get incidentally
deciding patterns in rising investigation territories,
exhibiting a few types of 'pinnacles of swelled assumptions
and enlightenment.
Few of our ways we included gamification in our
project square measure:
 Giving a token to the client on finishing of a
streak for a couple of set measures of your time
and occasion. This token might be utilised to ask
new courses and we'll add difficulties for clients
to complete a course in each time and present an
undertaking related with that course. On fruition
clients can get coins and that we square measure
keeping a presentation dashboard noticeable to
any or all clients this may expand the feeling of
among clients and that they can start interfacing
with our web application a ton of and a great deal
of.
 These higher than squares measure some of the
models related with this gamification being an
unvarying technique that keeps on consistently
changing and altering with time, pattern and
request. We'll continue to endeavour new
difficulties and rivalries on our foundation for
User collaboration like one of the gratitude to
expanded connection we've at the top of the
priority list is offering identifications to the
clients on the reason of their exhibition in tests
and rivalries facilitated by us in the application.
V. CONCLUSION
In this project, we scratched at a dataset of
students in an institute. Everyone's information are
provided in the dataset. To predict performance with
multiple features in the file using Logistic regression we
got a very good prediction.
We used classification to predict marks of every
student. Out of the 5 classification models used here;
Logistic Regression seemed to have a better prediction.
Finally, we looked at our solution to give
recommendations as to what features are best for providing
gamification data and feedback for personalised learning.
We observed that romantic status, student location, parents
education and study time plays a vital role in improving
learning.
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