The Wikipedia Adventure

Narayan, Sneha; Orlowitz, Jake; Morgan, Jennifer Craft; Hill, Benjamin Mako; Shaw, Aaron

doi:10.1145/2998181.2998307

Cited by 21 publications

(7 citation statements)

References 39 publications

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“…Prior work has considered how a specific subset of people learn (e.g., young adults), how people learn a specific subject, or how people use a particular resource for learning [27,36,50,54]. Yet, little is known about the full ecosystem of what adults learn about online and what educational resources they use to do so.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Online Learning Landscape

Kross

Hargittai

Redmiles

2021

Proc. ACM Hum.-Comput. Interact.

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Hundreds of millions of people learn something new online every day. Simultaneously, the study of online education has blossomed within the human computer interaction community, with new systems, experiments, and observations creating and exploring previously undiscovered online learning environments. In this study we endeavor to characterize this entire landscape of online learning experiences using a national survey of 2260 US adults who are balanced to match the demographics of the U.S. We examine the online learning resources that they consult, and we analyze the subjects that they pursue using those resources. Furthermore, we compare both formal and informal online learning experiences on a larger scale than has ever been done before, to our knowledge, to better understand which subjects people are seeking for intensive study. We find that there is a core set of online learning experiences that are central to other experiences and these are shared among the majority of people who learn online. We conclude by showing how looking outside of these core online learning experiences can reveal opportunities for innovation in online education.CCS Concepts: • Applied computing → E-learning; • Human-centered computing → Human computer interaction (HCI).

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Section: Introductionmentioning

confidence: 99%

Characterizing the Online Learning Landscape

Kross

Hargittai

Redmiles

2021

Proc. ACM Hum.-Comput. Interact.

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“…Although fully automated, the Wikipedia Adventure was designed to provide both comprehensive socialization and an impression of personalization through the use of self-guided interactive tutorials, realistic training scenarios, levels and badges, and a light, playful tone. Although the Wikipedia Adventure was widely used and rated favorably by the editors who played along, a controlled study of the system did not show any long-term impact [20]. The evaluation of the Wikipedia Adventure is notable because it was the first study of new editor onboarding on Wikipedia to combine an analysis of long-term retention impacts with a randomized controlled study design-a model we follow in our current study.…”

Section: Wikimedia Foundation Interventionsmentioning

confidence: 93%

Evaluating the impact of the Wikipedia Teahouse on newcomer socialization and retention

Morgan

Halfaker

2018

Proceedings of the 14th International Symposium on Open Collaboration

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Effective socialization of new contributors is vital for the long-term sustainability of open collaboration projects. Previous research has identified many common barriers to participation. However, few interventions employed to increase newcomer retention over the long term by improving aspects of the onboarding experience have demonstrated success. This study presents an evaluation of the impact of one such intervention, the Wikipedia Teahouse, on new editor survival. In a controlled experiment, we find that new editors invited to the Teahouse are retained at a higher rate than editors who do not receive an invite. The effect is observed for both lowand high-activity newcomers, and for both short-and long-term survival.

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“…Our analysis drew heavily from a body of research in econometrics that has used natural experiments to establish causality in observational data where researchers have traditionally been limited to making claims about correlation [3,28]. Recently HCI researchers have begun analyzing natural experiments as well [29]. Borrowing terminology from randomized controlled experiments in medicine, analyses of natural experiments typically describe their key independent variable as "the treatment."…”

Section: Analytic Planmentioning

confidence: 99%

How “Wide Walls” Can Increase Engagement

Dasgupta

Hill

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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A core aim for designing constructionist learning systems and toolkits is enabling "wide walls"-a metaphor used to describe supporting a diverse range of creative outcomes. Ensuring that a broad design space is afforded to learners by a toolkit is a common approach to achieving wide walls. We use econometric methods to provide an empirical test of the wide walls theory through a natural experiment in the Scratch online community. We estimate the causal effect of a policy change that gave a large number of Scratch users access to a more powerful version of Scratch data structures, effectively widening the walls for learners. We show that access to and use of these more powerful new data structures caused learners to use data structures more frequently. Our findings provide support for the theory that wide walls can increase engagement and learning.

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The Wikipedia Adventure

Cited by 21 publications

References 39 publications

Characterizing the Online Learning Landscape

Characterizing the Online Learning Landscape

Evaluating the impact of the Wikipedia Teahouse on newcomer socialization and retention

How “Wide Walls” Can Increase Engagement

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