Student Performance in Managerial Accounting: An Empirical Study at a U.S. Small Private College

ABSTRACT Accounting educators have long been interested in the effects of cognitive style on student performance. Research suggests that students' cognitive styles can moderate their success across a variety of assessment methods (i.e., multiple-choice versus written reports versus case study) (Au 1997) and instructional methodologies (Ott et al. 1990). Not clear, however, is the impact of cognitive style on a student's accounting task performance. Several studies have examined the relationship between accounting students' cognitive styles and their performance on accounting tasks, but the results have been mixed (Jones and Davidson 2007; Togo 1993; Arunachalam et al. 1997; Swanson et al. 2005). Using Chan's (1996) theory of cognitive misfit, this study proposes that diminished performance will occur when there is incongruence between a student's cognitive style and the cognitive demands of an accounting task. The Felder-Solomon Index of Learning Styles was used to classify students' cognitive styles as global or sequential. In an experiment involving 138 students, the effects of cognitive misfit negatively impacted performance on a managerial accounting task, and the effect was most pronounced for students with global styles. The current study improves our understanding of cognitive factors that impact students' accounting task performance.

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Empirical study on the effect of using synthetic attributes on classification algorithms

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-08-2016-0029 ◽

2017 ◽

Vol 10 (2) ◽

pp. 111-129 ◽

Cited By ~ 2

Author(s):

Ali Hasan Alsaffar

Keyword(s):

Data Mining ◽

Empirical Study ◽

Student Performance ◽

Software Tool ◽

Real Data ◽

Support Vector ◽

Classification Algorithms ◽

Past Performance ◽

Data Set ◽

Content Type

Purpose The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course. Design/methodology/approach The paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines. Findings The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable. Originality/value This paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

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