Introduction

Donors Choose is a website designed to help teachers request donations for technologies, textbooks, and other resources. Donors Choose provides access to data from all of their projects. One of the data sources is a list of all of the requested resources and their cost. Another contains information about donors to projects. There are also essays in which teachers describe their need and how their benefits their students.

In order to examine differences between resources requested from different groups of teachers, I analyzed the need statements from teachers in high poverty and low poverty classrooms. In order to reduce some of the variability in their responses, I examined only science teachers. Also, to maximize the potential differences from an analytic perspective, I analyzed teachers in what Donors Choose determined were the highest poverty schools (n = 1,315) with those in low poverty schools. I then randomly sampled for 1,315 requests from students in low poverty schools, so the analysis was based on 2,615 total need statements.

Method

To analyze the data, I adapted Sherin’s (2013) approach: sum(table(kfit$cluster)) 1. Process documents (in this case, the need statements) 2. Transform text into a term-document matrix 3. Cluster documents the documents (using hierarchical and k-means clustering) 4. Compare the groups using cosine similarity

Results

Here are the number of tweets and the top 10 words in each cluster (or topic):

## 
##    1    2    3    4    5    6 
## 1110  150  569  113  347  326
##    Cluster 1 Cluster 2  Cluster 3 Cluster 4 Cluster 5 Cluster 6
## 1       need   printer    science   include       kit     their
## 2    student  makerbot   material      cost       set     learn
## 3       ipad     print       math  shipping   machine   project
## 4   computer  filament   activate targetnew     build       own
## 5     camera    design       book     price      knex      them
## 6       case       ink     center   fulfill  electric   enhance
## 7     laptop    bundle    handson   science     class      work
## 8  projector     color       hand  proposal   circuit      ipad
## 9       mini  academic experience     delta     robot     skill
## 10   digital    create        lab   educate    simple      help

Here is a plot of the cosine similarity between the high poverty and low poverty groups and the clusters / topics:

High-poverty teachers seem more likely to request science laboratory / investigation-related materials (topic 3) and to make cost-sensitive requests (topic 4). Low-poverty teachers seem more likely to request digital technologies (topic 1), 3D printing / “Maker” technologies (topic 2), and materials for personalized learning (topic 6). It appears that robotics / K’nex kits (topic 5) are requested a bit more in high-poverty than low-poverty classrooms.

References

Donors Choose. (2015). Open data. Retrieved from http://data.donorschoose.org/docs/overview/

Sherin, B. (2013). A computational study of commonsense science: An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences, 22(4), 600-638.