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XII ESCOP - XVIII BPS Cognitive Section Joint Conference - Edinburgh - September 6th 2001

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 Laboratory Catholic University of the Sacred Heart

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XII ESCOP - XVIII BPS Cognitive Section Joint Conference - Edinburgh - September 6th 2001

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  1. 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@mi.unicatt.it - acalcat@libero.it

  2. 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 student’s cognitive profile.

  3. 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).

  4. STYLES COMPUTER USE . Left vs Right EXPERTISE . Route vs Survey BEHAVIOUR IN HYPERMEDIA NAVIGATION LEARNING OUTCOMES

  5. 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)

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. An example of the Virtual Reality section of the hypermedia

  12. An example of the Architectures section of the hypermedia

  13. 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)

  14. 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.

  15. 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

  16. 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 < .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.

  17. B - Relationships between left-right style and expertise and orientation style No significant correlations (Spearman’s 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 (sd=6.38) vs 14.43 (sd=6.37); low ability students scored higher than high ability students in the sequential scale of SOLAT: mean scores = 8.33 (sd=5.72) vs 6.95 (sd=4.95) Thus, the following relationships tended to emerge: - right style - high orientation ability; - left style - low orientation ability.

  18. A significant association between computer expertise (low-high) and orientation ability (low-high) was recorded (χ2 = 4.91, p < .05). This was supported also by: - a significant correlation between CUQ total scores and SQSOD ability scores (rho = .55, p < .001) and between CUQ total scores and non-spatial strategy scores (rho = -.42, p < .05); - a significant effect (F = 10.57, p <.005) of expertise on SQSOD ability scores (low expertise: mean = 35.55 (sd=6.90); high expertise: mean = 42.35 (sd=6.10).

  19. 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%

  20. Mean Computer expertise orientation Low High 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 < .005 Non spatial strategies 3.35 (2.30) 1.80 (1.77) 5.44, p < .05

  21. 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)

  22. 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 <.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.

  23. 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 <.05); a significant correlation between CUQ scores and speed scores was recorded: rho = .36, p <.05; - higher numbers of mouse movements and zooming in the Architectures section.

  24. 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 High F Number of jumps in Virtual Reality 0.67 (0.66) 1.16 (0.69) 5.32, p <.05 Frequency of zooming in Virtual Reality 0.05 (0.22) 0.42 (0.77) 4.56, p <.05 Frequency of zooming in Architectures 0.74 (0.87) 1.32 (0.89) 4.12, p <.05

  25. Mean times spent in navigating the sections of the hypermedia differed as follows: Orientation style Section Survey Route Over-flying 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 (χ2 = 6.27, p <.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.

  26. 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 85% 74% Over-flying 8% 11% Virtual Reality 0% 11% Architectures 0% 0% Photos 8% 6% Video 0% 0%

  27. E - Relationships between navigation style and learning High learners spent more time in navigating the Introduction (F = 19.67, p < .001) and re-visited the Architectures a higher number of times (F = 4.23, p < .05) than low learners. No significant differences in time spent visiting other sections between low and high learners were found

  28. 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 χ2 Over-flying 58% 42% 0.23, n.s. Photos 62% 38% 0.63, n.s. Introduction 33% 67% 4.81, p < .05 Virtual Reality 47% 53% 0.33, n.s. Architectures 27% 63% 3.99, p < .05 Video 33% 67% 1.67, n.s.

  29. High learners in Virtual Reality section performed: - lower numbers of mouse movements and of continuos movements; - a higher number of change of perspective.

  30. 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 < .05).

  31. 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.

  32. 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.

  33. 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 <.05) and route style was negatively associated with accuracy scores (rho= -.31, p <.05). Low orientation ability students obtained higher mean total learning scores (22.62, sd=4.79) than high ability students (19.37, sd=6.23) (F = 4.21, p <.05). Route style students achieved mean total learning scores (21.63, sd=5.65) higher than survey students (18.54, sd=4.07) 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 <.05. The whole acquired knowledge was represented through maps by the 45% of the survey students and by the 25 % of the route students.

  34. CONCLUSIONS Students’ personal features showed a consistent structure of relationships: High computer expertise Orientation ability Right thinking style Survey orientation style

  35. 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).

  36. 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.

  37. References Andris J. (1996) The relationship of indices of student navigational patterns in a hypermedia geology lab simulation to two measures of learning style. Journal of Educational Multimedia and Hypermedia, 5, 303-315. Antonietti A. (1998) Computer Use Questionnaire, manuscript. Bosco A. & Scalisi M. T. (1998) Situational Questionnaire on Sense of Direction, manuscript. Chuang Y. (1999) Teaching in a multimedia computer environment: A study of the effects of learning style, gender, and math achievement. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning,1, 1 Crosby M. & Stelovsky J. (1994) Hypermedia as a facilitator for retention: A case study using Kanji City. Computer Assisted Language Learning, 1, 3-14.

  38. Ford N. & Chen S. Y. (2000) Individual differences, hypermedia navigation, and learning: An empirical study. Journal of Educational Multimedia and Hypermedia, 9, 281-311. Frey D. & Simonson M. (1994) Assessment of cognitive style to examine student’s use of hypermedia within historic costume. Home Economics Research Journal, 21, 403-421. Korthauer R. D. & Koubek R. J. (1994) An empirical evaluation of knowledge, cognitive style, and structure upon the performance of a hypertext task. International Journal of Human-Computer Interaction, 6, 373-390. Liu M. & Reed W. M. (1994) The relationship between the learning strategies and learning styles in a hypermedia environment. Computers in Human Behavior, 10, 419-434. Monereo C., Fuentes M. & Sanchez S. (2000) Internet search and navigation strategies used by experts and beginners. Interactive Educational Multimedia, 1, 24-34.

  39. Torrance E. P. (1988), Your Style of Learning and Thinking. Bensenville, IL: Scholastic Testing Service. Valcke M. & Schellens T. (2000) Redesigning university education. Towards a dual mode approach: The congruency problem. I Research Workshop of European Distance Education Network (EDEN) Research and innovation in open and distance learning, Prague. Yu Ting I. & Underwood J. (1999) Learning styles and the use of the World Wide Web: Case studies from Singapore and the UK. CAL 99 - Virtuality in Education, London.

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