Towards Student Success Prediction

Hana Bydžovská

and Michal Brandejs

1,2

CVT and

KD Lab Faculty of Informatics, Masaryk University, Brno, Czech Republic

Keywords: Recommender System, Social Network Analysis, Data Mining, Prediction, University Information System.

Abstract: University information systems offer a vast amount of data which potentially contains additional hidden

information and relations. Such knowledge can be used to improve the teaching and facilitate the

educational process. In this paper, we introduce methods based on a data mining approach and a social

network analysis to predict student grade performance. We focus on cases in which we can predict student

success or failure with high accuracy. Machine learning algorithms can be employed with the average

accuracy of 81.4%. We have defined rules based on grade averages of students and their friends that

achieved the precision of 97% and the recall of 53%. We have also used rules based on study-related data

where the best two achieved the precision of 96% and the recall was nearly 35%. The derived knowledge

can be successfully utilized as a basis for a course enrollment recommender system.

1 INTRODUCTION

Educational systems are focused on providing a high

educational standard. Learning analytics is used to

enhance the teaching and the learning. One of the

most important issues often solved in educational

environment is finding what influences student

performance.

We are interested in designing a course

enrollment recommender system (Jannach et al.,

2011) that will help students with selecting courses

to enroll in. For such recommender system, it is

crucial not to recommend difficult courses for

particular students. The student can fail to meet the

minimum requirements and subsequently to

discontinue the study.

It is equally important to advise students on

mandatory courses that usually cause problems. The

task is to identify such courses precisely. Initially,

we intend to properly define the similarity between

students based on their achievements as the course

difficulty can be estimated from the results of

similar students enrolled in earlier.

Our novel approach utilizes not only student-

related data but also structured data inferred from

social behavior of students. In (Bydžovská et al.,

2014), we have confirmed that the impact of social

ties among students on their study results is really

not negligible.

The aim of the proposed method is to predict the

success or failure of students in the selected courses.

The preliminary work was published in (Bydžovská

et al., 2014). In comparison to it, this follow-up

work contains the description of new social feature

extraction and their usage to improve results.

Association rules and decision trees algorithms are

used for a subgroup discovery. The experiment is

also performed on a representative data sample that

provides interesting observations.

In the following section, we give an overview of

related work. In Section 3, we describe the course

enrollment recommender system. The data used in

our experiments is introduced in Section 4. In

Section 5, we describe the experiments and the next

section brings results. The discussion of results can

be found in Section 7. The summary and directions

for future work can be found in the last section.

2 RELATED WORK

Exploring student potential is an interesting task in

the educational environment. Researches explore

what influences students and what can be done to

help them to improve their achievements.

A typical way for discovering regularity in data

is using data mining techniques (Peña-Ayala, 2014)

suitable for exploring a vast amount of data. It

162

Bydžovská H. and Brandejs M..

Towards Student Success Prediction.

DOI: 10.5220/0005041701620169

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2014), pages 162-169

ISBN: 978-989-758-048-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

allows us to build predictive models by defining

valid and exact rules.

Researchers mostly examine study-related data

stored in university information systems which

contain for example grades, gender, field of study, or

age. However, exploring the influence of social ties

to students’ performance is an attractive topic.

Authors in (Poldin at al., 2014) obtain data about

students’ friendships from questionnaires. They

prove that such data can improve predictions of

student failure. Unlike (Poldin et al., 2014) whose

research depends on answers from questionnaires,

we use data obtained from an information system

that can be computed from user requests, e.g. the

publication co-authoring, statistics about e-mail

conversation, or files uploaded into someone else's

depository.

Sometimes researches are faced with unbalanced

classes. This problem often encountered when

analyzing educational data, also mentioned in

(Thammasiri et al., 2014). Using some over-

sampling techniques could overcome this problem.

Authors improve classification accuracy using

SMOTE, an algorithm that generates new examples

by interpolating among existing minority examples.

Authors in (Vialardi et al., 2009) aim at selecting

courses for students in order to obtain good exam

results. Difficulties of courses are compared with

student potentials. Both variables are computed from

grades. The work extension can be found in

(Vialardi et al., 2010) where the analysis is based on

the profile similarity.

We further extend the method from (Vialardi et

al., 2009) by addition of social data. In this way we

are able to compare students' data together with the

information about their friends. Therefore, we can

increase the prediction accuracy.

3 RECOMMENDER SYSTEM

DESCRIPTION

Students are interested in information resources and

learning tasks that could improve their skills and

knowledge. Therefore the recommender system

should monitor their duties and show them either an

easy or an interesting way to graduate.

The current version of the system contains two

modules: an extraction module and an analytical

module. Figure 1 presents the data flow between the

university information system, the recommender

system and external tools such as Weka and Pajek.

Figure 1: Data flow.

3.1 Motivation

The results of exploration can be used for a warning

against too difficult courses in which students are

enrolled in. When courses are mandatory, the

canceling the enrollment is not possible but the

knowledge is useful for students and they should

study hard. When courses are selective or optional,

students can revise their choices. They can select

other courses recommended by the system. The

system will recommend courses with respect to

students' potentials and interests. A short explanation

of the selection will be available for students to be

able to verify the recommendations.

Another possibility is using the prediction of

student success or failure to identify the best

students or weak students for the faculty

management. The best students represent candidates

for tutoring, scholarships or grants. Weak students

usually need help to be able to graduate successfully

and heads of faculty can encourage them for

example by providing the best students for tutoring

them. It can be also useful to assist teachers to be

able to organize students into seminar groups. On

the other hand it can be tricky when teachers make

conclusions based only on this information.

3.2 Extraction Module

The Information System of Masaryk University (IS

MU) is a complex system used to support not only

education at Masaryk University. Today, it serves

many purposes and its functionality involves

managing study-related records. The database of IS

MU contains many types of data, e.g. students’

grades, exams, electronic questionnaires, course

enrollments, discussion groups, shopping center,

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games, calendar, e-learning materials, publications,

course management, etc. Furthermore, the system

keeps the whole history of user requests.

The recommender system has its own database in

order not to influence the functioning of the

production system. The data we expect to define

student characteristics is periodically imported to its

database. The extraction module performs data pre-

processing and provides time-consuming

computations for study-related data.

The extraction module also manages social

behavior data, converts it into the format suitable for

Pajek (Nooy et al., 2011) which is a well-known tool

for social network analysis. Pajek reads the data and

creates sociograms where the nodes represent people

we are interested in and ties represent relations

between them. From such sociograms we can

compute social attributes defining the importance of

a person in the network. The extraction module also

prepares batch files in the format for Pajek in order

to launch the computations automatically. The

resulting social attributes are also stored into the

recommender system database to be ready for the

next processing.

3.3 Analytical Module

The data prepared by the extraction module is

further processed in the analytical module. This

module makes use of feature selection algorithms to

extract relevant features. It obtains also basic

statistics about the features, and it is able to run

machine learning algorithms from Weka (Witten et

al., 2011).

The analytical module will be finally composed

of two methods. The first one will provide finding

interesting courses for each student and the second

one will provide an estimation of the probability of

passing the course.

The current version of the module contains three

techniques described in this paper in Section 5 that

lead to the realization of the second goal. Firstly, we

use machine learning algorithms for mining

historical data. The second approach is based on

comparing student potential (student’s average

grade) with course difficulty (average grade

computed from all grades of students enrolled in a

course). The last method discovers student

subgroups for which the prediction can be more

accurate. The obtained accuracy is always compared

with the corresponding baseline, i.e. the case when

all instances of the data are classified into the

majority class.

The detailed description of these techniques can

be found in Section 5, the results are in Section 6

and the discussion of interesting effects is situated in

Section 7.

4 DATA

As it has been already mentioned, the data is

obtained by the extraction module from IS MU.

4.1 Student Characteristics

Each student can be described by a set of attributes

that precisely characterize student's qualities,

potentials and interests. We use three types of data:

the study-related data, the social behavior data and

the data about previously passed courses. Some of

these attributes were published in the previous work

(Bydžovská et al., 2014) in Section 5. The most

useful of them were: average of grades, weighted

average of grades, number of credits to gain, gained

credits and ratio between the last two attributes,

programme and field of study, closeness centrality,

degree, and weighted degree and betweenness

centrality (computed from the network formed by

students enrolled in the investigated course and all

their friends).

4.1.1 New Study-Related Attributes

In this section we present additional attributes

generated for the follow-up experiments introduced

in this paper. We add the following attributes:

 average number of enrolled/gained credits in a

term,

 difference of previous attributes and 30 credits

(recommended study workload per term),

 number of successful finished studies at the

faculty/university,

 number of failed courses per term,

 number of courses previously failed that have

to be passed in the future,

 number of successfully passed repeated

courses,

 information about successfully/unsuccessfully

utilized retakes (the second attempts to pass an

exam),

 information if a course is mandatory, selective

or optional for particular students with respect

to their year of admission and field of study,

 number of times, when a teacher of an

investigated course taught a particular student.

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4.1.2 New Social Behavior Attributes

Values defining relations between people already

exist in IS MU and they are successfully used for

personal search (Kasprzak et al., 2010). The value of

the relation can be in the interval of [100, 200] and

represents the measure of communication inside the

system between two people. The higher the number,

the stronger is the tie between the two people.

We used these values in the preliminary work

(Bydžovská et al., 2014) to create a sociogram

where students enrolled in the investigated course

and all their friends were included. This approach

had limitations: the sociogram was only a small part

of the global network and all friends of students

were included but not all of them really influenced

corresponding students (with weak ties).

To overcome these limitations we process data

about all people communicating in the information

system in this work. To favor explicitly expressed

friendships in IS MU, we add such relations to the

sociogram with the value of 300 (the strongest tie).

We also calculate different variants where rules

influence which ties are included in the sociogram.

We generate two types of a sociogram. The first

one contains data only about students of the Faculty

of Informatics (people who have an active study or

have finished a study in the last two years at the

faculty). The second one contains students, teachers

(people who taught in the previous years at the

faculty), academics and faculty staff.

For both sociogram types we build variants with

the following reductions of ties between people:

 all ties,

 ties stronger than the third of all

communication values,

 ties stronger than the median of all

communication values,

 ties stronger than the value 150,

 ties that were explicitly expressed (value 300).

For these 10 sociogram variants we extract the

following social features: degree, weighted degree,

centrality, betweenness. We also compute grade

averages of all neighbors.

We also extract the following information from

IS MU:

 the strongest tie with a teacher of the

investigated course,

 course marked as favorite,

 course attendance disclosure,

 course seminar group disclosure,

 course examination date disclosure,

 application for the study disclosure.

4.1.3 Previously Passed Courses

The subset of courses is selected for each

investigated course with respect to the rule that

almost 20 students are enrolled in the course. This

helps to reduce the number of courses and to select

only reliable ones. We added these attributes to

datasets to find out if there is a correlation between

these courses and the investigated course.

4.2 Used Data

We took 62 courses offered at the Masaryk

University for bachelor and master programmes. We

selected courses where many students were enrolled

in. The experiment comprised of 7457 enrolled

students in the years 2010-2012 and their 148750

grades. Figure 2 shows the course enrollment

statistics. The most students are enrolled in the

mandatory courses for bachelor programmes. The

largest course in the data set is Mathematics I with

1767 students.

200

400

600

800

1000

1200

1400

1600

1800

Course

Number of enrolled students

Figure 2: Course enrollment statistics.

5 STUDENT SUCCESS

PREDICTION

The aim is to precisely predict student success or

failure in the investigated course based on the

analysis of the historical data. Approaches using

machine learning algorithms and subgroup discovery

based on comparing grade averages, association

rules and decision trees are presented in this section.

5.1 Prediction using Machine Learning

Algorithms

5.1.1 Data Sets

We extracted six datasets for each course for

machine learning processing as follows:

 study-related data (SR),

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165

 social behavior data (SB),

 study-related and social behavior data (SS),

 study-related data enriched with data about

passed courses (SRC),

 social behavior data enriched with data about

passed courses (SBC),

 study-related and social behavior data and data

about passed courses (ALL).

5.1.2 Used ML Algorithms

We utilized different machine learning algorithms

implemented in Weka (Witten et al., 2011), namely

naive Bayes (NB), support vector machines (SMO),

instance based learning (IB1), classification rules

(PART), one rule (OneR) and decision trees (J48),

Random Forests, and also ensemble learning

methods, namely AdaBoost and Bagging. AdaBoost

algorithm achieved the best results in the

combination with Decision Stump and Bagging with

SMO or REPTree.

5.2 Subgroup Discovery

We aim at finding subgroups of students for which

the prediction could be more accurate than using ML

algorithms.

5.2.1 Grade Averages

The technique was inspired by Vialardi et al.

(Vialardi et al., 2009 and 2010). The method was

based on the comparison of the course difficulty and

values defining student potentials. The course

difficulty was defined as the average grade of all

students enrolled in the investigated course.

We have already made a similar experiment in

(Bydžovská et al., 2014) when we created an

ensemble learner from 3 classifiers (weighted

average of student grades, their friends’ weighted

average grades and weighted average grades of their

friends that attended the investigated course with the

corresponding student). The results were satisfactory

only for 4 of 5 investigated courses. That was the

reason why we extended our research with new

social attributes. As it was mentioned in Section

4.1.2, we computed average grades for each student

from all or selected student's neighbors from all

variants of sociograms. These values were able to

define the student potential.

When the student's potential was lower than the

course difficulty, it meant that the student or his or

her friends had better study results than students

attended the investigated course in the past. In this

situation we predicted success otherwise we did not

give any prediction.

5.2.2 Association Rules

For subgroup discovery (Lavrač et al., 2002; 2006)

we combined the technique of finding interesting

subsets of attribute values (by means of

discretization for continuous attributes and by

building subsets of values for categorical attributes)

with two learning algorithms—decision trees (J48)

and class association rules (Liu, 1998).

We created one data set for all investigated

courses and all students from the training set

(students enrolled in courses in 2010 and 2011) and

we found interesting rules that could be applied to

the test set (students enrolled in courses in 2012)

with high accuracy. This approach allowed us to find

general rules for the prediction of student success or

failure regardless of the specific course. It depended

only on student's study achievements. We also

explored decision trees and association rules based

on the Apriori algorithm for all courses in order to

improve the results for the corresponding subgroup

of courses.

6 RESULTS

Our goal was to precisely predict student success or

failure in the investigated courses. The most

important task was revealing such courses and such

students for which the prediction could be highly

confident.

6.1 Prediction using Machine Learning

Algorithms

Many experiments mentioned in Section 2 mined

study-related attributes for prediction. We enriched

data sets with social behavior data and data about

previously passed courses. We used 10-fold cross

validation. The prediction accuracy was 81.4% in

average.

Figure 3 presents the contribution of additional

data on the investigated courses. In comparison with

baseline, the accuracy was improved by 8% in

average. In comparison with study-related data, the

accuracy was improved by 2% in average. The

maximal improvement in accuracy was 27% in

comparison with baseline and more than 5% with

using only study-related data.

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100

Baseline Study-related data Best

Course

Accurac

Figure 3: Additional data contribution.

The distribution of best results of mining

different datasets (defined in Section 5.1.1) is

presented in Figure 4. The best results were often

obtained on study-related and social behavior data

(25 courses) and only on study-related data (24

courses). We did not improve accuracy for 8

courses.

Baseline

SRC

SBC

ALL

Figure 4: Best data sets.

The distribution of best ML algorithms (defined

in Section 5.1.2) is presented in Figure 5. The most

accurate algorithms were SMO (22 courses),

Bagging (11 courses), and Random Forests (8

courses).

Baseline

SMO

PART

AdaBoostM1

Bagging

J48

OneR

RandomForests

NaiveBayes

IB1

Figure 5: Best ML algorithms.

We also tested the data split to the training data

(students enrolled in courses in 2010 and 2011) and

the test data (students enrolled in courses in 2012).

Based on the training data, we defined three rules to

obtain course subgroup for which predictions using

machine learning algorithms were reliable:

 We were not capable of making prediction in

courses where conditions were changing

(different teacher, different students’

evaluation). The difference between success

rate of the test set and the training set was

more than 11.5%.

 For courses with more than 81% successful

students in the training set, the prediction is

not suitable. In comparison with baseline the

improvements were only about 1% in average

while the average accuracy for these courses

was almost 87% in the test set.

 If the course in the training set had success rate

lower than 61.5%, the algorithms improved

prediction by more than 16% in average but

the final accuracy was still lower than 80% in

the test set which was not good enough.

6.2 Subgroup Discovery

6.2.1 Grade Averages

We constructed 10 variants of the sociogram

described in 4.1.2 to be able to compare the

influence of different people to a student.

For all student averages (student's own, friends

or schoolmates with unlimited/limited ties) we

computed the precision and the recall. We were

interested in the highest precision. We observed that

the most frequently selected attributes for the

comparison with course difficulties were the

following:

 student weighted average grade,

 student average grade,

 weighted average of explicitly expressed

schoolmates,

 weighted average of schoolmates with ties

higher than 150,

 weighted average of explicitly expressed

friendship,

 weighted average of friends with ties higher

than 150.

Figure 6 represents the distribution of the best

results of the ties. We improved the results for more

than one fourth of all courses.

Weighted average

Average

Explicitly expressed

friendship

Ties >= 150

Rest

Figure 6: Importance of size of ties.

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167

This indicates that the student potential

calculated from closer friend ties was more accurate

than when all ties were considered.

The second observation was that this technique

did not meet the expectations for courses with the

baseline lower than 60%. These courses were very

difficult and the student's or the student friends'

knowledge might not be sufficient.

For all other courses the average precision

reached 92.86%. On the other hand, these classifiers'

recall reached only 53%.

6.2.2 Association Rules

Based on the previous findings a rule with a high

accuracy for subgroups of students was found. The

weighted average grade and average grade were the

most frequently selected as the beneficial attributes

defining the student potential. Because we calculated

the weighted average grade from all grades

regardless of studies, it was lower than if only the

particular study was considered because students

always had to pass difficult courses in the first years

of bachelor studies. The first defined rule:

 weighted average grade ≤ 2.4 → success.

The distribution of students' weighted average

grades can be seen in Figure 7. Grade 1 was the best

grade, grade 4 was the worst. The precision for this

rule was 96% and recall 24.86%.

1 1.4 1.8 2.2 2.6 3 3.4 3.8 4

100

200

300

400

500

Weighted average grade

Number of students

Figure 7: Weighted average grade distribution.

We also wanted to fill the gap for improvements

of very successful courses. The second defined rule:

 weighted average grade ≤ 3 AND ratio of

gained credits to credits to gain > 0.8 AND

courses with success rate > 80% → success.

When we considered the whole data set, the

precision for this rule was 96.85% and the recall

23.78%. When we omitted students' success

predicted by the first rule, the precision for this rule

was 94.8% and recall 11%.

When we used both rules in sequence, the

precision is slightly higher than 96% and the recall is

almost 35%.

7 DISCUSSION

The best fitting data set for machine learning

algorithms was study-related and social behavior

data together. The next observation was that there

were no noticeable improvements when we added

data about previously passed courses into the former

data sets except for five courses which will be

objects of our future interests. Based on the results

published in Bydžovká et al., 2014, we suppose that

these attributes have a stronger influence for

improving good/bad/failure prediction or the exact

grade prediction than for success/failure prediction

based on the previous experiments.

We explored many different rules with high

precision but they had small recall. For example:

 weighted average grade ≤ 3 AND successfully

finished studies at the university ≥ 2 AND

mandatory course for the specific field of

study = 'AP' → success (Precision: 97.2%,

Recall: 6.92%),

 successfully finished studies at the faculty ≥ 2

AND repeating the course → success

(Precision: 97.8, Recall: 0.8%).

However, the next step will be to apply such

precise rules in sequence. We can get more accurate

prediction and improve the global recall.

If we concerned a particular course, there were

plenty of rules explored for the specific course, for

example

 IA008: schoolmates attended the same course

in the past AND an average grade < 2.5 AND

a tie > 150 to a student → success (Precision:

93.75%, Recall: 7.8%).

This rule could not be applied for all courses

because of the low precision.

The next step will be finding subgroups of

courses for which such rules are suitable.

8 CONCLUSION AND FUTURE

WORK

The main contribution of this work is the

presentation of different approaches for student

success or failure prediction. The paper brings

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results and discusses advantages and disadvantages

of these methods.

Machine learning algorithms can be successfully

employed with the presented data set with the

average accuracy 81.4%.

We also split the data to the training and the test

set to identify courses for which ML cannot be

successfully applied to courses with more than 81%

or less than 61.5% successful students in the training

set. The results in the test set were also not so

convincing when there was a significant difference

between the training set and the test set.

On the other hand, we can apply the following

discovered rule for easier courses. All students with

the weighted average grade ≤ 3 and the ratio of

gained credits to credits to gain > 0.8 are successful.

This fills the gap.

We also defined rules based on the grade

averages of students and their friends. One

conclusion was that the prediction was more

accurate when only close friends were considered.

This approach offered the precision about 97% but

decreased the recall to 53%.

In the future work, we intend to find the

appropriate balance of using these methods and to

combine precise association rules to get the most

accurate predictions with a reliable recall. The

courses evinced the relations with other courses will

be explored. We also intend to enrich the data with

temporal features that can improve the current

results.

These predictions will constitute a part of the

course enrollment recommender system which will

help students to select courses and warn them

against difficult courses they have to pass.

ACKNOWLEDGEMENTS

We thank Lubomír Popelínský, colleagues of

Knowledge Discovery Lab, and also all colleagues

of IS MU development team for their assistance.

This work has been partially supported by Faculty of

Informatics, Masaryk University.

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