Nessun titolo diapositiva

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XII ESCOP - XVIII BPS Cognitive Section Joint
Conference - Edinburgh - September 6th 2001
Relationships Between Thinking Style and Net
Surfing Style Alessandro Antonietti and Andrea
Calcaterra Department of Psychology - Cognitive
Psychology LaboratoryCatholic University of the
Sacred Heart Largo Gemelli 1 - 20123
Milano antoniet_at_mi.unicatt.it - acalcat_at_libero.it
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INTRODUCTION An alleged instructional benefit
associated with learning from net-structured
knowledge (such as that provided by hypermedia)
concerns the opportunity to access information
according to the preferred style of thinking.
More precisely, when a student is given the
possibility to surf freely a hypermedia, - he/she
should develop personal navigation patterns whose
features should mirror his/her own cognitive
characteristics - personal navigation patterns,
regardless of their features, should enhance
learning because of the correspondences between
them and the students cognitive profile.
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Literature supports only partially these claims
(Andris, 1997 Chuang, 1999 Crosby Stelovsky,
1994 Ford Chen, 2000 Frey Simonson, 1994
Korthauer Koubek, 1994 Liu Reed, 1994
Valcke Schellens, 2000 Yu Ting Underwood,
1999). However, we must note that - only a
limited set of styles has been investigated - by
using only one kind of measures - by considering
only a part of the whole structure of the
relationships involved. The aims of the present
study were - to investigate an as yet not
adequately considered style (sequential-holistic)
- by using two distinct measures focused on
similar constructs even though embedded in
different contexts, respectively, a general one
(everyday thinking) and a specific one (spatial
orientation) - by considering in details the
relationships between style, surfing behaviour
and learning outcomes and by assuming computer
use expertise as a relevant variable to be tested
(Monereo, Fuentes Sanchez, 2000).
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STYLES
COMPUTER
USE . Left vs Right
EXPERTISE . Route
vs Survey BEHAVIOUR IN HYPERMEDIA
NAVIGATION LEARNING OUTCOMES
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METHOD PHASE 1 A sample of 306 students attending
university courses in different faculties were
administered the following questionnaires
measuring, respectively, computer use expertise
and left-right style of thinking - Computer Use
Questionnaire (CUQ) (Antonietti, 1998) - Your
Style of Learning and Thinking questionnaire
(SOLAT) (Torrance et al., 1988)
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CUQ The instrument asks to rate both the
frequency and the ability in computer using with
reference to various categories of usage (word
processing, calculation or statistical programs,
educational software, action and strategy video
games, web surfing, email, newsgroup and chat,
programming). Low, medium and high levels of
expertise were assessed by computing a total
score and by assuming the 33.3 and 66.6
percentiles of the frequency distribution of such
score as cut-off points.
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SOLAT Hypotheses derived from research into
brain lateralization were interpreted as
supporting the distinction between a left and a
right style of thinking. Left style is concerned
with logical-analytical thinking and implies
preference for sequential processing of
information and systematicity right style refers
to holistic thinking and implies preference for
parallel processing. SOLAT is a self-report
inventory designed to test the left-right style.
It consists of 28 items each reporting a pair of
statements (one referred to the left and the
other to the right style of thinking).
Respondents have to place a check mark whether
the statement is true of them they may check one
or both of the statements in a pair or neither.
SOLAT allows to compute distinct measures for the
left and right styles and to assess the relative
dependence of an individual either on the left
(sequential) or on the right ( holistic) mode
of thinking.
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Within such a sample, 40 students were selected
by considering scores in CUQ and SOLAT
questionnaires, so that the following groups were
constituted 10 high computer users - sequential
thinkers 10 high computer users - holistic
thinkers 10 low computer users - sequential
thinkers 10 low computer users - holistic thinkers
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PHASE 2 The 40 selected students were
administered the Situational Questionnaire on
Sense of Direction (SQSOD) (Bosco Scalisi,
1998) to identify both their orientation ability
and their orientation style (Route vs
Survey). SQSOD consists of a series of
situations (way finding, map drawing, direction
giving, and so on) respondents have to choose
the strategy (route, survey, or non-spatial) they
should employ to carry out the orientation task
described and have to evaluate their own ability
to perform the strategy. Total scores are
computed for each strategy and for whole ability
evaluation. On the basis of total scores students
were classified as showing a preference for the
route (sequential), the survey (holistic) or
the non-spatial strategy and as low or high in
orientation ability.

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PHASE 3 Students were requested to surf freely a
hypermedia concerning the ancient Maya
culture. The hypermedia provided students with -
an Introduction constituted by textual notions -
an Over-flying of the town - the Virtual Reality
reproduction of a Maya town to be freely
navigated - a series of Architectures
(tri-dimensional shapes which can be rotated)
accompanied by short expository texts - a gallery
of Photos (accompanied by explanatory notes) - a
series of short Videos
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An example of the Virtual Reality section of the
hypermedia
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An example of the Architectures section of the
hypermedia
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Navigation behaviour and strategies employed in
hypertextual navigation were recorded and
analysed by considering, for each section of the
hypermedia, the following parameters - time
spent in navigating the whole section and each of
its elements - zooming - changing the
perspective - mouse movements (direction, speed,
continuous vs jumping) - order according to which
the sections were navigated - navigating again
the same section (return)
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PHASE 4 Finally, students were asked some
questions concerning what they had learned
through the hypermedia navigation. Questions
concerned - recall of verbal notions - recall
of visual information - recall of information
presented in a visual-verbal format - inferences
drawn from declarative knowledge provided -
completeness, accuracy, and structural
organisation of the whole field of knowledge
acquired. Separate scores for each kind of
questions and a total score were computed
respondents were classified as low or high
learners by assuming the medians of the score
distributions as cut-off points.
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RESULTS Overview of the analyses carried out A -
Relationships between left-right style and
expertise B - Relationships between left-right
style and expertise and orientation style C -
Relationships between left-right styles and
expertise and navigation D - Relationships
between orientation style and navigation E -
Relationships between navigation and learning
outcomes F - Relationships between left-right
style and expertise and learning G -
Relationships between orientation style and
learning
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A - Relationships between left-right style and
expertise A significant negative correlation
between total computer use scores and left style
scores was recorded (rho -.15, p lt .05). Low
left thinkers were 22 of the low and 34 of the
high computer users High left thinkers were 54
of the low and 38 of the high computer
users Low right thinkers were 53 of the low
and 41 of the high computer users High right
thinkers were 23 of the low and 32 of the high
computer users In conclusion, computer expertise
tended to be associated with low left and with
high right thinking styles.
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B - Relationships between left-right style and
expertise and orientation style No significant
correlations (Spearmans rho) and associations
(chi-square) between SOLAT and SQSOD
emerged. However, high orientation ability
students scored higher than low ability students
in the right scale of SOLAT mean scores 15.11
(sd6.38) vs 14.43 (sd6.37) low ability
students scored higher than high ability students
in the sequential scale of SOLAT mean scores
8.33 (sd5.72) vs 6.95 (sd4.95) Thus, the
following relationships tended to emerge - right
style - high orientation ability - left style -
low orientation ability.
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A significant association between computer
expertise (low-high) and orientation ability
(low-high) was recorded (?2 4.91, p lt .05).
This was supported also by - a significant
correlation between CUQ total scores and SQSOD
ability scores (rho .55, p lt .001) and between
CUQ total scores and non-spatial strategy scores
(rho -.42, p lt .05) - a significant effect (F
10.57, p lt.005) of expertise on SQSOD ability
scores (low expertise mean 35.55 (sd6.90)
high expertise mean 42.35 (sd6.10).
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High experts tended to privilege the survey
strategy low experts the route and non-spatial
strategies Orientation Computer
expertise strategy preferred Low High Route
55 40 Survey 25 40 Non
spatial 15 5 No preference 5 15
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Mean Computer expertise orientation Low Hi
gh F scores Route 7.50 (2.21) 6.80
(1.58) 1.32, n.s. Survey 5.15 (2.32) 7.40
(2.16) 9.68, p lt .005 Non spatial strategies
3.35 (2.30) 1.80 (1.77) 5.44, p lt .05
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C - Relationships between left-right style and
expertise and navigation Detailed analysis of the
navigation behaviour showed coherent patterns of
relationships within each section of the
hypermedia and between the Virtual Reality and
Architectures sections (that is, the sections
which had similar surfing modalities).
Left-right style and expertise did not
influenced mean time spent in navigating any
section of the hypermedia, as revealed by 2 (left
vs right) X 2 (low vs high computer expertise)
Anovas, even though expert students tended to
spend less time in navigating the sections of the
hypermedia Computer expertise Section Low
High Introduction 87.55 (55.42) 66.70
(33.96) Over-flying 101.45 (38.70) 91.80
(38.81) Virtual Reality 211.75 (164.75) 190.75
(108.73) Architectures 178.06 (82.42) 171.75
(60.61) Photos 98.00 (40.66) 85.00
(37.31) Video 191.53 (64.54) 186.22 (52.13)
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However, right students, as compared to left
students, navigated later - the Introduction
mean order 3.00 (2.45) vs 1.15 (0.67), F
10.52, p lt.005 - the Photos mean order 4.74
(1.94) vs 4.53 (1.26), F 0.21, n.s. Right
students navigated earlier - the Over-flying
mean order 2.90 (1.97) vs 3.45 (1.79), F
1.19, n.s. - the Video mean order 3.90 (1.86)
vs 4.35 (1.93), F 0.61, n.s. The numbers of
returns in the Over-flying, Architectures and
Video sections were higher in the right than in
the left students.
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High computer users showed, in comparison to low
users - higher numbers of mouse movements,
changes, jumps, zooming in the Virtual Reality
section - faster movements in the Virtual
Reality section mean values - 0.55 (0.76) vs
0.01 (.31), F 5.83, p lt.05) a significant
correlation between CUQ scores and speed scores
was recorded rho .36, p lt.05 - higher numbers
of mouse movements and zooming in the
Architectures section.
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D - Relationships between orientation style and
navigation During navigation high orientation
ability students scored higher than low ability
students in the speed and in the numbers of jumps
and of mouse movements and in the frequency of
zooming. The following significant differences
emerged Orientation ability Low Hig
h F Number of jumps in Virtual Reality 0.67
(0.66) 1.16 (0.69) 5.32, p lt.05 Frequency of
zooming in Virtual Reality 0.05 (0.22) 0.42
(0.77) 4.56, p lt.05 Frequency of zooming in
Architectures 0.74 (0.87) 1.32 (0.89) 4.12, p
lt.05
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Mean times spent in navigating the sections of
the hypermedia differed as follows
Orientation style Section Survey Route Over-f
lying 109.62 (37.61) 86.42 (36.94) Introduction
66.23 (44.63) 81.00 (41.80) Virtual
Reality 178.00 (66.70) 233.74
(181.15) Architectures 165.54 (67.23) 183.35
(85.06) Photos 85.08 (27.23) 103.50
(42.72) Survey style students scored higher than
route students in the frequency of zooming (?2
6.27, p lt.05) and in the speed in the Virtual
Reality section Route students scored higher
than survey students in the numbers of mouse
movements and of continuous movements in the
Virtual Reality section.
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High orientation ability students tended to
navigate the Virtual Reality section and
Over-flying section - but not the Introduction,
Architectures, Photos and Video sections - as
first more frequently than low ability
students Orientation style Section
visited as first Survey Route Introduction 8
5 74 Over-flying 8 11 Virtual
Reality 0 11 Architectures 0 0 Photos
8 6 Video 0 0
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E - Relationships between navigation style and
learning High learners spent more time in
navigating the Introduction (F 19.67, p lt .001)
and re-visited the Architectures a higher number
of times (F 4.23, p lt .05) than low
learners.
No significant differences in time spent visiting
other sections between low and high learners were
found
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100 of students who began hypermedia surfing
from the Virtual Reality or from the Video were
high learners. 78 of students who began surfing
from Architectures were high learners. 67 of
students who began surfing from Photos were low
learners. Re-navigating the sections of the
hypermedia tended to be associated with high
learning rates Learning Sections Low High
?2 Over-flying 58 42 0.23,
n.s. Photos 62 38 0.63, n.s.
Introduction 33 67 4.81, p lt .05 Virtual
Reality 47 53 0.33, n.s. Architectures 27 6
3 3.99, p lt .05 Video 33 67 1.67, n.s.
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High learners in Virtual Reality section
performed - lower numbers of mouse movements
and of continuos movements - a higher number of
change of perspective.
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Students who represented the whole acquired
knowledge in a holistic way (e.g., with a map)
spent more time in navigating the Over-flying
section than students who derived a discursive
representation (both kinds of students did not
differ significantly from students who
represented knowledge through pictures) (F
3,14, p lt .05).
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Students who represented the acquired knowledge
through maps were 35 of the sample. Such a
representation was more frequent in students who
re-navigated particular sections of the
hypermedia Knowledge representation Section
revisited Picture Discourse Map Virtual
Reality 33 20 40 Over-flying 25 33 43
Architectures 27 27 45 Video 17 25 50
100 of students who navigated the Over-flying
section as first reproduced knowledge through a
map. In short, re-visiting overview sections
hinted at representing knowledge through maps.
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F - Relationships between left-right style and
expertise and learning Left-right style and
expertise did not influenced learning rates
(total score, questions about verbal notions,
questions about visual information, inferential
questions, completeness and accuracy scores) as
revealed by 2 (sequential vs holistic) X 2 (low
vs high computer expertise) Anovas.
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G - Relationships between orientation style and
learning No significant correlation coefficient
between orientation scores (ability and style)
and learning scores was found only orientation
ability and survey style were negatively
associated with learning from verbal questions
(respectively, rho - .32 and -.35, p lt.05) and
route style was negatively associated with
accuracy scores (rho -.31, p lt.05). Low
orientation ability students obtained higher mean
total learning scores (22.62, sd4.79) than high
ability students (19.37, sd6.23) (F 4.21, p
lt.05). Route style students achieved mean total
learning scores (21.63, sd5.65) higher than
survey students (18.54, sd4.07) as well as
higher partial (verbal, visual, verbal-visual,
inferential) scores. Mean verbal scores of route
and survey students were significantly different
respectively, 11.11 (2.42) vs 9.08 (3.12), F
4.28, p lt.05. The whole acquired knowledge was
represented through maps by the 45 of the survey
students and by the 25 of the route students.
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CONCLUSIONS Students personal features showed
a consistent structure of relationships High
computer expertise Orientation
ability Right thinking style Survey
orientation style
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Hypermedia navigation behaviour was linked to
skills rather than to styles - expert computer
users and high orientation ability students
showed dynamic surfing patterns (high number of
mouse movements, changes of perspective,
zooming) - left-right and route-survey styles
failed to influence surfing paths (apart from the
fact that right thinkers privileged the
navigation of overview sections of the
hypermedia).
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Good learning rates were not associated with
time spent in navigating the hypermedia but with
re-visiting its sections, visiting overview
sections as first, changing the
perspective. Learning outcomes were affected
neither by styles nor by abilities. Map
representation of the acquired knowledge was
developed by students who privileged overview
sections and who showed preferences toward
holistic processing. In conclusion, thinking
styles seem to play a minor role in modulating
personal hypermedia surfing path and in enhancing
learning.
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