scholarly journals Programming the platform university: Learning analytics and predictive infrastructures in higher education

2020 ◽  
pp. 003452372096562
Author(s):  
Carlo Perrotta
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Application Programming Interface ◽  
Educational Institutions ◽  
Social Scientific Study ◽  
Ethnographic Interviews ◽  
The Social ◽  
Application Programming ◽  
Social Scientific ◽  
Human Actors

This article offers a case study of how platforms and predictive infrastructures are emerging in higher education. It examines a Learning Analytics Application Programming Interface (API) from a popular Learning Management System. The API is treated firstly as an artefact based on the computational abstraction of educational principles, and secondly as an empirical entry point to investigate the emergence of a Learning Analytics infrastructure in a large Australian university. Through in-depth ethnographic interviews and the interpretative analysis of software development workflows, the paper describes an API-mediated platformisation process involving a range of actors and systems: computational experts, algorithms, data-savvy administrative staff and large corporate actors inserting themselves through back-ends and various other dependencies. In the conclusion, the article argues that the platformisation of higher education is part of a broader project that mobilises programmability and computation to re-engineer educational institutions in the interest of efficiency and prediction. However, the social-scientific study of this project cannot ignore the practical and compromised dimension where human actors and technical systems interact and, in the process, generate meaning.

scholarly journals Programming the platform university: Learning analytics and predictive infrastructures in higher education

2020 ◽  
Author(s):  
Carlo Perrotta
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Application Programming Interface ◽  
Educational Institutions ◽  
Social Scientific Study ◽  
Ethnographic Interviews ◽  
The Social ◽  
Application Programming ◽  
Social Scientific ◽  
Human Actors

This article offers a case study of how platforms and predictive infrastructures are emerging in higher education. It examines a Learning Analytics Application Programming Interface (API) from a popular Learning Management System. The API is treated firstly as an artefact based on the computational abstraction of educational principles, and secondly as an empirical entry point to investigate the emergence of a Learning Analytics infrastructure in a large Australian university. Through in-depth ethnographic interviews and the interpretative analysis of software development workflows, the paper describes an API-mediated platformisation process involving a range of actors and systems: computational experts, algorithms, data-savvy administrative staff and large corporate actors inserting themselves through back-ends and various other dependencies. In the conclusion, the article argues that the platformisation of higher education is part of a broader project that mobilises programmability and computation to re-engineer educational institutions in the interest of efficiency and prediction. However, the social-scientific study of this project cannot ignore the practical and compromised dimension where human actors and technical systems interact and, in the process, generate meaning.

Bringing Moral Culture into the Fray

2017 ◽  
Author(s):  
Justin Farrell
Keyword(s):  
Environmental Conflict ◽  
Scientific Study ◽  
Theoretical Argument ◽  
Moral Culture ◽  
Social Scientific Study ◽  
The Social ◽  
Social Scientific ◽  
Basic Level

This introductory chapter briefly presents the conflict in Yellowstone, elaborates on the book's theoretical argument, and specifies its substantive and theoretical contributions to the social scientific study of environment, culture, religion, and morality. The chapter argues that the environmental conflict in Yellowstone is not—as it would appear on the surface—ultimately all about scientific, economic, legal, or other technical evidence and arguments, but an underlying struggle over deeply held “faith” commitments, feelings, and desires that define what people find sacred, good, and meaningful in life at a most basic level. An overview of the subsequent chapters is also presented.

Nonprobability Sampling and Twitter

2017 ◽  
Vol 36 (2) ◽  
pp. 195-211 ◽  
Author(s):  
Patrick Rafail
Keyword(s):  
Social Sciences ◽  
Application Programming Interface ◽  
Sampling Strategies ◽  
Twitter Data ◽  
Large Databases ◽  
The Social ◽  
Application Programming ◽  
Data Source ◽  
Twitter Activity ◽  
Programming Interface

Twitter data are widely used in the social sciences. The Twitter Application Programming Interface (API) allows researchers to build large databases of user activity efficiently. Despite the potential of Twitter as a data source, less attention has been paid to issues of sampling, and in particular, the implications of different sampling strategies on overall data quality. This research proposes a set of conceptual distinctions between four types of populations that emerge when analyzing Twitter data and suggests sampling strategies that facilitate more comprehensive data collection from the Twitter API. Using three applications drawn from large databases of Twitter activity, this research also compares the results from the proposed sampling strategies, which provide defensible representations of the population of activity, to those collected with more frequently used hashtag samples. The results suggest that hashtag samples misrepresent important aspects of Twitter activity and may lead researchers to erroneous conclusions.

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