Kevin Broun
Learning Styles and Multiple Intelligences Literature Review
The addition of Howard Gardner’s Multiple Intelligences (MI) model to Carl Jung’s
Learning Styles model opened the floodgates for educational psychologists and educators to
devise and implement a series of assessment tools to determine students’ learning styles (Silver,
Strong & Perini, 1997). With the advent of computer- and web-based instruction, many of these
assessment tools incorporated the use of technology. Though widespread use of these
assessments is common, few have stopped to ask the important questions about reliability and
validity of such tests.
Riding’s Cognitive Styles Analysis (CSA) is the most frequently used computer-based
assessment of cognitive styles (Rezaei & Katz, 2004). The CSA tests two dimensions (WholistAnalytic and Verbal-Imager) with three subtests (one for Verbal-Imager and two for WholistAnalytic) (Rezaei). Accordingly, students are assessed and placed on a spectrum related to their
preference of text to pictures (Verbal-Imager) and big picture to component parts (WholistAnalytic). While many studies had been done on the test’s validity, the evidence on reliability is
sparse (Rezaei). Reliability data is critical in determining validity. Overall, the WholistAnalytic subtests were found to have higher reliability than the Verbal-Imager subtest (Rezaei).
Rezaei and Katz conclude that the CSA, while theoretically sound, may have defects in the area
of reliability and validity that warrant further research.
Another article calls into question the validity of the Verbal-Imager subtest of the CSA
(Massa & Mayer, 2005). According to Massa and Mayer, the test’s validity has not been
conclusively determined. In fact, the subtest lacks face validity, construct validity, and
predictive validity (Massa). The absence of predictive validity is disturbing in the context of
education and the classroom; if an assessment cannot diagnose students’ learning styles to
predict actual performance in a classroom, then it is of little help for a teacher who is attempting
to plan instruction that addresses multiple learning styles.
The CSA is not alone in its lack of predictive validity. The Multiple Intelligences
Development Assessment Scales (MIDAS) was developed to gauge an individual’s “intellectual
disposition” and is used for educational and career counseling (Shearer, 1997). MIDAS was
found to have “adequate” validity, but its lack of predictive validity is a cause for concern. “It is
possible that the MIDAS generally underestimates the students abilities” (Shearer).
The lack of conclusive reliability and validity research for cognitive styles and/or
multiple intelligence measures has led to the development of additional cognitive styles
assessments. In 2006, research was conducted at Harvard University, Rutgers UniversityNewark, George Mason University, New Jersey Institute of Technology, New York Institute of
Fashion and Design, and Stevens University about a proposed new dimension of learning styles.
Instead of only measuring students on the Verbal-Imager spectrum, the proposed new dimension
is a three-dimensional Object-Spatial-Verbal model (Blazhenkova & Kozhevnikov, 2008).
Blazhenjova and Kozhevnikov criticize the traditional Verbal-Imager questionnaires because of
their low internal reliability and lack of construct and predictive validity—the very problems
discussed above. One explanation offered for the failure of the traditional Verbal-Imager
cognitive style assessments is that the human brain actually contains two distinct subsystems for
processing and encoding visual information: the object imagery system & spatial imagery system
(Blazhenkova). Accordingly, the traditional assessments (e.g., CSA) that try to pin an individual
to a unitary construct of imagery miss the complexity of the human brain and fail to completely
explain cognitive style. The Object-Spatial Imagery and Verbal Questionnaire (OSIVQ) was
Kevin Broun
developed to reflect this complexity, and its reliability and validity have been examined in a
series of three studies (Blazhenkova). The results of the three studies seemed to indicate that the
OSIQV had higher reliability and validity than the earlier cognitive style assessments. “These
findings demonstrate the generalizability of the new cognitive style model to real-life activities
that extend beyond the laboratory testing settings” (Blazhenkova).
Just as new dimensions of cognitive styles are being evaluated, new technologies are
being developed to address individual learning styles. One of the benefits of technology-based
education is that the learning environment can be a dynamic place where new learning tools are
introduced often. “To be effective, e-earning systems should consider each student’s learning
preferences and skills” (Schiaffino, Garcia & Armandi, 2008). eTeacher is one example of a
new technology that is using the idea that every student has a different learning style to present
effective web-based instruction. eTeacher is an “intelligent agent,” a type of software/computer
program that learns “users’ interests, preferences, and habits and give them proactive,
personalized assistance with a computer application” (Schiaffiano). Instead of a web-based
questionnaire where students may choose arbitrary answers, eTeacher detects a student’s
learning style automatically through observations about how students learn and interact with a
web-based program (Schiaffino). eTeacher builds a student profile by collecting data about a
student during his/her completion of an e-learning course. “For example, if a student participates
in chat rooms and forums, eTeacher can infer that the student processes information actively and
not reflectively” (Schiaffino). Students are evaluated according to four dimensions devised by
Felder and Silverman: perception (intuitive-sensitive), input (visual-verbal) processing (activereflective), and understanding (global-sequential) (Schiaffino). One glaring flaw in eTeacher is
its inability to distinguish between visual and verbal learners at this time. The goal of eTeacher
thus far has been to recommend a course of action for students that coincide with their learning
styles; however, a new direction is being researched. Perhaps a better use of this technology is to
compliment a student’s learning style by suggesting actions that may not come naturally to a
student, thereby strengthening his/her weaknesses rather than only reinforcing existing strengths
(Schiaffino).
In conclusion, the use of multiple intelligences/cognitive styles assessments in education
needs further research. However, the fact that multiple intelligences and learning styles are
common topics of discussion within the field of education is a promising start to meeting the
needs of diverse learners. Educators must take into account the reality that not all students learn
the same way when planning instruction (Brandt, 1990). Diagnosing an individual’s learning
style is merely the first step; helping a teacher to use this data in a way that informs instruction is
the critical next step. The integration of technology in education—including computer and webbased assessment of learning styles—will only enhance the possibilities for both instructors and
students.
Kevin Broun
References
Blazhenkova, O. & Kozhevnikov, M. (2008). “The New Object-Spatial-Verbal Cognitive Style
Model: Theory and Measurement.” Applied Cognitive Psychology. Published online in
Wiley InterScience.
Brandt, R. (1990). “On Learning Styles: A Conversation with Pat Guild.” Association for
Supervision and Curriculum Development
Massa, L.J. & Mayer, R.E. (2005). “Three obstacles to validating the Verbal-Imager
Subtest of the Cognitive Styles Analysis.” Personality and Individual Differences 39(4),
845-848.
Rezaei, A.R. & Katz, L. (2004). “Evaluation of the reliability and validity of the cognitive styles
analysis.” Personality and Individual Differences 36, 1317-1327.
Schiaffino, S, P. Garcia & A. Amandi (2008). “eTeacher: Providing personalized assistance to
e-learning students.” Computers & Education 51, 1744-1754.
Shearer, C.B. (1997). “Reliability, Validity, and Utility of a Multiple Intelligences Assessment
for Career Planning.” Annual Meeting of the American Psychological Association 105th,
Chicago, IL, Aug 15-19, 1997.
Silver, H., R. Strong, and M. Perini (1997). “Integrating Learning Styles and Multiple
Intelligences.” Educational Leadership 55(1), 22-27.