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Analyzing Student Performance Trends in Progress Testing Data

This study examines patterns in student performance data from progress tests, using statistical tests and visualizations. Findings reveal insights on grades, gender, courses, and exams.

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Analyzing Student Performance Trends in Progress Testing Data

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  1. progress test 2011 borelli@units.it

  2. pie(table(annodicorso)) • annodicorso • 3 4 • 51 40

  3. table(annodicorso, genere) • table(annodicorso, genere) • genere • annodicorso F M • 3 25 26 • 4 17 19 • plot(table(annodicorso, genere))

  4. table(annodicorso, genere) • table(annodicorso, genere) • genere • annodicorso F M • 3 25 26 • 4 17 19 • fisher.test(table(annodicorso, genere)) • data: table(annodicorso, genere) • p-value = 1 • alternative hypothesis: true odds ratio • is not equal to 1 • 95 percent confidence interval: • 0.4201946 2.7551684 • sample estimates: • odds ratio • 1.073765

  5. boxplot(mediavoti)

  6. boxplot(mediavoti ~ genere)

  7. qqnorm(mediavoti)

  8. boxplot(mediavoti ~ genere) • fligner.test(mediavoti ~ genere) • Fligner-Killeen: • med chi-squared = 3.7769, df = 1, • p-value = 0.05197 • wilcox.test(mediavoti ~ genere) • data: mediavoti by genere • W = 885, p-value = 0.2971 • alternative hypothesis: true location shift is not equal to 0

  9. boxplot(mediavoti ~ annodicorso)

  10. boxplot(mediavoti ~ annodicorso) • fligner.test(mediavoti ~ annodicorso) • data: mediavoti by annodicorso • Fligner-Killeen:med chi-squared = 0.0053, df = 1, • p-value = 0.9418 • wilcox.test(mediavoti ~ annodicorso) • data: mediavoti by annodicorso • W = 615.5, p-value = 0.1713 • alternative hypothesis: true location shift is not equal to 0

  11. pie(table(deltaesami)) • deltaesami • 0 1 2 3 4 • 66 14 4 1 1

  12. table(genere, deltaesami) • deltaesami • genere 0 1 2 3 4 • F 35 5 1 0 1 • M 31 9 3 1 0 • chisq.test(table(genere, deltaesami)) • X-squared = 4.3411, df = 4, p-value = 0.3618 • Warning message: • In chisq.test(table(genere, deltaesami)) : • L'approssimazione al Chi-quadrato potrebbe essere inesatta

  13. table(genere, deltaesami) • deltaesami • genere 0 1 2 3 4 • F 35 5 1 0 1 • M 31 9 3 1 0 • fisher.test(table(genere, deltaesami)) • data: table(genere, deltaesami) • p-value = 0.3107 • alternative hypothesis: two.sided

  14. table(annodicorso, deltaesami) • table(annodicorso, deltaesami) • deltaesami • annodicorso 0 1 2 3 4 • 3 41 5 3 1 1 • 4 25 9 1 0 0 • fisher.test(table(annodicorso, deltaesami)) • data: table(annodicorso, deltaesami) • p-value = 0.2161 • alternative hypothesis: two.sided

  15. boxplot(indicegradim)

  16. table(indicegradim) • indicegradim • 4 4.5 5.5 6 6.5 7 7.5 8 8.5 9 • 1 1 1 18 1 27 5 22 1 1

  17. boxplot(indicegradim ~ annodicorso)

  18. boxplot(indicegradim ~ annodicorso) • fligner.test(indicegradim ~ annodicorso) • data: indicegradim by annodicorso • Fligner-Killeen:med chi-squared = 5.8816, df = 1, • p-value = 0.0153 • wilcox.test(indicegradim ~ annodicorso) • data: indicegradim by annodicorso • W = 658.5, p-value = 0.5145

  19. boxplot(indicegradim ~ genere)

  20. boxplot(indicegradim ~ genere) • fligner.test(indicegradim ~ genere) • med chi-squared = 0.4171, df = 1, • p-value = 0.5184 • wilcox.test(indicegradim ~ genere) • W = 735.5, p-value = 0.8029

  21. plot(jitter(indicegradim) , jitter(mediavoti))

  22. summary.lm • Call: • lm(formula = indicegradim ~ mediavoti) • Coefficients: • Estimate Std. Error t value Pr(>|t|) • (Intercept) 3.70487 2.31754 1.599 0.114 • mediavoti 0.12211 0.08454 1.444 0.153 • Residual standard error: 0.928 on 70 degrees of freedom • (19 observations deleted due to missingness) • Multiple R-squared: 0.02894, Adjusted R-squared: 0.01507 • F-statistic: 2.086 on 1 and 70 DF, p-value: 0.1531

  23. plot.lm

  24. plot(jitter(deltaesami), jitter(indicegradim))

  25. coplot(indicegradim ~ deltaesami |annodicorso)

  26. coplot(indicegradim ~ deltaesami | genere)

  27. c(sum(corrette), sum(nondate), sum(sbagliate)) • 7818 12526 6956

  28. boxplot(sommaesatte / 300) • performancestudente • <- sum(corrette[studente])

  29. plot(sommaesatte, sommasbagliate)

  30. plot(sommaesatte, sommasbagliate) • plot(reg)

  31. plot(sommaesatte, sommasbagliate)

  32. plot(sommaesatte, sommasbagliate) • plot(reg2)

  33. plot(sommaesatte, logsommasbagliate)

  34. plot(sommaesatte, sommasbagliate) • plot(reglog)

  35. boxplot(corrette / somma ~ insegnamento) • biochimica chir comportam farmaco fisiologia medint microimmuno morfologia ostgineco patofisio patologia pediatria sanita

  36. tapply(corrette / somma,insegnamento, median) • biochimica chir comportam • 0.3200000 0.1500000 0.3333333 • farmaco fisiologia medint • 0.2000000 0.4400000 0.1750000 • microimmuno morfologia ostgineco • 0.4800000 0.3500000 0.4000000 • patofisio patologia pediatria • 0.2800000 0.2000000 0.2000000 • sanita • 0.2000000

  37. boxplot(sbagliate / somma ~ insegnamento) • biochimica chir comportam farmaco fisiologia medint microimmuno morfologia ostgineco patofisio patologia pediatria sanita

  38. tapply(sbagliate / somma,insegnamento, median) • biochimica chir comportam • 0.2000000 0.2250000 0.2666667 • farmaco fisiologia medint • 0.2000000 0.3200000 0.2250000 • microimmuno morfologia ostgineco • 0.2400000 0.4000000 0.2500000 • patofisio patologia pediatria • 0.2400000 0.1000000 0.2000000 • sanita • 0.2000000

  39. glm (correttesusomma ~ scienze + annodicorso + genere + mediavoti + deltaesami + indicegradim, family = poisson) • Coefficients: • Estimate Std. Error z value Pr(>|z|) • (Intercept) 1.033868 0.152218 6.792 1.11e-11 *** • scienzecliniche -0.467722 0.012833 -36.447 < 2e-16 *** • annodicorso 0.297164 0.012229 24.301 < 2e-16 *** • genereM 0.019252 0.012217 1.576 0.115 • mediavoti 0.036037 0.005562 6.480 9.19e-11 *** • deltaesami -0.076604 0.013048 -5.871 4.33e-09 *** • indicegradim 0.079345 0.007022 11.300 < 2e-16 ***

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