An Instrument to Measure Math Attitudes of Computer Science Students

Ali, Parveen J.; Ali, Sanwar; Farag, Waleed

doi:10.7763/ijiet.2014.v4.450

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…The self-efficacy category and enjoyment category were taken from the survey by Ali et al (2014) in relation to measuring mathematics attitudes, the participation category from the National Survey of Student Engagement (Noohi et al, 2013), and the understanding category from the Mathematics Beliefs Scale (Lester, 2007). The survey made use of a 4-point Likert scale (1 = Strongly disagree, 2 = Disagree, 3 = Agree, 4 = Strongly agree) to measure the students' level of interest with collaborative learning and their attitudes towards learning mathematics.…”

Section: Surveymentioning

confidence: 99%

Utilising the Think-Pair-Share Technique in the Learning of Probability

Lee

Shahrill

2018

Int. J. Emerg. Math. Educ.

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Section: Surveymentioning

confidence: 99%

Utilising the Think-Pair-Share Technique in the Learning of Probability

Lee

Shahrill

2018

Int. J. Emerg. Math. Educ.

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“…Knowledge of logical mathematics and its application in programming are essential for complex tasks. Most students have difficulties with mathematical subjects; the correlation between success in programming courses and mathematical courses is still very high (Duran, 2016;Ali et al, 2014;Balmes, 2017).…”

Section: The Role Of Mathematics In Cs Educationmentioning

confidence: 99%

Successful Steps in Higher Education to Stop Computer Science Students from Attrition

Takács

Kárász²,

Takács³

et al. 2022

Interchange

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Currently, the dropout rate is crucial in the field of Computer Science (CS) higher education. In CS education it is usually the mathematically oriented subjects that are blamed for the high dropout rates. Implementing a theoretical framework into practice, we have been able to prevent 28% of our students from dropping out in the last 2 years due to our education reform. The aim of the present study is to analyse the results of the students through the curriculum of the CS program by factor analysis. Nearly 4000 first-year students’ results were analysed. One of the most important steps of the education reform was that all of the lectures became compulsory to attend. Another step was the introduction of a prevention and skills-training program for every first-year student in order to develop their study skills. Our findings highlighted that as a consequence of the education reform, more students stayed until the end of the first and second semesters and try taking exams in the exam period. Analysing the subjects as factors in the CS curriculum could (1) help faculty staff introduce an education reform, and (2) help decision-makers develop prevention and promotion programs in order to develop students’ study skills. The results reveal that we have managed to successfully engage first year students in the academic environments.

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“…It helps the students improve their problem-solving abilities as such studies confirmed that mathematics found to be one of the contributory factors on the success of the students taking information technology program (7,8) . Therefore, students must have a strong background in mathematics to have greater success in taking programming courses (9)(10)(11) .…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies suggest that mathematics is found to be significant in relation to computer programming wherein math is an avenue of learning essential skills that is necessary also for learning how to program (7,11) . Several works in literature on the relationship of mathematics and programming have been examined by the researchers to show the relevance of the present study.…”

Section: Introductionmentioning

confidence: 99%

Mining educational data in predicting the influence of Mathematics on the programming performance of University students

Cornillez¹,

e²,

Santos³

2020

IJST

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Objectives: The aim of this study was to investigate the influence of mathematics to the programming performance of Information Technology students and identified the relationships of their performance in programming among genders. Methods/Statistical analysis: The study utilized the data mining method using J48 classification algorithm and descriptive-correlation design. The data were gathered from Electronic Database of the University. Failure ratings of the students were removed as significant outliers and came up with 73 data sets. Pearson r and Point Biserial Correlations were used with 0.05 level of significance alpha to test the correlation between continuous measures of independent and dependent variables. Further, descriptive statistics were used to describe the level of performance in mathematics and programming. Findings: The results show that students demonstrated a high performance in their mathematics in the modern world course with a mean rating of 2.16 (SD=0.27), and a low performance in their mathematics enhancement 1 with a mean grade of 2.81 (SD=0.38). Similar result in their programming course with a mean grade of 2.64 (SD=0.39). The mathematics performance of the students is significantly correlated to their performance in programming. The low performance in mathematics enhancement 1 corresponds to the low performance in programming. Moreover, female students performed better in their programming course compared to males. Applications: The results could help the teachers improve the quality of instructions particularly in mathematics and programming that will improve the performance of the students in both subjects. Concerned University administrators should conduct frequent assessments and curriculum revisits to examine possible areas of improvement beneficial to the students.

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An Instrument to Measure Math Attitudes of Computer Science Students

Cited by 11 publications

References 10 publications

Utilising the Think-Pair-Share Technique in the Learning of Probability

Utilising the Think-Pair-Share Technique in the Learning of Probability

Successful Steps in Higher Education to Stop Computer Science Students from Attrition

Mining educational data in predicting the influence of Mathematics on the programming performance of University students

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