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. Nguyen, H. A., Hou, X., Stamper, J., & McLaren, B. M. (2020). Moving beyond Test Scores: Analyzing the Effectiveness of a Digital Learning Game through Learning Analytics. International Educational Data Mining Society.

  2. What types of data are associated with LA ?

    • connecting game play data to demographic and pre/post test surveys
    • mastery, accuracy, response time as students play games/solve problems

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

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

    • I am often interested in connecting the logs from game play to other student factors or assessments outside of the game.

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

    • This provides more of a formative view on what students are doing as they play games, rather than on more distal measures of 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?

    • game play behaviors, actions, mathematical perfomrance and response time, hint usage, etc.

  2. What is the purpose of your article?

    • What aspects of students game play are most related to longer term student acheivement?

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

    • The log data would need to be collected as students play games using technology. The student level data or assessment would need to be collected from school districts or classrooms.

  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?”

    • This would help us understand how particular math problem solving behaviors could be related to mathematical understanding and learning.

Part II: Data Product

After you finish the script file for lab1_badge add it to the community board.

Problem 1:

Create a data frame that includes two columns, one named “students” and the other named “foods.” The first column should be this vector (note the intentional repeated values): Thor, Rogue, Electra, Electra, Wolverine

The second column should be this vector: Bread, Orange, Chocolate, Carrots, Milk

# YOUR FINAL CODE BELOW
students <- c("thor", "rogue","electra", "electra", "wolverine")
foods <- c("bread", "orange", "chocolate", "carrots", "milk")

df <- data.frame(students, foods)

df
##    students     foods
## 1      thor     bread
## 2     rogue    orange
## 3   electra chocolate
## 4   electra   carrots
## 5 wolverine      milk

Problem 2

Using the data frame created in Problem 2, use the table() command to create a frequency table for the column called “students”

# YOUR FINAL CODE BELOW
frequencytable <- table(students)

frequencytable
## students
##   electra     rogue      thor wolverine 
##         2         1         1         1

Problem 3

Create a vector of five numbers of your choice between 0 and 10, save that vector to an object, and use the sum() function to calculate the sum of the numbers.

# YOUR FINAL CODE BELOW
vector <- c( 3, 6, 7,3,1 )
vector
## [1] 3 6 7 3 1
sum(vector)
## [1] 20

Problem 4

  1. Create code to read the data/sci-online-classes.csv file into R using function(s) from the tidyverse package. (Note: this requires the package tidyverse). Save the data as an object called sci_classes.

  2. Examine the contents of sci_classes in your console. Is your object a tibble? How do you know? (Hint: Check the output in the console.)

# YOUR FINAL CODE BELOW
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── 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
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.

Problem 5

Using the sci_classes data frame:

  1. Select all columns except subject and section.

  2. Assign to a new object with a different name.

  3. Examine your data frame.

# YOUR FINAL CODE BELOW
sci_classes_reduced <- sci_classes %>% select(-c(subject, section))
sci_classes_reduced
## # A tibble: 603 × 28
##    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 24 more variables: percentage_earned <dbl>, semester <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>

Knit & Submit

Congratulations, you’ve completed your Data Sources Badge!

Complete the following steps to submit your work for review by:

Complete the following steps to knit and publish your work:

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

  2. Next, click the knit button in the toolbar above to “knit” your R Markdown document to a HTML file that will be saved in your R Project folder. You should see a formatted webpage appear in your Viewer tab in the lower right pan or in a new browser window. Let’s us know if you run into any issues with knitting.

  3. Finally, publish your webpage on on Posit Cloud by clicking the “Publish” button located in the Viewer Pane after you knit your document. See screenshot below.

Foundations Learning Badge 1

Congratulations, you’ve completed Foundations Learning Badge 1! To receive credit for this assignment and earn the an official Foundations LASER Badge, share the link to published webpage under an empty Badge Artifact column on the 2023 LASER Scholar Information and Documents spreadsheet: https://go.ncsu.edu/laser-sheet. We recommend bookmarking this spreadsheet as we’ll be using it throughout the year to keep track of your progress.

Once your instructor has checked your link, you will be provided a physical version of the badge below!