Malleability of cognitive style

• Cognitive style is malleable.

• Cognitive style is malleable.

• Cognitive style is fixed.

• Research has failed to confirm whether cognitive style is fixed or malleable. Available evidence-based information is inconsistent and contradictory.

Information processing

• FD individuals prefer to process information at a slower pace than FI learners.

• FD individuals have a slower reaction time than FI individuals.

• FD individuals pay more attention to salient cues than to relevant ones.

• FD individuals tend to test fewer hypotheses, and experience more difficulties in recalling cues and information extracted.

• FD individuals tend to apply shallow level of information processing and inefficient strategies for restructuring contents.

• Cognitive style awareness of style is a precondition for metacognition.

• FD learners exhibit rigidity in information processing and are unable to consider alternate outcomes.

• FI experienced an enhanced sense of presence in a haptic virtual environment.
• FI were able to create ‘‘missing information’’ and to concentrate on relevant information only, ignoring “noisy” information (p. 247).
• This finding supports previous research indicating that FI are more creative than FD (Hecht & Reiner citing Rastogi, 1987 and Noppe, 1985).

• ‘‘cognitive style differences are more influential on the learning process than on the learning outcome in the virtual environment’’ (p. 172).

• FI learners tended to employ the internal reference strategy, while FD learners tended to rely on the external reference strategy when building a Home page.
• Learners’ performances deteriorated when they received an instructional strategy that contradicted with their cognitive styles. (Chen & Macredie, 2002, p. 10).

Learning

• FI students are more task-oriented than FD students and are more able to focus their attention on relevant aspects of the task.
• FI students were able to remember details and learn new rules.
• FI students can locate main ideas.

• FI learners focus their efforts on distinguished features, while FD learners tend to scan for more information and are easily distracted by irrelevant cues.

• FI students assimilated information more quickly in a complex and visual environment, preferring less the more linear style of tutorial and the multiple questions than FD students in a geology laboratory simulation. (Chen & Macredie, 2002, p. 5).

• FD/I ‘‘represent differences in cognitive abilities’’ (p. 1364).
• FI outperformed FD in problem solving ‘‘under instructional conditions that do not impose high extraneous cognitive load’’ (p. 1364).
• FI learners outperformed FD learners in problem solving tasks presented in different instructional formats (integrated and split) (p. 1364).

The authors stress the need for effective instructional designs to effectively support, facilitate, and guide all students’ learning irrespective of their FD/I. (p. 1364).

• Awareness of students’ cognitive style did not make a difference in their learning

outcomes. Most students need additional support regardless of their cognitive styles when completing complex tasks.

Academic achievement

• FD’s success rate on the General Educational Development Test (GED): 9.5% succeeded, 33.3% failed and 57.1% dropped out.
• FI’s success rate on the General Educational Development Test (GED): 52.6% succeeded, 15.8% failed and 31.6% dropped out.

• Support that FI have higher academic achievement in nonexistent social interdependence conditions than FD ‘‘who work in positive social interdependence conditions’’ (p.1).

• Strongly support that field independence contributes to academic achievement.

• Strongly support that field independence contributes to academic achievement.

• Strongly support that field independence contributes to academic achievement.

• Strongly support that field independence contributes to academic achievement in mathematics.

• There is evidence which indicates that if the individual’s cognitive style matches the information processing requirements of the situation or job, the individual will find it relatively easy to attend to and interpret relevant information and use it to decide how to act in order to perform effectively.

• Field-dependent-independent is a predictor of academic achievement.

• Field independence contributes to academic achievement in reading.

• FI learners succeed regardless of instructional strategies.

• FI outperformed FD in web-based instruction systems when using concepts maps and content outlines.

• Cognitive styles seem to have an effect on student achievements (p. 69)

• FI students performed better in the Internet treatment than in the CD-ROM and Text treatments (Parkinson & Redmond, 2002, p. 42);
• FI also performed better overall (p. 42);
• Witkin’s FD/FI is the most consistent predictor of final score irrespective of treatment (Parkinson & Redmond, 2002, p. 42);
• Witkin’s FD/FI is the most consistent predictor of final score in the Internet environment (p. 42).

• Experienced FI subjects outperformed Experienced-FD subjects in ergonomics, especially when explicit structure was not provided. (Chen & Macredie, 2002, p. 5)

• FI students showed higher scores for learning performance in distance learning course. (Chen & Macredie, 2002, p. 7)

• Cognitive style was significantly related to the development of cognitive maps in an introduction course on computer network. (Chen & Macredie, 2002, p. 7).
• FI students scored higher than FD students on the cognitive map in an introduction course on computer network. (Chen & Macredie, 2002, p. 7).

• There was no significant correlation between cognitive styles and learning achievement on human heart.
• FD learners scored lower than FI learners and intermediate learners in a condition mismatched with their preferred manipulation style. (Chen & Macredie, 2002, p. 10).

• FD/FI learners ‘‘with style awareness achieved higher scores than their counterparts who received no style awareness’’ (page ix).

• FI students tended to perform better than FD students in psychology course on all treatment levels.
• FD students performed at essentially equivalent levels as FI students in a congruent teaching method. (Chen & Macredie, 2002, p. 10)

Evidence-Based Instructional Strategies for Field-dependent/independent learners

• Providing a synergogic (social) learning environment;
• Offering deliberate structural support with salient cues, especially organizational cues such as advanced organizers;
• Providing clear, explicit directions and the maximum amount of guidance;
• Including orienting strategies before instruction;
• Providing extensive feedback (especially informative);
• Presenting advance organizers (verbal, oral, or pictorial);
• Presenting outlines or graphic organizers of content;
• Providing prototypic examples;
• Advising learner of instructional support needed (examples, practice items, tools, resources);
• Providing graphic, oral or auditory cues;
• Embedding questions throughout learning; and
• Providing deductive or procedural instructional sequences (p. 97).

Instructional conditions that capitalize on the preferences of the field-independent learners and challenge the field-dependent learners include:

• Providing an independent learning environment;
• Utilizing inquiry and discovery teaching methods;
• Providing abundant content resources and reference material to sort through;
• Providing independent, contract-based self-instruction;
• Providing minimal guidance and direction;
• Asking the learner to pose questions to be answered;
• Using inductive instructional sequence;
• Creating outlines, pattern notes, concept maps, etc.; and
• Using theoretical elaboration sequences (pp. 97-98).

Source: Adapted from (Summerville, 1999, p. 5)

• When introductory-level instruction emphasized an FD approach, both FD and FI complete successfully a visually-oriented tasks in online settings (p. iii).

• ‘‘trainability could be maximized by matching the FD/FI dimensions of cognitive style … with method of instruction’’ (p. 1).

• When better structured learning environments and support is provided to FD learners, FD and FI performed similarly in text and web environments.

• Concept attainment can be improved when instructional materials are designed for a specific cognitive style dimension.

• Results indicated a superiority of performance for field- independent students, regardless of the treatment. Attention directing strategies was found to be more effective for the field-independent than field-dependent students.

• A significant interaction was found between cognitive style FI/FD and methodology. The deductive approach was more effective with FI learners.

Implications of research to accommodate field-dependent adults in distant education

• Provide instructions on mnemonic and hierarchical memory techniques;
• Provide instruction on restructuring;
• Provide instruction on note taking and organizational;
• Provide instruction on a hypothetical approach to problem solving;
• Maximize instructor communication;
• Increase peer communication;
• Provide access to learning centers, labs, tutors;
• Provide technological support;
• Evaluate instruction using feedback from FD and FI learners;
• Humanize instruction;
• Provide charts, summaries, outlines, notes, and graphs;
• Diminish field factor;
• Provide navigation aids;
• Use cooperative learning techniques;
• Provide direct guidance;
• Teach both breadth and depth;
• Teach from global to specific;
• Provide reflective learning tasks;
• Model cognitive style flexibility;
• Interact face-to-face with learners;
• Use a variety of assessment tools;
• Minimize use of line graphs;
• Supplement instruction with sound whenever possible;
• Reduce interfering conditions;
• Give short learning tasks;
• Provide short due dates intervals;
• Balance use of inferential and factual questions;
• Closely monitor self-pacing;
• Colour-code instructional materials;
• Use animation.

• Navigational styles were not found to be significantly different between FD & FI, in distance learning course. (Chen & Macredie, 2002, p. 7).

• Providing navigational aids to accommodate FD users in a web-based interface reduce the disparity in the performance between FD and FI.
• These adaptations ‘‘did not adversely affect the performance of the FI individuals’’ (p. 75).

Internet – web search

• Cognitive style had greater impact on users’ web searching behaviours than other factors such as: information needs, information searching process, results evaluations, information search efficiency, level of web search experience. (p. 340).

• When assessing multi-document summaries, FD/FI users ‘‘have different assessments with regard to information coverage and the way that information is presented in both loosely and closely related document sets’’ (p. 1038).
• ‘‘User’s cognitive styles FD/FI affect a user’s ratings on the coherency of a summary’’ (p. 1038).

• FD users got confused more easily on the Web than FI users. (Chen & Macredie, 2002, p. 9).

• Both FD and FI students performed equally well in the use of HTML, but they demonstrated ‘‘strategic differences in navigation’’ (p. 281) and they ‘‘displayed characteristically different learning strategies’’ (p. 305).

• ‘‘Failed to demonstrate correlations between individuals ’ cognitive styles and their information seeking behaviors’’ (p. 5).
• Revealed individual differences in using the information retrieval systems, particularly the selection of navigation tools, the use of function keys, time to spend for each information retrieval (IR) stage, and evaluation of the retrieved information (p. 5, 6).

• ‘‘Spatial abilities and field independence appeared to be related to the use of computers, the WWW and e-mail” by older Japanese adults (p.71).

• Among individuals “who had little or no experience with online searches, the FI individuals tend to outperform the FDs” (p. 500).
• “In order to complete a search task, the FIs spent less time and needed to visit fewer nodes than the FDs.” (p. 500).
• “FDs with little or no online experience tended to use Home button more frequently than the rest. The use of Home button can be viewed as one possible indication of the user’s “getting lost”.
• This result implies that the FD novices get lost more often than the rest” (p. 500).
• “the difference created by participants’ cognitive style disappeared in those participants who had considerable online search experience” (p. 500).
• “Among the experienced online searchers, no significant difference was found between the FDs and the FIs in terms of the time spent and the number of nodes visited for the completion of a task” (p. 500).
• This finding “implies that difficulties that the FDs face in finding information on the Web may be overcome as the FDs gain experience and develop their search strategies while using online databases.” (p. 500).

• FI students tended to use search engines, find options, and URLs when performing web-based search.
• FD students tended to use home or back keys more frequently.
• FD students appeared to get lost and to be distracted on the Web when performing web-based search (Chen & Macredie, 2002, 9).

• Cognitive style is a major factor that influences student learning patterns within web-based instruction.
• FD and FI learners ‘‘have different preferences for locating information, especially for the selection of navigation tools and display options’’ (p. 23).

• Cognitive styles have a ‘‘significant effect on users’ preference for web-based instruction’’ (p. 2074).

• FI students scored significantly higher than FD users on information searching tasks.
• FI users especially had advantages over FD students when structural information was not conveyed through the interface design in the program (Chen & Macredie, 2002, 5).

• FI students did not differ from FD students in their motivation, learning strategies, and achievement in the web-based courses (Chen & Macredie, 2002, 8).

• FD users preferred a well-structured format when performing web-based search, especially for those with little or no experience in on-line searching (Chen & Macredie, 2002, 9).

& Duffin, 1996) in (Chen & Macredie, 2002)

• FD students used few new terms, but they retrieved many relevant references.
• FI students used many new terms. However, they obtained less relevant references (Chen & Macredie, 2002, p. 9).

Technology-based hypermedia instruction & learning

• When a more structured navigational map was used in a hypermedia learning environment FD students performed as well as FI students.
• When a more structured navigational map was used in a hypermedia learning environment, ‘‘Students in the navigational map group did not outperform students in the content list group as expected’’ (p. 4).
• Recommendations are made for hypermedia designers, developers and programmers on the need for more research to increase ‘‘the efficiency and effectiveness by meeting the needs of students with different cognitive styles’’ (p. 5).

• The level of difficulty experienced by FD learners in technology-based learning environment is proportional to the level of technology.
• Highly integrated technology-based instruction can result in 8 per cent differential learning gain between FD and FI learners.

• FI outperformed FD in a non-linear, unstructured learning environment (the Guided Inquiry Learning approach, GIA) (p. 4).
• FI performed better in a non-linear, unstructured environment than they did in a tutorial, linear and sequential approach. (p. 5).
• FI outperformed FD in the tutorial, linear and sequential approach (TuA) since students had to learn the material on their own; (p. 4).
• FI outperformed FD in independent learning task. (p. 5).

• FI performed better on recall and comprehension when cueing strategies are embedded in computer delivered text messages.
• The cueing strategies ‘‘did not improve field dependents’ performance on the assessments and actually hindered the performance of field independents’’ (p. 1).

• FI students perform better than FD students in technologically-based learning

• Cognitive styles do affect learners’ performance in different computer media (p. 42).

• FD learners (n=180) ‘‘perform significantly better when they have specific rather than general learning goals in a hypertext environment’’ (p. 661).
• ‘‘Specific versus general learning goals do not make a difference for FI learners’’ (p. 660-661).

• Style awareness did not affect the learning strategies used by field-independent students as they studied and carried out learning tasks in the Webquest (p. ix).
• ‘‘style awareness significantly influenced the learning strategies of field-dependent students as they studied and carried out learning tasks in the Webquest.
• FD students with style awareness used hypertext links and navigated the menu sequentially a greater number of times than their counterparts with no style awareness’’ (page ix).

• FD/FI “was shown to be a factor in visualization’’ (p. 186) when visual cues are used in multimedia environment to deliver lessons.
• “FI tend to perform better on visual-related tasks than FD learners’’ (p. 187).
• “FD learners were disadvantaged in high-load multimedia environment, as compared to FI learners’’ (p. 186).
• “FD may lack the cognitive capacity (working memory capacity or visual perception) to fully exploit the advantages of multiple representations, as compared to FI learners.”
• “Multiple external representations (MER) environment may even impede the learning performance of FD learners if it is too complex and impose a high cognitive load’’ (p. 187).

• In a ‘computer in education’ class, FD students took more linear steps than FI students. (Chen & Macredie, 2002, p. 5).

• FD and FI’s language learning performances were equally good.
• FD and FI chose different types of media, tools, and learning aids. (p. 8).
• FD students tended to follow the sequence provided by the language learning program.
• FI students tended to jump freely from one point to another using the index tool. (Chen & Macredie, 2002, p. 9)

• Cognitive style, FD/FI in particular, is a ‘‘key factor in the development of hypermedia learning system’’ (p. 14).
• The use of cognitive style is viewed as a way ‘‘to accommodate the learning needs of different learners’’ (p. 14).

• FI students outperformed FD students (verbal information), regardless of linking structures of hypermedia systems. (p. 7)

• FI learners benefited more than FD learners when animation, text and voice were combined
• FI learners developed their own structure in hypermedia environments. FD learners abide by the structure imposed by the software.
• FI learners performed better when exploration is encouraged. FD learners performed better with more directed tasks.
• FI learners learned more effectively than FD leaners.
• FI and FD learners displayed different ways of accessing information.
• FI learners demonstrated stronger information-seeking behavior than FD learners.
• FD learners have a preference for step-by-step instructions.
• FI learners are more actively engaged than FD learners.
• FI learners tend to cover more course content than their FD counterparts.
• FI learners spend more time on assessment than FD learners.
• FI learners read screen contents quicker than FD learners.
• FD learners benefited more form materials combining text and graphics.
• FI learners are more attracted to online instruction (pp. 3-5).

• FI learners achieved higher grades and had a lower failure rate than FD learners in a broadcast telecourse.

• ‘‘High use of the relatively specific/detailed keyword index.
• Low use of the global map.
• High proportion of time spent on lower levels in the subject hierarchy, and little time on higher levels.
• Low use of the top level Section buttons.
• Low use of the “Overview” topics within the sections’’ (p. 300).

FD learning behaviors identified:

• ‘‘High use of global map and low use of the more specific/detailed keyword index.
• High proportion of time spent on levels high in the subject hierarchy, and little time spent on lower levels.
• High use of the 3 top level Section buttons.
• High use of the ‘‘Overview’’ topics within the sections’’ (p. 300).

• FI students thought the structure of the hypermedia system was clear.
• FD students experienced more disorientation problems. (Chen & Macredie, 2002, p. 9).

Human computer interface and field-dependent field-independent

• The average number of search steps (AST) showed that ‘‘the disorientation of the FI learners was significantly less than of the FD learners’’ (p. 660).
• ‘‘The FI learners were more inclined to reconstruct the structure or resolve problems based on their cognition and their reconstruct abilities were better than those of FD learners’’ (p. 660).
• ‘‘The FD learners are also more passive than the FI learners and they prefer to study in known Structures’’ (p. 660.
• The maximum tolerable cognitive load is somewhat less than that of the FI, therefore, they easily become lost on the Hypermedia educational systems (HES) (p. 660).

• FD users seek more help.
• FD users adopt a trial-and-error approach to learning.
• FD users tend to avoid pre-planning.
• FD users do not actively seek extra information.
• FD users have a preference for systems that provide guidance.
• FD users have difficulty structuring information.
• FD users explore the system superficially.
• FI users FI users use an active investigation strategy.
• FI users more sophisticated learning strategies (pp. 2-3).

• FD user experience more difficulties learning a new system.
• FD users need extra assistance to progress within levels.
• FD users develop a fixation for a correct solution.
• FD users have problems in transferring acquired knowledge to similar but novel situations.
• FD and FI users employ different learning strategies to accomplish the same task (pp. 2-3).

Occupational training

• Paucity of research related to cognitive control and occupational training.

• FD law enforcement officers experienced ‘‘greater navigational difficulties than FI’’ (p. 171) FD ‘‘were more susceptible to distraction and disorientation caused by flaws in the navigational design of the Virtual Reality’’ (p. 171);
• ‘‘There was no significant differences in crime scene sketches … indicated that FD and FI both learned effectively from the Virtual Reality despite differences in the ways they explored the virtual crime scene’’ (p. 172);

• There are differences in cognitive styles due to cultural upbringing.

• The level of attrition among African-Americans and women is higher in highly integrated technology-based instruction.