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Power BI Educational Forecasting

The datasets were obtained from two identical surveys. One survey was conducted with maths students (dataset 1) and another with Portuguese language students (dataset 2). Secondary school students make up the sample. This data offers information about the pupils' social, gender, and academic backgrounds. This information was obtained from Kaggle.com. Before being imported into Power BI, the data was consolidated and cleaned in Excel, with the corresponding variables matched up and the column headers/values reformatted from a key to a comprehensive descriptor.

 

Hypothesis:

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•Academic support will have a positive impact on students' average final grade, while the number of past class failures will have a negative effect.

•Additionally, gender, parents' education, relationship quality, and family size are expected to significantly affect students' average final grade, without specific predictions regarding the direction of these effects.

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By adopting a non-directional hypothesis, I can comprehensively examine the effects of each variable on students' average final grade and provide insights into the dynamics between each.
 

Data
Data

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Raw Data sets
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Data
Combined data
Data
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multi-page report

Following the cleaned dataset being imported via the power query editor, a variety of visualisations were constructed to demonstrate the effect of each factor on Students' final grades. With page one covering Demographic factors, followed by Academic and finishing with Personal. From this, I was able to forecast achievement based on past class failures and demonstrate an effect of gender, family size, relationships, educational support, family background, and a variety of other factors on educational achievement across both subjects.

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It is important to note that there exists overlap between these variables. For instance. This report's demographic section, it first seems irrelevant to include alcohol use. But I was interested in looking at the relationship between this and demographic characteristics. According to my research, alcohol consumption habits can be correlated with a number of demographic characteristics, including age (Khamis et al., 2022).

The raw data  - a closer look.

Common variables for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:

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  1. school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)

  2. sex - student's sex (binary: 'F' - female or 'M' - male)

  3. age - student's age (numeric: from 15 to 22)

  4. address - student's home address type (binary: 'U' - urban or 'R' - rural)

  5. famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)

  6. Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart)

  7. Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)

  8. Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)

  9. Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')

  10. Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')

  11. reason - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')

  12. guardian - student's guardian (nominal: 'mother', 'father' or 'other')

  13. traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)

  14. studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)

  15. failures - number of past class failures (numeric: n if 1<=n<3, else 4)

  16. schoolsup - extra educational support (binary: yes or no)

  17. famsup - family educational support (binary: yes or no)

  18. paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)

  19. activities - extra-curricular activities (binary: yes or no)

  20. nursery - attended nursery school (binary: yes or no)

  21. higher - wants to take higher education (binary: yes or no)

  22. internet - Internet access at home (binary: yes or no)

  23. romantic - with a romantic relationship (binary: yes or no)

  24. famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)

  25. freetime - free time after school (numeric: from 1 - very low to 5 - very high)

  26. goout - going out with friends (numeric: from 1 - very low to 5 - very high)

  27. Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)

  28. Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)

  29. health - current health status (numeric: from 1 - very bad to 5 - very good)

  30. absences - number of school absences (numeric: from 0 to 93)

These grades are related with the course subject, Math or Portuguese:

  1. G1 - first period grade (numeric: from 0 to 20)

  2. G2 - second period grade (numeric: from 0 to 20)

  3. G3 - final grade (numeric: from 0 to 20, output target)

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