learning - Academic Conferences Limited
learning - Academic Conferences Limited 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. 188
3. Results of learning style analysis Michaela Drozdová et al. In this part, results obtained during the analysis are presented. 3.1 Association rules Association rules found many dependencies between properties. We were looking for rules with minimal support of 10% and minimal reliability of 70% and found total of 147 rules. The majority of these rules have very low level of depth learning in conclusion. Conclusions also contained visual type, strategic learning and motivation. Some of these rules are shown in Table 1. Of most interest are the rules with high support. For example almost half of non-visual type of students mostly do not possess depth learning style (see rule number 1, where support 43% means that this rule is applied to 43% of students and reliability 71% means that 71% of students with non visual learning also do not possess depth learning). Unfortunately 70% of all students do not possess depth learning style, so many of these rules are influenced by this fact. Because of this we cannot make any valid conclusions based on this analysis. But we can use its results to determine goal attributes for the decision tree analysis: depth and strategic learning, visual perception and motivation. Table 1: Selected rules with 70% minimal reliability and 10% minimal support, ordered by support Num Condition Conclusion Support Reliability 1 visual = very low depth = very low 43% 71% 2 strategic = very low depth = very low 38% 76% 3 social (alone - group) = middle depth = very low 38% 72% 4 verbal = low depth = very low 31% 73% 5 theoretical – experimental = high depth = very low 31% 76% … … … … … 32 motivation = high, theoretical – experimental = high depth = very low 17% 73% 33 social (alone - group) = middle, verbal = low depth = very low 17% 75% 34 auditive = middle visual = very low 16% 80% … … … … … depth = very low, systematic (sequential - random) = 69 very low strategic = very low 13% 72% visual = very low, motivation = high, strategic = very 70 low depth = very low 13% 73% … … … … … depth = very low, visual = very low, field = 87 University, pedagogical motivation = high 12% 70% 88 strategic = very low, visual = middle depth = very low 12% 79% … … … … … depth = very low, surface = very low, autoregulation = 107 visual = very low low 11% 71% … … … … … 147 autoregulation = low, social (alone - group) = low depth = very low 10% 87% 3.2 Decision trees analysis This method was used for four goal attributes based on the association rule analysis results: depth and strategic learning, visual perception and motivation. Further on we describe results of these analyses. By analyzing depth learning (see figure 1) we found out that group learning (left branch of node “social: alone/group”), holistic (left branch of node “detailistic/holistic”) students mostly have depth learning style (yellow color in leaf labeled “high”). Other results derived from this tree cannot unfortunately be used because 70% of students in this data sample do not have depth learning style. 189
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3. Results of <strong>learning</strong> style analysis<br />
Michaela Drozdová et al.<br />
In this part, results obtained during the analysis are presented.<br />
3.1 Association rules<br />
Association rules found many dependencies between properties. We were looking for rules with<br />
minimal support of 10% and minimal reliability of 70% and found total of 147 rules. The majority of<br />
these rules have very low level of depth <strong>learning</strong> in conclusion. Conclusions also contained visual<br />
type, strategic <strong>learning</strong> and motivation. Some of these rules are shown in Table 1. Of most interest<br />
are the rules with high support. For example almost half of non-visual type of students mostly do not<br />
possess depth <strong>learning</strong> style (see rule number 1, where support 43% means that this rule is applied to<br />
43% of students and reliability 71% means that 71% of students with non visual <strong>learning</strong> also do not<br />
possess depth <strong>learning</strong>). Unfortunately 70% of all students do not possess depth <strong>learning</strong> style, so<br />
many of these rules are influenced by this fact. Because of this we cannot make any valid conclusions<br />
based on this analysis. But we can use its results to determine goal attributes for the decision tree<br />
analysis: depth and strategic <strong>learning</strong>, visual perception and motivation.<br />
Table 1: Selected rules with 70% minimal reliability and 10% minimal support, ordered by support<br />
Num Condition Conclusion Support Reliability<br />
1 visual = very low depth = very low 43% 71%<br />
2 strategic = very low depth = very low 38% 76%<br />
3 social (alone - group) = middle depth = very low 38% 72%<br />
4 verbal = low depth = very low 31% 73%<br />
5 theoretical – experimental = high depth = very low 31% 76%<br />
… … … … …<br />
32 motivation = high, theoretical – experimental = high depth = very low 17% 73%<br />
33 social (alone - group) = middle, verbal = low depth = very low 17% 75%<br />
34 auditive = middle visual = very low 16% 80%<br />
… … … … …<br />
depth = very low, systematic (sequential - random) =<br />
69<br />
very low<br />
strategic = very<br />
low<br />
13% 72%<br />
visual = very low, motivation = high, strategic = very<br />
70<br />
low<br />
depth = very low 13% 73%<br />
… … … … …<br />
depth = very low, visual = very low, field =<br />
87<br />
University, pedagogical<br />
motivation = high 12% 70%<br />
88 strategic = very low, visual = middle depth = very low 12% 79%<br />
… … … … …<br />
depth = very low, surface = very low, autoregulation =<br />
107 visual = very low<br />
low<br />
11% 71%<br />
… … … … …<br />
147 autoregulation = low, social (alone - group) = low depth = very low 10% 87%<br />
3.2 Decision trees analysis<br />
This method was used for four goal attributes based on the association rule analysis results: depth<br />
and strategic <strong>learning</strong>, visual perception and motivation. Further on we describe results of these<br />
analyses.<br />
By analyzing depth <strong>learning</strong> (see figure 1) we found out that group <strong>learning</strong> (left branch of node<br />
“social: alone/group”), holistic (left branch of node “detailistic/holistic”) students mostly have depth<br />
<strong>learning</strong> style (yellow color in leaf labeled “high”). Other results derived from this tree cannot<br />
unfortunately be used because 70% of students in this data sample do not have depth <strong>learning</strong> style.<br />
189
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