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Cognitive Learning Recommendation System in
Indian Context
Anurag Gulati
Electrical Dept
DTU, Delhi , India
Anish Batra
I.T Dept
MSIT, Delhi, India
Abstract—In a huge population like India, every student grows
in a different psychological environment during formative
years. Further formal education system leads to diversification
of thought process and certain inclination towards a cognitive
sense. Due to this it becomes too ambiguous for students to find
the best suited methodology of learning. This paper presents
machine learning based recommendation system to decipher
previously hidden relationships between actual classroom
teaching and optimal methods of learning for individual
student. The proposed recommendation system provides a
student with a series of methods of learning ranked from high
to low based on the cognitive sense of the individual student
and helps him to find the best learning methods for an
individual student. The proposed recommendation system has
used extensively surveyed data set compiled from multiple
schools under the Indian education system for testing. The
proposed system has increased the knowledge among students
as a whole through recommended methods of learning, leading
to greatly improved class room results.
Index Terms— Recommendation system, collaborative
filtering, methods of learning, machine learning
I.
INTRODUCTION
I
N India, there is a specific pattern to teach students
wherein, they are categorized purely on the basis of marks
obtained in merit based examinations. The only aids that
are used to teach are black board and lecture along with
visual based presentation tools which are restricted to the
few elite schools that have adequate resources and
infrastructure to support the technology and operate it. The
problem here is that the students are collectively exposed to
any random method of learning available disregarding the
fact that each student has a different thought process and
knowledge grasping channel.
In this context, there is a requirement for recommending a
number of learning methods and not just one to students. If
just one learning method is recommended to a student, it
might not work well due to certain circumstances the student
might be in. Therefore, it would be appropriate to use a
recommender system having large collection of learning
methods out of which most suitable learning methods may
be recommended to the students along with the ranking of
the methods. Such a recommender system may work well as
a whole [1]. This type of recommender system might appear
to be simplistic but its design is much more complex in a
way that it has to handle a lot of unprocessed data. It also
978-1-5386-1922-3/17/$31.00 © 2017 IEEE
Rohit Khurana
CSE Dept
JIIT, Noida, India
Dr. MM Tripathi
Electrical Dept
DTU, Delhi, India
gives a shape to the profuse data since there is a lot of data
on the internet in the form of images, text and documents
[2], [3]. The quality of the recommendation system need to
be taken care as poor results can cause high cost problem
which do prevails in the world [4].
There are millions of students in India studying in the
schools and colleges, getting well structured education in a
customary fashion. This paper proposes a cognitive learning
recommending system based on machine learning for these
students to help them to evolve and grow with a holistic
perspective, leading to creation of leaders in diverse fields.
By categorizing students on the basis of multiple intelligent
questions and correlating them with a corresponding method
of learning, an extremely rich and informative data set is
generated. This data is fed to the proposed recommendation
system and further the experiences of several students are
also incorporated to predict the best category of learning
method for a student. The recommendation system also
gives other important outcome i.e names of students who are
more likely to be similar in cognitive abilities to the one
whose recommendations are being generated. The
experience factor helps to increase quality of results as
certain methods of learning which are common to a majority
students (studying under the same environment), can be
filtered by the recommendation system.
During the present work presented in the paper, a data set of
3000 students was collected via extensive surveys in
different schools (both government and private owned)
under the ambit of the standardized central board of
secondary education (CBSE) in India. These students were
provided with a common questionnaire consisting of 31
questions, of which 30 were self-scoring and based on
‘multiple intelligence’. These questions were used to deduce
the category of strengthened cognition in decreasing order of
likeliness. The 31st question was a general question to rate
financial status of parents from 1 to 4. This question helped
us deduce impact of resources available in shaping the
cognitive senses of an individual while growing up. The
overview can be more easily understood by the following
chart shown in fig. 1.
The data set was fed into the program designed to generate
recommendations and results. The program was written
using python and employing analytical tools such as
popularity model and personalized model to produce
recommendations. The results from the program produced
rank-wise recommendations for a particular student based on
the complete data set for each of the stated models.
Fig. 1: Weights given to Input
Subsequently an examination was conducted based on the
generated recommendations to test the validity of the
experiment. The students were allowed to attempt an exam
based on traditional techniques. In continuation to it another
test was given, with the key difference that this time the
student applied the best suited method of learning she/he
thinks most suitable from a range of recommended options
suggested to her/him.
Further, with help of analytical tools, some relations were
found within the data sets which were verified with observed
cases. Additionally, the impact of available resources in
shaping the cognitive abilities of a child during the growing
stages was studied. Based on the results it may be concluded
that the proposed recommendation system can yield higher
order benefits.
Rest of the paper is organized in following fashion. Section
II presents the theoretical aspect of the proposed
recommender system and section III discusses the modeling
and prediction algorithm for recommender system. Data
preparation is presented in section IV whereas results are
presented in section V. The conclusion is presented in
section VI.
II.
RECOMMENDER SYSTEM
In last decade, recommender system using content-based
filtering has got attention of many researchers [5], [6]. In
this paper, the collaborative filtering has been employed as
this approach looks promising. Collaborative filtering is
also used in other areas such as e-commerce, where each
user is recommended the products according to her/his own
prior choices, as well as the taste choices of other users
who bought some common product as the one in question
[7]. Since it is proposed to recommend the learning
methods to users using the data of the considerable
population, the concept of item based collaborative
filtering as shown in fig. 2 is employed in present work.
One similarity measure is used to find the similarity between
different items in the dataset which is employed to predict
ratings of user-item pairs that is not given in the dataset [8].
Fig. 2: Weights given to Input in item based collaborative filtering
Different types of similarity concepts are used while
implementing item based collaborative filtering [9]. One
type is cosine based similarity formulation that views items
and their ratings as vectors. It also considers the angle
between given vectors as similarity between them. Two
similarity measure methods are discussed below.
A. Correlation based similarity
The deviation of ratings of common users from average
ratings for a perticular item is used to predict the
similarity as shown in eq. 1 below.
(1)
B. Adjusted cosine similarity
In this theory, it is considered that some users may give
higher ratings to items, whereas others may rate low as a
preference [11]. Hence for a pair of item average rating of
a user are subtracted from each user's rating under
consideration as given in eq. 2
(2)
The rating of any user-item pair may be predicated, using
weighted sum as described in eq. 3, after model is ready
using any similarity measure.
(3)
From identified items similar to the target, the items rated
by active user is picked. The similarity between identified
and target item is used to assign weight to every user's
rating for each item. At the end, scaling of prediction is
done using the sum of similarities to achieve the closest
predicted rating.
III.
MODELLING AND PREDICTION
ALGORITHM
In this paper, the personalized algorithm of Python has been
used for prediction. The flow chart of proposed algorithm
and the method we have used is shown in fig. 3 which is
self-explanatory. The internal processing is done with a
combination of nearest neighbor searching, dense tables for
tracking item-item similarities, and sparse item-item tables.
A model is created that makes recommendations using
item popularity. When no target column is provided, the
popularity is determined by the number of observations
involving each item. When a target is provided, popularity is
computed using the item’s mean target value. When the
target column contains ratings, for example, the model
computes the mean rating for each item and uses this to rank
items for recommendation. In order to understand the
connectivity between the theory of the recommender system
and our implementation, consider the item based filtering at
basic wherein the item is the Learning Scheme described in
the Table 2. Now, imagine a student inclined towards
interpersonal – collaborative learning. The student is used to
studying with that technique and he is not focused upon
according to his mind, psychology and surroundings. Now,
there are several other students in the same/other schools
with similar matching circumstances who might be using
other learning techniques as that student. As a matter of fact,
that student is recommended with the other student’s
learning technique which might work well for him. In a
similar fashion, various techniques are recommended to him
in the decreasing order of the probabilities of the match. The
beauty of the recommendation relies in the fact that the items
recommended to the students are in the dataset itself and
used or pursued by other students/users with similar
psychological trends who might be producing different
results for the same task. In this manner, the users/students
learn from the other users; who are similar to the one in
consideration. This helps the user choose the best out of the
options amalgamated by both environmental factors as well
as the ideal scenario. Refer to fig.2 and the formulas used in
the section II.
IV.
DATA PREPARATION
Data was collected from different schools of Delhi, India
(both private and government) for the research. Collected
data was molded into the required datasets so as to analyze it
in the required manner. The learning schemes were
categorized into 30 categories, hereafter named as LSCAT,
which could tell about the present learning methods of the
students and recommend them the best suited learning
methods using the collaborative filtering [12].
Table 1: Parent’s Income Data
Parent’s Income Category
Parent’s income
Category 1
0-4 lakhs per year
Category 2
4-7 lakhs per year
Category 3
Category 4
7-10 lakhs per year
Above 10 lakhs per year
The details of the students and the schools is not presented
due to privacy reasons. The income of the parents of the
students was also taken into account so as to find out the
relation between financial condition, type of the society and
different learning methods. The parent’s income has been
categorized as given in table 1.
Fig. 3: Flow chart of prediction algorithm
Table 2: Learning method data set sample
LSCAT
Learning scheme
1
Interpersonal Collaborative learning
Interpersonal Connected learning
Interpersonal Cooperative learning
Interpersonal - Inside
outside circle learning
Interpersonal - Jigsaw
technique
Kinesthetic –
Experimental learning
Kinesthetic - Interpretive
dance
Kinesthetic - Role
playing
Kinesthetic - Serious
play
Kinesthetic - Tactical
2
3
4
5
6
7
8
9
10
Parent’s
User ID Score Income
Category
301
5
2
301
3
2
301
5
2
301
3
2
301
5
2
301
5
2
301
5
2
301
4
2
301
5
2
301
5
2
A sample learning methods data set for a student (user ID
301) is shown in table 2 which contains 10 categories out of
31 categories. The score in table 2 is a self-rated entity given
by student for corresponding learning scheme. It ranges from
1 to 5, wherein 1 is strongly disinterested and 5 is strongly
interested. The learning methods of the students were
analyzed and rated using the psychometric exam (which was
a 31 question exam) and each question corresponded to a
particular learning method [13]. The questions were rated by
the students in the form of marks from 1 to 5. There were
3000 students in totality in the sample space.
The best suited learning methods were recommended to all
the students using popularity and personalized methods.
Table 3 shows the recommendations made to a particular
user using popularity method whereas table 4 shows the
recommendations made to the same user using personalized
method. It is evident from both tables that both the models
recommend the learning methods of user id 15 but with
different learning schemes. The recommendations using
personalized method recommends the category 23 as rank 1
but the popularity method recommends the category 29 to
the same user as preferred rank.
Table 3: Recommendation to user using popularity method
V.
RESULTS AND DISCUSSIONS
Data analysis was performed on a data set experimented
upon aaprox. 3000 students. The students were provided
with a common questionnaire consisting of 31 questions, of
which 30 were self-scoring and based on ‘multiple
intelligence’ to deduce the category of strengthened
cognition in decreasing order of likeliness. The 31st question
was a general question to rate financial status of parents
from 1 to 4.
Based on the experimental results, the graph in fig. 4 is
constructed for a sample of 100 students, where x-axis
depicts category id and y-axis depicts sum of scores of all
respective category ids of all students in a sample set.
Fig. 4: User ID vs. learning scheme category ID
The fig. 4 indicates the inclination of students towards to
method of learning of category id 5 which is Jigsaw
Technique. Jigsaw technique is a classroom activity wherein
students are dependent on each other to succeed. Students
with different abilities work in together on small problems,
each topic at a time to finish over the entire syllabus or
course.
A total of 30 different samples were considered and a
significant majority of them shows the same result as shown
in fig. 4. Result indicates that the Jigsaw technique has a
significant overlap to the Indian education system. This
shows that due to growing up under this system, the students
have been moulded in a way through which students have
got used to this type of learning method even if their
cognitive abilities prove otherwise.
User ID
15
15
15
15
15
15
15
15
15
15
LSCAT
29
28
11
8
20
10
5
30
23
21
Score
85.0
85.0
85.0
85.0
84.0
84.0
84.0
83.0
82.0
82.0
Rank
1
2
3
4
5
6
7
8
9
10
Table 4: Recommendation to user using personalized method
User ID
15
15
15
15
15
15
15
15
15
15
Code
23
19
21
14
1
13
18
2
17
27
Score
0.709072499828
0.705763818942
0.705517884365
0.703353118817
0.69693447047
0.696670768696
0.68471544573
0.68346608484
0.679943936084
0.671205678618
Rank
1
2
3
4
5
6
7
8
9
10
The 31st question of the surveyed data set was the parent’s
income on a rated scale of 1 to 4. Each student was made to
answer this question, this helped to deduce a basic relation
between available resources and methods of learning
acquired during the early growth stages of life. An
assumption was made that resources for some categories
such as graphical visual techniques or audio books etc
require more resources over some other categories which
required relatively lower resources such as read aloud or
collaborative learning. The scatter plots which relates
category ID to mean score of all students relative to each
category ID were plotted.
Fig. 5 shows that the interpersonal jigsaw technique is the
most preferred by the students lying in parent’s income
category 1. Since, these students have scarce availability of
resources and knowledge acquisition sources through other
means, therefore, these students are highly dependent on the
classroom study of the Indian education system. Hence, they
have evolved to accept this as a common learning scheme.
Linguistic cramming is the most preferred learning method
by the students lying in parent’s income category 2 as
indicated by fig. 6. They have the availability of sufficient
reading material and text books, and since, books are an easy
source of knowledge, the thorough reading and cramming is
the common practice in this category.
were made for both popularity and personalized models and
comparison of results is as shown in table 5
Fig. 8: Scatter plot corresponding to parents’ income category = 4
. The test with conventional learning scheme is categorized
as A and test with recommended learning scheme is
categorized as B. The maximum mark was taken as 50.
Fig. 5: Scatter plot corresponding to parents’ income category = 1
Fig. 6: Scatter plot corresponding to parents’ income id category = 2
Fig. 7: Scatter plot corresponding to parents’ income category = 3
No. of
student
1
2
3
4
5
6
7
8
9
10
Total
Table 5: Comparison of test results
Popularity Model
Personalized
A
B
A
40
44
40
26
27
26
45
44
45
45
47
45
34
39
34
29
41
29
17
16
17
22
19
22
31
38
31
42
44
42
331
359
331
Model
B
45
28
43
49
49
45
20
17
45
50
391
These students were given a subjective test and their results
were evaluated by a teacher, and then the same students
were given access to our software which made them suitable
recommendations. Then another test of similar level and
subject was given to them and results obtained are
compared. It is clear that in both the models the results of the
students have improved using recommended learning
scheme.
Fig. 7 shows that the music with ideas is the most preferred
by the students lying in the parent’s income category 3.
Access to audio and visual techniques due to the availability
of smart phones, internet, and computers prompts them to
use videos and audio techniques and hence they use the
musical: music with ideas techniques for their learning.
Experimental learning is the best suited for the students
lying in this parent’s income category 4 as shown in fig. 8.
Learning in the private schools and availability of enough
resources in the schools and their home allows them to learn
through equipment and physical activities rather than mere
text books. Hence, this learning for the category 4 is
completely justified.
An examination was conducted on 10 samples from the
sample space both before and after the recommendations
Fig. 9: Efficiency of popularity model
The difference between the conventional and the
recommended results were also plotted as shown in fig. 9
and fig. 10. In Popularity model there was 8.4592 percent
increase in the overall result of the students using as shown
by fig. 9. In the Personalized model there was 18.1268
percent increase in the overall result of the students using the
results of personalized model as evident in fig. 10.
[2]
[3]
[4]
[5]
[6]
[7]
Fig. 10: Efficiency of the personalized model
As it is seen from the graphs, the recommended models
undoubtedly work much better than the conventional
models. Moreover, the personalized model works better than
the popularity model in the Indian education system.
VI.
CONCLUSION
Indian education system is very complex due to large
variation in terms of social, economic and geographical
conditions. Students may develop a learning pattern which
may not be best method for them. In this paper it is proposed
to develop a recommender system based on machine
learning which can recommend them better learning
schemes. Data set of 3000 students was collected and
experiments were performed on sample data sets. Results
indicate that Indian education system is more inclined to the
Jigsaw technique which is related to the observable
classroom based study techniques where topic wise syllabus
is covered in groups of mixed students. The recommender
system produced multiple suggestions for the students.
Students who appeared for the tests chose themselves, the
most suited methodology, among the ones available. The
algorithms help filter inclinations of cognitive senses due to
environmental factors and embrace the strengthened
cognitive ability of the student. The tests results shows better
performance via popularity model of 8.4592 and 18.1268 via
personalized model. Further a relation between financial
status and preferred method of learning was observed.
It may be concluded that the classrooms can be divided
based on the recommendations of the algorithm presented in
this paper to yield better results and optimize the learning
time. Instead of dividing the students into class sections in
random order or by merit (as traditionally done in our Indian
education system), students can be grouped by learning
abilities and have a teacher specialized in that particular
learning technique using the required tools to teach them.
VII.
[1]
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