Researching educational apps: ecologies, technologies, subjectivities and learning regimes

ABSTRACT

Apps are becoming increasingly important in education. However, critical educational research has hardly ever undertaken up-close analyses of educational apps. This article provides an extensive analysis of one ‘learning to code' app called Grasshopper. Drawing from the field of Science and Technology Studies, the article adopts a theoretical framework that situates app analyses at the dimensions of apps' situated ecologies, their platform and algorithmic technologies, the enacted user subjectivities and, as the sum of these previous dimensions, the projected learning regimes. Making use of the methodological entry points of app websites, stores and interfaces, the article makes visible and analyzes the app’s infrastructural settings, actively invokes different app situations, and uses these to advance the inquiry by offering a multidimensional point of view that navigates different scalar dimensions of educational apps. In doing so, the article examines learning to code through its concrete sociomaterial practices, and the specific pedagogies and sorts of education that are employed in order to bring about such learning.

KEYWORDS:

• Apps
• science and technology studies
• learning to code

Disclosure statement

No potential conflict of interest was reported by the author.

Notes on contributor

Mathias Decuypere is an Assistant Professor at the Methodology of Educational Sciences Research Group (KU Leuven, Belgium). Primary research interests are directed at developing and making use of innovative qualitative research methods that allow to analyze new educational technologies, higher and regular education policy, open education and Education for sustainable development.

ORCID

Mathias Decuypere http://orcid.org/0000-0002-0983-738X

Notes

1 https://grasshopper.codes/.

2 https://area120.google.com/.

3 As with all apps, usage statistics are difficult to obtain, but some information about the Android version can be found here: https://www.appbrain.com/app/grasshopper%3A-learn-to-code-for-free/com.area120.grasshopper

4 https://wiki.digitalmethods.net/Dmi/ToolGooglePlaySimilar/.

5 In other words, the Google Play Similar Apps tool allows to investigate which apps are algorithmically associated with which other apps without making use of a user's personal data. Our analysis is not immediately directed at inquiring the mutability of algorithms based on personal recommender systems – we do not intend to analyze how algorithms’ output shifts, and is co-shaped by, users’ moving through data (Mackenzie 2015). Rather, of particular interest here is the agency that is present within algorithmic ordering itself in so far as this ordering is displaying the relational embeddedness of the app in a store network of similar apps. Whilst stabilizing the mutability of algorithmic agency, this allows to make the processes of algorithmic associating, and the effects that such associating brings about, amenable to empirical scrutiny (Dieter et al. 2018; Ziewitz 2016).

6 Network visualizations were constructed in Gephi (gephi.org) using the ForceAtlas algorithm (Bastian, Heymann, and Jacomy 2009). Figure 2 shows this network with a filter of 2, i.e., only apps with more than one relation are being displayed. Size of the nodes is set proportionally to their betweenness centrality. In order to perform a visual network analysis, app type was manually categorized (and colored) based on app descriptions as found in the Play Store (Decuypere 2019c).

7 Between September and October 2018. All footage discussed in this article (website, store and interface) was collected within that timeframe. Figure 1 consists of two screenshots from the Grasshopper website (https://grasshopper.codes/); Figures 3–6 consists of screenshots taken within the Grasshopper app itself.

8 https://support.grasshopper.codes/t/super-charge-your-coding-skills/58.

9 https://play.google.com/store/apps/details?id=com.area120.grasshopper.

10 As a single user walking through Grasshopper, it is impossible to track down the concrete functioning of each algorithm. Rather, conform STS principles, the focus is on the effects of such algorithmic agency. In order to give the reader a general idea about the algorithms’ functioning, the following brief description is based on Malysheva (2017), a Google employee who has provided some insight into how Grasshopper's algorithms function.

Additional information

Funding

This work was supported by Research Council, KU Leuven [grant number C14/18/041].