實驗結果

朗讀和默讀分數結果分析

獨立樣本t檢定

aloud <- dat$aloud_point
silent <- dat$silent_point

var.test(aloud, silent)

## 
##  F test to compare two variances
## 
## data:  aloud and silent
## F = 0.75717, num df = 27, denom df = 27, p-value = 0.4747
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.3503905 1.6362117
## sample estimates:
## ratio of variances 
##          0.7571744

t.test(aloud, silent, alternative = "less", var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  aloud and silent
## t = -0.73669, df = 54, p-value = 0.2322
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
##       -Inf 0.3633522
## sample estimates:
## mean of x mean of y 
##  5.500000  5.785714

朗讀分數默讀分數的平均數比較

mean_data <- dat %>%
  summarise(silent_mean = mean(silent_point),
            aloud_mean = mean(aloud_point))

plot_data <- data.frame(Method = c("Silent Point", "Aloud Point"),
                        Average_Point = c(mean_data$silent_mean, mean_data$aloud_mean))

graph <- ggplot(plot_data, aes(x = Method, y = Average_Point, fill = Method)) +
  geom_bar(stat = "identity", width = 0.5) +
  labs(x = "Reading Method", y = "Average Point") +
  theme_minimal()+
  ggtitle("Comparison of average reading score and silent reading score")

graph

郎讀和默讀分數的比較(Box Plot)

comparison_boxplot <- ggplot(dat, aes(x = factor(1), y = aloud)) +
  geom_boxplot(fill = "lightblue", color = "black") +
  geom_boxplot(aes(x = factor(2), y = silent), fill = "lightgreen", color = "black") +
  scale_x_discrete(labels = c("Aloud", "Silent")) +
  ylab("Point") +
  theme_minimal()+
  ggtitle("Comparison of Aloud and Silent Reading Points")+
  xlab("Method")

comparison_plotly <- ggplotly(comparison_boxplot)

comparison_plotly

郎讀和默讀分數的比較(ScatteR Plot)

sc <- ggplot(dat, aes(x = silent_point, y = aloud_point, color = gender)) +
  geom_point() +
  geom_abline(intercept = 0, slope = 1, linetype = "dashed") +
  labs(x = "Silent Reading Score", y = "Aloud Reading Score", color = "Gender")

sc <- ggplotly(sc)

sc

朗讀和默讀分數的頻率分佈

各條柱的高度（y軸）表示該範圍內數據的出現頻率。頻率表示該範圍內的數據個數。在這種情況下，x軸表示”Point（分數）“，表示數據值的範圍。y軸值表示各範圍的頻率。

data <- data.frame(
  Method = rep(c("Aloud", "Silent"), each = length(aloud)),
  Point = c(aloud, silent)
)

plot <- ggplot(data, aes(x = Point, fill = Method)) +
  geom_histogram(binwidth = 1, position = "identity", alpha = 0.5) +
  labs(x = "Point", y = "Frequency", fill = "Method") +
  theme_minimal()+
  ggtitle("Frequency Distribution of Scores for Silent and Aloud Reading")

animation <- plot +
  transition_states(Method, transition_length = 2, state_length = 1) +
  enter_fade() +
  exit_fade()

animate(animation)

朗讀和默讀之間的關係分析：性別、閱讀順序、時間

性別與郎讀/默讀的關係

這是一個按性別比較朗讀和默讀分數關係的Box Plot。

mean_data_g <- dat %>%
  group_by(gender) %>%
  summarise(silent_mean = mean(silent_point),
            aloud_mean = mean(aloud_point))

gender_graph1 <- ggplot(mean_data_g, aes(x = gender, y = silent_mean, fill = gender)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(x = "Gender", y = "Silent Point", fill = "Gender") +
  theme_minimal()

gender_graph2 <- ggplot(mean_data_g, aes(x = gender, y = aloud_mean, fill = gender)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(x = "Gender", y = "Aloud Point", fill = "Gender") +
  theme_minimal()

gender_graph <- grid.arrange(gender_graph1, gender_graph2, nrow = 1)

gender_graph

## TableGrob (1 x 2) "arrange": 2 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]

這是一個按性別比較朗讀和默讀分數差異的Box Plot。通過Box Plot的位置和範圍，可以看出不同性別之間分數分佈和變異程度的差異。圖中使用不同顏色區分不同閱讀順序，可以比較不同閱讀順序之間的性別差異。閱讀順序(Reading order)的A是朗朗默默，B是默默朗朗

gender_plot <- ggplot(dat, aes(x = gender, y = silent_point - aloud_point, fill = module)) +
  geom_boxplot() +
  labs(x = "Gender", y = "Difference in Points (Silent - Aloud)", fill = "Reading order") +
  theme_minimal()+
  ggtitle("Relationship between Gender and Difference in Points (Silent - Aloud)") +
  xlab("Gender") +
  ylab("Difference in Points")

gender_plot_animated <- gender_plot +
  transition_states(
    states = module,
    transition_length = 0.5,
    state_length = 1
  ) +
  enter_fade() +
  exit_fade()

animate(gender_plot_animated)

閱讀順序與朗讀/默讀的關係

這是比較每個閱讀順序(Reading order)的朗讀分數和默讀分數平均值的Bar Plot。閱讀順序(Reading order)的A是朗朗默默，B是默默朗朗

mean_data_o <- dat %>%
  group_by(module) %>%
  summarise(silent_mean = mean(silent_point),
            aloud_mean = mean(aloud_point))

order_graph1 <- ggplot(mean_data_o, aes(x = module, y = silent_mean, fill = module)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(x = "Reading Order", y = "Silent Point", fill = "Order") +
  theme_minimal()

order_graph2 <- ggplot(mean_data_o, aes(x = module, y = aloud_mean, fill = module)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(x = "Reading Order", y = "Aloud Point", fill = "Order") +
  theme_minimal()

order_graph <- grid.arrange(order_graph1, order_graph2, nrow = 1)

order_graph

## TableGrob (1 x 2) "arrange": 2 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]

這是一個按閱讀順序比較朗讀和默讀分數差異的Box Plot。通過Box Plot的位置和範圍，可以看出不同模組之間分數分佈和變異程度的差異。圖中使用不同顏色區分不同性別，可以比較不同性別之間的模組差異。閱讀順序(Reading order)的A是朗朗默默，B是默默朗朗

module_plot <- ggplot(dat, aes(x = module, y = silent_point - aloud_point, fill = gender)) +
  geom_boxplot() +
  labs(x = "Reading order", y = "Difference in Points (Silent - Aloud)", fill = "Gender") +
  theme_minimal()+
  ggtitle("Relationship between Reading order and Difference in Points (Silent - Aloud)") +
  xlab("Reading order") +
  ylab("Difference in Points")

module_plot_interactive <- ggplotly(module_plot)

module_plot_interactive

時間與朗讀/默讀的關係

這是一個顯示考試所花時間與朗讀和默讀分數差異關係的Scatter Plot。每個數據點代表一個受測者，點的位置反映了時間和分數差異的關係。圖中使用不同顏色區分不同性別，可以比較不同性別之間時間和分數差異的關係。

convert_time <- function(time_str) {
  time_components <- strsplit(time_str, "m")[[1]]
  minutes <- as.numeric(time_components[1])
  seconds <- as.numeric(sub("s", "", time_components[2]))
  total_seconds <- minutes * 60 + seconds
  return(total_seconds)
}

dat$time <- sapply(dat$time, convert_time)

time_plot <- ggplot(dat, aes(x = time, y = silent_point - aloud_point, color = gender)) +
  geom_point() +
  labs(x = "Time", y = "Difference in Points (Silent - Aloud)", color = "Gender") +
  theme_minimal()+
  ggtitle("Relationship between Time and Difference in Points (Silent - Aloud)") +
  xlab("Time(seconds)") +
  ylab("Difference in Points")

time_plot_interactive <- ggplotly(time_plot)

time_plot_interactive