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.

    • Valle, N., Antonenko, P., Valle, D., Sommer, M., Huggins-Manley, A. C., Dawson, K., ... & Baiser, B. (2021). Predict or describe? How learning analytics dashboard design influences motivation and statistics anxiety in an online statistics course. Educational Technology Research and Development, 69(3), 1405-1431.

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

    • How does learning analytics dashboard design influence motivation and statistics anxiety in an online statistics course

  3. What are some common approaches EDA approaches used and what did they entail?

    • learning analytics dashboard

  4. How were data visualization or feature engineering used to support analysis, if at all?What were the key findings or conclusions?

    • the study explored the influence of predictive and descriptive learning analytics dashboards on graduate students’ motivation and statistics anxiety in an online graduate-level statistics course. The results of Bayesian multiple regression model indicate that the predictive dashboard significantly reduced learners’ interpretation anxiety and had an effect on intrinsic goal orientation.

  5. Finally, what value, if any, might education practitioners find in these results?

    • Education practitioners might find value in the results of this study as it suggests that the use of predictive and descriptive learning analytics dashboards can have a positive impact on graduate students’ motivation and statistics anxiety in an online statistics course.

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

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

    • How effective are dashboards in identifying programs that can enhance student achievement?

  2. Briefly describe any steps of the EDA approach that will be used..

    • Determine purpose-Identify data sources-Choose a dashboard tool-Design the dashboard-Determine algorithms-Test and refine-Deploy and train

  3. What elements of EDA might require human judgement and decision making?

    • Decision-making by researchers is required when deciding on algorithms and designing dashboards.

Part II: Data Product

In our Learning Analytics code-along, we only 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 Data Visualizations using ggplot for Learning Analytics. You have three options for completing the Data Product portion: You can create the visualization exercise provided. Create a visualization of your choice using a data set from the data folder Create a visualization using your own data.

I highly recommend creating a new R script in your lab-3 folder to complete this task. When your code is ready to share, use the code chunk below to share the final code for your model and answer the questions that follow.

Exercise 1: - Using the `sci-online to create a basic visualization that: + Examine the relationship between two categorical variables. + Add an appropriate title to your chart. + Add a caption that poses a question educators may have about this data that your visualization could help answer.

# YOUR FINAL CODE HERE
# Load the package
library(ggplot2)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.1     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ lubridate 1.9.2     ✔ tibble    3.2.0
## ✔ purrr     1.0.1     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
# Create a sample data frame with two categorical variables
library(readr)
sci_online_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.
# Create a bar chart using ggplot
ggplot(sci_online_classes,
           aes(x = FinalGradeCEMS, y = TimeSpent_hours)) +
       geom_point()+ geom_point(shape=0.5, color = "green") + 
       labs(title="The Relationship between Course Time Spent and Final Grade", x = "Final Grade", y="Time Spent")
## Warning: Removed 30 rows containing missing values (`geom_point()`).
## Removed 30 rows containing missing values (`geom_point()`).

Exercise 2: - Using the `sci-online to create a basic visualization that: + examines the relationship between two continuous variables. (scatterplot with layers, #’ a log-log or line plot, or one using coord functions.) + Add an appropriate title to your chart. + Add a caption that poses a question educators may have about this data that your visualization could help answer. + Add or adjust any aesthetics to improve the readability of visual appeal of your viz. + Use a color scale if appropriate to modify the default colors used by ggplot. + Adjust or remove your legend as appropriate.

ggplot(sci_online_classes, aes(x = TimeSpent_hours, y = percentage_earned)) +
  geom_point() +
  ggtitle("The Relationship between Course Time Spent and the Percentage Earned") +
  labs(caption = "Caption:Is there a correlation?") +
  theme(plot.title = element_text(hjust = 0.5)) +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position = "none")
## Warning: Removed 5 rows containing missing values (`geom_point()`).

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.