LA Foundations badge

LASER Institute Foundation Learning Lab 1

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:

• In Part I, you will reflect on your understanding of key concepts and begin to think about potential next steps for your own study.

• In Part II, you will create a simple data product in R that demonstrates your ability to apply a data analysis technique introduced in this learning lab.

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.

   Lewis, A., Ochoa, X., & Qamra, R. (2023). Instructor-in-the-loop exploratory analytics to support group work. LAK23: 13th International Learning Analytics and Knowledge Conference, 284–292. https://doi.org/10.1145/3576050.3576093

2. What types of data are associated with LA ?

   Video, audio, and the direction of arrival (DOA) of sound of each group

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

   Current Speaker: Identified through DOA information.

   Textual Transcripts: Derived from audio using speech recognition.

   Body Posture and Gaze Direction: Analyzed using computer vision algorithms like OpenPose for body posture and OpenFace for gaze direction.

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

   Becasue I am interested in desiging Storyline based course and analyze student data to improve learning. This paper provides the methods of Multimodal Data Collection, I probabay could also use various data collection techniques within the Storyline 360 course. for example, I could collect data such as user clicks, time spent on specific slides, quiz response to check student engagement and understanding.Then I could do feature extraction, and generatt different analyses and visualizations to show to the course instuctors to help them understand students’ performance and finds ways to improve the course.

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

   Unlike traditional assessment methods in most classrooms, this paper did multimodal data analytics, automated feature extraction, and created exploratory metrics and visualizations, then used this information to improve the course iteratively.The multimodal analysis provides deeper insights into how students work together and how these interactions affect learning.

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

   How does the integration of mutlimodal analytics and real-time feedback within a Storyline-based STEM course affect student engagement and learning outcomes?

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

   progression data, learner dashboard, course pre- and post-grades, in-course feedback

8. What is the purpose of your article?

   The purpose of this research is to examine the impact of real-time learning analytics and feedback mechanisms integrated into a Storyline-based STEM course on university students’ engagement and learning outcomes.

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

   I could use statistical methods to examine students’ learning patterns, engagement levels using these data.

10. 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?”

    I could employ learning analytics to monitor student interactions with the course material by tracking their clicks, the duration they spent on specific slides or quizzes. Additionally, by analyzing data from quizzes and tests, I can obtain insights into students’ learning outcomes and identify areas where they feel struggled.

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

3. 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

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.

#import


#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>

4. 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
new_data <- sci_classes %>%
  select(student_id, subject, semester, FinalGradeCEMS)

new_data

# A tibble: 603 × 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  
 7      52326 AnPhA   S216               83.6
 8      52446 PhysA   S116               97.8
 9      53447 FrScA   S116               96.1
10      53475 FrScA   S116               NA  
# ℹ 593 more rows

#inspect your data

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.}

There are some data missing from the FinalGradeCEMS.

5. 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.

new_select_2 <- sci_classes %>%
  select(-subject, -section)

new_select_2

# 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>

6. 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.

ocna_courses <- sci_classes %>%
  filter(subject == "OcnA")

ocna_courses

# A tibble: 111 × 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
 7      54567 OcnA-S216-02                  3871                3286
 8      57981 OcnA-S116-01                  3587                2879
 9      58178 OcnA-S116-01                  3940                3348
10      62175 OcnA-S216-01                  3169                2249
# ℹ 101 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>

head(ocna_courses)

# 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.

• The head function displays 6 rows of data

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

7. 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.

no_na <- sci_classes %>%
  drop_na(total_points_earned)
glimpse(no_na)

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…

#inspect data 

8. 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_data <- sci_classes %>%
  arrange(desc(subject))
str(arranged_data)

spc_tbl_ [603 × 30] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ student_id           : num [1:603] 48797 52446 54434 55140 57489 ...
 $ course_id            : chr [1:603] "PhysA-S116-01" "PhysA-S116-01" "PhysA-S116-01" "PhysA-S116-01" ...
 $ total_points_possible: num [1:603] 2207 2086 3228 1245 2970 ...
 $ total_points_earned  : num [1:603] 1910 1719 2506 945 2050 ...
 $ percentage_earned    : num [1:603] 0.865 0.824 0.776 0.759 0.69 ...
 $ subject              : chr [1:603] "PhysA" "PhysA" "PhysA" "PhysA" ...
 $ semester             : chr [1:603] "S116" "S116" "S116" "S116" ...
 $ section              : chr [1:603] "01" "01" "01" "01" ...
 $ 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] 84 97.8 93.1 73.8 94.8 ...
 $ Points_Possible      : num [1:603] 438 10 30 5 5 10 10 10 273 15 ...
 $ Points_Earned        : num [1:603] 399 10 21 3 NA 10 7 NA 232 9 ...
 $ Gender               : chr [1:603] "F" "F" "F" "F" ...
 $ q1                   : num [1:603] 4 3 4 4 3 3 4 4 NA NA ...
 $ q2                   : num [1:603] 3 3 2 3 2 4 3 3 NA NA ...
 $ q3                   : num [1:603] 3 3 2 3 2 2 3 2 NA NA ...
 $ q4                   : num [1:603] 4 3 4 4 4 4 5 4 NA NA ...
 $ q5                   : num [1:603] 4 3 4 4 4 3 3 3 NA NA ...
 $ q6                   : num [1:603] 4 4 4 3 4 3 3 3 NA NA ...
 $ q7                   : num [1:603] 4 3 4 3 3 4 2 2 NA NA ...
 $ q8                   : num [1:603] 4 3 4 4 4 3 4 3 NA NA ...
 $ q9                   : num [1:603] NA 3 2 2 4 1 1 1 NA NA ...
 $ q10                  : num [1:603] 3 3 4 3 3 4 5 2 NA NA ...
 $ TimeSpent            : num [1:603] 1482 1390 1232 2083 1864 ...
 $ TimeSpent_hours      : num [1:603] 24.7 23.2 20.5 34.7 31.1 ...
 $ TimeSpent_std        : num [1:603] -0.2347 -0.3023 -0.4191 0.2093 0.0471 ...
 $ int                  : num [1:603] 3.8 3 4 3.8 3.6 3.4 4.2 3.7 4 NA ...
 $ pc                   : num [1:603] 3.5 3 3 3 2.5 3 2.5 2.5 3 NA ...
 $ uv                   : num [1:603] 3.5 3.33 3 2.67 3.33 ...
 - 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> 

#inpsect data

9. 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.

final_data <- sci_classes %>%
  select(student_id, subject,semester, FinalGradeCEMS) %>%
    filter(subject == "OcnA") %>%
   arrange(desc(subject))
final_data

# A tibble: 111 × 4
   student_id subject semester FinalGradeCEMS
        <dbl> <chr>   <chr>             <dbl>
 1      44638 OcnA    S116               81.7
 2      47979 OcnA    S216               81.9
 3      54066 OcnA    S116               93.9
 4      54282 OcnA    S116               93.4
 5      54342 OcnA    S116               64.8
 6      54346 OcnA    S116               51.1
 7      54567 OcnA    S216               65.8
 8      57981 OcnA    S116               66.7
 9      58178 OcnA    S116               92.7
10      62175 OcnA    S216               73.8
# ℹ 101 more rows

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