Demo Data from EDBT 2026 / reviewers in the wild / expert
Christine Pisarczyk
dblp:264/7059
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Collaborative and social computing › collaborative learning
computer-supported collaborative learning |
0.4 | 1 | 2020 | LIFT: Integrating Stakeholder Voices into Algorithmic Team Formation · CHI 2020 |
Design research and methods
stakeholder engagement |
0.1 | 1 | 2020 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | LIFT: Integrating Stakeholder Voices into Algorithmic Team FormationabstractTeam 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 |
CHI | 4 |