Designing online species identification tools for biological recording: the impact on data quality and citizen science learning

Nirwan Sharma (Corresponding Author), Laura Colucci-Gray, Advaith Siddharthan, Richard Comont, René van der Wal

Research output: Contribution to journalArticle

1 Citation (Scopus)
5 Downloads (Pure)

Abstract

In recent years, the number and scale of environmental citizen science programmes that involve lay people in scientific research have increased rapidly. Many of these initiatives are concerned with the recording and identification of species, processes which are increasingly mediated through digital interfaces. Here, we address the growing need to understand the particular role of digital identification tools, both in generating scientific data and in supporting learning by lay people engaged in citizen science activities pertaining to biological recording communities. Starting from two well-known identification tools, namely identification keys and field guides, this study focuses on the decision-making and quality of learning processes underlying species identification tasks, by comparing three digital interfaces designed to identify bumblebee species. The three interfaces varied with respect to whether species were directly compared or filtered by matching on visual features; and whether the order of filters was directed by the interface or a user-driven open choice. A concurrent mixed-methods approach was adopted to compare how these different interfaces affected the ability of participants to make correct and quick species identifications, and to better understand how participants learned through using these interfaces. We found that the accuracy of identification and quality of learning were dependent upon the interface type, the difficulty of the specimen on the image being identified and the interaction between interface type and ‘image difficulty’. Specifically, interfaces based on filtering outperformed those based on direct visual comparison across all metrics, and an open choice of filters led to higher accuracy than the interface that directed the filtering. Our results have direct implications for the design of online identification technologies for biological recording, irrespective of whether the goal is to collect higher quality citizen science data, or to support user learning and engagement in these communities of practice.
Original languageEnglish
Article numbere5965
Number of pages24
JournalPeerJ
Volume6
DOIs
Publication statusPublished - 28 Jan 2019

Fingerprint

learning
Learning
Decision making
taxonomic keys
Bombus
Biota
type collections
Aptitude
decision making
Ecology
Decision Making
Identification (Psychology)
Data Accuracy
species identification
Technology
Research
methodology

Keywords

  • Citizen science
  • User learning
  • Species identification
  • Identification keys
  • Field guides
  • Data quality
  • Learning
  • Cognition
  • Bumblebees
  • Biological recording
  • COMMUNITIES
  • INTEGRATION
  • THINK-ALOUD

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Neuroscience(all)

Cite this

Designing online species identification tools for biological recording : the impact on data quality and citizen science learning. / Sharma, Nirwan (Corresponding Author); Colucci-Gray, Laura; Siddharthan, Advaith; Comont, Richard; van der Wal, René.

In: PeerJ, Vol. 6, e5965, 28.01.2019.

Research output: Contribution to journalArticle

@article{81967c914d084b0d919b690d6741ef2e,
title = "Designing online species identification tools for biological recording: the impact on data quality and citizen science learning",
abstract = "In recent years, the number and scale of environmental citizen science programmes that involve lay people in scientific research have increased rapidly. Many of these initiatives are concerned with the recording and identification of species, processes which are increasingly mediated through digital interfaces. Here, we address the growing need to understand the particular role of digital identification tools, both in generating scientific data and in supporting learning by lay people engaged in citizen science activities pertaining to biological recording communities. Starting from two well-known identification tools, namely identification keys and field guides, this study focuses on the decision-making and quality of learning processes underlying species identification tasks, by comparing three digital interfaces designed to identify bumblebee species. The three interfaces varied with respect to whether species were directly compared or filtered by matching on visual features; and whether the order of filters was directed by the interface or a user-driven open choice. A concurrent mixed-methods approach was adopted to compare how these different interfaces affected the ability of participants to make correct and quick species identifications, and to better understand how participants learned through using these interfaces. We found that the accuracy of identification and quality of learning were dependent upon the interface type, the difficulty of the specimen on the image being identified and the interaction between interface type and ‘image difficulty’. Specifically, interfaces based on filtering outperformed those based on direct visual comparison across all metrics, and an open choice of filters led to higher accuracy than the interface that directed the filtering. Our results have direct implications for the design of online identification technologies for biological recording, irrespective of whether the goal is to collect higher quality citizen science data, or to support user learning and engagement in these communities of practice.",
keywords = "Citizen science, User learning, Species identification, Identification keys, Field guides, Data quality, Learning, Cognition, Bumblebees, Biological recording, COMMUNITIES, INTEGRATION, THINK-ALOUD",
author = "Nirwan Sharma and Laura Colucci-Gray and Advaith Siddharthan and Richard Comont and {van der Wal}, Ren{\'e}",
note = "Funding This work was supported by the University of Aberdeen’s Environment and Food Security Theme PhD studentship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/peerj.5965#supplemental-information.",
year = "2019",
month = "1",
day = "28",
doi = "10.7717/peerj.5965",
language = "English",
volume = "6",
journal = "PeerJ",
issn = "2167-8359",
publisher = "PEERJ INC",

}

TY - JOUR

T1 - Designing online species identification tools for biological recording

T2 - the impact on data quality and citizen science learning

AU - Sharma, Nirwan

AU - Colucci-Gray, Laura

AU - Siddharthan, Advaith

AU - Comont, Richard

AU - van der Wal, René

N1 - Funding This work was supported by the University of Aberdeen’s Environment and Food Security Theme PhD studentship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/peerj.5965#supplemental-information.

PY - 2019/1/28

Y1 - 2019/1/28

N2 - In recent years, the number and scale of environmental citizen science programmes that involve lay people in scientific research have increased rapidly. Many of these initiatives are concerned with the recording and identification of species, processes which are increasingly mediated through digital interfaces. Here, we address the growing need to understand the particular role of digital identification tools, both in generating scientific data and in supporting learning by lay people engaged in citizen science activities pertaining to biological recording communities. Starting from two well-known identification tools, namely identification keys and field guides, this study focuses on the decision-making and quality of learning processes underlying species identification tasks, by comparing three digital interfaces designed to identify bumblebee species. The three interfaces varied with respect to whether species were directly compared or filtered by matching on visual features; and whether the order of filters was directed by the interface or a user-driven open choice. A concurrent mixed-methods approach was adopted to compare how these different interfaces affected the ability of participants to make correct and quick species identifications, and to better understand how participants learned through using these interfaces. We found that the accuracy of identification and quality of learning were dependent upon the interface type, the difficulty of the specimen on the image being identified and the interaction between interface type and ‘image difficulty’. Specifically, interfaces based on filtering outperformed those based on direct visual comparison across all metrics, and an open choice of filters led to higher accuracy than the interface that directed the filtering. Our results have direct implications for the design of online identification technologies for biological recording, irrespective of whether the goal is to collect higher quality citizen science data, or to support user learning and engagement in these communities of practice.

AB - In recent years, the number and scale of environmental citizen science programmes that involve lay people in scientific research have increased rapidly. Many of these initiatives are concerned with the recording and identification of species, processes which are increasingly mediated through digital interfaces. Here, we address the growing need to understand the particular role of digital identification tools, both in generating scientific data and in supporting learning by lay people engaged in citizen science activities pertaining to biological recording communities. Starting from two well-known identification tools, namely identification keys and field guides, this study focuses on the decision-making and quality of learning processes underlying species identification tasks, by comparing three digital interfaces designed to identify bumblebee species. The three interfaces varied with respect to whether species were directly compared or filtered by matching on visual features; and whether the order of filters was directed by the interface or a user-driven open choice. A concurrent mixed-methods approach was adopted to compare how these different interfaces affected the ability of participants to make correct and quick species identifications, and to better understand how participants learned through using these interfaces. We found that the accuracy of identification and quality of learning were dependent upon the interface type, the difficulty of the specimen on the image being identified and the interaction between interface type and ‘image difficulty’. Specifically, interfaces based on filtering outperformed those based on direct visual comparison across all metrics, and an open choice of filters led to higher accuracy than the interface that directed the filtering. Our results have direct implications for the design of online identification technologies for biological recording, irrespective of whether the goal is to collect higher quality citizen science data, or to support user learning and engagement in these communities of practice.

KW - Citizen science

KW - User learning

KW - Species identification

KW - Identification keys

KW - Field guides

KW - Data quality

KW - Learning

KW - Cognition

KW - Bumblebees

KW - Biological recording

KW - COMMUNITIES

KW - INTEGRATION

KW - THINK-ALOUD

UR - http://www.scopus.com/inward/record.url?scp=85061385008&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/designing-online-species-identification-tools-biological-recording-impact-data-quality-citizen-scien

U2 - 10.7717/peerj.5965

DO - 10.7717/peerj.5965

M3 - Article

VL - 6

JO - PeerJ

JF - PeerJ

SN - 2167-8359

M1 - e5965

ER -