A Comparative Study of Educational Data Mining Techniques for Skill-based Predicting Student Performance

A Comparative Study of Educational Data Mining
Techniques for skill-based Predicting Student
Performance
Mustafa M. Zain Eddin1
, Nabila A. Khodeir1
, Heba A. Elnemr2
1
Dep. Of Informatics research, Electronics Research Institute, Giza, Egypt
2
Computers and Systems Dep., Electronics Research Institute, Giza, Egypt
mustafa@eri.sci.eg
nkhodeir@eri.sci.eg
heba@eri.sci.eg
Abstract— Prediction of student performance has become an
essential issue for improving the educational system. However,
this has turned to be a challenging task due to the huge quantity
of data in the educational environment. Educational data mining
is an emerging field that aims to develop techniques to
manipulate and explore the sizable educational data.
Classification is one of the primary approaches of the
educational data mining methods that is the most widely used for
predicting student performance and characteristics. In this work,
three linear classification techniques; logistic regression, support
vector machines (SVM), and stochastic gradient descent (SGD),
and three nonlinear classification methods; decision tree, random
forest and adaptive boosting (AdaBoost) are explored and
evaluated on a dataset of ASSISTment system. A k-fold cross
validation method is used to evaluate the implemented
techniques. The results demonstrate that decision tree algorithm
outperforms the other techniques, with an average accuracy of
0.7254, an average sensitivity of 0.8036 and an average specificity
of 0.901. Furthermore, the importance of the utilized features is
obtained and the system performance is computed using the most
significant features. The results reveal that the best performance
is reached using the first 80 important features with accuracy,
sensitivity and specificity of 0.7252, 0.8042 and 0.9016,
respectively.
I. INTRODUCTION
Intelligent Tutoring Systems (ITSs) are computer-assisted
tutoring systems that permit personalization of the system
interactions. ITSs consider a student model as an input to
different tutoring systems components to adapt their
behaviours accordingly. Student model is a representation of
the student features [1] such as knowledge [2, 3], performance,
interests [4], goals [5, 6] and individual traits [7].
Diverse techniques are utilized in student modelling
process. They are generally classified into two categories:
cognitive science and data mining approaches [8]. Cognitive
Science approaches modelled how humans learn based on
domain modelling and expert systems [9]. Model-tracing (MT)
and constraint-based modelling (CBM) are the two common
techniques in cognitive science approach. Student modelling
in MT is represented in terms of rules that represent the
procedural knowledge of the domain. The model reasons for
student knowledge through tracing student execution of the
defined domain rules [10]. In CBM, student modelling is
based on recognizing the student errors in terms of violations
of defined domain constraints. Domain constraints are used to
represent not only the procedural knowledge as MT but also
the declarative knowledge [11].
On the other hand, educational data mining student
modelling approaches are based on the generated data through
interactions between the system and a large number of
students. For example, the student answers to the presented
questions and different assistance types that are requested by
the student. Reasonable data mining techniques are used as a
tool to understand and model the student properties of interest
using such data. The focus of student models that are based on
cognitive science is modelling of the student knowledge. On
the other hand, data mining techniques model different
features such as student performance, student affect, and
student learning [12, 13].
Generally, the student model is used to predict the student
response to tutorial actions and to assess the student or the ITS.
Data mining-based student models can achieve such tasks [14].
Different data mining techniques are designed to achieve
predictive tasks. Based on the values of different independent
variables, the value of a target dependent variable is predicted
[12, 14]. In ITSs, the most popular predicted variables are the
correctness of a student’s response to a question.
The focus point of this paper is to discuss the
implementation of educational data mining techniques for
predicting student performance. In this study, we appraise and
compare some of the most widespread classification
algorithms that tackle this problem. Six data mining
techniques: logistic regression, support vector machine (SVM),
stochastic gradient descent (SGD), decision tree, random
forest and adaptive boosting (AdaBoost), are investigated and
assessed. Furthermore, the feature importance method is
accomplished to reduce the feature space as well as selecting
the significant features to enhance the system performance.
The paper is organized as follows: A short overview of
educational data mining techniques is presented in section II,
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while section III reviews some related work on using
classification techniques in students' performance prediction.
The procedure for the comparative study is exhibited in
section IV. The conclusion and future work are provided in
section V.
II. EDUCATIONAL DATA MINING TECHNIQUES
Educational data mining is a procedure applied to distill
valuable information and patterns from a massive educational
database [15]. The valuable information and patterns may be
exploited for predicting students' performance. Accordingly,
this can help the educationalists in affording an effectual
teaching methodology.
Typically, in educational data mining technique, predictive
modelling is applied to portend the student performance.
Several tasks are commonly used to build the predictive
model, including classification, categorization and regression.
Yet, classification is considered the most widespread
method to predict students' performance. Several classification
techniques can be applied to portend the students'
performance. Among these algorithms are the logistic
regression, SGD, SVM, decision tree, random forest and
AdaBoost classifier.
Classification aims to take an input vector 𝑥 and assign it
to one of 𝐾 discrete classes 𝐶 𝑘 where 𝑘 = 1, 2, … , 𝐾 .
Typically, the classes are separated and therefore each input is
assigned to one and only one class. In other words, the input
space is thereby divided into decision regions whose
boundaries are called decision surfaces. In linear models for
classification, the decision surfaces are linear functions of the
input vector.
Linear prediction methods, such as logistic regression and
SVM, have been widely used in statistics and machine
learning. In this paper, we will focus on logistic regression [16,
17, 18], SVM [19] and SGD [20].
On the other hand, the decision tree has the ability to
capture the non-linearity in the data by dividing the space into
smaller sub-spaces based on the taken decision. In this work,
the decision tree technique is explored for predicting the
student performance [21, 22]. Ensembles of decision trees,
random forest and AdaBoost, which combine the predictions
of several base estimators, are also considered. [23, 24]. A
brief description of the utilized data mining methods is
presented in the next section
A. Logistic Regression
Regression aims to estimate the conditional expectations of
continuous variables using input-output relationships. Logistic
regression is used for categorical or binary outcomes.
Therefore logistic regression is a linear model for
classification rather than regression. It is extensively utilized
owing mainly to its simplicity. Logistic regression investigates
the relation between a categorical dependent variable and
several independent variables, and predicts the probability of
incidence of an event by fitting the data into a logistic curve.
Generally, logistic regression is categorized into two models:
binary logistic regression and multinomial logistic regression.
The binary logistic regression is normally used if the
dependent variable is dichotomous, whether the independent
variables are continuous or categorical. Whereas, the
multinomial logistic regression are used if the dependent
variable is not dichotomous and contains more than two
classes. [16, 17, 18].
B. Support Vector Machine
SVM is a supervised learning method that analyses the
training data and builds a model, which in turn assigns the
unlabelled data to the appropriate class. For a binary class
learning task, the SVM aims to obtain the superior
classification function to discriminate between the two classes
elements in the training data. In this work, we have adopted
linear SVM, where a linear classification function is used to
create a separating hyperplane that goes along the middle path
separating the two classes. Outwardly, there exist infinitely
several hyperplanes that may detach the training data. The
SVM technique attempts to find the hyperplanes that
maximize the gap between both classes so as to achieve the
testing classification without perpetrating misclassification
errors. Maximizing the margin to get the largest possible
distance between the separating hyperplane and the instances
on either side of it is proven to reduce an upper bound on the
expected generalization error [19].
C. Stochastic Gradient Descent Machine
SGD is a linear classifier beneath convex loss functions like
SVM and Logistic Regression. It is considered an effective
approach to discriminative learning. Despite the availability of
this approach in the machine learning society since long time,
it has lately got more attention owing to its potency with
large-scale and sparse data mining problems. It has been
successfully exploited in issues related to natural language
processing and text classification.
Generally, gradient descent is deemed the best method used
insomuch that the parameters cannot be determined
analytically and need to be searched for through an
optimization algorithm [20]. Therefore, SGD is considered an
optimization algorithm used to find the values of parameters
of a function (f) that minimizes a cost function (cost). The aim
of the algorithm is to get model parameters that reduce the
error of the model on the training dataset. That is achieved by
changing the model that moves it through a gradient or slope
of errors down on the way to a minimum error value. This
provides the algorithm with its name of “gradient descent".
D. Decision Trees
Decision tree is a supervised learning scheme based on a
non-parametric model and utilized for classification and
regression. Decision tree constructs a model to portend the
target variable value through learning plain decision rules
inferred from the data features [21].
Decision tree algorithms start with a set of cases and create
a tree data structure that can be utilized to classify new cases.
Each case is defined by a set of features which can have
numeric or symbolic values. A label representing the name of
a class is associated with each training case [22]. Decision
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Tree is characterized by simplicity to understand and to
interpret. Decision Tree is able to handle both numerical and
categorical data. Other techniques are usually specialised in
analysing datasets that have only one type of variable. On the
other hand, decision Tree model can create over-complex
trees that do not generalise the data well (overfitting). Setting
the minimum number of samples required at a leaf node or
setting the maximum depth of the tree is necessary to avoid
this problem.
Small deviations in the data may generate a completely
distinct decision tree, which causes instability to the tree. This
problem is alleviated by adopting an ensemble of the decision
tree.
E. Ensemble Methods
To enhance generalizability robustness over a single
classifier, ensemble methods are used which combine the
predictions of several base estimators built with a learning
algorithm.
Principally, the ensemble methods are categorized into the
averaging methods and the boosting methods. In the averaging
methods, several estimators are created independently and
then their average prediction is computed. Random forests
classifier is an example of the average methods. Conversely,
the base estimators in the boosting methods are constructed
sequentially and one attempts to decrease the bias of the
joined estimator. AdaBoost is an example of boosting
methods.
The ensemble methods target to combine several feeble
models to generate a powerful ensemble. Generally, the joined
estimator is usually superior to any of a single base estimator
as its variance is reduced [23, 24].
1) Random forests
Random Forest algorithm is an example of averaging
ensemble methods. Random Forests is more robust than
decision trees and able to model large feature spaces.
Random Forests is a bagged classifier linking a collection
of decision tree classifiers which constitute a forest of trees
[23]. The varied set of classifiers is created by introducing
randomness in the classifier construction. The prediction
of the ensemble is given as the averaged prediction of the
discrete classifiers. In random forests, each tree in the
ensemble is grown on a different bootstrap sample that
containing randomly drawn instances with replacement
from the original training sample. In addition, random
forest uses random feature selection where at each node of
the decision tree t, m features are nominated at random out
of the M features and the best split selected out of this m.
When splitting a node during the building of the tree, the
split that is selected is no longer the best split among all
features. Alternatively, the selected split is the best split
between a random subset of features. Accordingly, the
forest bias typically increases to some extent regarding the
bias of an individual non-random tree. However, averaging
is usually more than compensating for the increase in bias
that gives an overall better model
2) Adaptive Boosting (AdaBoost)
Boosting is a general ensemble method that produces a
strong classifier from a number of weak classifiers. It is
based on building a model from the training data, then
creating a second model that tries to correct the errors
from the first model. Models are added up to the training
set is predicted perfectly or a maximum number of models
are added. AdaBoost was the first really successful
boosting algorithm established for binary classification. It
can be used in combination with many other types of
learning algorithms to increase performance. The output of
the other learning algorithms is combined into a weighted
sum that denotes the final output of the boosted classifier.
AdaBoost is adaptive in the sense that subsequent weak
learners are tweaked in favour of those instances
misclassified by preceding classifiers [24].
For each consecutive iteration, the weights of sample
data are singly modified and then, the learning process is
reapplied to the reweighted sample. The incorrectly
predicted training examples induced by the boosted model
at the previous step possess increased weights, while the
correctly predicted ones hold decreased weights. Usually,
this permits decreasing the variance within the model.
III. RELATED WORK
Predicting student performance in solving problems is the
focus of a number of literature. Different educational data
mining techniques, such as decision trees [25], artificial neural
networks [26], matrix factorization [27], collaborative filters
[28] and probabilistic graphical models [29], have been
applied to develop prediction algorithms. These classifiers can
be used to identify weak students and thus assist the students
develop their learning activities.
Pereira et al, utilizes decision tree classifiers to predict the
student marks based on previous semester marks and internal
grades [25]. The accuracy of the classifiers was computed and
it was shown that the decision tree classifier CHAID has the
highest accuracy followed by C4.5.
Thai et al, compared the accuracy of decision tree and
Bayesian Network algorithms for predicting the academic
performance of undergraduate and postgraduate students at
two very different academic based on their grades and
demographic information. They concluded that the decision
tree was more accurate than the Bayesian Network [30].
Xu et al. developed an ongoing prediction of the students’
performance using ensemble learning technique.
Exponentially Weighted Average Forecaster (EWAF) is used
as a building block algorithm to enable progressive prediction
of students’ performance [31].
Feng and Heffernan utilized skill model for predicting
student performance in a large scale test Massachusetts
Comprehensive Assessment System (MCAS). Skill model is a
matrix that relates questions to the needed skills to solve the
problem. Based on the assessed skills of the students, the
model of the performance of the predicted MCAS test score is
measured. They used Mixed-effects logistic regression model
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to predict the student response based on student response data
through time, and skills parameter. Two different grain-sized
skill models were tried. Skill model that has large grain-sized
of skills gave more accurate prediction [32].
Ostrow et al. implemented partial credit models within
ASSISTments system to predict student performance. Partial
credit scoring is defined based on penalties for hints and
attempts. The maximum likelihood probabilities for the next
problem correctness within each test fold are used as predicted
values. Implementing partial credit scoring improves
prediction of student performance within adaptive tutoring
systems [33].
Wang et al. introduced the Opportunity Count Model
(OCM) and investigated the significance of considering OC in
student models [34]. The OCM built separate models for
differing OCs by using random forest to determine
fluctuations in the importance of student performance details
across a dataset stratified by OC.
IV.DATASET AND METHOD
The ASSISTment system is based on Intelligent Tutoring
System technology and is delivered through the web. The
main feature of ASSISTments is that they offer instructional
assistance in the process of assessing students. ASSISTments
utilizes amount and type of the assistance that students receive
as a way to assess the student knowledge. In addition, the
questions and related needed skills to be solved are defined in
[33]. As shown in table I, The dataset contained performance
details for 96,331 transaction log logged by 2,889 unique
students for 978 problems spanning 106 unique Skill Builders
and 269 of these problems are multi-skilled problems. Multi-
skilled problems are problems that need more than one skill to
be solved. Such problems are called original problems.
TABLE I
USED DATASET STATISTICS
Students Questions Original Questions Logs
2889 978 269 96331
ASSISTment system provides a number of original
questions and associated scaffolding questions. The original
questions typically have the same text as in MCAS test
whereas the scaffolding questions were created by content
experts to train students who fail to answer the original
question. The process starts when the student submits an
incorrect answer, then the student is not allowed to try the
original question further, but instead must then answer a
sequence of scaffolding questions that are presented one at a
time. Students work through the scaffolding questions,
probably with hints, until he finally gets the problem correct.
Scaffolding questions allow tracking the learning of
individual skills where each question is related and tagged by
the needed skills to be solved. That is used to express the skill
model. The dataset of our experiment has 103 skills that are
defined for all used questions.
The next sections indicate the reason of for selecting
dataset features. The conducting of the comparative study of
applying various classification techniques on the addressed
dataset is considered also for predicting the student
performance.
A. Dataset Features
Our model works in predicting either the student will
answer a question or not based on a set of features. These
features are the student-id who is accessing the system, the
skills of the question if the question is a multi-skilled question
or single-skill and either the student tried to solve this
question before or not (is_done).
The system based on this information can learn and predict
the student’s behavior against different questions even a
newly added question, as the question itself is an independent
factor in our learning progress, but we care about the skills. So
whatever the question is even the questions not included in the
learning process or added later, we can predict student’s
behavior only based on the skills of a question.
B. Comparative Analysis
This work presents a comprehensive comparative study of
the applicability of the data mining techniques for predicting
student performance. Several data mining techniques, are
investigated and evaluated, logistic regression, SVM and SGD
as linear prediction methods as well as decision tree, random
forest and AdaBoost classifier as non-linear prediction
methods
C. Evaluation methods
The evaluation stage is an essential step for selecting the
appropriate classifier for a given data. In this work, we
adopted several methods to achieve the evaluation task; mean
squared error (MSE), accuracy, sensitivity and specificity.
MSE measures the average of the squares of the errors or
deviations. The model is initially fit on a training dataset,
which is a set of examples used to fit the classifier parameters.
The resulting model is run with the training dataset and
produces a result, which is then compared with the target, for
each input vector in the training dataset, to measure the MSE
for the training data. Then, the fitted model is used to predict
the responses for the observations in another part of the
dataset that called the validation dataset. The obtained results
are compared with each input vector in the validating dataset
to compute the Testing MSE. The MSE is obtained as follows
𝑀𝑆𝐸(𝑦, 𝑦̂) =
1
𝑛 𝑠𝑎𝑚𝑝𝑙𝑒𝑠
∑ (𝑦𝑖, 𝑦̂𝑖)2
(1)
𝑛 𝑠𝑎𝑚𝑝𝑙𝑒𝑠−1
𝑖=0
where 𝑛 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 is the number of predictions, 𝑦̂ is the vector of
observed values, and y ̂ is the vector of predicted values.
Estimation of the accuracy, sensitivity and specificity is
based on four terms, namely:
True positives (TP): positive instances that are correctly
predicted.
True negatives (TN): negative instances that are correctly
predicted.
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False positives (FP): positive instances that are incorrectly
predicted.
False negatives (FN): negative instances that are incorrectly
predicted.
The accuracy is an empirical rate of correct prediction, the
sensitivity is the ability to correctly classify the output to a
particular class, and the specificity is the ability to predict that
the outputs of other classes are not part of a stated class. These
performance measures are computed as follows:
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃 + 𝑇𝑁
𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁
(2)
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 =
𝑇𝑃
𝑇𝑃 + 𝐹𝑁
(3)
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 =
𝑇𝑃
𝑇𝑃 + 𝐹𝑁
(4)
A k-fold cross-validation is performed to assess the overall
performance of the implemented classifier techniques. The
dataset is split randomly into k consecutive subsets called
folds of approximately the same size. The model is trained
and tested k times. In this paper, 10-fold cross-validation is
applied.
D. Result and Discussion
TABLE II
THE AVERAGE TRAINING AND TESTING MSE FOR DIFFERENT CLASSIFIERS
Classifier Training MSE Testing MSE
Linear SVM 0.5069 0.5079
SGD Classifier 0.4922 0.4929
Logistic Regression 0.3085 0.3089
Decision Tree 0.1158 0.2746
Random Forest Classifier 0.1263 0.2717
AdaBoost Classifier 0.281 0.2817
In the light of the training and testing MSE illustrated in
figure 1 and table II, we can see that the linear SVM has the
worst training and testing MSE (0.507 & 0.508, respectively),
which means that the linear SVM has high variance and high
bias. Decision tree classifier, on the other hand, achieves the
least MSE for the training phase (0.116), while random forest
classifier attains the least MSE for the testing phase (0.272)
with an insignificant difference of the decision tree (0.275).
Fig. 1. The average Training and Testing MSE for the different classifiers
Furthermore, the accuracy, sensitivity and specificity for
the different classifiers are recorded in Table II and displayed
in figure 2, figure 3 and figure 4, respectively. It can be
observed that the worst accuracy value (0.49) is made using
the linear SVM classifier. Additionally the poorest
specificity (~0.0) is produced when applying the linear SVM
and SGD techniques. Conversely, both have the highest
sensitivity, ~1.0. While the minimum sensitivity (0.66) is
reached using the logistic regression method. Regarding the
accuracy, the decision tree and random forest classifiers
realize the best performance (~0.73) with a trivial difference.
On the other hand, the specificity accomplishes its maximum
value (0.9) using the decision tree.
TABLE III
AVERAGE ACCURACY, SENSITIVITY AND SPECIFICITY FOR THE DIFFERENT
CLASSIFIERS
Classifier Accuracy Sensitivity Specificity
Linear SVM 0.4921 0.9975 0.0042
SGD classifier 0.5071 1 0
Logistic Regression 0.6911 0.6641 0.7485
Decision tree 0.7254 0.8036 0.901
Random Forest classifier 0.7283 0.8378 0.8562
AdaBoost classifier 0.7183 0.671 0.764
Fig. 2. Accuracy of the different classifiers
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Fig. 3. Sensitivity of the different classifiers
Fig. 4. Specificity of the different classifiers
The perfect sensitivity and deficient specificity of the linear
SVM and SGD, signifies that both classifiers are good for
capturing actual true cases, but they fail to catch false
positives. Furthermore, the results indicate that the decision
tree and random forest classifiers have roughly the best
training MSE, testing MSE, accuracy and specificity as well
as a high sensitivity. Nevertheless, their sensitivity is less than
that of linear SVM and SGD classifiers. However, the SVM
and SGD Classifiers have very small accuracy and poor
specificity, as well as very high training and testing MSE.
Additionally, despite that the random forest classifier has a
comparable performance to decision tree approach, it
disburses a long time for training large datasets. Therefore, it
may be emphasized that the decision tree classifier is the best
techniques for predicting new data that hadn’t been fitted
before.
E. Feature Selection
Feature selection is the process of selecting the most
significant features that can be used for model construction.
Furthermore, feature selection techniques delivers a way for
reducing the computation time as well as improving the
prediction performance. In order to reduce the feature domain,
the feature importance for the triumphed classifier (decision
tree) is detected using the Gini coefficient.
The Gini importance reveals the frequency of selecting a
specific feature for a split and the potential of this feature for
discrimination during the classification problem. As stated this
criterion, the features are ranked and nominated preceding to
the classification procedure. Every time a split of a node is
made on variable, the Gini impurity criterion for the two
descendant nodes is less than the parent node. Adding up the
Gini decreases for each individual variable over all trees in the
forest, gives a fast variable importance that is often very
consistent with the permutation importance measure. The Gini
importance principle demonstrated robustness versus noise
and efficiency in selecting beneficial features.
Each feature has an importance in detecting the student’s
behavior against questions, the table below shows the most
important features in predicting student’s behavior. The higher
ratio indicates the more important feature.
As shown in Fig. 5, the most important features for our
model is the student-id, then whether the student fulfilled this
question or not, without caring about his previous answer was
wrong or not, thereafter, if this question is multi_skilled or not.
Fig. 5. Different features important for decision trees classification
The impact of the feature selection on the student
performance prediction is investigated. The features are first
rated descending according to their importance. Then, the
effect of selecting different number of the first ranked features
on predicting the student performance is recorded. Fig. 6
portrays the effect of the number of features on the system
performance. The results reveal that the best performance is
achieved using the initial 80 important features. Despite the
slight improvement in the prediction accuracy (0.7252),
sensitivity (0.8042) and specificity (0.9016), the feature space
is reduced significantly.
Fig. 6. The system performance using different number of features
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V. CONCLUSION
Students’ performance prediction is typically valuable to
assist the instructors and learners refining their teaching and
learning process. This paper discusses the performance of
different data mining techniques in predicting the student
performance. Logistic regression, SVM, SGD, decision-Trees,
and AdaBoost classifier are investigated and their
classification performance is compared. The obtained results
unveiled that data mining tools may broadly be employed by
education institutions to predict the students’ performance.
The uppermost classification accuracy (0.7254), sensitivity
(0.8036) and specificity (0.901) are produced using decision
tree scheme. Feature selection technique based on Gini
coefficient is performed to reduce the feature space and
improve the system performance. The superior performance is
attained using the first 80 important features.
For future work more extended datasets may be utilized.
Furthermore, hybrid classification techniques can be applied
for predicting the students’ performance.
VI.ACKNOWLEDGMENT
We used the 'Assistments Math 2006-2007 (5046 Students)'
dataset accessed via DataShop [35].
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Vol. 16, No. 3, March 2018
ISSN 1947-5500

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