Research 0


Posted by on Nov 6, 2014 in Blog, Brain Training Controversy

Adoption of Brain Training is Running Ahead of Research 

You have probably come across the recent consensus statement made by 69 cognitive scientists and neuroscientists, which argued that there is no compelling scientific evidence supporting the claims that playing brain games may actually help people to become smarter. While these scientists believe that we need the illumination of sound research to determine the effectiveness of brain training game, a respected scholar has recently torpedoed what used to be considered the iron clad protecting the claims of scientific research. The researcher concluded that most published research findings are false. Obviously it is irresponsible to claim that brain training games are bogus based on the lack of research, which in itself is being claimed as bogus.

While scientists are arguing about the merits of brain training in the basis of scientific research, teachers and parents who are confronted in helping children experiencing difficulties on a daily basis are running ahead of the evidence-based discourse and adopting a variety of brain training games for helping these children to enhance their cognitive skills.

Drawing from our award-winning research in cognitive skills augmentation, we have developed the Neuro-Ludus brain training game by dovetailing important discoveries in neuroplasticity, gaming and mobile technology. Neuro-Ludus enables youth and adults to augment their analytical/information processing skills to learn more effectively and achieve peak performance.

To succeed in the information society, people need to have well-developed analytical/information-processing skills. Employers view these skills as essential to achieve a competent and productive workforce. Educators also recognize the importance of these skills to learn effectively throughout life. Additionally, the use of digital technology involves the application of advanced cognitive skills for processing huge amounts of complex information quickly and effectively. These skills regulate the information processing habits representing a person’s typical mode of perceiving, visualizing, thinking, problem solving, and remembering.

To mitigate this widening cognitive overload, Neuro-Ludus offers cognitive augmentation brain training to improve analytical/information-processing skills. Neuro-Ludus has a built-in pre-test and post-test to enable players to assess their progress.

Neuro-Ludus contributes to the democratization of brain training in two important ways. First it provides fun, continuous, on-demand brain-training anywhere, anytime, and free for everyone. Second it puts players in the driver’s seat to improve their cognitive skills, and assess their progress in real time, with the help of a highly valid and reliable standardized pre and post-test.

Non-personal information collected from the gameplays will provide important learning analytics, which would help to shed light on the effectiveness of brain training. It is irresponsible for scientists to claim that brain training is bogus, based on presumed quality and paucity of evidence-based information. We caution against blind confidence in “text book” research design and methodology. We have to acknowledge that recent experimental research from the medical field is indicating that subjects in control, non-treatment condition groups, and receiving placebos can show significant improvements. This of course seriously undermines both the internal and external validity of the research.

We need to learn form e-learning research. For several years, research comparing e-learning to traditional instruction was yielding statistically non-significant results. Critics were using these results to claim that e-learning does not work. What they failed to realize is that the effectiveness of an e-learning intervention is incremental and that summative evaluation results are used for the continuous improvement of e-learning interventions. Learning analytics now provides instructional designers with an important tool to identify strengths and weaknesses of e-learning programs. The effectiveness of brain training programs should be viewed from a similar perspective.

During our first involvement in brain training a young student who was struggling in school and was identify as a potential dropout asked us: “Why are we not all born smart”. At the end of the intervention another students told us: “I was always told that I am dumb, now I feel that I am smart”. We believe that brain training is an avenue to help people unlock their full potential. We invite people to play Neuro-Ludus for free.

The Neuro-Ludus English/French apps are FREE from: iTunes , Google Play and online: http://www.neuroludus.comdigitalbackgd

An animated video demonstration of Neuro-Ludus is accessible from:

Read our blogs to learn more:

Download our Press Kit:

Contacts/Interviews: Chinien, Ph.D.,; Boutin, Ph.D., UQAM,



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Understanding adult learners

Posted by on Oct 10, 2014 in Blog, Understanding Adult Learners

Labout market inclusion and extension of older workers

Labour Market Inclusion and Extension of Older Workers

The economic prosperity, which is helping Canadians sustain a high standard of living, is being threatened by diminishing skill stocks due to the aging of baby boomers, rising retirement rates and slow population growth (Expert Panel on Older Workers, 2008). To sustain growth in productivity the “economic output must be achieved by a smaller and older workforce” (Prskawetz, Fent, Guest, n.d. p. 1). Research also indicates that the entry of young people into the workforce is being delayed because they are taking much longer to complete their education and training (McMullin, J.A.; Cooke, 2004). Estimates suggest that 20% of the Canadian population will be over the age of 65 by 2015. Others project that by 2031, 25% of the population will be over the age of 65. It is also anticipated that the labour force participation rate will drop to 57% in 2025 as a result of the aging workforce (McMullin, J. A.; Tomchick., n.d.). The ratio of workers to retirees is expected to be two-to-one by 2031. People tend to retire earlier in spite of the fact that they are living longer due to continuing medical advances. For example, it is estimated that Canadians may now live a healthy life two or more decades after retirement; however, the average retirement age in Canada is 62 (Canadian Broadcasting Corporation, 2008).

Due to the economic uncertainty, Canadians appear to be giving second thought to retirement. A recent survey conducted by Sun Life reveled that “Canadians expecting to be retired at age 66 declines by almost 50 per cent in five years” (Sun Life Financial, 2013, p. 1), due to the fear of outliving their retirement savings. There are growing concerns among policy makers regarding the dependency ratio of the aging population and governments’ ability to sustain the social safety net as life expectancy continues to grow in industrialized countries (Ghosheh, N.; Lee, S.; McCann, 2006). Human Resources and Skills Development Canada (HRSDC) estimates that in the next 10 years, two out of every three job openings will be attributed to the aging workforce (Standing Committee on Human Resources Social Development and the Status of Persons with Disabilities, 2008). A consensus is also emerging among government and industry leaders that skill shortages will intensify and become more widespread, due to the aging of the Canadian workforce (Government of Canada, 2008). There is also a widespread recognition that immigration, Canada’s traditional source of labour, will have limited impact in offsetting the labour force deficit because of the worldwide competition for talent (Expert Panel on Older Workers, 2008; Human Resources Professionals Association of Ontario, 2007). Several countries are taking bold steps to address the population aging issues, by extending working lives and postponing the labour market exit of older workers.

Across the G7 countries there has been a wave of reforms to remove disincentives to early labour market exit, to combat the labour market exclusion and to promote inclusion and participation of older workers. These reform efforts have primarily targeted aspects of pension plans and social assistance, which tend to encourage early withdrawal from work life. The most common changes include increasing the age of eligibility and introducing penalties for earlier retirement. On the other hand, a whole range of incentives has been introduced to promote labour market extension beyond normal retirement age, by enabling retired employees who are working full-time or part-time to contribute to their pension plans. Most G7 countries have also abolished a mandatory retirement age. Various legislations are also being enacted to ban age discrimination. Canada has implemented some of these reforms and the implementation of others is under consideration (WDM-Consultants, 2011).

The trend toward ‘Freedom 55’ seems to be wearing down as a growing number of Canadians are becoming uncertain about their economic future during their retirement years. However, research indicating that two-thirds of older Canadian workers intend to work past retirement age may be overestimating the potential pool of older workers. Approximately one third of older workers end their work lives due to illnesses or disabilities; another third retire from work and are simply not interested in maintaining any form of labour market attachment. It is the remaining third that constitutes the estimated potential pool of older workers who are fit and willing to work beyond normal retirement age (WDM-Consultants, 2011).

There are some major employer barriers that act as impediments to the hiring and retention of older workers and that also push the workers to sever their labour market attachment earlier. The most notable of these barriers include: discriminatory employment practices, discriminatory re-employment practices, the generalized perception that older workers earn more and are less productive than younger workers, intergenerational divide at the workplace, and company policy restricting the rehire of retired workers (WDM-Consultants, 2011).

There are various employee barriers impinging on the labour market extension of older workers. Human capital deficiencies encapsulate three main employee barriers, namely: skills and learning deficiencies, deficiencies in workability, and some personal issues that are not conducive to labour market integration and extension. In spite of various initiatives for promoting lifelong and life-wide learning, older workers have little access to workplace training to learn new skills and to continuously improve acquired skills. Even when they have access to training, they may lack the information-processing skills to fully benefit from the opportunity (WDM-Consultants, 2011). The OECD has identified this problem as being a major challenge that needs to be overcome in order to facilitate the labour inclusion and extension of adult workers:

Rapid societal change is…increasingly requiring older adults to acquire and use complex information with new technologies, not just in the workplace but also in many aspects of home and everyday life. These requirements can pose considerable challenges to older adults faced with declining sensory, perceptual, and cognitive abilities as they aged. Consequently, there are compelling reasons for understanding the effects of aging on adult learning, both from psychological and educational perspectives and from the point of view of the underlying brain mechanisms that support cognition and learning” (OECD, 2007, p. 212). 

According to Camusso, although many people who enter the world of work have the necessary capacity to successfully adapt to workplace requirements, their cognitive skills may decay over time, resulting in a decline in their ability to adapt to changes. It is important therefore to give adult workers the opportunity to develop and hone their cognitive skills (Camusso, 2001).

The modern workplace has been altered significantly by automation and computerization. Many physical job tasks have disappeared and have been replaced by cognitive tasks that involve the transmission and processing of information from a computer screen or a numerical control system. As the job tasks become more complex and mental, the requirements for sound basic essential skills are also becoming more stringent. However, a significant proportion of adult workers lack the basic literacy and numeracy skills to successfully cope with their job requirements. The reorganization of labour is a third factor that significantly affects skills requirement. Specialized job roles are being replaced by a variety of job tasks performed within autonomous production teams. This new work environment calls for versatility and autonomy (Camusso, 2001).

Research has shown that workers need to deploy complex information processing skills and meta-cognitive strategies for solving problems in technology rich environment (Chinien & Boutin, 2011). Hong and Liu identified three types of meta-cognitive strategies that may be deployed by computer game players. Following is a brief description of each:

  • Trial-and-error thinking: Players determine problem attributes through trial-and error tactics, and make no attempts to reflect and revise their thinking process;
  • Heuristic thinking: Players apply experience gained form trial-and error mode to avoid repeating the same mistake; and
  • Analogical thinking: Players solve problems through an analytical mental process (Hong, Hwang, Tam, Lai, & Liu, 2012)
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Brain-mind connection 0

Brain-mind connection

Posted by on Oct 10, 2014 in Blog, The Human Brain

This entry is part 2 of 2 in the series The architecture of the human brain

Brain – Mind Connection 

For many years the scientific community has struggled to make the mind-brain connection. Not surprisingly, there is not a clear definition of the mind because of the mystery which surrounds it. From a religious perspective, mind is synonymous with soul, spirit and divine principle. Scientists have so far been unable or unwilling to define the mind. Daniel Siegel has probably provided the most comprehensive definition of the mind, which is gaining widespread acceptance amongst scientists. He defined the mind as follows: “a core aspect of the mind is an embodied and relational process that regulates the flow of energy and information’’ (p. 2). Siegel’s conceptualization of the mind, encapsulating the mind, brain and relationship elements, is illustrated in the diagram below.







Source: Adapted from Siegel (Siegel, 2010). 

In this conception of the mind, the brain is seen as an organ having a variety of parts designed to perform some specific functions by themselves in combination with other parts. The mind is conceptualized as the flow of energy and information. This assumes that the mind regulates the energy. By regulating, Siegel meant monitoring what is happening with the energy and exerting an influence to alter the way things are happening. The information component represents the symbolization of what is happening. The relationship element of the model suggests a mind-body connection, rather than a limited perspective of mind-brain connection. According to Siegel, the mind regulates energy and information flow not only throughout the whole body of an individual, but also between and among people. The relationship function is for sharing of energy and information flow (Campbell, 2008). This brain-mind conception proposes the brain as a flexible, modifiable, malleable organ that can be altered with new experiences and thoughts. As mentioned earlier, the mind is the flow of energy and information. Therefore the dynamics of brain plasticity can be explained as the effects of the mind over the brain. New experiences and thoughts generate a flow of energy and information (mind), which acts on the neurons to trigger the development of new neurons and new neural connections.

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Research on Digital Skills and the Cognitive Style Field-Dependent / Independent

Posted by on Oct 10, 2014 in Blog, Cognitive Control Regulating Information Processing

This entry is part 2 of 3 in the series Cognitive control regulating information processing

Research on Digital Skills and the Cognitive Style Field-Dependent / Independent 

In 1985, Van der Veer, Tauber, Waerns, and Muylwijk posited the impact of users’ cognitive style construct field-dependent/field-independent on human-computer interaction:


Field-dependent persons develop a fixation to a certain solution that is valid in one situation and so encounter difficulties in a new one that is analogous but has some minor differences. They lack the ability to focus on analogies and to transfer their solution methods. With the user interface we predict field-dependent users to need extra help in transfer from one level of the interface to the next … Experienced users learning a new system will also encounter difficulties if they are field-dependent. They will lack the tendency to use mental models of a familiar system as an analogy for the acquisition of new ones, without adequate support (i.e. metacommunication about similarities and differences). We expect field-independent users to overcome these difficulties without support (Van der Veer et al., 1985, p. 295).

Since then, a considerable amount of research has been conducted, on the cognitive style construct field-dependent/independent (Chinien, 1990). This section of the review of literature will focus only on studies that are of more direct relevance to the objectives of this project, which is to establish the importance of the cognitive style construct field-dependence/field-independence in the acquisition and practice of digital skills.

The search and selection process to identify empirical research focused on digital skills and the cognitive style field-dependent / field-independent consisted of four stages as summarized in Table 2. In the first stage, we conducted a thorough search of dissertation abstracts using ProQuest, and a systematic search of selected electronic journals including IEEE Explore, PsycNet, ACM Digital library, EDITLIB, and ERIC. Using the online search tools EndNote and Mendeley, the UQAM’s search engine (Virtuose) and search engine on the internet (Google Scholar), we made a first selection of empirical studies focused on digital skills and the cognitive style field-dependent / field-independent, conducted with an adult target population. The search targeted mainly studies from the year 2000 to date and was performed with the following four groups of keywords:

(1) digital technology, computer, internet, CHI (Computer Human Interaction);

(2) cognitive style, analytical, field-dependent, field-independent;

(3) adults, workers, men/women, males/females;

(4) learning, acquisition, application, deploying, using.

These search terms were combined using the Boolean AND, OR, NOT to identify relevant documents based on titles and abstracts. This first search resulted in a total of two hundred and eighty six (286) studies.

In the second stage, a review of the selected research titles was performed to identify all studies that appeared to reveal a relationship between cognitive style field-dependent/field-independent and digital technology. Following this second stage, one hundred and thirty seven (137) studies remained in the selection.

In the third stage, the abstracts of the selected studies were skimmed to assess their relevance to the objective of this research and to verify the targeted research population. The selection included studies conducted with an adult population only. As a result of this assessment, ninety five (95) studies were identified.

Given the poor quality of some of the abstracts, in stage four of the selection process we read in more depth the selected studies to assess their relevance to the objective of this review and to ascertain that the sample consisted of an adult population. Following this analysis, seventy nine (79) studies were considered for this review.

Table 2. The search and selection process for the literature review.

Stage 1 Search electronic journal and dissertation abstracts using search engines and four groups of search terms, based on date of publication (year 2000 to date). 286 studies
Stage 2 Review title from selected research and exclude non relevant studies. 137 studies
Stage 3 Skim abstract of selected studies, assess their relevance and verify the targeted population. 95 studies
Stage 4 Read in more depth selected papers to assess their relevance to the objective of this review and to an adult population. 79 studies
Total 79 studies


Given that the purpose of this review of literature was to establish the importance of the cognitive style field-dependent and field-independent for the acquisition and practice of digital skills, only the summary findings of the studies reviewed are reported in Table 3. These research findings were analyzed and categorized in order to provide a better understanding of the importance of the cognitive skill field-dependent / independent in the acquisition and practice of digital skills.

Two comprehensive reviews of literature were used as a starting point for this section. Jonassen and Grabowski (1993) reviewed several studies assessing differences in learning between field-dependent and field-independent learners and made a summary of the implications of the style characteristics. The summary of his findings as cited by (Summerville, 1999, p. 5) is reported in Table 3.

Isaak-Ploegman (2003) conducted a comprehensive integrated review of literature to ascertain the effects of cognitive style field-dependent/ independent in the context of distance education for adult learners. The researcher identified several implications for educational practice that can be beneficial to field-dependent learners. These implications are also listed in Table 3 below (Isaak-Ploegman, 2003).


Table 3. Research findings on digital skills and the cognitive style Field-Dependent / Independent


Malleability of cognitive style

Research References Findings
(Agor, 1989)
  • Cognitive style is malleable.
(Allison & Hayes, 1996)
  • Cognitive style is malleable.
(Messick, 1976; Kogan, 1980; Robertson, 1985; Kirton, 1989)
  • Cognitive style is fixed.
(Zhang, 2005)
  • Research has failed to confirm whether cognitive style is fixed or malleable. Available evidence-based information is inconsistent and contradictory.

Information processing

Research References Findings
(Avilio, Alexander,   Barrett, & Sterns, 1979)
  • FD individuals prefer to process information at a slower pace than FI learners.
(Boysen & Thomas, 1980)
  • FD individuals have a slower reaction time than FI individuals.
(Dickstein, 1968)
  • FD individuals pay more attention to salient cues than to relevant ones.
(Davis & Frank, 1979)
  • FD individuals tend to test fewer hypotheses, and experience more difficulties in recalling cues and information extracted.
(Beuhring & Kee, 1987)
  • FD individuals tend to apply shallow level of information processing and inefficient strategies for restructuring contents.
(Fyle, 2009)
  • Cognitive style awareness of style is a precondition for metacognition.
(Spanger & Tate, 1988)
  • FD learners exhibit rigidity in information processing and are unable to consider alternate outcomes.
(Hecht & Reiner, 2007)
  • 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).
(Kroutter, 2010)
  • ‘‘cognitive style differences are more influential on the learning process than on the learning outcome in the virtual environment’’ (p. 172).
(Lee 2000) in (Chen & Macredie, 2002)
  • 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).


Research References Findings
(Berger & Goldberger, 1979); (Isaak-Ploegman, 2003)
  • 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.
(Davis, 1987)
  • FI learners focus their efforts on distinguished features, while FD learners tend to scan for more information and are easily distracted by irrelevant cues.
Andris, 1996)
  • 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).
(Angeli & Valanides, 2013)
  • 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).

(Summerville, 1999)
  • 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

Research References Findings
(Donnarumma, Cox, & Beder, 1980) in (Cao, 2006)
  • 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.
(Bai, 2008)
  • Support that FI have higher academic achievement in nonexistent social interdependence conditions than FD ‘‘who work in positive social interdependence conditions’’ (p.1).
(Bal, 1988)
  • Strongly support that field independence contributes to academic achievement.
(Paramo & Tinajero, 1990)
  • Strongly support that field independence contributes to academic achievement.
(Savage, 1983)
  • Strongly support that field independence contributes to academic achievement.
(Abdollahpour & Kadivar, 2006)
  • Strongly support that field independence contributes to academic achievement in mathematics.
(Zhang, 2005)
  • 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.
(Paramo & Tinajero, 1990)
  • Field-dependent-independent is a predictor of academic achievement.
(Robeck, 1982)
  • Field independence contributes to academic achievement in reading.
(Hayes & Allinson, 1997)
  • FI learners succeed regardless of instructional strategies.
(Archer, 2005)
  • FI outperformed FD in web-based instruction systems when using concepts maps and content outlines.
(Hammoud, Love, & Brinkman, (2009)
  • Cognitive styles seem to have an effect on student achievements (p. 69)
(Parkinson & Redmond, 2002)
  • 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).
(Korthaure & Koubek, 1994) in (Chen & Macredie, 2002)
  • Experienced FI subjects outperformed Experienced-FD subjects in ergonomics, especially when explicit structure was not provided. (Chen & Macredie, 2002, p. 5)
(Boyce, 1999) in (Chen & Macredie, 2002)
  • FI students showed higher scores for learning performance in distance learning course. (Chen & Macredie, 2002, p. 7)
(Chou & Lin, 1997) in (Chen & Macredie, 2002)
  • 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).
(Fullerton, 2000) in (Chen & Macredie, 2002)
  • 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).
Fyle, 2009
  • FD/FI learners ‘‘with style awareness achieved higher scores than their counterparts who received no style awareness’’ (page ix).


(Witten, 1989) in (Chen & Macredie, 2002, p. 10)
  • 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

Research References Findings
(Jonassen & Grabowski, 1993) cited by (Summerville, 1999) Instructional conditions that capitalize on the preferences of the field-dependent learners and challenge the field-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)

(Lee, 2006)
  • When introductory-level instruction emphasized an FD approach, both FD and FI complete successfully a visually-oriented tasks in online settings (p. iii).
(Manfredo, 1987)
  • ‘‘trainability could be maximized by matching the FD/FI dimensions of cognitive style … with method of instruction’’ (p. 1).
(Redmond, Walsh, & Parkinson, 2003)
  • When better structured learning environments and support is provided to FD learners, FD and FI performed similarly in text and web environments.
(Elliot, 1976)
  • Concept attainment can be improved when instructional materials are designed for a specific cognitive style dimension.
(Greco & McClung, 1979)
  • 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.
(Grieve & Davis, 1971)
  • 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

Research References Findings
(Isaak-Ploegman, 2003) 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.
(Boyce, 1999) in (Chen & Macredie, 2002, p. 7)
  • Navigational styles were not found to be significantly different between FD & FI, in distance learning course. (Chen & Macredie, 2002, p. 7).
(Parkinson, Redmond, & Walsh, 2004)
  • 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

(Kinley, Tjondronegoro, & Partridge, 2010)


  • 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).
(Nguyen, Santos, & Russell, 2011)
  • 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).
(Wang, Hawk, & Tenopir, 2000) in (Chen & Macredie, 2002)
  • FD users got confused more easily on the Web than FI users. (Chen & Macredie, 2002, p. 9).
(Ford & Chen, 2000)
  • 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).
(Oh & Albright 2004)
  • ‘‘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).
(Umemuro, 2004)
  • ‘‘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).
(Kim, 2000)
  • 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).
(Kim, 1997) in (Chen & Macredie, 2002)
  • 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).
(Chen & Liu, 2008)
  • 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).
(Clewley, Chen, & Liu, 2011)
  • Cognitive styles have a ‘‘significant effect on users’ preference for web-based instruction’’ (p. 2074).
(Chang, 1995) in (Chen & Macredie, 2002)
  • 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).
(Shih & Gamon, 1999) in (Chen & Macredie, 2002)
  • FI students did not differ from FD students in their motivation, learning strategies, and achievement in the web-based courses (Chen & Macredie, 2002, 8).
(Palmquist & Kim, 2000) in (Chen & Macredie, 2002)
  • 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).
(Wood, Ford, Miller, Sobczyk,

& 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

Research References Findings
(Wang, 2007)
  • 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).
(Burnett III, 2010)
  • 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.
(Umar & Maswan, 2006)
  • 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).
(Cao, 2006)
  • 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).
(Burnett III, 2010)
  • FI students perform better than FD students in technologically-based learning
(Parkinson & Redmond, 2002)
  • Cognitive styles do affect learners’ performance in different computer media (p. 42).
(Ku & Soulier, 2009)
  • 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).
(Fyle, 2009)
  • 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).


(Tze Wei & Sazilah, 2012)
  • 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).
(Reed & Oughton, 1997) in (Chen & Macredie, 2002)
  • In a ‘computer in education’ class, FD students took more linear steps than FI students. (Chen & Macredie, 2002, p. 5).
(Liu & Reed, 1995) in (Chen & Macredie, 2002)
  • 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)
(Lee, Cheng, Rai, & Depickere, 2005)
  • 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).
(Lin & Davidson, 1996)
  • FI students outperformed FD students (verbal information), regardless of linking structures of hypermedia systems. (p. 7)
(Handal & Herrington, n.d)
  • 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).
(Spanger & Tate, 1988)
  • FI learners achieved higher grades and had a lower failure rate than FD learners in a broadcast telecourse.
(Ford & Chen, 2000) FI learning behaviors identified:

  • ‘‘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).
(Chen and Ford, 1998) in (Chen & Macredie, 2002)
  • 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

Research References Findings
(Qin & Rau, 2008)
  • 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).
(Coventry, 1989) as reported by (Dufresne &Turcotte, 1997)
  • 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).
(Nielsen, 1990), (van der Veer et al., 1985) as reported by (Dufresne & Turcotte, 1997)
  • 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

Research References Findings
(Hayes & Allinson, 1997)
  • Paucity of research related to cognitive control and occupational training.
(Kroutter, 2010)
  • 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);
Cultural differences
Research References Findings
(Allinson and Hayes, 2000)
  • There are differences in cognitive styles due to cultural upbringing.
(Burnett III, 2010)
  • The level of attrition among African-Americans and women is higher in highly integrated technology-based instruction.
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Posted by on Oct 10, 2014 in Blog, Cognitive Control Regulating Information Processing

This entry is part 3 of 3 in the series Cognitive control regulating information processing

Field-dependent and Field-independent

There is a considerable body of knowledge that has been accumulated on cognitive style through empirical research (Chinien, 1990), and various cognitive style dimensions have been identified. One of the cognitive style dimensions which has received more widespread attention and which has been more extensively researched is the construct field-dependent/independent (Learning & Skills Research Centre, 2004), (Wallace & Gregory, 1985), (Witkin, et al., 1977), (Witkin & Goodenough, 1981), (Ragan et al., 1979). Field-dependent/independent (FD/FI) is a psychological construct related to a “global versus an analytical way of perceiving, [and] entails the ability to perceive items without being influenced by the background” (Kirby, 1979, p. 52). There is considerable empirical evidence regarding stable and profound differences between the field-dependent and field-independent construct (Witkin et al., 1977).

Herman Witkin is credited for the identification of the field-dependent and field-independent constructs. During World War II, Witkin was intrigued by the fact that when some fighter pilots were flying long distances in thick clouds or fog, they could maintain their orientation and reference to the horizontal. Others however became disoriented when they lost sight of the horizon. Witkin conducted a series of laboratory experiments to study this phenomenon. His early approach to test his subjects was to place them in a dark room, seated on a chair. Both room and chair could be tilted. During the experiment, subjects were asked to locate their body position to the true vertical, while the chair or the room or both were tilted. The subjects who were able to maintain their orientation were designated as “field-independent”. Those subjects who were unable to do so were called “field-dependent” (Witkin & Goodenough, 1981).

Witkin’s laboratory test was simplified over time to a paper and pencil instrument, called the Embedded Figures Test (EFT). Subjects using the EFT are asked to identify simple figures embedded in a series of complex designs. The Fl individuals are more successful in disembedding the figures from these designs. The FD persons, on the other hand, are less successful in performing that task. Research has shown that the EFT is a valid and reliable instrument for measuring the cognitive style dimension FD/FI (Witkin et al., 1971). The EFT is administered on an individual basis. Therefore it is not a practical instrument for testing large groups of subjects. Another version of the same test, the Group Embedded Figures Test (GEFT) was developed for group testing. The GEFT contains almost the same items of the EFT, and has a reliability estimate of .82 for both males and females. The concurrent validity of the test (r = .82 for males and r = .63 for females) was estimated by correlating subjects’ scores on the GEFT and EFT (Witkin et al., 1971).

Research has shown that field-dependent individuals are drawn to people and like to have people around them. They exhibit more non-verbal behaviors, prefer occupations which require involvement with others (e.g., social sciences); they also demonstrate a preference for academic areas that are people-oriented (e. g., teaching, selling). On the other hand, the relatively field-independent people demonstrate a preference for impersonal and abstract school subjects (e. g., mathematics and physical sciences); they are more impersonal, and prefer occupations in which interaction with others is not important (e.g., astronomy, engineering) (Witkin & Moore, 1974). Relatively field-dependent persons have a global perception, take a long time for solving problems (Witkin & Moore, 1974). They tend to use “external referents for self-definition, and are therefore externally motivated; make less use of mediators in the coding process of knowledge acquisition; and prefer a spectator approach to concept attainment” (Caliste, 1985, p. 26). Furthermore, the field-dependent individuals are extremely alert to social cues, have highly developed interpersonal skills, and like to study and work in groups. In addition they are extremely sensitive to social criticism and are strongly influenced by others around them (Witkin & Moore, 1974, Witkin & Goodenough, 1981). In contrast, the relatively field-independent persons are analytical, tend to solve problems rapidly, and learn better when content is abstract (Witkin & Moore, 1974). They are also “less attentive to social cues and more abstract- analytical in orientation, use external referents for self-definition and therefore are more intrinsically motivated, make frequent use of mediators and engage in a hypothesis-testing approach to concept attainment rather than spectator analysis” (Caliste, 1985, p. 26). The field-independent individuals tend to be “aloof, theoretical and not sensitive to others around them. They prefer to work alone. They avoid group interaction if given a choice. They are almost oblivious to social criticism. In contrast to the field-dependents, field-independents will restructure any random or non-hierarchically presented information for better retention and retrieval” (Wallace & Gregory, 1985, p. 22). According to Witkin and Moore (1974) the FD/FI individuals are not different “in sheer learning ability or memory” (p. 6).

Earlier studies (Witkin, Lewis, Hertzman, Machover, Meissner, & Wapner, 1954) indicated that the cognitive style dimension FD/FI was related to gender differences. Males appeared to be more field-independent that females. However, evidence from more recent studies indicates that gender differences in field-dependence is inconclusive (Naditch, 1976).

The cognitive style dimension field-dependent/independent has also some important educational implications, which can affect what one learns and how one learns (Witkin et al., 1977; Ragan et al., 1979). This cognitive style construct is also a source of considerable individual differences among individuals. These differences reflect the psychological and personality factors, perceptual patterns and social orientation (Ausburn & Ausburn, 1978), all of which affect people’s ability to learn. 

Although cognitive style is considered to be a determinant factor for individual and organizational performance by some industrial and organizational psychologists, it has received little attention in the literature in the past. However, there is currently sustained interest for research investigating cognitive style in business and management (Armstrong, Cools, & Sadler-Smith, 2012). Streufert and Nogami (1989) have argued that there must be another dimension, independent of related task-specific knowledge and skills, that explains successes and failures to perform in novel situations and in task after task, and job after job (Streufert & Nogami, 1989) in (Hayes, 1998). They describe this dimension in the following terms: “these variables must reflect stable cognitive and action tendencies that are applied across tasks. Rather than attitudes, specific knowledge or practical skills, which a person might apply to some given job, these “personality” variables must reflect a characteristic style, which an individual might employ across tasks and settings. These are variables, which control how a person in general perceives, processes, and organizes information and how that person would act” (Streufert & Nogami, 1989, p. 94) in (Hayes, 1998).

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Cognitive Styles and Cognitive Controls

Posted by on Oct 10, 2014 in Blog, Cognitive Control Regulating Information Processing

This entry is part 1 of 3 in the series Cognitive control regulating information processing

Cognitive Styles

The first part of this section presents a discussion on cognitive styles. The second part examines the cognitive style construct field-dependent and field-independent. The third part reviews the importance of this cognitive style dimension for the acquisition and deployment of digital skills.

For thousands of years educators have been preoccupied with individual differences among learners (Keef, 1982). Zhang (2005) traced back the foundation of the theory of cognitive style research and development to Kurt Lewin’s work in the 1920s: A Dynamic Theory of Personality. In his attempt to set the foundation for a theory of differentiation among human beings, Lewin (1923) defined differentiation as: “a function of the conditions of the environment as well as the individual peculiarities of the person” (p. 226). Zhang (2005) summarized Kevin’s general law of psychology, which she argued had a significant influence on research related to individual differences among individuals: “a person’s behavior B is a function of a person’s personality P an environmental situations E (B= f (PE))” (Zhang, 2005, p. 11). 

Researchers’ interest in cognitive styles can also be traced back to C. Jung in 1923 when he advocated a theory of psychological types, which differentiated individuals along two types of attitudes (extraversion and introversion); two perceptual functions (intuition and sensing); and two judgment functions (thinking and feeling) (Sternberg & Grigorenko, 2001). Herman Witkin is credited as being the father of cognitive style and his works have been a catalyst for the exponential growth in cognitive style research. Cognitive styles are the information processing habits representing the learner’s typical mode of perceiving, thinking, problem solving, and remembering (Messick, 1985). They are also described as: “high-level heuristics that organize and control behavior across a wide variety of situations” (Dufresne & Turcotte, 1997, p. 1). A panel of experts reached a consensus on a definition of cognitive styles through a Delphi study:

Cognitive styles are individual differences in processing that are integrally linked to a person’s cognitive system. More specifically, they are a person’s preferred way of processing (perceiving, organizing and analyzing) information using cognitive brain-based mechanisms and structures (Peterson & Rayner, 2009, p. 520).

These styles constitute important dimensions of individual differences among students. In an attempt to explain the cognitive style construct, Cross (1976) notes:

People see and make sense of the world in different ways. They give their attention to different aspects of the environment; they approach problems with different methods for solution; they construct relationships in distinctive patterns; they process information in different but personally consistent ways. … Style has a broad influence on many aspects of personality and behavior: perception, memory, problem solving, interest, and even social behaviors and self-concepts. (p. 115-116).

There have been thousands of research studies conducted on cognitive styles over the years, through which a large number of cognitive style dimensions have been identified. The Learning & Skills Research Centre (2004) made an inventory, which included 71 models (Kirby, 1979); (Zhang, Sternberg, & Rayner, 2012) also provide also a comprehensive summary of several cognitive style constructs that have been identified and researched. The Learning & Skills Research Centre (2004) has noted that the “enormous size of these literatures presents very particular problems for practitioners, policy-makers and researchers who are not specialists in this field” (p .2).

Gregoroc (1982) indicates that brain behavior research provides “strong evidence that individual differences do indeed exist and that some of our instructional approaches are inappropriate for many individuals” (p. 7). Ginsburg (1985) states: “individual differences is at the heart of education. To a large degree education is or should be concerned with developing meaningful forms of learning for individuals who differ in important ways” (p. 57).

In discussing the issues and concerns related to individual differences among learners and learning, Belland, Taylor, Canelos, Dwyer, & Baker (1985) note that:

Accommodating learners’ individual differences remains a concern for all teachers at all levels. Whether the individual difference is defined as genetic intelligence, as a cognitive style or as an attitude, these individual difference variables have a significant influence upon learning and overall academic progress (p. 185).

Cognitive styles are psychological constructs “usually conceptualized as characteristic modes of perceiving, remembering, thinking, and problem solving, reflective of information processing regularities that develop in congenial ways around underlying personality trends” (Messick, 1985, p. 90). Ragan et al.(1979) argue that since cognitive style determines the way we acquire and process information, the “individual may encounter tasks that require the processing of information in a way that they are unable to accomplish, simply because their cognitive style restrict the availability of the processing technique” (p. 2). This argument suggests that instructional tasks can be style-biased, and is supported by research findings showing differential effects of matching and mismatching instructional tasks to cognitive styles. Several researchers have criticized the cognitive control FD-FI because they have found significant relations between the construct and measures of academic achievement (Learning & Skills Research Centre, 2004b). Others have acknowledged that there might be an interaction of field-independence and achievement and directed their effort to assist field-dependent individuals to overcome their information processing deficit (Learning & Skills Research Centre, 2004b).

Despite the importance of cognitive styles as a framework for address significant issues related to individual differences among people, this field of research has been: “constantly repeatedly criticized for the myriad of tests; contested, confused and overlapping definitions and terminology; inappropriate measurement and lack of independent evaluation” (Peterson, Rayner, & Armstrong, 2009, p. 18). After conducting a comprehensive review of research on 13 major cognitive constructs, the Learning & Skills Research Centre (2004) arrived at similar conclusions. The major problem in the field of cognitive style research relates to the inappropriate conceptualization and operationalization of the construct for research purposes. The work of Jonassen and Grabowski (1993) has contributed to bring conceptual clarity among the four main categories of constructs that they have identified through their research. Following is a brief description of each:

Table 1. Categories of constructs

Categories of constructs Description
Cognitive controls Because of the rapidity with which this flow of information takes place, a person needs to have highly developed cognitive controls to be able to cope with this information processing demands effectively and efficiently. Cognitive controls influence and regulate perception.
Cognitive styles Cognitive styles describe learner traits.
Learning styles Learning styles refers to preferred modes of acquiring knowledge in a learning environment.
Personality types Personality type refers to learner’s attention, engagement, and expectations.
Source: Adapted from (Ayersman & Minden, 1995).


While it has been demonstrated that all these categories of cognitive constructs contribute in one way or another to learning and performance, the Witkin cognitive control field-dependent and field-independent is the primary interest of this project because it is by far the most researched and most influential construct (Learning & Skills Research Centre, 2004). As Messick (1986) noted earlier, many unfulfilled promises had been made in the name of cognitive styles. He stressed that these false promises “may be true for some cognitive styles, perhaps even most of them, but it is not true for field dependence-independence. Its early promise has been fulfilled, and its potential continues to offer ample collateral for exciting new forays” (Messick, 1986, p. 117).

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