LA Foundations - KEY

LASER Institute Foundation Learning Lab 1 - KEY

Author

YOUR NAME HERE

Published

July 17, 2024

The final activity for each learning lab provides space to work with data and to reflect on how the concepts and techniques introduced in each lab might apply to your own research.

To earn a badge for each lab, you are required to respond to a set of prompts for two parts:

Part I: Reflect and Plan

Use the institutional library (e.g. NCSU Library), Google Scholar or search engine to locate a research article, presentation, or resource that applies learning analytics analysis to an educational context or topic of interest. More specifically, locate a study that makes use of one of the data structures we learned today. You are also welcome to select one of your research papers.

  1. Provide an APA citation for your selected study.

  2. What types of data are associated with LA ?

  3. What type of data structures are analyzed in the educational context?

  4. How might this article be used to better understand a dataset or educational context of personal or professional interest to you?

  5. Finally, how do these processes compare with what teachers and educational organizations already do to support and assess student learning?

Draft a research question of guided by techniques and data sources that you are potentially interested in exploring in more depth.

  1. What data source(s) should be analyzed or discussed?

  2. What is the purpose of your article?

  3. Explain the analytical level at which these data would need to be collected and analyzed.

  4. How, if at all, will your article touch upon the application(s) of LA to “understand and improve learning and the contexts in which learning occurs?”

Part II: Data Product

In our Learning Analytics code-along, we scratched the surface on the number of ways that we can wrangle the data.

Using one of the data sets provided in the data folder, your goal for this lab is to extend the Learning Analytics Workflow from our code-along by preparing and wrangling different data.

Or alternatively, you may use your own data set to use in the workflow. If you do decide to use your own data set you must include:

  • Show two different ways using select function with your data, inspect and save as a new object.

  • Show one way to use filter function with your data, inspect and save as a new object.

  • Show one way using arrange function with your data, inspect and save as a new object.

  • Use the pipe operator to bring it all together.

Feel free to create a new script in your lab 2 to work through the following problems. Then when satisfied add the code in the code chunks below. Don’t forget to run the code to make sure it works.

Instructions:

  1. Add your name to the document in author.

  2. Set up the first (or, two if using an Introduction) phases of the LA workflow below. I’ve added the wrangle section for you. You will need to Prepare the libraries necessary to wrangle the data.

Wrangle

  1. In the chunk called read-data: Import the sci-online-classes.csv from the data folder and save as a new object called sci_classes. Then inspect your data using a function of your choice.
# Type your code here
#load todyverse
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
#import
sci_classes <- read_csv("data/sci-online-classes.csv")
Rows: 603 Columns: 30
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (6): course_id, subject, semester, section, Gradebook_Item, Gender
dbl (23): student_id, total_points_possible, total_points_earned, percentage...
lgl  (1): Grade_Category

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#inspect your data
sci_classes
# A tibble: 603 × 30
   student_id course_id     total_points_possible total_points_earned
        <dbl> <chr>                         <dbl>               <dbl>
 1      43146 FrScA-S216-02                  3280                2220
 2      44638 OcnA-S116-01                   3531                2672
 3      47448 FrScA-S216-01                  2870                1897
 4      47979 OcnA-S216-01                   4562                3090
 5      48797 PhysA-S116-01                  2207                1910
 6      51943 FrScA-S216-03                  4208                3596
 7      52326 AnPhA-S216-01                  4325                2255
 8      52446 PhysA-S116-01                  2086                1719
 9      53447 FrScA-S116-01                  4655                3149
10      53475 FrScA-S116-02                  1710                1402
# ℹ 593 more rows
# ℹ 26 more variables: percentage_earned <dbl>, subject <chr>, semester <chr>,
#   section <chr>, Gradebook_Item <chr>, Grade_Category <lgl>,
#   FinalGradeCEMS <dbl>, Points_Possible <dbl>, Points_Earned <dbl>,
#   Gender <chr>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>,
#   q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, TimeSpent <dbl>,
#   TimeSpent_hours <dbl>, TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>
  1. In the select-1 code chunk: Use the ‘select’ function to select student_id, subject, semester, FinalGradeCEMS. Assign to a new object with a different name (you choose the name).
# Type your code here
selected_data <- select(sci_classes, student_id, subject, semester, FinalGradeCEMS)

#inspect your data
summary(selected_data)
   student_id      subject            semester         FinalGradeCEMS  
 Min.   :43146   Length:603         Length:603         Min.   :  0.00  
 1st Qu.:85612   Class :character   Class :character   1st Qu.: 71.25  
 Median :88340   Mode  :character   Mode  :character   Median : 84.57  
 Mean   :86070                                         Mean   : 77.20  
 3rd Qu.:92730                                         3rd Qu.: 92.10  
 Max.   :97441                                         Max.   :100.00  
                                                       NA's   :30

What do you notice about FinalGradeCEMS?(*Hint: NAs?)

  • Answer here {possible answer: I notice NA values indicating missing data. This requires handling either by imputation or removal depending on the analysis requirements.}
  1. In code chunk named select-2 select all columns except subject and section. Assign to a new object with a different name. Inspect your data frame with a different function.
# Type your code here
reduced_data <- select(sci_classes, -subject, -section)

#inspect data
str(reduced_data)
tibble [603 × 28] (S3: tbl_df/tbl/data.frame)
 $ student_id           : num [1:603] 43146 44638 47448 47979 48797 ...
 $ course_id            : chr [1:603] "FrScA-S216-02" "OcnA-S116-01" "FrScA-S216-01" "OcnA-S216-01" ...
 $ total_points_possible: num [1:603] 3280 3531 2870 4562 2207 ...
 $ total_points_earned  : num [1:603] 2220 2672 1897 3090 1910 ...
 $ percentage_earned    : num [1:603] 0.677 0.757 0.661 0.677 0.865 ...
 $ semester             : chr [1:603] "S216" "S116" "S216" "S216" ...
 $ Gradebook_Item       : chr [1:603] "POINTS EARNED & TOTAL COURSE POINTS" "ATTEMPTED" "POINTS EARNED & TOTAL COURSE POINTS" "POINTS EARNED & TOTAL COURSE POINTS" ...
 $ Grade_Category       : logi [1:603] NA NA NA NA NA NA ...
 $ FinalGradeCEMS       : num [1:603] 93.5 81.7 88.5 81.9 84 ...
 $ Points_Possible      : num [1:603] 5 10 10 5 438 5 10 10 443 5 ...
 $ Points_Earned        : num [1:603] NA 10 NA 4 399 NA NA 10 425 2.5 ...
 $ Gender               : chr [1:603] "M" "F" "M" "M" ...
 $ q1                   : num [1:603] 5 4 5 5 4 NA 5 3 4 NA ...
 $ q2                   : num [1:603] 4 4 4 5 3 NA 5 3 3 NA ...
 $ q3                   : num [1:603] 4 3 4 3 3 NA 3 3 3 NA ...
 $ q4                   : num [1:603] 5 4 5 5 4 NA 5 3 4 NA ...
 $ q5                   : num [1:603] 5 4 5 5 4 NA 5 3 4 NA ...
 $ q6                   : num [1:603] 5 4 4 5 4 NA 5 4 3 NA ...
 $ q7                   : num [1:603] 5 4 4 4 4 NA 4 3 3 NA ...
 $ q8                   : num [1:603] 5 5 5 5 4 NA 5 3 4 NA ...
 $ q9                   : num [1:603] 4 4 3 5 NA NA 5 3 2 NA ...
 $ q10                  : num [1:603] 5 4 5 5 3 NA 5 3 5 NA ...
 $ TimeSpent            : num [1:603] 1555 1383 860 1599 1482 ...
 $ TimeSpent_hours      : num [1:603] 25.9 23 14.3 26.6 24.7 ...
 $ TimeSpent_std        : num [1:603] -0.181 -0.308 -0.693 -0.148 -0.235 ...
 $ int                  : num [1:603] 5 4.2 5 5 3.8 4.6 5 3 4.2 NA ...
 $ pc                   : num [1:603] 4.5 3.5 4 3.5 3.5 4 3.5 3 3 NA ...
 $ uv                   : num [1:603] 4.33 4 3.67 5 3.5 ...
  1. In the code chunk named filter-1, Filter the sci_classes data frame for students in OcnA courses. Assign to a new object with a different name. Use the head() function to examine your data frame.
#Type your code here

ocna_students <- filter(sci_classes, subject == "OcnA")

head(ocna_students)
# A tibble: 6 × 30
  student_id course_id    total_points_possible total_points_earned
       <dbl> <chr>                        <dbl>               <dbl>
1      44638 OcnA-S116-01                  3531                2672
2      47979 OcnA-S216-01                  4562                3090
3      54066 OcnA-S116-01                  4641                3429
4      54282 OcnA-S116-02                  3581                2777
5      54342 OcnA-S116-02                  3256                2876
6      54346 OcnA-S116-01                  4471                3773
# ℹ 26 more variables: percentage_earned <dbl>, subject <chr>, semester <chr>,
#   section <chr>, Gradebook_Item <chr>, Grade_Category <lgl>,
#   FinalGradeCEMS <dbl>, Points_Possible <dbl>, Points_Earned <dbl>,
#   Gender <chr>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>,
#   q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, TimeSpent <dbl>,
#   TimeSpent_hours <dbl>, TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>

Q: How many rows does the head() function display? Hint: Check the dimensions of your tibble in the console.

  • Answer here

{Possible answerr: The head function displays 5 rows of data}

  1. In code chunk named filter-2, filter the sci_classes data frame so rows with NA for points earned are removed. Assign to a new object with a different name. Use glimpse() to examine all columns of your data frame.
# Type your code here

no_na_points <- filter(sci_classes, !is.na(total_points_possible))

#inspect data 
glimpse(no_na_points)
Rows: 603
Columns: 30
$ student_id            <dbl> 43146, 44638, 47448, 47979, 48797, 51943, 52326,…
$ course_id             <chr> "FrScA-S216-02", "OcnA-S116-01", "FrScA-S216-01"…
$ total_points_possible <dbl> 3280, 3531, 2870, 4562, 2207, 4208, 4325, 2086, …
$ total_points_earned   <dbl> 2220, 2672, 1897, 3090, 1910, 3596, 2255, 1719, …
$ percentage_earned     <dbl> 0.6768293, 0.7567261, 0.6609756, 0.6773345, 0.86…
$ subject               <chr> "FrScA", "OcnA", "FrScA", "OcnA", "PhysA", "FrSc…
$ semester              <chr> "S216", "S116", "S216", "S216", "S116", "S216", …
$ section               <chr> "02", "01", "01", "01", "01", "03", "01", "01", …
$ Gradebook_Item        <chr> "POINTS EARNED & TOTAL COURSE POINTS", "ATTEMPTE…
$ Grade_Category        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ FinalGradeCEMS        <dbl> 93.45372, 81.70184, 88.48758, 81.85260, 84.00000…
$ Points_Possible       <dbl> 5, 10, 10, 5, 438, 5, 10, 10, 443, 5, 12, 10, 5,…
$ Points_Earned         <dbl> NA, 10.00, NA, 4.00, 399.00, NA, NA, 10.00, 425.…
$ Gender                <chr> "M", "F", "M", "M", "F", "F", "M", "F", "F", "M"…
$ q1                    <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q2                    <dbl> 4, 4, 4, 5, 3, NA, 5, 3, 3, NA, NA, 5, 3, 3, NA,…
$ q3                    <dbl> 4, 3, 4, 3, 3, NA, 3, 3, 3, NA, NA, 3, 3, 5, NA,…
$ q4                    <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 3, 5, NA,…
$ q5                    <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 4, 5, NA,…
$ q6                    <dbl> 5, 4, 4, 5, 4, NA, 5, 4, 3, NA, NA, 5, 3, 5, NA,…
$ q7                    <dbl> 5, 4, 4, 4, 4, NA, 4, 3, 3, NA, NA, 5, 3, 5, NA,…
$ q8                    <dbl> 5, 5, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q9                    <dbl> 4, 4, 3, 5, NA, NA, 5, 3, 2, NA, NA, 5, 2, 2, NA…
$ q10                   <dbl> 5, 4, 5, 5, 3, NA, 5, 3, 5, NA, NA, 4, 4, 5, NA,…
$ TimeSpent             <dbl> 1555.1667, 1382.7001, 860.4335, 1598.6166, 1481.…
$ TimeSpent_hours       <dbl> 25.91944500, 23.04500167, 14.34055833, 26.643610…
$ TimeSpent_std         <dbl> -0.18051496, -0.30780313, -0.69325954, -0.148446…
$ int                   <dbl> 5.0, 4.2, 5.0, 5.0, 3.8, 4.6, 5.0, 3.0, 4.2, NA,…
$ pc                    <dbl> 4.50, 3.50, 4.00, 3.50, 3.50, 4.00, 3.50, 3.00, …
$ uv                    <dbl> 4.333333, 4.000000, 3.666667, 5.000000, 3.500000…
  1. In the code chunk called arrange-1, Arrange sci_classes data by subject then percentage_earned in descending order. Assign to a new object. Use the str() function to examine the data type of each column in your data frame.
# Type your code here
arranged_classes <- arrange(sci_classes, subject, desc(percentage_earned))

#inpsect data
str(arranged_classes)
spc_tbl_ [603 × 30] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ student_id           : num [1:603] 70192 86488 96690 91175 86267 ...
 $ course_id            : chr [1:603] "AnPhA-S116-02" "AnPhA-S116-01" "AnPhA-S216-01" "AnPhA-S116-02" ...
 $ total_points_possible: num [1:603] 1936 3342 4804 3199 3045 ...
 $ total_points_earned  : num [1:603] 1763 3033 4309 2867 2705 ...
 $ percentage_earned    : num [1:603] 0.911 0.908 0.897 0.896 0.888 ...
 $ subject              : chr [1:603] "AnPhA" "AnPhA" "AnPhA" "AnPhA" ...
 $ semester             : chr [1:603] "S116" "S116" "S216" "S116" ...
 $ section              : chr [1:603] "02" "01" "01" "02" ...
 $ Gradebook_Item       : chr [1:603] "POINTS EARNED & TOTAL COURSE POINTS" "POINTS EARNED & TOTAL COURSE POINTS" "POINTS EARNED & TOTAL COURSE POINTS" "POINTS EARNED & TOTAL COURSE POINTS" ...
 $ Grade_Category       : logi [1:603] NA NA NA NA NA NA ...
 $ FinalGradeCEMS       : num [1:603] 96 87.4 64.8 82.2 35.1 ...
 $ Points_Possible      : num [1:603] 10 28 10 5 50 15 10 10 353 460 ...
 $ Points_Earned        : num [1:603] 7 26 3 5 50 11 8 10 330 452 ...
 $ Gender               : chr [1:603] "F" "M" "F" "F" ...
 $ q1                   : num [1:603] 4 4 4 5 5 4 5 4 NA NA ...
 $ q2                   : num [1:603] 3 4 3 3 5 2 4 4 NA NA ...
 $ q3                   : num [1:603] 3 2 2 3 3 3 4 3 NA NA ...
 $ q4                   : num [1:603] 4 3 5 5 5 4 5 4 NA NA ...
 $ q5                   : num [1:603] 4 3 4 5 5 4 5 4 NA NA ...
 $ q6                   : num [1:603] 3 3 4 4 5 3 5 4 NA NA ...
 $ q7                   : num [1:603] 3 3 3 3 4 4 5 4 NA NA ...
 $ q8                   : num [1:603] 5 2 4 5 5 4 4 4 NA NA ...
 $ q9                   : num [1:603] 2 3 3 3 5 1 4 4 NA NA ...
 $ q10                  : num [1:603] 5 3 2 5 5 2 5 4 NA NA ...
 $ TimeSpent            : num [1:603] 1537 3600 1970 1315 406 ...
 $ TimeSpent_hours      : num [1:603] 25.62 60 32.83 21.92 6.77 ...
 $ TimeSpent_std        : num [1:603] -0.194 1.328 0.125 -0.358 -1.029 ...
 $ int                  : num [1:603] 4.4 3 3.8 5 5 3.9 4.6 4 4.8 4.6 ...
 $ pc                   : num [1:603] 3 2.5 2.5 3 3.5 3.5 3.75 3.5 3.5 4.5 ...
 $ uv                   : num [1:603] 2.67 3.33 3.33 3.33 5 ...
 - attr(*, "spec")=
  .. cols(
  ..   student_id = col_double(),
  ..   course_id = col_character(),
  ..   total_points_possible = col_double(),
  ..   total_points_earned = col_double(),
  ..   percentage_earned = col_double(),
  ..   subject = col_character(),
  ..   semester = col_character(),
  ..   section = col_character(),
  ..   Gradebook_Item = col_character(),
  ..   Grade_Category = col_logical(),
  ..   FinalGradeCEMS = col_double(),
  ..   Points_Possible = col_double(),
  ..   Points_Earned = col_double(),
  ..   Gender = col_character(),
  ..   q1 = col_double(),
  ..   q2 = col_double(),
  ..   q3 = col_double(),
  ..   q4 = col_double(),
  ..   q5 = col_double(),
  ..   q6 = col_double(),
  ..   q7 = col_double(),
  ..   q8 = col_double(),
  ..   q9 = col_double(),
  ..   q10 = col_double(),
  ..   TimeSpent = col_double(),
  ..   TimeSpent_hours = col_double(),
  ..   TimeSpent_std = col_double(),
  ..   int = col_double(),
  ..   pc = col_double(),
  ..   uv = col_double()
  .. )
 - attr(*, "problems")=<externalptr>
  1. In the code chunk name final-wrangle, use sci_classes data data and the %>% pipe operator:
  • Select student_id, subject, semester, FinalGradeCEMS.
  • Filter for students in OcnA courses.
  • Arrange grades by section in descending order.
  • Assign to a new object.
  • Examine the contents using a method of your choosing.
#Type your code here

final_data <- sci_classes %>%
  select(student_id, subject, semester, FinalGradeCEMS) %>%
  filter(subject == "OcnA") %>%
  arrange(desc(FinalGradeCEMS))
print(final_data)
# A tibble: 111 × 4
   student_id subject semester FinalGradeCEMS
        <dbl> <chr>   <chr>             <dbl>
 1      66740 OcnA    S116               99.3
 2      91163 OcnA    S216               97.4
 3      94744 OcnA    S216               96.8
 4      91818 OcnA    S116               96.5
 5      90090 OcnA    S116               96.3
 6      88168 OcnA    S116               96.0
 7      89114 OcnA    S116               95.0
 8      86758 OcnA    S116               94.6
 9      68476 OcnA    S116               94.6
10      79893 OcnA    T116               94.5
# ℹ 101 more rows

Render & Submit

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