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.

    • Sense, F., van der Velde, M., & van Rijn, H. (2021). Predicting university students' exam performance using a model-based adaptive fact-learning system. Journal of Learning Analytics, 8(3), 155-169

  2. What types of data are associated with LA ?

    • assessment

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

    • Digital learning environments

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

    • It predicts university students' exam performance using a model-based adaptive fact-learning system

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

    • This helps make earlier interventions to help 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?

    • LMS learning behaviors

  2. What is the purpose of your article?

    • how learning behaviors predict their success

  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?”

    • Yes.

Part II: Data Product

After you finish the script file for lab1_badge add the code fo reach problem into the correct code chunks below. # Making sure not to change the code chunk name.

# YOUR FINAL CODE HERE

df <- data.frame( Students =  c("Thor", "Rogue", "Electra", "Electra", "Wolverine"),
                  Foods = c("Bread", "Orange", "Chocolate", "Carrots", "Milk")
                 )
print (df)
##    Students     Foods
## 1      Thor     Bread
## 2     Rogue    Orange
## 3   Electra Chocolate
## 4   Electra   Carrots
## 5 Wolverine      Milk
# YOUR FINAL CODE HERE

table(df$Students)
## 
##   Electra     Rogue      Thor Wolverine 
##         2         1         1         1
# YOUR FINAL CODE HERE

mynum = c(3,4,5,7,9)
mysum = sum(mynum)
print (mysum)
## [1] 28
# YOUR FINAL CODE HERE

library (tidyverse)
## Warning: replacing previous import 'lifecycle::last_warnings' by
## 'rlang::last_warnings' when loading 'pillar'
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.3.5     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.7
## ✔ tidyr   1.1.3     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0
## Warning: package 'ggplot2' was built under R version 3.6.2
## Warning: package 'tidyr' was built under R version 3.6.2
## Warning: package 'purrr' was built under R version 3.6.2
## Warning: package 'dplyr' was built under R version 3.6.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
setwd("/Users/hb8648/Documents/foundational-skills/foundation_lab_1")
sci_classess <- read_csv("./data/sci-online-classes.csv")
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   course_id = col_character(),
##   subject = col_character(),
##   semester = col_character(),
##   section = col_character(),
##   Gradebook_Item = col_character(),
##   Grade_Category = col_logical(),
##   Gender = col_character()
## )
## See spec(...) for full column specifications.
# YOUR FINAL CODE HERE

newdf <-  select(sci_classess, -c("subject", "section"))
print(newdf)
## # A tibble: 603 × 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-S21…    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-S21…    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-S11…    2207    1910   0.865 S116    POINTS… NA         84  
##  6      51943 FrScA-S21…    4208    3596   0.855 S216    POINTS… NA         NA  
##  7      52326 AnPhA-S21…    4325    2255   0.521 S216    POINTS… NA         83.6
##  8      52446 PhysA-S11…    2086    1719   0.824 S116    POINTS… NA         97.8
##  9      53447 FrScA-S11…    4655    3149   0.676 S116    POINTS… NA         96.1
## 10      53475 FrScA-S11…    1710    1402   0.820 S116    POINTS… NA         NA  
## # … with 593 more rows, 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
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

Knit & Submit

Congratulations, you’ve completed your Data Sources Badge!

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.