Research question one
What is the relationship between teacher’s perspectives on the school’s attitude toward using ICT in teaching and technology use in the classroom?
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# Summary statistics
rq1_clean$it2g14a <- factor(rq1_clean$it2g14a,
levels = c(1,2,3,4),
labels = c("Strongly agree",
"Agree",
"Disagree",
"Strongly disagree"))
rq1_clean$it2g05a <- factor(rq1_clean$it2g05a,
levels = c(1,2,3,4),
labels = c("Never", "Less than two years", "Between two and five years", "More than five years"))
rq1_clean$it2g05b <- factor(rq1_clean$it2g05b,
levels = c(1,2,3,4),
labels = c("Never", "Less than two years", "Between two and five years", "More than five years"))
rq1_clean$ii2g12a <- factor(rq1_clean$ii2g12a,
levels = c(1,2),
labels = c("Yes", "No"))
rq1_clean$partt <- factor(rq1_clean$partt,
levels = c(1,2,3),
labels = c("Both Cycles",
"Only 2018",
"Only 2020"))
rq1_clean <- rq1_clean %>%
rename(`Country ID` = cntry,
`Year of Response` = year,
`Teacher Participation` = partt,
`School Consider ICT a Priority` =it2g14a,
`ICT Use During Lesson` = it2g05a,
`ICT Prepare Before Lesson` = it2g05b,
`ICT Self-support` = ii2g12a)A descriptive table for the Research Question one.
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datasummary_skim(rq1_clean,
type = "categorical",
title = "Table 1. Summary of School and Teacher's Perspective on ICT",
notes = "Source: International Computer and Information Literacy Study(ICILS)")| N | % | ||
|---|---|---|---|
| Country ID | DNK | 1617 | 23.6 |
| FIN | 3194 | 46.5 | |
| URY | 2051 | 29.9 | |
| Teacher Participation | Both Cycles | 4318 | 62.9 |
| Only 2018 | 2132 | 31.1 | |
| Only 2020 | 412 | 6.0 | |
| School Consider ICT a Priority | Strongly agree | 2710 | 39.5 |
| Agree | 3073 | 44.8 | |
| Disagree | 681 | 9.9 | |
| Strongly disagree | 59 | 0.9 | |
| ICT Use During Lesson | Never | 188 | 2.7 |
| Less than two years | 537 | 7.8 | |
| Between two and five years | 1700 | 24.8 | |
| More than five years | 4342 | 63.3 | |
| ICT Prepare Before Lesson | Never | 97 | 1.4 |
| Less than two years | 366 | 5.3 | |
| Between two and five years | 1170 | 17.1 | |
| More than five years | 4993 | 72.8 | |
| ICT Self-support | Yes | 4944 | 72.0 |
| No | 576 | 8.4 | |
| Source: International Computer and Information Literacy Study(ICILS) | |||
A Table for Missing Values in Research Question one:
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rq1_table_missing| Table 2. Missing values in key ICT related variables | ||||||
| Insights into digital infrastructure and support disparities | ||||||
|
Missing Values by Year and Participation Cycle
|
Country | School Consider ICT a Priority | ICT Use During Lesson | ICT Prepare Before Lesson | ICT Self-support | |
|---|---|---|---|---|---|---|
| Response Year | Teacher Participation Cycle | |||||
| 2018 | Both Cycles | DNK | 6 | 1 | 12 | 48 |
| 2018 | Both Cycles | FIN | 21 | 3 | 18 | 46 |
| 2018 | Both Cycles | URY | 27 | 12 | 17 | 117 |
| 2018 | Only 2018 | DNK | 19 | 7 | 26 | 82 |
| 2018 | Only 2018 | FIN | 10 | 5 | 17 | 21 |
| 2018 | Only 2018 | URY | 59 | 23 | 46 | 237 |
| 2020 | Both Cycles | DNK | 30 | 6 | 16 | 199 |
| 2020 | Both Cycles | FIN | 17 | 1 | 13 | 71 |
| 2020 | Both Cycles | URY | 78 | 18 | 31 | 279 |
| 2020 | Only 2020 | DNK | 7 | 2 | 5 | 35 |
| 2020 | Only 2020 | FIN | 1 | 1 | 3 | 6 |
| 2020 | Only 2020 | URY | 64 | 16 | 32 | 201 |
Research Question One
Initial Visualization version:
Show code
rq1_clean_vis <- rq1_clean %>%
filter(!is.na(`School Consider ICT a Priority`) &!is.na(`ICT Use During Lesson`) & !is.na(`ICT Prepare Before Lesson`) &!is.na(`ICT Self-support`)) %>%
filter(`Teacher Participation` == "Both Cycles")
p1 <- ggplot(data = rq1_clean_vis, aes(x = `ICT Use During Lesson`, fill= `School Consider ICT a Priority`)) +
geom_bar(position = "dodge")+
facet_wrap(`Year of Response`~ `Country ID`)+
labs(title = "Figure 1. Frequency of ICT Use during lessons by School ICT Priority across countries",
x = "ICT Use During Lessons",
y = "Count",
fill = "School ICT Priority")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
scale_fill_OkabeIto()
p1
Research Question One
Final Visualization version:
What I did is to combining the two plots could provide a more comprehensive view of how ICT use evolved across different contexts before and during lessons, while also showing school ICT priority changes from 2018 to 2020. By this point, I realize my data for Research Question one is not tidy enough.
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rq1_clean_vis_long <- rq1_clean_vis %>%
pivot_longer(cols = c(`ICT Use During Lesson`, `ICT Prepare Before Lesson`),
names_to = "ICT Use",
values_to = "ICT Frequency")
rq1_clean_vis_long$`Year of Response` <- as.factor(rq1_clean_vis_long$`Year of Response`)
p2 <- rq1_clean_vis_long %>%
ggplot(aes(x = `Year of Response`,
group = interaction(`Country ID`, `School Consider ICT a Priority`),
color = `School Consider ICT a Priority`,
shape = `Country ID`)) +
geom_line(stat = "count") +
geom_point(stat = "count", size = 3) +
facet_wrap(~ `Country ID`) +
labs(title = "Figure 1. Frequency of ICT Use Before Lessons by School ICT Priority Across Countries (Pre- vs. Post-Pandemic)",
x = "Year of Response",
y = "Count",
color = "School ICT Priority",
linetype = "Country",
shape = "Country") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
scale_fill_OkabeIto()
p2
My null hypothesis for research question one: School ICT Priority has no influence on teacher’s ICT use before and during lessons.
Run ANCOVA analysis to test my null hypothesis.
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rq1_analysis <- rq1 %>%
filter(!is.na(idteach) &!is.na(partt) &!is.na(it2g14a) &!is.na(it2g05a)&!is.na(it2g05b)&!is.na(ii2g12a)) %>% rename(`Country ID` = cntry,
`Year of Response` = year,
`Teacher Participation` = partt,
`School Consider ICT a Priority` =it2g14a,
`ICT Use During Lesson` = it2g05a,
`ICT Prepare Before Lesson` = it2g05b,
`ICT Self-support` = ii2g12a) %>%
filter(`Teacher Participation` == 3)
ancova_fit1 <- aov(`ICT Use During Lesson` ~ `School Consider ICT a Priority` + `Year of Response` + `Country ID` + `ICT Self-support`, data = rq1_analysis)
summary(ancova_fit1) Df Sum Sq Mean Sq F value Pr(>F)
`School Consider ICT a Priority` 1 9.75 9.746 28.470 3.74e-07
`Country ID` 2 11.56 5.778 16.878 2.71e-07
`ICT Self-support` 1 0.11 0.108 0.317 0.575
Residuals 140 47.93 0.342
`School Consider ICT a Priority` ***
`Country ID` ***
`ICT Self-support`
Residuals
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Show code
Df Sum Sq Mean Sq F value Pr(>F)
`School Consider ICT a Priority` 1 5.05 5.053 16.174 9.41e-05
`Country ID` 2 2.20 1.102 3.527 0.032
`ICT Self-support` 1 0.07 0.071 0.226 0.635
Residuals 140 43.73 0.312
`School Consider ICT a Priority` ***
`Country ID` *
`ICT Self-support`
Residuals
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1