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Christine Pisarczyk

dblp:264/7059 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Collaborative and social computing · 77% Design research and methods · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing › collaborative learning
computer-supported collaborative learning
0.412020
LIFT: Integrating Stakeholder Voices into Algorithmic Team Formation · CHI 2020
Design research and methods
stakeholder engagement
0.112020
LIFT: Integrating Stakeholder Voices into Algorithmic Team Formation · CHI 2020

Methods — techniques the papers use, named apart from their topics

team formation algorithms · 0.4interviews · 0.4experiment · 0.4
YearPublicationVenuePosition
2020 LIFT: Integrating Stakeholder Voices into Algorithmic Team Formation
abstract
Team formation tools assume instructors should configure the criteria for creating teams, precluding students from participating in a process affecting their learning experience. We propose LIFT, a novel learner-centered workflow where students propose, vote for, and weigh the criteria used as inputs to the team formation algorithm. We conducted an experiment (N=289) comparing LIFT to the usual instructor-led process, and interviewed participants to evaluate their perceptions of LIFT and its outcomes. Learners proposed novel criteria not included in existing algorithmic tools, such as organizational style. They avoided criteria like gender and GPA that instructors frequently select, and preferred those promoting efficient collaboration. LIFT led to team outcomes comparable to those achieved by the instructor-led approach, and teams valued having control of the team formation process. We provide instructors and designers with a workflow and evidence supporting giving learners control of the algorithmic process used for grouping them into teams.
Emily M. Hastings, Albatool A. Alamri, Andrew Kuznetsov, Christine Pisarczyk, Karrie Karahalios, Darko Marinov, Brian P. Bailey
CHI4