learning - Academic Conferences Limited

Michaela Drozdová et al.
to the order of learning there can be either detailistic or holistic tactics. For this characteristic a part of
the TSI (Thinking Style Inventory) questionnaire was used (Sternberg, 1999).
The conception of student’s learning can be divided into three levels: deep, strategic and surface.
This characteristic is tested by ASSIST (Approaches and Study Skills Inventory for Students)
questionnaire (Tait, 1996).
The learning auto-regulation defines, to which extent the student is able to manage his learning
independently. This implies his need of external supervision of the processes of learning where, on
one side, there are those who appreciate punctual instructions and on the other side those who prefer
to manage the learning processes themselves. For this characteristic a part of the LSI questionnaire
was used (Mareš, 1993).
2.1 Testing and data analysis of individual learning styles
To measure aforesaid characteristics, it is best to use questionnaires. In the pilot phase we used the
above mentioned questionnaires, but their combination was not applicable due to their length. This
led to inaccurate filling of the questionnaires, shown by the results of their analysis (Takács, 2009).
These reasons led to compiling a new shortened Learning Profile Questionnaire - LPQ (Novotný,
2010). The usual duration of filling the LPQ is only ten minutes while measuring all properties needed.
This questionnaire is inspired by following questionnaires: LSI, ILS, TSI and ASSIST. Only three to
five questions measures one property of the student. This questionnaire was not verified on students
yet.
Most questions in the LPQ questionnaire are made of statements together with scales of agreement
to this statement. These scales are usually from 1 (I don’t agree) to 5 (I agree). Some questions have
four variants, one for each type of perception.
This questionnaire was converted into electronic form. Before filling the questionnaire, its purpose was
introduced to the students. A group of university students from different fields of study were asked to
fill this questionnaire, as well as a group of high school students. A total of 508 students filled this
questionnaire, 190 from those being grammar school students, 196 pedagogical university students,
68 high school students and 62 informatics university students. The duration of filling the
questionnaires varied between two minutes and half an hour. To exclude suspiciously fast filling
students we sat aside 45 questionnaires filled in less than five minutes.
From filled questionnaires we saved data about students’ answers and questionnaire results that were
computed according to a scoring key of this questionnaire. Values of all properties ranged from 0 to
100, zero meaning that the student does not possess this characteristic and one-hundred meaning
that he does.
For the data analysis association rules method, which looks for dependencies between attributes and
conveys them in form of rules, was used. Each rule consists of a condition and a conclusion and is
assigned with reliability and support. Reliability provides information about how many percent of
records found with this condition being true meet also the conclusion. Support provides information
about for how many percent of records the condition is true.
Principal component analysis was used to identify possible dependent attributes measuring the same
or very similar student’s characteristics. This method searches for principal components – new
attributes which values are determined by linear combination of former attributes. We can distinguish
composition of each component by the size of its coefficients. Every principal component is also
described by standard deviation that indicates significance of the component.
Decision tree analysis was also performed. This analysis finds numbers of interesting rules for one
goal attribute. Rules are then represented in a form of a tree. Leaves gives value of the goal attribute,
nodes are conditions of rules and branches are values of conditions. Finally, a cluster analysis was
performed, which tries to find groups of similar objects that are different from other group‘s objects in
data. For the computation of the distance was used Euclidian metric and then two grouping methods
were used: single link and Ward-Wishard Ward’s minimum variance method (Ward, 1963) aims at
finding compact, spherical clusters. The single linkage method adopts a ‘friends of friends’ clustering
strategy.
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