What motivates enrolment in programming MOOCs?

Learners who enrol in massive open online courses (MOOCs) have different backgrounds and tend to have different motivations than learners in traditional courses. Based on value-expectancy theory, an instrument was developed to measure motivation for enrolling in a programming MOOC. A study with 1229 adult participants in Estonian-language programming course “About Programming” was conducted to validate the instrument. Results of confirmatory factor analysis validated the 7-factor scale named factors influencing enrolment in MOOC (FIEM). FIEM comprises three factors of expectancies, three factors of values and one factor of social influence. The highest and lowest rated motivational factors influencing enrolment in programming MOOC are discussed in the paper. Interest in and expectations for the course, personal suitability of distance learning and suitability for family and work are the highest-rated motivational factors for those who enrol in MOOC. Usefulness related to own children, social influence and usefulness to related to certification were the lowest rated. The results of this study can be useful for designers of programming MOOCs and the developed scale might be used in future studies.     

Best of both worlds or refusal to comply? The rich kids of Tehran on Instagram

ABSTRACT

This article offers a textual analysis and semiotic reading of the Rich Kids of Tehran's (RKOT’s) Instagram page. Contributing to scholarship on Iranian youth media practices, this article interrogates how the RKOT navigate urban and rural space to engage in everyday processes of resistance against global and local systemic oppression. Grounding their visual representations on Instagram in historical and cultural context, the author questions how and when quotidian actions are transformed into political transgressions when posted on social media. This article emphasizes the RKOT's agency in shaping their brand by analyzing representations of gender performance, intertextuality, and national identity on Instagram.     

Using the Information Structure Model to Compare Profile-Based Information Filtering Systems

In the IR field it is clear that the value of a system depends on the cost and benefit profiles of its users. It would seem obvious that different users would prefer different systems. In the TREC-9 filtering track, systems are evaluated by a utility measure specifying a given cost and benefit. However, in the study of decision systems it is known that, in some cases, one system may be unconditionally better than another. In this paper we employ a decision theoretic approach to find conditions under which an Information Filtering (IF) system is unconditionally superior to another for all users regardless of their cost and benefit profiles.It is well known that if two IF systems have equal precision the system with better recall will be preferred by all users. Similarly, with equal recall, better precision is universally preferred. We confirm these known results and discover an unexpected dominance relation in which a system with lower recall will be universally preferred provided its precision is sufficiently higher.