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The contribution of intelligence, learning strategies, and personal development to engineering students' academic performanceSkuy, Melissa Ann January 2003 (has links)
A research report submitted to the Faculty of Humanities,
University of the Witwatersrand, Johannesburg, in partial
fulfilment of the requirements for the degree of Masters of
Education (Educational Psychology), 2003 / Previous studies have addressed the question whether intellectual ability (as
measured by the Raven's Progressivp Matrices Tests) is related to academic
performance in engineering (Rushton & Skuy, 2000; Rushton, Skuy & Fridjhon,
2002; Rushton, Skuy & Fridjhon, 2003). The question arose of whether nonintellective
(personality and attitudinal factors) playa larger role at this level,
than intelligence, in determining academic performance in engineering
university students. Accordingly, data were yielded for 93 percent (N=100) of
the second year Chemical Engineering class in terms of their performance on
various measures. These included two measures of intellectual ability, namely
the Ravens Advanced Progressive Matrices (RAPM) and the Organiser (of The
Learning Propensity Assessment Device), together with a measure of learning
strategies and attitudes (Learning and Study Strategies Inventory), locus of
control (Locus of Control Inventory) and self-esteem (Coopersmith Self-Esteem
Inventory). The students' academic results comprised the December 2002 and
June 2003 examination results. The current research results demonstrated that
while neither the RAPM nor the Organiser yielded any significant correlations
with academic results, certain of the non-intellective measures did, and were
able to differentiate between high and low academic performers. Motivation,
Autonomy and Freedom from Anxiety were found to be significantly related to
academic performance, and contributed 26 percent of the variance. This
indicates that these factors play a role in academic achievement, and that
exploration of personality and motivational factors constitutes a potentially
fruitful avenue of research. However, it also seems that 74 percent of variance
was unaccounted for, and therefore future studies should explore other factors,
not included in this study, in relation to engineering students' academic
performance. Furthermore, it emerged that it is unrealistic to attempt to predict
academic performance at midyear (June results).
KEY WORDS: Intelligence, learning strategies, locus of control, self-esteem,
engineering students, second year, and academic performance. / AC2017
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