![](bg2.png)
The difference in learning characteristics is
influenced by individual characteristics as well. In
addition to behaviour, personal characteristics play a
major role in the learning characteristics. Personal
characteristics show special behaviour in each
individual. Various studies have revealed the
importance of understanding student characteristics
on the effectiveness of the learning process
(Kauffman, 2015; VanSickle et al., 2015; Apple,
Duncan and Ellis, 2016). However, there is still not
much literature that explore student characteristics by
applying data mining technique. According to (Sin
and Muthu, 2015), data mining techniques can be
used to improve academic quality, including
predicting student performance in learning, data
visualization, detecting student failures in learning
and even investigating student behaviour in learning.
Therefore, data mining techniques are also potential
to be used to segment student characteristics in order
to understand their learning behaviour. This study
aims to identify the learning characteristics pattern of
engineering students using data mining clustering
technique. The cluster resulted from this research can
be used to figure out the existing differences among
cluster and provide faculty members with some
insight of their student characteristics.
2 METHOD
This study uses an online questionnaire to collect
data. The online questionnaire is administered via
Universitas Negeri Malang Academic Information
System (SIAKAD) in April - May 2018. The target
respondents are all registered students in Faculty
Engineering at Universitas Negeri Malang, which are
approximately 5,300 students. Among of those
registered students, only 2,934 students fill out the
questionnaire (55.34% participation rates). The data
mining clustering model is built following the
SEMMA procedure, which are Sample, Explore,
Modify, Model, and Assess.
The first step, sample is conducted by determining
the target object of the study, which are all registered
students in Universitas Negeri Malang. The explore
step aims to understand the nature of collected data,
which is performed by plotting the collected data. The
modify step includes data preparation and data
transformation when needed. Data preparation steps
include data cleaning and data imputation. Data
cleaning aims to delete uncompleted responses and
outlier responses. Data imputation is performed to
impute missing responses with the mode or mean
responses. After data preparation steps, only 1,914
responses (65.23% usable rate) are complete and can
be used for further analysis. To identify the learning
characteristics pattern, this study uses three clustering
data mining technique. The clustering models built in
this study are K-means cluster, Kohonen cluster
analysis, and Two step cluster analysis. The last step
is to determine how to assess the model performance
(accuracy). Regarding the model accuracy, this study
use Silhouette index as suggested by (Pereda and
Estrada, 2018). Silhouette measures distance of an
element to its own cluster (cohesion) and compares it
to other clusters (separation). The higher value
indicates that the element is well matched to its own
cluster and poorly matched to other defined clusters.
The questionnaire that is used to collect data
contains 4 questions about respondent’s profile
(gender, level of study period, study program, and
GPA). In addition, the questionnaire contains 18
closed-ended question that ask about the learning
characteristic of respondent. Briefly, the item list of
the questions in the questionnaire is shown in Table
1.
Table 1: Item list in the questionnaire.
Characteristics of
discussion activity
Understanding during
learning process
Characteristics of
independent study
Table 2: Descriptive statistics of the respondent’s profile.
Identification of Learning Characteristics Pattern of Engineering Students using Clustering Techniques
275