How learn and process information the students in technical universities.

Mazilescu, Crisanta Alina ; Draghici, Anca ; Draghici, George 等

1. INTRODUCTION

Developing an information society requires first and foremost a technical and scientific culture throughout the society. Although for this purpose time and money have been invested, researches on technical skills and scientific knowledge have revealed the shipwreck of scientifique education. In stimulating interest for science and technology, the school is considered to be the most influential factor, followed closely by family and media (Mazilescu et al., 2009).

With the same goal of increasing the interest in scientific and technological knowledge we have focused in this work to study the learning style and cognitive complexity of "polytechnic" students.

1.1 Cognitive complexity

Cognitive complexity is a variable which describes processing information. Constructivist perspective of cognitive complexity is involved in building environmental representations, together with three other characteristics of the personal constructs: integration, organization and discrimination. Personal constructs theory proposed by Kelly (1955) postulates that individuals use a number of personal constructs to perceive and to structure events experienced. From the perspective of complexity, cognitive structure has three types of organization: complex structure is when constructs are well defined, organized hierarchically, but with little relationship between them; mid structure reflect the capacity to perceive quite well both the differences and similarities between phenomena and events received, is a structure in which concepts are well defined; simple structure is when constructs interact among themselves due to their insufficient definition and to overlapping of their axes of reference; Individuals who have developed personal constructs have a better ability to process information.

While cognitive complexity is the ability to use information stored in memory, the quality of this information is not considered and cognitive complexity is necessary but not sufficient for intelligence (Haase et al., 1979).

1.2 Cognitive style, parent learning style

According to Keefe (1979), Gordon Allport was the first author to propose the term "cognitive style". In 1976, Messick published a study on cognitive styles referring to "constant individual differences in how to organize and process information and experiences" and to "stable attitudes, preferences and stable strategies determining typical modes of a person to perceive, memorize, think and solve problems" (Messick, 1976). Interest to researchers since 70-80 years, passed from general cognitive functioning during the learning to individual learning needs of students. On this occasion, appeared and was based the difference between cognitive style and learning style.

1.3 Learning style

Learning style was a topic of interest to many researchers, which is why they appeared and there are plenty of theories:

--The learning styles depending on the learning environment

--The learning styles depending on modality of encoding and representation

--The learning styles based on how information processing

- The learning styles based on a model of experiential learning

- The learning styles based on a theory of personality--The mixed models of learning styles

The VAK concept, theories and methods were first developed by psychologists and teaching specialists in the 1920's. The VAK multi-sensory approach depending on modality of encoding and representation. This dimension style is also present in the mixed models of Hill (1972) and Dunn and Dunn (1978).

Theory VAK preferred learning styles defines the existence of three learning styles: visual, auditory and kinesthetic, but only one of which occurs predominantly in a situation of learning. The practic interest for this teory is to determine the best sensorial modality prefered to learning by students for using this in teaching.

This is the argument to realize this study, which has aims to identify learning styles and ways of processing social information to students in technical sciences.

2. METHOD

2.1 Subjects

The sample of subjects who participated in this research was composed of 115 students of techical university: 35 girls and 80 boys (18 to 24 years). The subjects are students in automation and computers, 1st and 3rd year of license. The average age is 20.5 years.

2.2 Instruments

The instruments used are "Questionnaire for cognitive complexity" (Virga, 2005) and "The questionnaire styles learning by vision, hearing and touch "(Vanderspelden, 2002). In what regards the cognitive complexity, the author established 5 factors (meta-cognitive ability, desire of knowledge, social influence, openess to new and tolerance for ambiguity). Upon these factors which are in interdependence, there is cognitive complexity, whose global score is calculated by summing up the partial scores of its facets.

To identify preferred learning styles of students from the Faculty of Automation and Computers we used the questionnaire developed by the Human Resources Department Staff and Skills Development (the Government of Canada) team led by Jean Vanderspelden (2002).

3. RESULTS

In part teachers tend to favor their own learning styles. The mismatches between the prevailing teaching style in most science courses and the learning styles of most of the students have several serious consequences. Because it the society loses potentially excellent scientists. To achieve an accord between teaching and learning styles we want to identify the learning styles of students of technical universities.

Regarding the sensory system preferred by "polytechnic" students we found that they used in relatively equal proportions all three learning styles: visual, auditory and kinesthetic. In a detailed analysis we observed that the most of students train all three sensory systems when studying.

The differences between averages obtained were statistically insignificant. In table 1 we present the descritive statistics of the results obtained for each studied learning style: visual, auditory and kinesthetic.

Taking into the analysis all three learning styles, visual, auditory and kinesthetic, we presented here the percents for each learning style. Note that all learning styles are present in almost equal proportions:

--34,85% auditory style

--34,43% visual style

--30,72% kinesthetic style.

Analysis of cognitive complexity gives us an insight into how the students use the information available in memory to perceive and evaluate people and events in their environment. Regarding the cognitive complexity (CCG) are presented the descriptive statistic for all their 5 factors: meta-cognitive ability (Mcgn), desire of knowledge (DesKnow), social influence (SocInfl), openess to new (Openess) and tolerance for ambiguity (TolAmbig) (table 1).

In Table 2 are presented the results of Significance Test of the difference between the average lot of girls and boys respectively. These results show that the girls, students in Automation and Computers have a "tolerance for ambiguity" less than boys, their peers.

Regarding the differences between 1st and 3rd year of study we observe that just openess to new tend to recede with the completion of the licence studies (tab 2).

4. CONCLUSION

In terms of meta-cognitive skills of students in technical sciences, we can say that they tend to develop reflective activities, to observe different aspects of reality and developed an interest in understanding and explaining the reasons for human behavior and events production.

Your intellectual curiosity and their need for knowledge could be increased during university studies. In fact, we observe a decrease in "opening the new" to the end of your studies, that can be explained by the abundance of new things learned in faculty.

Reference to the learning style of students, teachers must adapt their speech and teaching methods to the student's predominantly sensory system. This is because it was concluded that students have better results when teaching methods are adapted to their learning styles.

But, from the results presented in this study, students in technical science used in relatively equal proportions all three learning styles: visual, auditory and kinesthetic. Therefore, trainers would present information in a manner useful to all three learning styles. This would create for all students, regardless of their preferred style, the opportunity to be involved.

Learning is a complex process and is difficult to include in analysis all its dimensions. Because it we mention that our study considered learning analysis only in terms of information processing and sensory system preferred.

5. REFERENCES

Dunn, R. & Dunn, K. (1978). Teaching students through individual learning styles. Reston Pub, Virginia

Haase, R.F.; Lee, D.Y. & Banks D.L. (1979). Cognitive correlates of polychronicity, Perceptual and Motor Skills, Vol. 49 pp 271-282

Hill, J. (1972). The Educational Sciences. Oakland Community College, Detroit

Keefe,J.W. (1979). Student learning styles : diagnosing and prescribing programs, Reston, VA: National Association of Secondary School Principals (NASSP), pp 1-17

Kelly, G.A. (1955) .The Psychology of Personal Constructs, vol 1 and 2. Norton, New York.

Mazilescu, C.A.; Draghici, A.; Draghici, G.; Mihartescu, A.A. & Constantin, D. (2009). Aspects of scientifique and technological culture at students in technical sciences, Proceedings of Balkan Region Conference on Engineering and Business Education & International Conference on Engineering and Business Education, Oprean,C., Grunwald,N & Kifor,C.V. (Ed.) pp 527-530, ISBN 978973-739-848-2, Lucian Blaga University of Sibiu, october 2009, Sibiu

Messick (1976). Individuality in learning, San Francisco: Jossey-Bass, pp 4-22.

Virga,D. (2005). Cognitive complexity--the construction and validation of a questionnaire, Review of Applied Psychology. 3-4., pp 14-19 West University Publishing House, Timisoara

Tab. 1. Descriptive statistic for visual, auditory and kinesthetic
learning style and for cognitive complexity and their subfactors

Questionnaire Dimensions Mean Std. Dev

V.A.K visual 9,45 2,58
(Vanderspelden,
2002) auditory 9,56 2,54
 kinesthetic 8,43 2,79

Q.C.C Mcgn 22,95 4,556
(D.Virga, 2005)
 DesKnow 16,41 3,825
 SocInfl 16,13 3,923
 Openess 18,71 3,648

 TolAmbig 15,85 3,226

 CCG 90,06 13,101

Tab. 2. Differences between boys and girls; differences
between 1st and 3rd year of study

Tolerance for N = 80 m = 16,26 [delta] = 3,04 t = 2,47
ambiguity
(Diff boys-girls) N = 35 m = 14,81 [delta] = 3,47 p <0.01

Openess to new N = 61 m = 19,26 [delta] = 3,81 t = 1,92
(Differences 1st--
3rd year of study) N = 54 m = 18,08 [delta] = 3,38 p <0.1