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 the Learning Analytics Workflow 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 educational issue, “problem of practice,” and/or questions were addressed?

    • The aim of this study is to develop a Radial Basis Function Neural Network for prediction of students’ performance using their past academic records as well as their cognitive and psychomotor abilities.

  3. Briefly describe any steps of the data-intensive research workflow that detailed in your article or presentation.

      1. school data, 2) encoding and missing value imputation 3) preprocessed data 4) training set 5) Radial basis function neural net 6) model 7) testing set 8) model evaluation

  4. What were the key findings or conclusions? What value, if any, might education practitioners find in these results?

    • Under technique of Radial Basis Function Neural Network (RBFNN) considering Principal Component Analysis (PCA), the experiment method of Feature Extraction with Psychomotor Ratings showed good accuracy, sensitivity, specificity.

  5. Finally, how, if at at, were educators in your self-selected article involved prior to wrangling and analysis?

    • Students’ raw scores were extracted from the school’s database. In addition, the teachers’ ratings for each student were collected.

Draft a new research question of guided by the the phases of the Learning Analytics Workflow. Or use one of your current research questions.

  1. What educational issue, “problem of practice,” and/or questions is addressed??

    • identifying at-risk students and support their learning

  2. Briefly describe any steps of the data-intensive research workflow that can be detailed in your article or presentation.

    • Import data, Wrangle data, Explore data, ML model, Evaluate data and model

  3. 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?”

    • Machine learning is a branch of artificial intelligence that uses data and algorithms to learn from patterns and make predictions. By using machine learning techniques such as classification and prediction, educators can group students based on their needs, interests and abilities, and design effective teaching strategies that cater to their diverse learning styles.

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:

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
install.packages("readr")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)
library(readr)

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.
head(sci_classes, 5)
## # A tibble: 5 × 30
##   student_id course_id   total…¹ total…² perce…³ subject semes…⁴ section Grade…⁵
##        <dbl> <chr>         <dbl>   <dbl>   <dbl> <chr>   <chr>   <chr>   <chr>  
## 1      43146 FrScA-S216…    3280    2220   0.677 FrScA   S216    02      POINTS…
## 2      44638 OcnA-S116-…    3531    2672   0.757 OcnA    S116    01      ATTEMP…
## 3      47448 FrScA-S216…    2870    1897   0.661 FrScA   S216    01      POINTS…
## 4      47979 OcnA-S216-…    4562    3090   0.677 OcnA    S216    01      POINTS…
## 5      48797 PhysA-S116…    2207    1910   0.865 PhysA   S116    01      POINTS…
## # … with 21 more variables: 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>, and abbreviated
## #   variable names ¹​total_points_possible, ²​total_points_earned,
## #   ³​percentage_earned, ⁴​semester, ⁵​Gradebook_Item
  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
install.packages("dplyr")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
selected_data <- select(sci_classes, student_id, subject, semester, FinalGradeCEMS)

head(selected_data)
## # A tibble: 6 × 4
##   student_id subject semester FinalGradeCEMS
##        <dbl> <chr>   <chr>             <dbl>
## 1      43146 FrScA   S216               93.5
## 2      44638 OcnA    S116               81.7
## 3      47448 FrScA   S216               88.5
## 4      47979 OcnA    S216               81.9
## 5      48797 PhysA   S116               84  
## 6      51943 FrScA   S216               NA
selected_grade <- sci_classes %>% select(student_id, subject, semester, FinalGradeCEMS)

head(selected_data)
## # A tibble: 6 × 4
##   student_id subject semester FinalGradeCEMS
##        <dbl> <chr>   <chr>             <dbl>
## 1      43146 FrScA   S216               93.5
## 2      44638 OcnA    S116               81.7
## 3      47448 FrScA   S216               88.5
## 4      47979 OcnA    S216               81.9
## 5      48797 PhysA   S116               84  
## 6      51943 FrScA   S216               NA

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

  1. In code chunk named select-2 select all columns except subject and section. Assign to a new object with a different name. Examine your data frame with a different function.
# Type your code here
selected_data_2 <- select(sci_classes, -subject, -section)

head(selected_data_2)
## # A tibble: 6 × 28
##   student_id course_id   total…¹ total…² perce…³ semes…⁴ Grade…⁵ Grade…⁶ Final…⁷
##        <dbl> <chr>         <dbl>   <dbl>   <dbl> <chr>   <chr>   <lgl>     <dbl>
## 1      43146 FrScA-S216…    3280    2220   0.677 S216    POINTS… NA         93.5
## 2      44638 OcnA-S116-…    3531    2672   0.757 S116    ATTEMP… NA         81.7
## 3      47448 FrScA-S216…    2870    1897   0.661 S216    POINTS… NA         88.5
## 4      47979 OcnA-S216-…    4562    3090   0.677 S216    POINTS… NA         81.9
## 5      48797 PhysA-S116…    2207    1910   0.865 S116    POINTS… NA         84  
## 6      51943 FrScA-S216…    4208    3596   0.855 S216    POINTS… NA         NA  
## # … with 19 more variables: 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>,
## #   and abbreviated variable names ¹​total_points_possible,
## #   ²​total_points_earned, ³​percentage_earned, ⁴​semester, ⁵​Gradebook_Item,
## #   ⁶​Grade_Category, ⁷​FinalGradeCEMS
  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
filtered_data <- filter(sci_classes, subject == "OcnA")

head(filtered_data)
## # A tibble: 6 × 30
##   student_id course_id   total…¹ total…² perce…³ subject semes…⁴ section Grade…⁵
##        <dbl> <chr>         <dbl>   <dbl>   <dbl> <chr>   <chr>   <chr>   <chr>  
## 1      44638 OcnA-S116-…    3531    2672   0.757 OcnA    S116    01      ATTEMP…
## 2      47979 OcnA-S216-…    4562    3090   0.677 OcnA    S216    01      POINTS…
## 3      54066 OcnA-S116-…    4641    3429   0.739 OcnA    S116    01      ATTEMP…
## 4      54282 OcnA-S116-…    3581    2777   0.775 OcnA    S116    02      POINTS…
## 5      54342 OcnA-S116-…    3256    2876   0.883 OcnA    S116    02      POINTS…
## 6      54346 OcnA-S116-…    4471    3773   0.844 OcnA    S116    01      ATTEMP…
## # … with 21 more variables: 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>, and abbreviated
## #   variable names ¹​total_points_possible, ²​total_points_earned,
## #   ³​percentage_earned, ⁴​semester, ⁵​Gradebook_Item

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

  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
filtered_data_2 <- filter(sci_classes, !is.na(Points_Earned))

glimpse(filtered_data_2)
## Rows: 511
## Columns: 30
## $ student_id            <dbl> 44638, 47979, 48797, 52446, 53447, 53475, 53475,…
## $ course_id             <chr> "OcnA-S116-01", "OcnA-S216-01", "PhysA-S116-01",…
## $ total_points_possible <dbl> 3531, 4562, 2207, 2086, 4655, 1710, 1209, 4641, …
## $ total_points_earned   <dbl> 2672, 3090, 1910, 1719, 3149, 1402, 977, 3429, 2…
## $ percentage_earned     <dbl> 0.7567261, 0.6773345, 0.8654282, 0.8240652, 0.67…
## $ subject               <chr> "OcnA", "OcnA", "PhysA", "PhysA", "FrScA", "FrSc…
## $ semester              <chr> "S116", "S216", "S116", "S116", "S116", "S116", …
## $ section               <chr> "01", "01", "01", "01", "01", "02", "01", "01", …
## $ Gradebook_Item        <chr> "ATTEMPTED", "POINTS EARNED & TOTAL COURSE POINT…
## $ Grade_Category        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ FinalGradeCEMS        <dbl> 81.70184, 81.85260, 84.00000, 97.77778, 96.11872…
## $ Points_Possible       <dbl> 10, 5, 438, 10, 443, 5, 12, 10, 5, 10, 220, 30, …
## $ Points_Earned         <dbl> 10.00, 4.00, 399.00, 10.00, 425.00, 2.50, 12.00,…
## $ Gender                <chr> "F", "M", "F", "F", "F", "M", "M", "M", "F", "F"…
## $ q1                    <dbl> 4, 5, 4, 3, 4, NA, NA, 4, 3, 5, NA, 4, 4, NA, 4,…
## $ q2                    <dbl> 4, 5, 3, 3, 3, NA, NA, 5, 3, 3, NA, 2, 4, NA, 3,…
## $ q3                    <dbl> 3, 3, 3, 3, 3, NA, NA, 3, 3, 5, NA, 2, 3, NA, 3,…
## $ q4                    <dbl> 4, 5, 4, 3, 4, NA, NA, 5, 3, 5, NA, 4, 5, NA, 4,…
## $ q5                    <dbl> 4, 5, 4, 3, 4, NA, NA, 5, 4, 5, NA, 4, 4, NA, 4,…
## $ q6                    <dbl> 4, 5, 4, 4, 3, NA, NA, 5, 3, 5, NA, 4, 4, NA, 3,…
## $ q7                    <dbl> 4, 4, 4, 3, 3, NA, NA, 5, 3, 5, NA, 4, 5, NA, 3,…
## $ q8                    <dbl> 5, 5, 4, 3, 4, NA, NA, 4, 3, 5, NA, 4, 4, NA, 4,…
## $ q9                    <dbl> 4, 5, NA, 3, 2, NA, NA, 5, 2, 2, NA, 2, 4, NA, 2…
## $ q10                   <dbl> 4, 5, 3, 3, 5, NA, NA, 4, 4, 5, NA, 4, 4, NA, 3,…
## $ TimeSpent             <dbl> 1382.7001, 1598.6166, 1481.8000, 1390.2167, 1479…
## $ TimeSpent_hours       <dbl> 23.04500167, 26.64361000, 24.69666667, 23.170278…
## $ TimeSpent_std         <dbl> -0.30780313, -0.14844697, -0.23466291, -0.302255…
## $ int                   <dbl> 4.2, 5.0, 3.8, 3.0, 4.2, NA, NA, 4.4, 3.4, 4.7, …
## $ pc                    <dbl> 3.50, 3.50, 3.50, 3.00, 3.00, NA, NA, 4.00, 3.00…
## $ uv                    <dbl> 4.000000, 5.000000, 3.500000, 3.333333, 2.666667…

  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.

    arranged_data <- arrange(sci_classes, subject, desc(percentage_earned))

str(arranged_data)
    ## 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>

  2. In the code chunk name final-wrangle, use sci_classes data data and the %>% pipe operator:

#Type your code here
# final-wrangle
final_data <- sci_classes %>%
  select(student_id, subject, semester, FinalGradeCEMS) %>%
  filter(subject == "OcnA") %>%
  arrange(desc(FinalGradeCEMS))

head(final_data)
## # A tibble: 6 × 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

Knit & Submit

Congratulations, you’ve completed your Foundation Badge on Learning Analytics Workflow! Complete the following steps to submit your work for review by

  1. Change the name of the author: in the YAML header at the very top of this document to your name. As noted in Reproducible Research in R, The YAML header controls the style and feel for knitted document but doesn’t actually display in the final output.

  2. Click the yarn icon above to “knit” your data product to a HTML file that will be saved in your R Project folder.

  3. Commit your changes in GitHub Desktop and push them to your online GitHub repository.

  4. Publish your HTML page the web using one of the following publishing methods: Publish on RPubs by clicking the “Publish” button located in the Viewer Pane when you knit your document. Note, you will need to quickly create a RPubs account. Publishing on GitHub using either GitHub Pages or the HTML previewer.

  5. Post a new discussion on GitHub to our Foundations Badges forum. In your post, include a link to your published web page and write a short reflection highlighting one thing you learned from this lab and one thing you’d like to explore further.