BibSLEIGH — Seth

Travelled to:

1 × USA

Collaborated with:

Anurag Sarkar Z.Popovic Sebastian Deterding Josh Aaron Miller Britton Horn Pratheep Kumar Paranthaman E.Andersen Y.Liu R.Snider M.S.El-Nasr Aaron William Bauer Theo Tsapakos D.Hsiao C.Ballweber M.Williams G.Smith Janos Barbero Firas Khatib D.Baker Amy L. R. Sterling Robert Kleffner William M. Silversmith Justin B. Siegel Uttkarsh Narayan Matthew Hantsbarger R.Szeto Zhengxing Chen Christopher Amato Truong-Huy D. Nguyen Y.Sun Aditya Ponnada Binod Thapa-Chhetry Dinesh John S.S.Intille E.O'Rourke J.Lowdermilk D.Truong Ilya Makedon H.Lü J.Fogarty Foldit players Jonathan Barone Colin Bayer Rowan Copley Nova Barlow Matthew Burns Sundipta Rao Georg Seelig Adrien Treuille Andrew Leaver-Fay K.Tuite Alex Cho Snyder Michael Beenen D.Salesin

Talks about:

game (14) player (7) level (5) design (4) comput (4) skill (4) human (4) rate (4) matchmak (3) analysi (3)

Person: Seth Cooper

DBLP: Cooper:Seth

Contributed to:

2012

2010

2011

2013

2014

2015

2016

2017

2018

2019

Wrote 22 papers:

CHI-2012-AndersenOLSLTCP #complexity #game studies: The impact of tutorials on games of varying complexity (EA, EO, YEL, RS, JL, DT, SC, ZP), pp. 59–68.
FDG-2010-CooperTBLTKSBSBP #challenge #design #game studies: The challenge of designing scientific discovery games (SC, AT, JB, ALF, KT, FK, ACS, MB, DS, DB, ZP), pp. 40–47.
FDG-2011-AndersenLSSCP #game studies #on the: On the harmfulness of secondary game objectives (EA, YEL, RS, RS, SC, ZP), pp. 30–37.
FDG-2011-CooperKMLBBFPp #analysis #metaprogramming #social: Analysis of social gameplay macros in the Foldit cookbook (SC, FK, IM, HL, JB, DB, JF, ZP, Fp), pp. 9–14.
FDG-2011-LiuASCP #analysis #effectiveness: Feature-based projections for effective playtrace analysis (YEL, EA, RS, SC, ZP), pp. 69–76.
FDG-2013-BauerCP #automation: Automated redesign of local playspace properties (AWB, SC, ZP), pp. 190–197.
FDG-2014-HsiaoCBP #behaviour: User behavior transformation through dynamic input mappings (DYH, SC, CB, ZP).
FDG-2015-BaroneBCBBRSPC #design #evaluation #game studies #named: Nanocrafter: Design and Evaluation of a DNA Nanotechnology Game (JB, CB, RC, NB, MB, SR, GS, ZP, SC).
DiGRA-FDG-2016-CooperDT #game studies #rating #testing: Player Rating Systems for Balancing Human Computation Games: Testing the Effect of Bipartiteness (SC, SD, TT).
AIIDE-2017-SarkarC #game studies #predict: Level Difficulty and Player Skill Prediction in Human Computation Games (AS, SC), pp. 228–233.
CHI-PLAY-2017-HornCD #adaptation #analysis #elicitation #game studies: Adapting Cognitive Task Analysis to Elicit the Skill Chain of a Game (BH, SC, SD), pp. 277–289.
FDG-2017-SarkarWDC #game studies #rating: Engagement effects of player rating system-based matchmaking for level ordering in human computation games (AS, MW, SD, SC), p. 10.
AIIDE-2018-HornMSC #approach #automation #monte carlo: A Monte Carlo Approach to Skill-Based Automated Playtesting (BH, JAM, GS, SC), pp. 166–172.
CIG-2018-ChenANCSE #game studies #named #performance #recommendation: Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games (ZC, CA, THDN, SC, YS, MSEN), pp. 1–8.
FDG-2018-CooperSKSS #game studies #tool support: Repurposing citizen science games as software tools for professional scientists (SC, ALRS, RK, WMS, JBS), p. 6.
FDG-2018-SarkarC #rating: Meet your match rating: providing skill information and choice in player-versus-level matchmaking (AS, SC), p. 8.
FDG-2018-SarkarC18a #game studies #volunteer: Comparing paid and volunteer recruitment in human computation games (AS, SC), p. 9.
CHI-PLAY-2019-ParanthamanC #crowdsourcing #development #game studies #named #towards: ARAPID: Towards Integrating Crowdsourced Playtesting into the Game Development Environment (PKP, SC), pp. 121–133.
CHI-PLAY-2019-PonnadaCTMJI #crowdsourcing #design #game studies #process #recognition #research: Designing Videogames to Crowdsource Accelerometer Data Annotation for Activity Recognition Research (AP, SC, BTC, JAM, DJ, SSI), pp. 135–147.
FDG-2019-MillerNHCE #game studies: Expertise and engagement: re-designing citizen science games with players' minds in mind (JAM, UN, MH, SC, MSEN), p. 11.
FDG-2019-SarkarC #array #rating #using: Using rating arrays to estimate score distributions for player-versus-level matchmaking (AS, SC), p. 8.
CoG-2019-SarkarC #game studies #using: Inferring and Comparing Game Difficulty Curves using Player-vs-Level Match Data (AS, SC), pp. 1–4.