Title: Ready or not, here come new K-8 computing education standards
Authors: Joshua Rosenberg, Lynn Hodge, Mehmet Aydeniz, Anthony Schmidt, Katie Rich
Abstract: Around the United States (and the world), educators and other educational stakeholders are at work to prepare students as a part of new computing (or computer science) education initiatives. In Tennessee, new standards (*K-8 Digital Readiness Standards), which are distinguished from past standards through their emphasis new learning targets, such as computational thinking and computer programming, were released for Fall, 2019. While now the official standards for computing educaton-related teaching and learning at the K-8 level, the readiness of educators and administrators is an important - and open - question, particularly when it comes to areas that are under-resourced with respect to access to curricula and tools and support through professional learning opportunities. In this community-engaged project, we developed and administered a needs survey for K-8 teachers and administrators at any level regarding their interest and preparedness regarding providing computing education learning opportunities for their students. We received responses from 98 teachers and 32 administrators primarily from rural (though also from somewhat rural and metropolitan-based) school districts in East Tennessee. Teachers and administrators reported that computer science is highly important to them, but that they were not very aware of the new standards, and felt only moderately prepared to enact instruction to meet the standard’s aims. Analysis of qualitative items for what resources related to computing education teachers and administrators possess, and what needs they have, reveal an opportunity and demand for curricula, the availability of coursework and licensure opportunities, and professional development opportunities to learn about both the technical aspects of computing education as well as how computing ideas may be embedded across the K-8 curriculum. In addition to a discussion of these findings, we will highlight implications for administrators, researchers, and educational leaders across Tennessee, and best practices for supporting the implementation of new computing education stadards identified from research throughout the nation.
library(qualtRics)
library(tidyverse)
# only need to run once
# qualtRics::qualtrics_api_credentials(api_key = "",
# base_url = "utk.co1.qualtrics.com",
# install = TRUE)
d <- qualtRics::fetch_survey("SV_1S9KAi38aD366cR", convert = FALSE, force_request = TRUE)
dt <- d %>%
filter(str_detect(ROLE, "teacher"))
da <- d %>%
filter(str_detect(ROLE, "admin"))
urban <- "Knox County Schools"
somewhat_rural <- c("Anderson County Schools",
"Union County Schools",
"Blount County Schools",
"Sevier County Schools",
"Grainger County Schools",
"Jefferson County Schools",
"Loudon County Schools")
d1 <- qualtRics::fetch_survey("SV_1S9KAi38aD366cR", label = FALSE, convert = FALSE, force_request = TRUE)
dt1 <- d1 %>%
filter(ROLE == 1 & T_SCHOOL_TYPE != 4)
da1 <- d1 %>%
filter(ROLE == 2)
qualtRics::survey_questions('SV_1S9KAi38aD366cR')d %>%
filter(str_detect(ROLE, "teacher"),
T_SCHOOL_TYPE != "Neither/Other",
T_CONSENT == "Yes") %>%
count(ROLE) ## # A tibble: 1 x 2
## ROLE n
## <chr> <int>
## 1 Teacher (please select this role if you a classroom teacher, STEM/STEAM… 98d %>%
filter(str_detect(ROLE, "admin"),
Q10 == "Yes") %>%
count(ROLE) ## # A tibble: 1 x 2
## ROLE n
## <chr> <int>
## 1 Administrator (please select this role if you are a superintendent, dir… 32dt %>%
mutate(rural = case_when(
T_DISTRICT %in% urban ~ "metro",
T_DISTRICT %in% somewhat_rural ~ "somewhat_rural",
is.na(T_DISTRICT) ~ NA_character_,
TRUE ~ "rural"
)) %>%
janitor::tabyl(rural)## rural n percent valid_percent
## metro 10 0.08474576 0.1219512
## rural 58 0.49152542 0.7073171
## somewhat_rural 14 0.11864407 0.1707317
## <NA> 36 0.30508475 NAda %>%
mutate(rural = case_when(
A_DISTRICT %in% urban ~ "metro",
A_DISTRICT %in% somewhat_rural ~ "somewhat_rural",
is.na(A_DISTRICT) ~ NA_character_,
TRUE ~ "rural"
)) %>%
janitor::tabyl(rural)## rural n percent valid_percent
## metro 2 0.05405405 0.0952381
## rural 16 0.43243243 0.7619048
## somewhat_rural 3 0.08108108 0.1428571
## <NA> 16 0.43243243 NAselect(dt1, T_AWARE, T_IMPORTANCE, T_TEACH_PREPARED) %>%
apaTables::apa.cor.table()##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2
## 1. T_AWARE 1.73 0.59
##
## 2. T_IMPORTANCE 2.73 0.45 .13
## [-.07, .33]
##
## 3. T_TEACH_PREPARED 2.12 0.59 .50* .11
## [.13, .74] [-.29, .48]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## select(da1, A_FAMILIAR = A_FAMILAIR, A_IMPORTANCE, A_PREPARED) %>%
apaTables::apa.cor.table()##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2
## 1. A_FAMILIAR 1.66 0.67
##
## 2. A_IMPORTANCE 2.77 0.50 .04
## [-.33, .40]
##
## 3. A_PREPARED 2.20 0.55 -.06 .30
## [-.42, .31] [-.07, .59]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## dt %>%
janitor::tabyl(T_XTEACH_KINDS)## T_XTEACH_KINDS n percent valid_percent
## a stand-alone CS class 2 0.01694915 0.03448276
## CS as a part of a technology class 11 0.09322034 0.18965517
## CS integrated with math or science 24 0.20338983 0.41379310
## CS integrated with other subjects 16 0.13559322 0.27586207
## other (please specify) 5 0.04237288 0.08620690
## <NA> 60 0.50847458 NAda %>%
janitor::tabyl(A_CS_THISYEAR)## A_CS_THISYEAR n percent valid_percent
## No 4 0.1081081 0.2222222
## Yes 14 0.3783784 0.7777778
## <NA> 19 0.5135135 NAda %>%
janitor::tabyl(A_CSLEADER)## A_CSLEADER n percent valid_percent
## No 12 0.3243243 0.6315789
## Yes 7 0.1891892 0.3684211
## <NA> 18 0.4864865 NAselect(dt1, T_RESOURCES, T_NEEDS, T_BARRIERS) %>%
DT::datatable()da %>% select(A_RESOURCES, A_NEEDS_ADMIN) %>%
DT::datatable()