In almartin82/mapvizieR: Visualization and Data Analysis tools for NWEA MAP student data

---

title: r paste(params$measurementscale, 'MAP Class Growth Goals')

knitr::opts_chunk$set(
  echo = FALSE,
  warning = FALSE,
  message = FALSE,
  error = FALSE,
  dpi = 300
)

library(gridExtra)

baseline_cdf <- mapvizieR::mv_limit_cdf(
  mapvizieR_obj = params$mv,
  studentids = params$studentids,
  measurementscale = params$measurementscale
) %>%
dplyr::filter(
  termname == params$baseline
)

#hacky holdover hotfixes
constant_grade <- baseline_cdf$grade %>% table() %>% sort(TRUE) %>% names() %>% extract(1) %>% as.numeric()
baseline_cdf$grade <- constant_grade

baseline_cdf <- baseline_cdf %>% 
  dplyr::group_by(
    measurementscale, map_year_academic, 
    fallwinterspring, termname, grade
  )

baseline_rit <- mean(baseline_cdf$testritscore, na.rm = TRUE) 
baseline_sum <- mapvizieR::summary.mapvizieR_cdf(baseline_cdf)

num_students <- length(params$studentids %>% unique())
valid_mask <- params$studentids %in% baseline_cdf[!is.na(baseline_cdf$testritscore), ]$studentid
missing_ids <- params$studentids[!valid_mask] %>% unique()
num_missing <- length(missing_ids)
missing_logical <- num_missing > 0
nys_logical <- params$grade >= 3

start_season <- gsub(" .*$", "", params$baseline)
end_season <- gsub(" .*$", "", params$endpoint)

cgp_slim <- calc_cgp(
  measurementscale = params$measurementscale,
  end_grade = params$grade,
  growth_window = paste0(start_season, ' to ', end_season),
  baseline_avg_rit = baseline_sum$mean_testritscore,
  calc_for = c(1, 5, 20, 50, 80, 95, 99)
)

cgp_targets <- cgp_slim$targets

goal_growth <- cgp_targets[cgp_targets$cgp == params$growth_percentile_target, ]$growth_target
goal_endpoint_rit <- baseline_rit + goal_growth
goal_halfway_rit <- baseline_rit + (goal_growth / 2)

goal_endpoint_cohort_status_npr <- cohort_mean_rit_to_npr(
  params$measurementscale, 
  params$grade, 
  end_season,
  goal_endpoint_rit
)

p_cgp_targets <- cgp_target_spread_plot(
  cgp_prep = cgp_slim
)

#shitty holdover logic
temp_mv <- params$mv

if (start_season == 'Spring') {
  temp_mv$cdf <- temp_mv$cdf %>%
    dplyr::filter(
      grade_level_season <= params$grade - 1
    )
}

#make plots
rit_trace_simple <- cohort_rit_trace_plot(
  mapvizieR_obj = temp_mv,
  studentids = params$studentids,
  measurementscale = params$measurementscale,
  match_method = 'no matching',
  first_and_spring_only = FALSE,
  entry_grade_seasons = c(-99),
  collapse_schools = TRUE,
  retention_strategy = 'collapse',
  small_n_cutoff = -1
)

status_trace_rit <- cohort_status_trace_plot(
  mapvizieR_obj = temp_mv,
  studentids = params$studentids,
  measurementscale = params$measurementscale,
  match_method = 'no matching',
  first_and_spring_only = TRUE,
  entry_grade_seasons = c(-99),
  collapse_schools = TRUE,
  retention_strategy = 'collapse',
  small_n_cutoff = -1,
  plot_labels = 'RIT'
)

status_trace_npr <- cohort_status_trace_plot(
  mapvizieR_obj = temp_mv,
  studentids = params$studentids,
  measurementscale = params$measurementscale,
  match_method = 'no matching',
  first_and_spring_only = TRUE,
  entry_grade_seasons = c(-99),
  collapse_schools = TRUE,
  retention_strategy = 'collapse',
  small_n_cutoff = -1,
  plot_labels = 'NPR'
)

About these students

There are r num_students students in this group. r length(missing_ids) of them do not yet have a baseline MAP score.

r params$measurementscale Prior Achievement

Here is the historical class average RIT for these students. (This plot shows all historical data for all r num_students students - it is not limited to continuously enrolled students. It does, however, exclude students who have transferred out of the school and are no longer part of the presently enrolled r num_students-student group.)

rit_trace_simple

The background reference lines on the plot above show the grade/class/cohort attainment percentiles. Another way to visualize the historical data is to track the attainment percentile of this group, rather than the RIT. To be clear: this is still the same data, derived from the same underlying RIT scores. We're simply showing this data as an mean attainment percentile, instead of using the RIT scale.

status_trace_npr

The intuition for this plot is very straighforward - where the line is flat, it means that the class made exactly typical growth for that time period. When the line moves up, it means that growth was faster than peer grade/classes group. If the line slopes down, it means that growth was slower.

Baseline Achievement

The r num_students students in this group have a baseline (r params$baseline) average RIT of r baseline_rit %>% round(1).

For grade r params$grade students, that baseline puts this class at the r baseline_sum$cohort_status_npr %>% round(0) %>% toOrdinal::toOrdinal() attainment percentile. That means that this class's absolute achievement is higher than r baseline_sum$cohort_status_npr %>% round(0)% of other classes in the country.

2016-17 Goals

Our network goal is for each class to grow at the r params$growth_percentile_target %>% toOrdinal::toOrdinal() percentile. That's means our growth would be in the top r 100 - params$growth_percentile_target percent of classrooms nationally!

To reach that goal by r params$endpoint, your students will need to make r goal_growth %>% round(1) points of RIT growth on average. That means growing from an average RIT of r baseline_rit %>% round(1) to an average RIT of r goal_endpoint_rit %>% round(1).

If your students grew to an average RIT of r goal_endpoint_rit %>% round(1) by this Spring, they would be in the r goal_endpoint_cohort_status_npr %>% round(0) %>% toOrdinal::toOrdinal() attainment percentile.

The chart below shows how much growth is needed to hit other cohort growth benchmarks.

More growth scenarios

p_cgp_targets

Progress monitoring

When we test students again in the Winter, the target is for students to be halfway toward their Spring goal. If the average RIT score for these students is at or above r goal_halfway_rit %>% round(1), that will indicate that your students are on track to reach the growth goal.

Student detail

The tables below shows the baseline achievement data for each of the r num_students in this report.

Quartile 1 (1st percentile - 24th percentile) Students

baseline_cdf <- roster_to_cdf(baseline_cdf, params$mv, 'studentfirstlast')

cdf_cols_pipe <- . %>%
  dplyr::ungroup() %>%
  dplyr::select(
    studentfirstlast, studentid, testritscore, testpercentile
  ) %>% 
  knitr::kable()

baseline_cdf %>%
  dplyr::filter(testquartile == 1)  %>%
  cdf_cols_pipe

Quartile 2 (25th percentile - 49th percentile) Students

baseline_cdf %>%
  dplyr::filter(testquartile == 2)  %>%
  cdf_cols_pipe

Quartile 3 (50th percentile - 74th percentile) Students

baseline_cdf %>%
  dplyr::filter(testquartile == 3)  %>%
  cdf_cols_pipe

Quartile 4 (75th percentile - 99th percentile) Students

baseline_cdf %>%
  dplyr::filter(testquartile == 4)  %>%
  cdf_cols_pipe

almartin82/mapvizieR documentation built on June 3, 2023, 10:53 a.m.