5_Capstone_Analysis_Ashlie_Steffen.qmd

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Ashlie Steffen

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title:"Capstone Analysis"
author: "Ashlie Steffen"
format: html
editor: visual
---

Introduction The educational problem I am approaching is the challenge students face when they struggle on assignments and need different types of support. In my Week 4 alignment post, I discussed how I want to use data to help teachers understand assignment patterns and communicate their needs in a positive way. This problem is important because it can help teachers, students, and administrators see where students are academically. When teachers know where students are struggling, they can provide interventions to help close learning gaps. This can also help students better understand their own learning. The question I am tryng to answer using the data: How can students assignment performance help teachers identify students need and differentiated support? # Data Overview

set.seed(123)

data <- data.frame(
  Student_ID = paste0("Student_", 1:40),
  matrix(sample(6:20, 40 * 16, replace = TRUE), nrow = 40, ncol = 16)
)

names(data)[2:17] <- paste0("Week_", 1:16)

head(data)

  Student_ID Week_1 Week_2 Week_3 Week_4 Week_5 Week_6 Week_7 Week_8 Week_9
1  Student_1     20     15     11     10     11     17     12     16     10
2  Student_2     20     18     11     13     13     11     15      6     18
3  Student_3      8     12     12      8     10      6     18     15      7
4  Student_4     19     14     20     15     12     15     17     15     15
5  Student_5      8     14      6      7     16     16     15      6     20
6  Student_6     15     15     11     15      9      9     13     15     11
  Week_10 Week_11 Week_12 Week_13 Week_14 Week_15 Week_16
1      18       6       8       6      17      12       9
2      11       8      15      16       8       9      11
3      15      17      12       9      19      17      20
4      15       7      16      13      13      19      17
5      11      10       6      17      11      14       6
6      17       6      17      18       9      16       8

## Visualization: Heat Map of Student Scores

week_columns <- grep("Week_", names(data), value = TRUE)

heatmap_data <- as.matrix(data[, week_columns])

rownames(heatmap_data) <- data$Student_ID

heatmap(
  heatmap_data,
  Rowv = NA,
  Colv = NA,
  col = colorRampPalette(c("red", "pink", "white"))(20),
  scale = "none",
  main = "Heat Map of Student Scores",
  xlab = "Week",
  ylab = "Student"
)

Findings:

The heat map shows students’ scores on assignments during the week. Red shows low scores, and pink shows high scores. The teacher can see which assignments students struggled with and provide extra support for each assignment. This shows how teachers can use data to provide additional support. This graph aligns with my week four post because teachers are using data to provide additional support. They can see where students are based on each week’s assignments and provide support to help students improve their scores.

## Visualization: Lower Performing Students

week_columns <- grep("Week_", names(data), value = TRUE)

data$Average_Score <- rowMeans(data[, week_columns])

lower_students <- data[order(data$Average_Score), ][1:10, ]

barplot(
  lower_students$Average_Score,
  names.arg = lower_students$Student_ID,
  las = 2,
  main = "Lower Performing Students",
  xlab = "Student",
  ylab = "Average Score",
  col = "blue"
)

Findings:

This graph shows the lower-performing students in the class and their average scores. Based on this data, the teacher can provide additional support. The teacher can provide additional support by grouping students based on their averages. The teacher could do this at the end of the unit to help students stay on track. This aligns with the week 4 post because the teacher is providing students with support based on the data. The teacher can also communicate what students need by showing students data. This data connects to my questions because it is showing how teachers can use data to provide additional support.

Summary of Findings

Based on the data, the findings show students’ performance on assignments. The heat map showed where each student scored on each assignment. The teacher can see which students are struggling with assignments. This allows the teacher to provide support. The bar showed that lower-performing students in the class were based on their assignment scores. This allows the teacher to provide additional support tailored to students’ needs. The findings connect to my question. They show how teachers can use assignment data to identify students who need support and differentiate instruction. The findings also connect to my week four discussion post because the main goal is to use data to help students. Using the heat map and bar graph allows the teacher to provide support, differentiate instruction, and communicate students’ learning needs in a positive way.