Drawing on insights from the transdisciplinary project 'SENSE: Sensory Explorations of Nature in School Environments', this paper articulates a novel approach that addresses current calls for meaningful participation of children in citizen science activities in schools. We scaffolded more typical data collection activities within diverse digital and natural haptic experiences aimed at developing observational skills through arts and science-based methods, such as clay modelling, digital haptic tree identification and textural mapping exercises of the school grounds. Data were collected in three primary schools in Scotland, through audio and video-recording, and observation notes in the field. Findings showed how incorporating touch focuses attention differently to vision, leading to different scientific questions and inquiries. In effect, touch experiences may serve to balance the aims of citizen science beyond the intentional identification and enumeration of species towards the more taxing, epistemic and ethical questions of 'who decides what matters in nature observation' and 'for whom' is the learning, as students are invited to participate and contribute on their own terms. Implications for this form of citizen science to open significantly new directions for children's participation, and make its way into existing teaching practices in schools, are discussed.

This paper explores the use of Generative AI and ChatGPT for improving the production of distance learning materials. We have conducted participatory design workshops involving cross-disciplinary teams of academics and different stakeholders, such as industry partners and learning material designers that are involved in course production at the UK Open University. The main outcome of these workshops is a set of use cases on how Generative AI tools, such as ChatGPT, can be integrated and augment the existing course production and delivery processes to make them more agile and efficient. Following the workshops, we have developed a proof-of-concept tool that can instantly generate introductions and summaries of course material, automatically generate quizzes and tests, as well as automatically identify, categorise, and transform learning activities. A preliminary evaluation performed with members of the course production teams indicated that in 40% of cases AI generated text of 250-500 words is suitable for use in distance learning materials. Overall, the evaluation participants welcome this use of Generative AI, but there are concerns mainly centred on potential for bias, misinformation, and copyright infringement in the generated learning materials.

We investigate the potential of a new citizen science paradigm that facilitates collaborative learning between humans and artificial intelligence (AI). Recognising the potential of AI to support and empower rather than replace human participation, we explore the integration of image recognition as a ‘dialogic AI partner’ in citizen science (CS) projects, interacting with participants in real time. We study this in the context of a biodiversity monitoring project that relies on volunteers to identify biological species from images taken in the wild. Guided by the idea of Bakhtin’s dialogism and Bayesian inference principles, we developed a web interface that integrated an image recognition model, fine-tuned for classifying 22 UK bumblebee species, into an interactive interface based on visual feature keys to enable real-time dialogue between humans and AI. We report a significant improvement in identification accuracy for both humans and AI when they engage in such dialogue and retain the ability to reach independent conclusions rather than achieve consensus. Given the inherent need for convergence in decision-making within scientific processes such as species identification tasks, we augmented the dialogic process with a Bayesian model that unifies potentially divergent human and AI perspectives post collaboration to achieve a more accurate consensus decision than that achieved by either AI or citizens. Our work provides new understandings around the design of a dialogic space for CS practice that effectively builds on the complementary strengths of human and AI visual recognition approaches.

As the citizen science (CS) community flourishes, there is an opportunity to reflect on how practitioners can widen participation and work with participants as co-researchers to investigate and take action around global challenges. Through the lens of one CS case study, the X-Polli:Nation project, we report on how technologists, ecologists, and education specialists repurposed older projects by cross-pollinating ideas with children and teachers in the UK and in Italy to create Artificial Intelligence–enhanced tools appropriate for teaching sustainability in schools. Taking part in an actionable CS cycle, children learn about pollinating insects, record scientific data, create flowering habitats, and communicate their importance. Through this process, X-Polli:Nation demonstrates relevance across a number of Sustainable Development Goals (e.g., SDG 4, Quality Education; SDG 10, Reducing Inequality; and SDG 15, Life on Land), and applies the underlying SDG principle “leave no one behind.” We go on to investigate if, and how, young people would like to deepen their engagement with the SDGs, and we report that taking action and communicating the importance of the SDGs were of paramount interest. The challenge of building sustainability into an already crowded curriculum can be alleviated by understanding its value, considering the audience, and adapting to new contexts. The considerable benefits include raising awareness about global sustainability issues and giving children the confidence to become passionate environmental stewards, all the while extending the life of older projects and thus making CS methods sustainable too.

A number of initiatives invite members of the public to perform online classification tasks such as identifying objects in images. These tasks are crucial to numerous large-scale Citizen Science projects in different disciplines, with volunteers using their knowledge and online support tools to, for example, identify species of wildlife or classify galaxies by their shapes. However, for complex classification tasks, such as this case study on identifying species of bumblebee, reaching an agreement between volunteers - or even between experts~-~may require consensus-building processes. Collaboration and teamwork approaches to problem solving and decision-making have been widely documented to improve both task performance and user learning in the real world. Most of these processes and projects are mediated online through feedback delivered in an asynchronous manner, and this article thus addresses a central research question: How do participants involved in species identification tasks respond to different forms of feedback provided in online collaboration, designed to support peer-learning and improve task performance? We tested four different approaches to feedback within a collaboration task, where participants reviewed their previously annotated data based on information curated from their peers on a long running online citizen science initiative. The selected interfaces have a strong foundation in social science and psychology literature and can be applied to citizen science practices as well as other online communities. Results showed that while all four approaches increased accuracy, there were differences based on the types of consensus that existed before collaboration. Such differences highlight the usefulness of different forms of feedback during collaboration for increasing data accuracy of identification and furthering users' expertise on identification tasks. We found that anonymised and goal-directed free text comments posted on social learning interfaces were most effective in improving data accuracy as well as creating opportunities for peer-learning, particularly where the species identification task was more difficult. This study has significant implications for extending the practice of citizen science across formal and informal learning environments and reaching out to a variety of users.

Widespread concern over declines in pollinating insects has led to numerous recommendations of which “pollinator-friendly” plants to grow and help turn urban environments into valuable habitat for such important wildlife. Whilst communicated widely by organisations and readily taken up by gardeners, the provenance, accuracy, specificity and timeliness of such recommendations remain unclear. Here we use data (6429 records) gathered through a UK-wide citizen science programme (BeeWatch) to determine food plant use by the nations’ bumblebee species, and show that much of the plant use recorded does not reflect practitioner recommendations: correlation between the practitioners’ bumblebee-friendly plant list (376 plants compiled from 14 different sources) and BeeWatch records (334 plants) was low (r = 0.57), and only marginally higher than the correlation between BeeWatch records and the practitioners’ pollinator-friendly plant list (465 plants from 9 different sources; r = 0.52). We found pollinator-friendly plant lists to lack independence (correlation between practitioners’ bumblebee-friendly and pollinator-friendly lists: r = 0.75), appropriateness and precision, thus failing to recognise the non-binary nature of food-plant preference (bumblebees used many plants, but only in small quantities, e.g. lavender—the most popular plant in the BeeWatch database—constituted, at most, only 11% of records for any one bumblebee species) and stark differences therein among species and pollinator groups. We call for the provision and use of up-to-date dynamic planting recommendations driven by live (citizen science) data, with the possibility to specify pollinator species or group, to powerfully support transformative personal learning journeys and pollinator-friendly management of garden spaces.

Against a backdrop of accelerating digital innovation in nature conservation and environmental management, a real-world experiment was conducted with the research aims of assessing: 1) the effects of introducing a digital data-entry platform on volunteer data submission; and 2) the extent to which coordinators influence digital platform use by their volunteers. We focussed on a large-scale volunteer-based initiative aimed at eradicating the non-native American mink (Neovison vison) from northern Scotland. This geographically dispersed conservation initiative adopted a digital platform that allowed volunteers to submit records to a central database. We found that the platform had a direct and positive effect on volunteer data submission behaviour, increasing both the number and frequency of submissions. However, our analysis revealed striking differences in coordinator engagement with the platform, which in turn influenced the engagement of volunteers with this centrally introduced digital innovation. As a consequence, the intended organisation-wide rolling out of a digital platform translated into a diversely-implemented innovation, limiting the efficacy of the tool and revealing key challenges for digital innovation in geographically-dispersed conservation initiatives.

This short paper summarizes the development of ColloCaid (www.collocaid.uk), a text editor that supports writers with academic English collocations. After a brief introduction, the paper summarizes how the lexicographic database underlying ColloCaid was compiled, how text editor integration was achieved, and results from initial user studies. The paper concludes by outlining future developments.

Identifying private gardens in the U.K. as key sites of environmental engagement, we look at how a longer-term online citizen science programme facilitated the development of new and personal attachments of nature. These were visible through new or renewed interest in wildlife-friendly gardening practices and attitudinal shifts in a large proportion of its participants. Qualitative and quantitative data, collected via interviews, focus groups, surveys and logging of user behaviours, revealed that cultivating a fascination with species identification was key to both ‘helping nature’ and wider learning, with the programme creating a space where scientific and non-scientific knowledge could co-exist and reinforce one another.

We report on an in‐depth corpus linguistic study on ‘multiple views’ terminology and word collocation. We take a broad interpretation of these terms, and explore the meaning and diversity of their use in visualisation literature. First we explore senses of the term ‘multiple views’ (e.g., ‘multiple views’ can mean juxtaposition, many viewport projections or several alternative opinions). Second, we investigate term popularity and frequency of occurrences, investigating usage of ‘multiple’ and ‘view’ (e.g., multiple views, multiple visualisations, multiple sets). Third, we investigate word collocations and terms that have a similar sense (e.g., multiple views, side‐by‐side, small multiples). We built and used several corpora, including a 6‐million‐word corpus of all IEEE Visualisation conference articles published in IEEE Transactions on Visualisation and Computer Graphics 2012 to 2017. We draw on our substantial experience from early work in coordinated and multiple views, and with collocation analysis develop several lists of terms. This research provides insight into term use, a reference for novice and expert authors in visualisation, and contributes a taxonomy of ‘multiple view’ terms.

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.

In this short paper, we discuss the user-centred design process in the development of an online learning environment for learners of English for Academic Purposes (EAP). The ColloCaid project is a research collaboration between researchers from Applied Linguistics and Lexicography, Human-computer Interaction and Visualisation to develop a learning tool which provides users of academic English language an online environment to provide real-time suggestions to improve the vocabulary and fluency of their texts. Although still being developed, our online environment has received great interest from a range of users interested in improving their academic writing. The collaboration has revealed design insights which may be of interest to the researchers interested in the development of interactive learning environments.

Writing is a cognitively challenging activity that can benefit from lexicographic support. Academic writing in English presents a particular challenge, given the extent of use of English for this purpose. The ColloCaid tool, currently under development, responds to this challenge. It is intended to assist academic English writers by providing collocation suggestions, as well as alerting writers to unconventional collocational choices as they write. The underlying collocational data are based on a carefully curated set of about 500 collocational bases (nouns, verbs, and adjectives) characteristic of academic English, and their collocates with illustrative examples. These data have been derived from state-of-the-art corpora of academic English and academic vocabulary lists. The manual curation by expert lexicographers and reliance on specifically Academic English textual resources are what distinguishes ColloCaid from existing collocational resources. A further characteristic of ColloCaid is its strong emphasis on usability. The tool draws on dictionary-user research, findings in information visualization, as well as usability testing specific to ColloCaid in order to find an optimal amount of collocation prompts, and the best way to present them to the user.

Corpora have given rise to a wide range of lexicographic resources aimed at helping novice users of academic English with their writing. This includes academic vocabulary lists, a variety of textbooks, and even a bespoke academic English dictionary. However, writers may not be familiar with these resources or may not be sufficiently aware of the lexical shortcomings of their emerging texts to trigger the need to use such help in the first place. Moreover, writers who have to stop writing to look up a word can be distracted from getting their ideas down on paper. The ColloCaid project (www.collocaid.uk) aims to address these problems by integrating information on collocation with text editors. In this paper, we share the research underpinning the initial development of ColloCaid by detailing the rationale of (1) the lexicographic database we are compiling to support the collocation needs of novice users of English for Academic Purposes (EAP) and (2) the preliminary visualisation decisions taken to present information on collocation to EAP users without disrupting their writing. We conclude the paper by outlining the next steps in the research.

Several citizen science projects engage with the public around pollinator species, typically requesting data (e.g. in the form of photo-records of different species tagged by place and date). While such projects help scientists collect data, these data are rarely fed back to the public in any meaningful manner. In this paper, we address this through a recommender system based on Matrix Factorization over a matrix of observed bumblebee-plant interactions derived from data submitted to a citizen science project BeeWatch. The system recommends pollinator-friendly plants for domestic gardens and takes into account both the fact that different bumblebee species exhibit differing preferences for flowers, and that plants flower at different times of the year. The goal is to attract a range of bumblebee species to a garden and to ensure that these species have sufficient food sources through the season.

The rapid rise of citizen science, with lay people forming often extensive biodiversity sensor networks, is seen as a solution to the mismatch between data demand and supply while simultaneously engaging citizens with environmental topics. However, citizen science recording schemes require careful consideration of how to motivate, train, and retain volunteers. We evaluated a novel computing science framework that allowed for the automated generation of feedback to citizen scientists using natural language generation (NLG) technology. We worked with a photo-based citizen science program in which users also volunteer species identification aided by an online key. Feedback is provided after photo (and identification) submission and is aimed to improve volunteer species identification skills and to enhance volunteer experience and retention. To assess the utility of NLG feedback, we conducted two experiments with novices to assess short-term (single session) and longer-term (5 sessions in 2 months) learning, respectively. Participants identified a specimen in a series of photos. One group received only the correct answer after each identification, and the other group received the correct answer and NLG feedback explaining reasons for misidentification and highlighting key features that facilitate correct identification. We then developed an identification training tool with NLG feedback as part of the citizen science program BeeWatch and analyzed learning by users. Finally, we implemented NLG feedback in the live program and evaluated this by randomly allocating all BeeWatch users to treatment groups that received different types of feedback upon identification submission. After 6 months separate surveys were sent out to assess whether views on the citizen science program and its feedback differed among the groups. Identification accuracy and retention of novices were higher for those who received automated feedback than for those who received only confirmation of the correct identification without explanation. The value of NLG feedback in the live program, captured through questionnaires and evaluation of the online photo-based training tool, likewise showed that the automated generation of informative feedback fostered learning and volunteer engagement and thus paves the way for productive and long-lived citizen science projects.