Academic Impact Part 2

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.title[
# Academic Impact Part 2
]
.subtitle[
## Data Visualization and Analysis in the Era of COVID-19
]
.author[
### Damian Betebenner, Adam VanIwaarden & Nathan Dadey
]
.institute[
### Center for Assessment
]
.date[
### March 22nd, 2022 (updated: April 20th, 2022)
]

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# Academic Impact

---

## Learning Deceleration/Acceleration

- Summarization of assessment data (either for diagnostic or accountability purposes) of emphasizes two types of data results: Status (i.e. student attainment) and Growth (i.e., student academic progress).

- With the pandemic, emaphasis has changed to look at _academic impact_.

- In yesterday's introduction we discussed status impact (odometer) and growth impact (speedometer).

- We emphasize that Impact is synonymous with _decleration_ and Recovery is synonymous with _acceleration_.

- Deceleration, by definition, is the change in velocity (i.e. growth).

- Improvement (increasing student attainment/status in a system over time) is indicative of acceleration (unless the population is changing leading to the increased attainment/status)

- One of the most informative and least appreciated papers that discusses this is [Focusing State Educational Accountability Systems: Four Methods of Judging School Quality and Progress](https://www.nciea.org/sites/default/files/publications/Dale020402.pdf)

---

---

# Data to Investigate Academic Impact

- The data set we will utilize is called __sgpData_LONG_COVID__ and is a part of the __SGPdata__ package you've installed.

- Load the package into your R environment:

- Explore the dataset:

```r
summary(sgpData_LONG_COVID)
```

---

# Status Based Academic Impact

- To analyze and visualize status based impact we will investigate changes to the Empirical Cummulative Distribution Functions (ECDFs) for grade by cohort pairs.

- We begin by analyzing ECDFs for Grade 8 mathematics in 2019 versus Grade 8 mathematics in 2021.

```r
math_g8_2019 <- sgpData_LONG_COVID[CONTENT_AREA=="MATHEMATICS" & GRADE=="8" & YEAR=="2019"]
math_g8_2021 <- sgpData_LONG_COVID[CONTENT_AREA=="MATHEMATICS" & GRADE=="8" & YEAR=="2021"]
math_g8_2019_quantiles <- quantile(math_g8_2019$SCALE_SCORE, probs=0:100/100)
math_g8_2021_quantiles <- quantile(math_g8_2021$SCALE_SCORE, probs=0:100/100)
```

---

## Academic Impact: Status

---

## Create Plot of Status Based Academic Impact

```r
par(mai=c(0.5, 0.5, 0.25, 0))
plot(x=50, y=500, type="n", xlim=c(0,100), ylim=c(350, 700), axes=FALSE, main="Status Academic Impact: Mathematics Grade 8")
points(0:100, math_g8_2019_quantiles, type="l", col="black")
points(0:100, math_g8_2021_quantiles, type="l", col="red")
axis(1, at=0:10*10)
axis(2, at=c(350, 400, 450, 500, 550, 600, 650, 700), cex.axis=0.7)
```

---

## Create Plot of Status Based Academic Impact

---

## Identifying percent at/above proficient

- Normally, you will have the percentage of students in a grade/content area at above proficient.
- However, they can be derived from the data we are using.
- From above, we know that 577 is the proficient cutscore for grade 8, mathematics
- We can use ECDFs to calculate these

```r
math_g8_2019_ecdf <- ecdf(math_g8_2019$SCALE_SCORE)
math_g8_2021_ecdf <- ecdf(math_g8_2021$SCALE_SCORE)
100 - 100*math_g8_2019_ecdf(500)
```

```
## [1] 55.26433
```

```r
100 - 100*math_g8_2021_ecdf(500)
```

```
## [1] 43.5762
```

---

---

## To facilitate plotting convert LONG data to WIDE data

```r
SGP::outputSGP(sgpData_LONG_COVID, output.type='WIDE')
```

---

## Growth Based Academic Impact

```r
load('Data/Demonstration_COVID_SGP_WIDE_Data.Rdata')
g8_2019_2021_data <- Demonstration_COVID_SGP_WIDE_Data[GRADE.2019.MATHEMATICS=="6" & GRADE.2021.MATHEMATICS=="8"]
g8_2017_2019_data <- Demonstration_COVID_SGP_WIDE_Data[GRADE.2017.MATHEMATICS=="6" & GRADE.2019.MATHEMATICS=="8"]
attach(g8_2019_2021_data)
plot(SCALE_SCORE.2019.MATHEMATICS, SCALE_SCORE.2021.MATHEMATICS, pch=".", cex=0.5, xlab="Scaled Score 2019: Grade 6", ylab="Scaled Score 2021: Grade 8")
```

```r
detach(g8_2019_2021_data)
```

---

## Calculate conditional status splines

```r
load('Data/Demonstration_COVID_SGP_WIDE_Data.Rdata')
g8_2019_2021_data <- Demonstration_COVID_SGP_WIDE_Data[GRADE.2019.MATHEMATICS=="6" & GRADE.2021.MATHEMATICS=="8"]
g8_2017_2019_data <- Demonstration_COVID_SGP_WIDE_Data[GRADE.2017.MATHEMATICS=="6" & GRADE.2019.MATHEMATICS=="8"]
attach(g8_2019_2021_data)
spline_2019_to_2021 <- lm(SCALE_SCORE.2021.MATHEMATICS ~ bs(SCALE_SCORE.2019.MATHEMATICS))
detach(g8_2019_2021_data)

attach(g8_2017_2019_data)
spline_2017_to_2019 <- lm(SCALE_SCORE.2019.MATHEMATICS ~ bs(SCALE_SCORE.2017.MATHEMATICS))
detach(g8_2017_2019_data)
```

---

```r
attach(g8_2019_2021_data)
plot(SCALE_SCORE.2019.MATHEMATICS, SCALE_SCORE.2021.MATHEMATICS, pch=".", cex=0.5, xlab="Scaled Score 2019: Grade 6", ylab="Scaled Score 2021: Grade 8")
x_coors <- min(SCALE_SCORE.2019.MATHEMATICS, na.rm=TRUE):max(SCALE_SCORE.2019.MATHEMATICS)
points(x_coors, predict(spline_2017_to_2019, data.frame(SCALE_SCORE.2017.MATHEMATICS=x_coors)), col="black", type="l")
points(x_coors, predict(spline_2019_to_2021, data.frame(SCALE_SCORE.2019.MATHEMATICS=x_coors)), col="red", type="l")
detach(g8_2019_2021_data)
```

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<img src="Academic_Impact_Part_2_files/figure-html/unnamed-chunk-13-1.png" width="3151" />
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