Educational Data Mining Rule based Recommender Systems

Ghadeer Mobasher

, Ahmed Shawish

1,2

and Osman Ibrahim

Faculty of Informatics and Computer Science, The British University in Egypt, Cairo, Egypt

Ain Shams University, Cairo , Egypt

Keywords:

Educational Data Mining, Decision Trees, Reduced Error Pruning and Rule based Recommender System.

Abstract:

Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which data mining tech-

niques are deployed to extract knowledge from educational information systems to help decision makers to

improve the learning process and enhance the academic performance of the students. The available studies

mainly focused on predicting the academic performance based on demographic and study related attributes.

Most of the previous work adopted the decision trees as one of the most famous data mining techniques to

predict rather than extracting real knowledge that reveals the reasons behind student’s dropout. On the other

hand, there were other studies in the psychological track to measure the mental health score based on the

educational environment. This paper proposes a complete EDM framework in a form of a rule based recom-

mender system that is not developed to analyze and predict the student’s performance only, but also to exhibit

the reasons behind it. The proposed framework analyzes the students’ demographic data, study related and

psychological characteristics to extract all possible knowledge from students, teachers and parents.Seeking the

highest possible accuracy in academic performance prediction using a set of powerful data mining techniques.

The framework succeeds to highlight the student’s weak points and provide appropriate recommendations.

The realistic case study that has been conducted on 200 students proves the outstanding performance of the

proposed framework in comparison with the existing ones.

1 INTRODUCTION

Educational Data Mining (EDM)helps decision mak-

ers to improve the learning process and enhance the

academic performance of students in different edu-

cational programs by applying powerful data mining

techniques . The greatest challenge in EDM is to iden-

tify the main factors affecting the student’s academic

performance and hence being able to better predict

the student’s achievement level in a pro-active man-

ner (Prabha and Shanavas, 2014).

Most of the available EDM researches focused

on two categories of data sets as inputs for analysis.

Firstly, the data set related to the student’s educational

related attributes, such as the study and homework

hours , Reading and Writing skills,etc.Second the de-

mographic data set like gender, family related infor-

mation, life style, etc. (Kovacic, 2010).

Despite the enormous researches done based on

these two types of data sets, they only focused on ex-

ploring as much as possible attributes for the purpose

of enhancing the accuracy of their prediction. Their

results were mainly limited to predict the student’s

performance rather than reveling the real reasons be-

hind it (Nasiri and Minaei, 2012).

It is also worth to note that most of the data sets

of these researches were mainly acquired from one

source only, which is the student’s registration forms

without any involvement of other parties like teachers

and parents that participate in the student’s learning

process.

More and above, none of the previous studies in-

cluded the student’s mental health as part of the anal-

ysis. According to the Canadian Mental Health Asso-

ciation, approximately one in ﬁve children and youth

has a mental health challenge that directly affects the

student’s learning capabilities. As a result, early iden-

tiﬁcation is so critical and can lead to improvements

in school and better health outcomes in life (Ramirez,

2014). Regarding the previous educational psychol-

ogy researches that focused on assessing the student’s

mental health score based on the educational settings

using statistical analysis, authors have analyzed the

mental health based on the student’s educational set-

tings, to come out with results that reveal interesting

directly proportional relationships between the stu-

dent’s educational settings of their study program and

their mental health(Li et al., 2008).

292

Mobasher, G., Shawish, A. and Ibrahim, O.

Educational Data Mining Rule based Recommender Systems.

DOI: 10.5220/0006290902920299

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 292-299

ISBN: 978-989-758-239-4

We can easily notice the isolation between the two

ﬁelds despite their correlation. It’s crucial to add

value to the identiﬁcation of factors that have great

impact on the student’s academic performance with

the usage of the powerful techniques of data mining

to early alert weak students. Moreover, to provide a

proactive decision making based on the extracted pat-

terns to enhance the student’s learning outcomes.

In this paper we propose a complete framework in

the form of a rule based recommender system, which

is developed not only to analyze the student’s per-

formance, but also to reveal the reasons behind it.In

the proposed framework, we diversify the input data

by combining the student’s demographic data, edu-

cational activities and psychological characteristics.

Moreover, the data set is not only extracted from the

student’s registration forms, but also from the teach-

ers and parents to acquire their evaluation based on

student’s behavior at class and home.

The proposed recommender system will provide a

proactive approach to early alert the issues that might

affect the students academic performance in his/ her

educational life. Moreover, it will provide the student,

parents and the school with the best recommendation,

that points out the weak points of the student that need

to be considered with the appropriate best treatments

based on the corresponding cases. This paper high-

lights three main contributions as follows:

• Concluding the most relevant attributes from the

previous researches that have the highest inﬂu-

ence on the student’s academic performance.

• Deploy a set of very powerful data mining

techniques to accurately predict the student’s

academic performance and extract all possible

knowledge from the input data.

• Develop a recommender system to provide the

student, parents and teachers with the appropri-

ate recommendation based on the corresponding

problems.

An extensive simulation studies have been con-

ducted on a realistic real cases including 200 Elemen-

tary Students, revealing the outstanding performance

of the proposed framework in comparison with the

previous ones.

This paper is organized as follows: Section 2 illus-

trates the background. Section 3 introduces the pro-

posed techniques and methodologies to develop the

EDM Rule Based Recommender System. Section 4

describes the experiment and the results. Finally, Sec-

tion 5 discusses the conclusion and the future work.

2 RELATED WORK

In this section we provide a comprehensive study of

all the previous EDM Researches and we also review

the available educational recommender systems.

2.1 Previous Predictive Models

The previous predictive models only focused on us-

ing the student’s demographic data like gender, age,

family status, family income and qualiﬁcations. In

addition to the study related attributes including the

homework and study hours as well as the previous

achievements and grades. Bhardwaj and pal predicted

the student’s performance at the end of the semester

through student’s data such as attendance, assign-

ments marks and class test marks (Baradwaj and Pal,

2012). From diverse literature, the observed predicted

poor performance has been mostly traced to poor pre-

vious scores, demographic data as well as the level of

intelligence(Ahmed and Elaraby, 2014). In addition

to other background factors such as socioeconomic

status, family education and occupation, religion and

even ethnicity have been identiﬁed as factors that have

highest implications on the student’s academic per-

formance(Berger and Archer, 2016). These previous

work were only limited to provide the prediction of

the academic success or failure, without illustrating

the reasons of this prediction. Most of the previous

researches have focused to gather more than 40 at-

tributes in their data set to predict the student’s aca-

demic performance. These attributes were from the

same type of data category whether demographic ,

study related attributes or both , that lead to lack of

diversity of predicting rules. As a result, these gener-

ated rules did not fully extract the knowledge for the

reasons behind the student’s dropout.

Apart from the previously mentioned work, there

were previous statistical analysis models from the per-

spective of educational psychology that conducted a

couple of studies to examine the correlation between

the mental health and the academic performance. Pre-

vious researches studied the academic performance

of students with respect to the correlation with their

mental health behavior. Their results demonstrate

that feelings of anxiety, depression, and time pres-

sure negatively affected the performance of these stu-

dents. However, participating in extracurricular ac-

tivities alongside having a good support system pos-

itively affected the academic performance(McLeod

et al., 2012). Moreover, The Center for Addiction and

Mental Health found through surveys that ﬁnal year

students were least likely to report these symptoms as

compared to students in other years (Weare and Nind,

Educational Data Mining Rule based Recommender Systems

293

2011).

After surveying the previous related work, we

conclude that the previous attempt’s main goal was

the identiﬁcation of the highest impact factors on aca-

demic performance. Some of them were from the ed-

ucational data mining perspective , while others from

the educational psychology methodology . Both ﬁelds

have the same goal, but each of them have different

methodology .

2.2 Previous Educational Data Mining

Recommender Systems

Concerning the previous recommender systems, most

of them are student oriented and they predict the per-

formance of a speciﬁc subject or provide a single

learning resource rather than predicting student’s per-

formance in each course and providing overall pre-

diction to the student’s ﬁnal score. For example,

Piedade (Piedade and Santos, 2008) that proposed a

SRM model provide support in the form of an ef-

fective student institution relationship through mon-

itoring of the students and their academic activities.

Also,Kanokwan (KuyoroShade et al., 2013) built on

the previously mentioned SRM model to propose

an intelligent recommendation system framework for

student relationship management that can assess the

performance of the students and provide the appropri-

ate recommendation for the choice of single course.

The existing systems mostly focused on predict-

ing the student’s performance in a particular course

with respect to the student’s data from the admission .

Student’s oriented systems have the purpose of aiding

students to the best decision for speciﬁc course regis-

tration enrollment. The type of the recommendations

was too brief, they missed illustrating the methodolo-

gies to apply them.

We believe that the real recommender system

should provide the suitable advises to enhance the

student’s overall academic performance based on the

type of the challenges that the student is suffering

from during the academic year.

3 PROPOSED FRAMEWORK

This section describes the proposed framework

through four sub sections. First, the new combination

of input data set is explained. As well as, detailed il-

lustration of data pre-processing is presented. Second

subsection describes the data analysis and processing.

Finally, the implementation details of the proposed

framework is discussed.

3.1 Input Data Model

The proposed framework ﬁrstly focuses on merg-

ing the demographic and study related attributes with

the educational psychology ﬁelds, by adding the stu-

dent’s psychological characteristics to the previously

used data set (i.e., the students’ demographic data

and study related ones). After Surveying the previ-

ously used factors for predicting the student’s aca-

demic performance, we picked the most relevant

attributes based on their rationale and correlation

with the academic performance (Osadan and Bur-

rage, 2013) (Cimmiyotti, 2013). Concerning the stu-

dent’s psychological characteristics, they were ex-

tracted through group of symptoms that asses the stu-

dent’s mental health based on children fact sheet from

National Alliance on Mental Health (NAMI) (Deb

et al., 2015). According to NAMI, the top 5 men-

tal illness that the school student’s might suffer from

are Anxiety, Hyperactivity, Bipolar, Conduct, and Re-

active Attachment Disorders. The previous studies

proved that low mental health score has negative aca-

demic performance implications

To remove any unnecessary attribute, we conduct

a feature selection analysis using Information Gain as

an attribute evaluator and Ranker as a search method.

The feature selection analysis dropped 8 attributes

from the data set, that includes 45 attributes as they

report zero gain information gain.

Three questionnaires have been designed to col-

lect the above listed data for the student, parents and

teachers. The ﬁrst one was questioned to the stu-

dent, mainly focusing on the set of pre-mentioned de-

mographic data. The other two questionnaires, were

targeting parents and teacher’s evaluation for the stu-

dent’s psychological characteristics and study related

skills.

The input model is divided into three main cate-

gories, the ﬁrst is the demographic data which is il-

lustrated in Table 1. The second category is the study

related attributes illustrated in Table 2. Finally, the

third category is psychological characteristics shown

in Table 3.

3.2 Data Analysis

This paper proposes a hybrid system which is based

on data mining techniques using classiﬁcation algo-

rithms, specially focusing on four rule induction al-

gorithms One R, Zero R, JRIP and PART. And four

decision algorithms J48, Random tree, REP tree and

Decision stump. The ﬁrst four classiﬁers belong to

Rules and the second ones belong to Decision Trees.

These classiﬁcation algorithms belong to the ”white

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Table 1: Demographic Arributes.

Attribute Name Expected Values

Stage 1,2,3,4,5,6 Primary

Gender Male, female

Home Type Flat, villa

Father qualiﬁcation

Mother qualiﬁcation

School level,

BSc.

MSc

PHD

Family income

Poor,

Middle,

High

Parental status

Married,

Divorced,

Separated.

No. of siblings Numeric

Table 2: Study Related Attributes.

Variable Name Variable format

Number of courses

NumericHomework hours

Study hours

Counting skills

Poor,

Average,

Excellent

Arithmetic knowledge

Reading skills

Writing skills

Hand-writing skills

box” classiﬁcation model and can be used directly for

decision making. To validate that our proposed work

outperform the previous related work, after merging

between the demographic, study related and psycho-

logical characteristics and after applying 8 different

powerful data mining techniques to predict the aca-

demic performance.

In order to choose the best data mining technique,

there are three main concerns for the model evalua-

tion : the tree size, tree complexity and prediction ac-

curacy of the decision tree.

The proposal aims to analyze student’s demo-

graphic data, study related details and psychological

characteristics in terms of ﬁnal state to ﬁgure whether

the student is on the right track or struggling or even

failing. In addition to extensive comparison of our

proposed model with the other previous related mod-

els.

3.3 Framework Implementation

The proposed recommender system is implemented

with java language using Net Beans version 7.3 as a

java environment. WEKA Java API is be used to im-

plement the data mining and machine learning algo-

rithm. The intended user is the Data Analyst. The

recommender system enables the data analyst to up-

load the data set and then view the predicted set of

Table 3: Psychological Attributes.

Variable Name Variable

format

Talkative

Yes/No

Lack of motivation

lack of organization

History of frequent suspension

Mood swinging

Frequent Abscence

Failure to ﬁnish work

Bossy

Easily distracted

Always,

Never,

Often

disability to listen

Blurt Out

Trouble playing quietly

Makes a lot of mistakes

Lying

Physical ﬁghts

Stealing

Unusual speech patterns

Toileting issues

Refusal to answer simple questions

Memory abilities Poor,

Average,

Excellent.

Attention abilities

Eye-contact abilities

rules and the generated tree. Moreover, the data ana-

lyst can add student’s information to predict the over-

all academic performance, the predicted value will

be displayed. The recommender system isn’t only

limited to the prediction of state, but also provides

the student, parents and teachers with the correspond-

ing recommendations and strategies to follow to im-

prove the learning outcomes. These recommenda-

tions are based on experimented studies for enhancing

the student’s academic performance. In addition to

the mentioned above functionalities, the System will

also alert all parties with the possible upcoming men-

tal illnesses that the student might suffer from. Due

to the gathering of the psychological characteristics

that formulate set of symptoms to couple of mental

illnesses that might negatively impact the academic

performance, as mentioned in section 2.

4 CASE STUDY

This section presents the realistic case study and dis-

cusses its results. The Student’s academic perfor-

mance prediction and recommendations will be illus-

trated with a comparative analysis between our pro-

posed framework and the previous related work.

Educational Data Mining Rule based Recommender Systems

295

4.1 Experiment Settings

Once the questionnaires have been acquired from the

pre-mentioned sources and after the data set passed

through the pre-processing phase. The experiment is

conducted on 200 elementary students , after elimi-

nating the inconsistencies between Teachers and Par-

ent’s Response on the study related data and psycho-

logical characteristics.

The data is then analyzed for knowledge extrac-

tion; the data is ﬁrst classiﬁed on the hybrid 8 clas-

siﬁer’s algorithms using 10- fold cross validation

method. Data were randomly divided into 10 parts.

Each held holds the next and learning scheme trained

nine-tenth of the rest of the data set, then the error rate

is calculated in the holdout set. The learning proce-

dure is performed 10 times on different random train-

ing set. Finally, an average of 10 error estimates to

produce estimates of the overall error. Detailed result

analysis will be discussed in the coming subsection.

4.2 Predictive Model Results and

Validation

In terms of prediction accuracy, REP Tree outperform

with a prediction accuracy of 73.6 %. While the least

prediction was for Zero R with 45.5 % as prediction

accuracy. Detailed bar chart graph is illustrated in ﬁg-

ure 1.

Figure 1: Classiﬁer’s Prediction Accuracy.

Regarding the classiﬁer’s tree size, REP tree has

the smallest tree size. While, Random Tree has the

largest tree size. The complexity of the tree is mea-

sured by the size of the tree, the total number of leaves

and the tree’s depth. The less the tree size, total leaves

and tree depth the less complicated the tree. In ﬁgure

2, detailed comparison between classiﬁer’s tree size

and complexity.

Although Zero R has the least tree size and com-

plexity, however it is the least reliable classiﬁer to ap-

ply, because the extracted rules lost structural com-

plexity. Only one rule is generated to predict the aca-

demic performance.

Figure 2: Classiﬁer’s tree size and complexity.

It is very clear to conclude that REP tree is the

best classiﬁer to apply.As the size of the REP tree

is the smallest with the lowest complexity and high-

est prediction accuracy. REP tree is a post-pruning

method; this decision tree ﬁnds the smallest version

of the most accurate sub-tree with respect to the prun-

ing set. During the pruning the size of the tree will

get reduced and reduces the complexity and thus im-

proves the prediction accuracy. Regarding the Rep’s

prediction accuracy results analysis 73.5 % were pre-

dicted true when they were actually true, while 12.5 %

were predicted true however, they were actually false.

In Table 4 , the generated REP Decision Tree.

Table 4: Generated Rep Rules.

Generated Rules Predicted State

If the Arithmetic Knowl-

edge == Excellent

Then the state == On the

Right Track

If the Arithmetic Knowl-

edge == Average and Read-

ing Skills==Excellent ==

Excellent

Then the state == Strug-

gling

If the Arithmetic Knowl-

edge == Average and Read-

ing Skills==Poor

Then the state == Fail-

ing

If the Arithmetic Knowl-

edge == Average and Read-

ing Skills== Average and

History of Frequent Suspen-

sion==Yes

Then the state == Fail-

ing

If the Arithmetic Knowl-

edge == Average and Read-

ing Skills== Average and

History of Frequent Suspen-

sion==No

Then the state == Strug-

gling

If the Arithmetic Knowl-

edge == Poor and Makes

lots of Mistakes ==Always

Then the state == Fail-

ing

If the Arithmetic Knowl-

edge == Poor and Makes

lots of Mistakes ==Never

Then the state == Strug-

gling

If the Arithmetic Knowl-

edge == Poor and Makes

lots of Mistakes ==Often

Then the state == Strug-

gling

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If we applied the previous related research strat-

egy that used the demographic data to predict the aca-

demic performance on our data set. The best clas-

siﬁer is J48 with a correctly classiﬁed instances and

the predicted rules mainly depend on the fathers and

mother’s qualiﬁcations, home type, family income

and frequent absence. While if we used the demo-

graphic data and the study related attributes to predict

the academic performance, the best classiﬁer is the

REP tree. Arithmetic knowledge, Number of courses,

Writing Skills, Homework Hours and stage have the

highest impact on the prediction of performance.

Finally, if we applied the third strategy of using

the psychological characteristics to predict the perfor-

mance, the best classiﬁer was J48 correctly classiﬁed

instances. History of frequent suspension, Failure to

ﬁnish work, blurt out, Memory Abilities, Lying and

disability to listen were mainly considered in the pre-

diction of performance.

In ﬁgure 3, detailed comparative prediction accu-

racy between old strategies vs our work.

Figure 3: Comparison between previous work and our

model’s prediction accuracy.

Beyond the out performance of our proposed strat-

egy in terms of accuracy percentage and our extracted

predictive rules , which are consistent with the theo-

retical aspects of the educational principles. We can

assert that our work added great values to the predic-

tion of the academic performance. After applying our

proposed methodology to merge between educational

data mining and educational psychology perspectives,

new results have been generated that better predicted

the student’s academic performance.

4.3 Recommeder System Discussion

The data analyst ﬁrst logs in and browses for the ﬁle to

upload it. Then the system automatically applies the

8 classiﬁers and uses the highest best classiﬁer’s pre-

diction accuracy, which is REP Tree in our case. Then

the data analyst can have the option to see the clas-

siﬁer’s prediction rules and generated decision tree

as well. Also a detailed message is displayed with

detailed classiﬁcation’s accuracy. In addition to the

functionalities mentioned above, the data analyst can

enter the data of the student who wants to predict

his/her overall academic performance. After the an-

alyst ﬁlls in the student’s information, the system dis-

plays the predicted academic state of the student and

presents the appropriate recommendations for the stu-

dent, the parents and the teacher.

Moreover, it alerts all parties with the mental dis-

order that the student might face in the future. It is

possible that the student might not face any academic

problems currently, but in the future, the student will

be facing academic performance challenges due the

mental illness disorder. So the system will not only

provide the analyst with the predicted academic state

and the appropriate recommendations, but highlights

any upcoming mental illness that the student might

shown in the future. Snippets of the system’s inter-

face are illustrated in ﬁgure 4, which presents an ex-

ample of the parent’s , teacher’s and student’s recom-

mendation after entering student’s data and predict-

ing performance. The proposed system avoided the

Figure 4: Screenshot of framework interface .

drawbacks of other system. It provides recommenda-

tion for all parties that participate in the student’s ed-

ucational life, including the student, the teachers and

parents. Samples of the recommendations addressed

to the student are illustrated in table 5. Table 6 il-

lustrates sample of recommendations for the teachers

and parents.

The extracted recommendations are based on

proven classroom fact sheets (for Disease Control

et al., 2013) (Hite, 2009) (De Corte et al., ).

Educational Data Mining Rule based Recommender Systems

297

Table 5: Samples of the suggested recommendations for

Students.

Problem Suggested Recommendation

The student’s arith-

metic knowledge

and reading skills

are average or poor

• Play arithmetic games so

that you can have fun and

at the same time learn and

practice.

• Write the small steps as a

draft while solving rather

than memorizing the num-

bers to avoid mistakes.

• Scan before you read.

• Practice, the more you read

the better reader you will

become and smarter too, so

feed your mind.

The student is

suffering from

AD/HD ,(s)he is

easily distracted

,blurt out ,poor at-

tention and memory

abilities,talkative

,lacks organization

, having trouble

to play quietly

and makes lots of

mistakes

• Enhance your social skills

and try to get involved with

other peers.

• When you feel low or sad,

try to divide your stud-

ies into small manageable

parts.

Moreover, most of the previous attempts were fo-

cusing on predicting the student’s performance in a

speciﬁc course with respect to the student’s informa-

tion gathered from the admission. It only focuses on

identifying the factors that have high impact on the

student’s success or failure in certain subject. Based

on the no-free theorem, the system that was able to

predict students performance in a particular course

may not do so in the overall performance of the stu-

dents at the end of each academic year.

One of the previous recommender system at-

tempts, let the user add the IF-ELSE rules manually

and this of course increases the human error. while

the implemented system let the data analyst loads the

data set, enabling dynamic prediction rules extraction,

even if the data set changes.

5 CONCLUSION AND FUTURE

WORK

This paper proposes a complete EDM framework in

the form of a rule based recommender system that

analyze the student’s academic performance to point

out the student’s weak points and provide appropriate

Table 6: Samples of the suggested recommendations for

Parents and Teacher.

Problem Suggested Recommendation

The student’s arith-

metic knowledge

and reading skills

are average or poor

• Implement a coherent read-

ing program level.

• Focus on ﬂuency and com-

prehension.

• Create a culture that encour-

ages learning, thinking, re-

ﬂection and self-analysis

The student is

suffering from

AD/HD ,(s)he is

easily distracted

,blurt out ,poor at-

tention and memory

abilities,talkative

,lacks organization

, having trouble

to play quietly

and makes lots of

mistakes

• Provide the student with

recorded books as an alter-

native to self-reading when

the student’s concentration

is low.

• Break assigned reading

into manageable segments

and monitor the student’s

progress, checking compre-

hension periodically.

• Devise a ﬂexible curricu-

lum that accommodates the

sometimes rapid changes in

the student’s ability to per-

form consistently in school.

• When energy is low, reduce

academic demands; when

energy is high, increase op-

portunities for achievement.

• Identify a place where the

student can go for privacy

until he/she regains self-

control.

recommendations for treatment.The paper combined

three major factors that affect the student’s academic

performance: demographic data, educational related

attributes and psychological characteristics. The real-

istic case-study conducted on 200 students assured the

outstanding capabilities of the academic performance

prediction, depth of knowledge extraction, and great

beneﬁt of recommendations provided by the proposed

framework in comparison with the available work.

The generated rules also showed a perfect matching

with the scientiﬁc proved facts. The diversiﬁcation of

the data sources and the involvement of teachers and

parents present a notable improvement in this work.

Regarding the future work, a larger data set

needed to be used in different academic stages. More-

over, new psychological characteristics need to be

added with the supervision of professional psychol-

ogist to better discover new patterns and enhance the

prediction results.

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