Identifying Factors that Influence Performance of Non-Computing Majors in the Business Computer Information Systems Course

Cronan, Timothy Paul; Embry, P. R.; White, Sharon D.

doi:10.1080/08886504.1989.10781892

Cited by 43 publications

(4 citation statements)

References 17 publications

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“…Usually, when using regression, one tries to predict the dependent variables' values using independent attributes of different types. The number of independent variables is very large and includes age and gender [4,5], GPA grades [6], educational level of parents [7], emotional and social factors [8], and even the complexity measure of teachers' lecture notes [9]. Dependent variables include course grade [5,6,10,11], exam grade [8,12] and even aptitude in computer programming [13].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Student Exam’s Scores by Analyzing Social Network Data

Fire

Katz

Elovici

et al. 2012

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a novel method for the prediction of a person's success in an academic course. By extracting log data from the course's website and applying network analysis methods, we were able to model and visualize the social interactions among the students in a course. For our analysis, we extracted a variety of features by using both graph theory and social networks analysis. Finally, we successfully used several regression and machine learning techniques to predict the success of student in a course. An interesting fact uncovered by this research is that the proposed model has a shown a high correlation between the grade of a student and that of his "best" friend.

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Section: Introductionmentioning

confidence: 99%

“…Other methods used in tackling the grade prediction problem are the factor analysis or other classification schemes with statistical analysis [5,10,14,15]. For example, Rountree et al [15] used Decision Trees analysis in order to identify combinations of factors that may assist in predicting success or failure in a CS1 class.…”

Section: Introductionmentioning

confidence: 99%

Predicting Student Exam’s Scores by Analyzing Social Network Data

Fire

Katz

Elovici

et al. 2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Identifying employees who might best profit from additional training [17] 5. Improving computer classes for non-CIS majors [7] 6. Determining the importance of such oft-cited predictors of computer competency as gender or math ability [3,9] 7.…”

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confidence: 99%

“…For example, studies before 1975 tended to explore demographic background and past high school achievement. In this regard, the most ambilious study is that of Cronan et al [7]. who examined '£'1989 ACM 0001-078Z/89/1100-1322 $1.50 54 such variables as possible predictors of student performance.…”

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confidence: 99%