Welcome to our presentation!
In this paper,
we try to understand
how interest-driven content creation
shape opportunities for informal learning.
We present a case study of novices usage of data structures in in the scratch online community
In recent years in computing education
there is a focus on computational participation
learning programming is not limited to classrooms
students could learn through making and sharing projects in a socially supported context
Many see the potential of interest-driven online communities to promote computational participation
Typically in those communities
users can create and share their works, consume content, as well as interacting with members to exchange feedback and socialize
An overarching question is that how can online communities support computational learning, especially when regarding challenging computational concepts
Some suggest that novices can mentor each other and provide each other with technical and social support
Others found that online discussion can be inefficient and often distracted by socialization
The community that we focused on is Scratch
an online community where novices can make and share projects that can be seen, interacted with, and remixed by others
The Scratch community is built around a block-based programming system that is designed so that each type of blocks is mapped to a specific programming concept
One of the most important but challenging computational concepts for novices is data structure
In Scratch’s case, data structures consist of scalar variables and lists
Only less than fifteen percent of users have ever used data structures in their projects in Scratch
In this study, we specifically explored if and how interactions in the scratch community
support novices to learn data structures
We designed our study to be a series of mixed-method analyses
In our first study
we conducted a qualitative analysis on a sample of discussion threads
retrieved from the Scratch forum
The goal is to identity practices in community-generated learning resources about variable and lists
The output of study 1 is a proposed theory
we proposed a social feedback loop
that describes how community-generated resources may actually constrain innovative computational participation
Then in Study 2
we performed a series of quantitative hypothesis testing analysis
and found general support for our theory
We sampled 400 threads in that topic using keywords related to variables and lists
and performed grounded theory analysis
Here are some of our key findings
We found novices in the online discussion use specific examples to mentor each other about variables and list
They learn through the concrete elements that they are making, usually are specific to games
For example, one user is suggesting others to create a variable for the lives feature in a game
In the other quote, a user is teaching another user to use list to store armors in an inventory, also for a game
As a result, user generated examples and tutorials are often framed in game-making scenarios
For example, a lot of sample code are games with popular elements like lives and inventory
When users are helping each other on issues with variables and lists
they often use examples involving the same popular game elements
While these community-produced learning resources can offer concrete examples
we further found that they may not support everyone’s interest
Since most of the examples are about popular game-related elements
users with different interest may not be able to find useful resources
For example, in this thread, a user asked about how to use variables in their project
other users offer them examples making score counter in a game
However, the user wanted to use variables in their story telling project, which is not a game
They ended up never getting helped because there is no example in the community
We synthesize our findings as a social feedback loop
Stage 1 suggests that in an online interest-driven learning community, some types of creation, we call it Use Case A, will be more popular than others
When seeking community support, learners will tend to ask questions framed specifically around Use Case A and use community resources framed in the same use case
Stage 2 shows the results of this process
As learners receive support, they produce new artifacts with Use Case A that can serve as learning resources for others
Then, users in the future can use these learning resources, becoming even more likely to create the same use case
The outcome of the feedback loop is that, as certain use case becomes popular
the community's learning resources are increasingly focused on the same use cases
After proposing the theory from our qualitative insights, we did a second study, a series of quantitative analyses to test our theory
First, we hypothesize that certain genres of projects involving data structures will become more popular over time
specifically more and more projects involving variables and lists in the community will be games
We tested the hypothesis using logistic regression on the odds of a project being game over time
For all hypotheses, we conducted the analysis on projects with variables and projects with lists respectively
The first hypothesis is partially supported by projects with Lists
Over the years, the proportion of projects with lists has been increasing in the community
Second, we hypothesize that over time, the names that users give to variables and lists will become more concentrated
We measured concentration using the Gini coefficient of the count of variable and list names in the community
We tested the hypothesis using linear regression of the Gini coefficient over time
We found general support for this hypothesis
In addition to testing the outcome, in our third hypothesis, we tested the mechanism of the loop
We speculate that users exposed to popular use cases will create similar artifacts
Therefore, we hypothesize that users who have been exposed to projects involving popular variable and list names
will be more likely to use those names in their own projects
We measured exposure using whether the user has downloaded projects with top popular variables or list names
Users who have been exposed to popular list names, represented by the the solid line in the figure,
will be more likely to use - in other words, less likely to never use, popular names in their projects,
compared to users who have never downloaded projects with popular list names, represented by the dashed line
In summary, our findings suggests that in online communities, novices are able to mentor each other using concrete examples that support common interests.
However, additional regulation and supports as listed below need to be implemented so that it could mitigate the effects of the social feedback loop that we discovered
Please see our full paper for the full set of findings and insights. Thanks for listening!