Previous studies reported that mathematics contributes to the success of students in their programming courses. One of the cognitive prerequisites of learning programming is mathematics
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
There are also discussions by some researchers in the gender gap between males and females in the field of computing, particularly information technology
Works on literatures on the prediction of factors of computer programming achievement revealed that cognitive and academic variables, computer exposures and demographics are strongly predicted class performance. Other studies considered variables like the learning styles of the students
Furthermore, educational data mining (EDM) was used in the present study. EDM has been on the realm of education research
The results of the study may help teachers improve the quality of instructions in mathematics and programming courses to improve students’ programming skills and outcomes. This will also guide on the revision of curriculum for the Bachelor of Science in Information Technology program in a Philippine State University that will help the students acquire necessary skills in their programming courses. EDM using J48 classification algorithm and descriptive - correlational was used in the present study to examine the influence of the mathematics performance and the programming ability of the information technology students in a State University. This study also looked into the performance of the students in mathematics and programming between genders.
This study aimed to determine whether the performance of the BS Information Technology students in mathematics is significantly correlated to their programming performance. This study also looked into the relationship of students’ gender to their programming performance. Specifically, this study has the following specific objectives:
To identify the level of performance of the students in mathematics and programming;
To determine the relationship on the programming performance of the students between genders;
To measure the significant relationship between the level of mathematical performance and programming performance among the respondents, and;
To present patterns useful for predicting the influence of mathematics in the programming performance of the students based on the decision tree model.
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To obtain the objectives covered in the study, a descriptive-correlational design was employed
The datasets used in the study were extracted from the database of the Electronic Management System of the university. The datasets are composed of the final ratings of the undergraduate information technology students from the three different courses, namely, Mathematics in the Modern World, Mathematics Enhancement 1, and Computer Programming 1 who were enrolled during the first semester of the school year 2019 – 2020. The actual 87 datasets were used for the decision tree model using J48 Algorithm, however, only 73 were selected for descriptive-correlational part of the study. Those are the students who passed the programming course, this is in order to examine the influence of the independent variable in passing the programming course. Students with failure ratings in their mathematics courses were eliminated as it was considered as significant outliers. Further, the datasets of the students are composed of 60.3 percent of females and 39.7 percent males. Most of the students are between the age of 18 and 21 years old (83.6%), while only 2.7 percent are aged above or equal to 29 years old.
In compliance of the Data Privacy Act of the Philippines, the researchers ensured that the protocols of conducting the study and collecting the data were followed. The utmost confidentiality of all the data gathered was assured and was solely used for the study purpose. After the analysis, the data were deleted from the computers of the researchers.
The data used in the study were collected from the database of the Electronic Management System of the University. The data were collected from the Registrar Office of the campus on the submitted grade sheets of faculty of the students involved in the study. There were 87 datasets used for generating patterns from the decision tree model with four variables (Gender, Mathematics Enhancement 1, Mathematics in the Modern World, and Programming 1).
The researchers hypothesized that the gender of the students and their performance in two mathematics courses do not have significant bearing on their programming course. The study employed the correlation analyses using Pearson r correlation and Point Biserial Correlation. These methods were used to estimate the correlation between continuous measure of independent and dependent variables, and between categorical independent variable and continuous dependent variable, respectively
Furthermore, the data were encoded in Microsoft Excel application and saved in a CSV format and transformed into a nominal type of data for algorithmic analysis. Waikato Environment for Knowledge Analysis software was used to generate patterns and decision tree model based on the J48 algorithm.
The J48 algorithm is the implementation of ID3 (Iterative Dichotomiser 3) algorithm developed by the WEKA project team. It is the improved algorithm from ID3 which deals with both discrete and continuous variables, missing values and the pruning process of the tree after construction. The classifier used by J48 is a decision tree that is built from root to leaves. It uses information gain as its attribute selection measure by letting a node to hold the tuples of partition D. The attribute with the highest information gain is chosen as the splitting attribute for the node or the root node. The expected information needed to classify a tuple is given by
This study employed educational data mining method utilizing J48 classification algorithm and descriptive and correlation analyses to obtain the goal of the study. Particularly, we utilized the point biserial and Pearson r correlations in order test the hypothesis. The sequence of the results starts with the mathematics performance of the students in the mathematics enhancement 1 and mathematics in the modern world, followed with the correlation analysis results, and patterns from the generated tree model.
Rating | Frequency | Percentage | Qualitative Description |
---|---|---|---|
1.0 – 1.4 | 1 | 1.4 | Excellent |
1.5 – 1.9 | 4 | 5.5 | Superior |
2.0 – 2.4 | 4 | 5.5 | Very Good |
2.5 – 2.9 | 12 | 16.4 | Good |
3.0 | 52 | 71.2 | Passed |
Total | 73 | 100 |
Notes: M = 2.81; SD = 0.38; & CV = 13.52%
Rating | Frequency | Percentage | Qualitative Description |
---|---|---|---|
1.5 – 1.9 | 19 | 26.0 | Superior |
2.0 – 2.4 | 38 | 52.1 | Very Good |
2.5 – 2.9 | 16 | 21.9 | Good |
Total | 73 | 100 |
Notes: M = 2.16; SD = 0.27; & CV = 12.5%
Rating | Frequency | Percentage | Qualitative Description |
---|---|---|---|
1.0 – 1.4 | 1 | 1.4 | Excellent |
1.5 – 1.9 | 4 | 5.5 | Superior |
2.0 – 2.4 | 14 | 19.2 | Very Good |
2.5 – 2.9 | 32 | 43.8 | Good |
3.0 | 22 | 30.1 | Passed |
Total | 73 | 100 |
Notes: M = 2.64; SD = 0.39; & CV = 14.8%
In terms of programming performance as seen in
Gender | N | M | SD | CV |
---|---|---|---|---|
Male | 29 | 2.66 | 0.40 | 15.0% |
Female | 44 | 2.63 | 0.38 | 14.4% |
N = 73
The results in
Variable | r – value | p – value | Interpretation |
---|---|---|---|
Gender | -0.039 | 0.746 | Not significant |
Mathematics Enhancement 1 | 0.316 | 0.001 | Significant |
Mathematics in the Modern World | 0.061 | 0.611 | Not significant |
Notes: Programming performance as dependent variable
N = 73; Significant at p ≤ 0.05
The low academic performance of respondents on mathematics enhancement 1 course corresponds to a low academic performance on programming course
J48 Decision tree patterns |
---|
Math_En = ME_Superior: P_Good Math_En = ME_Passed: P_Good Math_En = ME_Very_Good: P_SuperiorMath_En = ME_Good| Math_World = MW_Very_Good| | Sex = M: P_Good| | Sex = F: P_Very_Good| Math_World = MW_Superior: P_Very_Good| Math_World = MW_Good: P_Passed| Math_World = MW_Passed: P_Very_GoodMath_En = ME_Excellent: P_Very_Good |
1.0 – 1.4 – Excellent, 1.5 – 1.9 – Superior, 2.0 – 2.4 – Very good, 2.5 – 2.9 – Good, 3.0 – Passed, 3.0 below - Failed
Using the 10-fold cross validation, the results in
The results of data analyses in this study shows the direct relationship between mathematics performance and programming performance among university students. The extent of relationships among constructs can be observed on their students’ performance (grade ratings) on three subjects studied
The Mathematics Enhancement 1 revealed a medium correlation to programming performance on computational thinking skills learned in College Algebra and Trigonometry
In Eastern Visayas State University Programming I is offered at the early stage of the information technology curriculum which focuses on C Programming Language. This is a low-level programming language that serves as algorithm platform to known programming languages such as Java, PHP, Python and other higher programming languages. It covers basic programming principles on problem solving, algorithm, flowcharting, writing source code and program simulation
Several studies support this claim on students critical thinking skills (computational and rigorous thinking skills) on its impact to programming performance among students in the University
On the other hand, the study reveals to be significantly no relationship on the performance of the students in programming between genders. It can be associated to the students learning interest and self-efficacy on learning programming is a critical factor in determining their programming performance as supported by existing literature
University curriculum aims to provide quality education to students by constantly evaluating its learning outcomes
Currently the University is using blended learning to the students. Students access to mobile learning technology, learning management system and access to video content on YouTube Education; offers self-paced learning approach that elevate students’ learning experience in the university and thus achieve academic performance
Conforming to the results of previous studies, curriculum writers for the information technology program should give mathematics an importance as it contributes to the success in programming particularly to beginning programmers. Possible improvements to help students acquire a deeper understanding to mathematics would result to an increase of performance not only in their programming courses but in their other general mathematics subjects as well. If further results to other studies revealed that performance in mathematics is significantly correlated to the performance in programming, mathematics should come to be a requirement in talking programming course in the University.
It is concluded that the low performance of the students in programming course was affected by their poor performance in mathematics. This gave emphasis to the significance of prior knowledge in the students understanding that allows them to link with new information. Mathematical knowledge and skills as one of the pre-requisite courses and core skills on programming, it plays integral part on achieving effective learning outcome in programming. Programming performance was directly and positively associated on the level of acquired mathematical skills by the students. Students gender, on the other hand, has no significantly bearing on programming performance.
In light of the findings and conclusions of the study, the researchers recommend that the information technology department faculty members of the university should continue improving its quality of instructions particularly in teaching and learning motivation towards mathematics and programming courses in order to improve the performance and outcome. It is also recommended to conduct a frequent general assessment to the students in order to further investigate factors that lead to student’s difficulty on the concerned learning areas. Develop mobile learning environment, learning management systems, various YouTube video contents, and other self-paced learning strategies to further enhance the programming performance of the students. However, the question on the other factors that affects the student’s performance in Mathematics courses, which indirectly influence their performance in Programming 1 course, remained to be investigated. Also, the conduct of related research is highly recommended that will focus on determining other factors that may affect student’s programming performance which are not considered under study.
We would like to acknowledge the Eastern Visayas State University – Tanauan Campus for allowing us to conduct and gather the necessary data for the study.