activity
20172021
most citedBoomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms

38 citations · 54 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC2021

Towards Teachable Conversational Agents

Nalin Chhibber, Edith Law

The traditional process of building interactive machine learning systems can be viewed as a teacher-learner interaction scenario where the machine-learners are trained by one or mo…

cs.HC201911 cited

Using Conversational Agents To Support Learning By Teaching

Nalin Chhibber, Edith Law

Conversational agents are becoming increasingly popular for supporting and facilitating learning. Conventional pedagogical agents are designed to play the role of human teachers by…

cs.CY201938 cited

Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms

Snehalkumar, S. Gaikwad, Durim Morina +35

Paid crowdsourcing platforms suffer from low-quality work and unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores,…

cs.IR2018

Human Perception of Surprise: A User Study

Nalin Chhibber, Rohail Syed, Mengqiu Teng +3

Understanding how to engage users is a critical question in many applications. Previous research has shown that unexpected or astonishing events can attract user attention, leading…

cs.HC20175 cited

Prototype Tasks: Improving Crowdsourcing Results through Rapid, Iterative Task Design

Snehalkumar "Neil" S. Gaikwad, Nalin Chhibber, Vibhor Sehgal +26

Low-quality results have been a long-standing problem on microtask crowdsourcing platforms, driving away requesters and justifying low wages for workers. To date, workers have been…