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Learning Styles in Relation to Academic Performance in Middle School Mathematics

2007· article· en· W1991620613 on OpenAlexaboutno aff
Eisa M. Al‐Balhan

Bibliographic record

VenueDigest of Middle East Studies · 2007
Typearticle
Languageen
FieldPsychology
TopicLearning Styles and Cognitive Differences
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationStyle (visual arts)Quarter (Canadian coin)Variety (cybernetics)PsychologyLearning stylesComputer scienceGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Research was conducted with middle‐school Kuwaiti children to assess the effectiveness of student learning styles in predicting students' academic performance in Mathematics. A group of middle school students who had received first quarter grades and enrolled in an after‐school tutoring program were studied, with half of the students in a traditional tutoring program and the other half in a Markova learning style‐tutoring program. Results show that the students in the experimental group (mean = 45.91), whose learning styles were accommodated for, performed better than the students in the control group who studied using the traditional method (mean = 43.80) of teaching. Gender, type of school attended, and area in which the students lived were all analyzed within the experimental group. The experimental group results show that the highest‐grade improvement in Mathematics was found to be predominately male students attending private institutions, and living in the urban areas of Kuwait. Students learn in a variety of ways, and their ability to attain this information also varies. A student's capacity to learn is impacted by the teacher's style of conveying information. Unfortunately, little attention has been given to how children think (Markova, 1992). Often, it is assumed that students' minds operate in the same way as the teacher's does. So much of student failure in school comes directly out of the larger failure to stimulate all those areas in the children's brains, stimulation which could open up their minds in so many ways (Markova, 1992). Student's academic performance is a matter of concern to educators, parents, and students themselves. The ways in which an individual characteristically acquires, retains, and retrieves information are collectively referred to as his or her learning style (Felder and Henriques, 1995). Unfortunately, the manner in which children acquire the information to perform well academically is too often ignored. Considerable research has examined the relationship between students' learning styles and their academic performance (Witkin, 1973; Gregorc, 1979; Claxton and Murrell, 1987; Brunner and Majewski, 1990; Schroeder, 1993; Klavas, 1993). These studies have consistently found that when learning styles were considered in the teaching process, academic performance increased. Schroeder states that accommodating the variations in learning styles could improve curricula and the teaching process (1993). The results of a study by Dunn et al. (1995) suggested that students whose learning styles are accommodated would be expected to achieve 75% of a standard deviation higher than students for whose learning style had not been accommodated. Many researchers have reported that students often classified as poor achievers, learning disabled, at‐risk youth, or dropouts were able to improve their academic performance when instruction was redesigned to respond to their particular learning style preferences (Stone, 1992; Perrin, 1990; Elliot, 1991; Andrews, 1990). Children suffer deeply when their natural way of thinking, of absorbing and processing information, of creating and expressing is criticized, mocked, or ignored (Markova, 1992). However, learning efficiently empowers children to gain confidence since many believe they have learned a skill only after they can perform it easily. Markova acknowledges that many approaches to understanding individual differences include something about the fact that most of us have one sense we are most comfortable using in the learning process. Understanding these patterns of processing information is crucial to finding the most effective ways to educate our children. Markova has identified six patterns of personal thinking, which are different combinations of the perceptual kinesthetic (K), auditory (A), and visual (V) channels. He posits that information is first received by the conscious mind, sorted by the unconscious mind and finally integrated by the subconscious mind (Markova, 1992). The six different combinations (KAV, KVA, AVK, AKV, VKA, and VAK) are referred to as personal thinking patterns and determine the most comfortable and effective way for each learner to learn.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.095
GPT teacher head0.340
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2007
Admission routes1
Has abstractyes

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