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The progressive move of higher education institutions (HEIs) towards blended and online environments, accelerated by COVID-19, and their access to a greater variety of student data has heightened the need for ethical learning analytics (LA). This need is particularly salient in light of a lack of comprehensive, evidence-based guidelines on ethics that address gaps voiced in LA ethics research. Studies on the topic are predominantly conceptual, representing mainly institutional rather than stakeholder views, with some areas of ethics remaining underexplored. In this paper, we address this need by using a case of four years of interdisciplinary research in developing the award-winning Early Alerts Indicators (EAI) dashboard at a distance learning university. Through a lens focused on ethical considerations and informed by the practical approach to ethics, we conducted a case study review, using 10 relevant publications that report on the development and implementation of the tool. Our six practical recommendations on how to ethically engage with LA can inform an ethical development of LA that not only protects student privacy, but also ensures that LA tools are used in ways that effectively support student learning and development.

Participation in authentic research in the field and online through Community and Citizen Science (CCS) has shown to bring learning benefits to volunteers. In online CCS, available platforms present distinct features, ranging from scaffolding the process of data collection, to supporting data analysis and enabling volunteers to initiate their own studies. What is yet not well understood is how best to design CCS programmes that are educational, inclusive, and accessible by diverse volunteers, including young people and those with limited prior science experiences who are rather few in CCS. In this study, we interviewed 31 young people, aged 7-20 years old, who used iNaturalist, an online biodiversity monitoring platform, and identified how different forms of participation online and in the field facilitated (or inhibited) certain forms of learning, as defined by the Environmental Science Agency framework. Findings revealed that iNaturalist enabled participation of young people including those with limited science experiences and facilitated science learning such as the development of science competence and understanding. A blended learning framework for biodiversity monitoring in CCS is presented as a means to support the development of hybrid, educational, and inclusive CCS programmes for young people.

Educational applications (apps) are ubiquitous within children's learning environments and emerging evidence has demonstrated their efficacy. However, it remains unclear what the active ingredients (ie, mechanisms), or combination of ingredients, of successful maths apps are. The current study developed a new, open-access, three-step framework for assessing the educational value of maths apps, comprised of type of app, mathematical content and app design features. When applied to a selection of available maths apps previously evaluated with children in the first 3 years of school (the final sample included 23 apps), results showed that practice-based apps were the most common app type tested (n = 15). Basic number skills, such as number representation and relationships, were the most common area of mathematics targeted by apps (n = 21). A follow-up qualitative comparative analysis showed observed learning outcomes with maths apps were enhanced when apps combined the following: a scaffolded and personalised learning journey (programmatic levelling) and explanations of why answers were right or wrong (explanatory feedback), as well as praise, such as 'Great job!' (motivational feedback). This novel evidence stresses the significance of feedback and levelling design features that teaching practitioners and other stakeholders should consider when deciding which apps to use with young children. Directions for future research are discussed.

Community and citizen science in online settings could be seen as a means for young people to engage with and contribute to authentic science. Yet, there is a limited understanding of who takes part in citizen science among young people, what they learn, and through which processes, particularly in online settings. In this exploratory study, we analysed 34 in-depth interviews and log files of young people aged 11-19 years old who took part in citizen science projects, hosted on the Zooniverse platform. Data analysis suggested that participation in online citizen science can bring environmental science learning benefits to young people, with some participants reporting evidence of agency with science, highlighted by taking action to do science in another context. Many participating youth exhibited substantial previous science experiences that helped them to take part and learn from citizen science projects. Considering findings from this study, we present a first working framework of how environmental science learning is enabled or hindered by certain types of participation, as a means to guide the design of online citizen science for young people. We recommend that the future project design, publicity and recruitment in online citizen science activities explicitly target the needs and interests of young people with diverse characteristics and competencies to truly open science to all.

Despite the potential of Predictive Learning Analytics (PLAs) to identify students at risk of failing their studies, research demonstrating effective application of PLAs to higher education is relatively limited. The aims of this study are: (1) to identify whether and how PLAs can inform the design of motivational interventions; and (2) to capture the impact of those interventions on student retention at the Open University UK. A predictive model -- the Student Probabilities Model (SPM) -- was used to predict the likelihood of a student remaining in a course at the next milestone and eventually completing it. Undergraduate students (N=630) with a low probability of completing their studies were randomly allocated into the control (n=312) and intervention groups (n=318), and contacted by the university Student Support Teams (SSTs) using a set of motivational interventions such as text, phone, and email. The results of the randomized control trial showed statistically significant better student retention outcomes for the intervention group, with the proposed intervention deemed effective in facilitating course completion. The intervention also improved the administration of student support at scale and low cost.

Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders (i.e., managers, teachers, students) about PLA. This paper presents an "at scale" implementation of PLA at a distance learning higher education institution and details, in particular, the perspectives of 20 educational managers involved in the implementation. It concludes with a set of recommendations about how best to adopt and apply large-scale PLA initiatives in higher education.

Online community and citizen science (CCS) projects have broadened access to scientific research and enabled different forms of participation in biodiversity research; however, little is known about whether and how such opportunities are taken up by young people (aged 5-19). Furthermore, when they do participate, there is little research on whether their online activity makes a tangible contribution to scientific research. We addressed these knowledge gaps using quantitative analytical approaches and visualisations to investigate 249 youths' contributions to CCS on the iNaturalist platform, and the potential for the scientific use of their contributions. We found that nearly all the young volunteers' observations were 'verifiable' (included a photo, location, and date/time) and therefore potentially useful to biodiversity research. Furthermore, more than half were designated as 'Research Grade', with a community agreed-upon identification, making them more valuable and accessible to biodiversity science researchers. Our findings show that young volunteers with lasting participation on the platform and those aged 16-19 years are more likely to have a higher proportion of Research Grade observations than younger, or more ephemeral participants. This study enhances our understanding of young volunteers' contributions to biodiversity research, as well as the important role professional scientists and data users can play in helping verify youths' contributions to make them more accessible for biodiversity research.

Online citizen science projects have broadened options for accessing science and enabled different forms of participation in scientific research for adult and young volunteers. Yet, little is known regarding participation patterns among youth participants. Quantitative approaches were used to investigate the contribution of 183 young volunteers to citizen science on the iNaturalist platform and the participation behaviour that relates to their contribution. The participants accessed and used iNaturalist as part of one-day field-based events (bioblitzes) facilitated by museums. Compared to the observation behaviour of all iNaturalist users, as documented on the platform, the young volunteers observe fewer plants and birds, and more molluscs, arachnids and insects. The average daily contributions of young volunteers were found to be positively associated with a large proportion of active days on iNaturalist and a systematic contribution behaviour, yet negatively related to a long duration on the platform. This study enhances our understanding of young volunteers' contributions to citizen science and provides insights for research on participation in online citizen science. Our findings have implications on how museums design the field-based events to encourage follow-up systematic participation and maintain active contribution.

The importance of teachers in online learning is widely acknowledged to effectively support and stimulate learners. With the increasing availability of learning analytics data, online teachers might be able to use learning analytics dashboards to facilitate learners with different learning needs. However, deployment of learning analytics visualisations by teachers also requires buy-in from teachers. Using the principles of technology acceptance model, in this embedded case-study, we explored teachers' readiness for learning analytics visualisations amongst 95 experienced teaching staff at one of the largest distance learning universities by using an innovative training method called Analytics4Action Workshop. The findings indicated that participants appreciated the interactive and hands-on approach, but at the same time were skeptical about the perceived ease of use of learning analytics tools they were offered. Most teachers indicated a need for additional training and follow-up support for working with learning analytics tools. Our results highlight a need for institutions to provide effective professional development opportunities for learning analytics.

Citizen Science (CS) is an increasingly popular activity enacted either in the field or online. Volunteers participate in research activities such as data processing and analysis by, for example, identifying plants and animals. In this paper we examine young people's participation in online CS projects hosted on the Zooniverse platform. This is an exploratory study, the first of its kind that focuses on young people, mainly 16-19 years old. It uses data analytics and visualisation techniques to capture participation in online CS, and in particular to answer the following questions: (a) What does young people's participation look like in CS projects? (b) What Zooniverse projects do young people choose to participate in? and (3) What Zooniverse projects do young people choose together? Findings revealed five distinct engagement profiles characterising young people's participation and identified certain projects as been more popular across participants. Implications for the design of online citizen science projects targeting young people are discussed.

Learning analytics dashboards (LADs) can provide learners with insights about their study progress through visualisations of the learner and learning data. Despite their potential usefulness to support learning, very few studies on LADs have considered learners' needs and have engaged learners in the process of design and evaluation. Aligning with that, there is a limited understanding of what specific student cohorts, in particular distance and online learners, may seek from LADs to effectively support their studies. In this study, we present findings from 21 interviews with undergraduate distance learners, mainly high performers, that aimed to capture student perceptions about the usefulness of specific LAD features and the factors that explain these perceptions. Our findings revealed that amongst the LAD features favoured by students was the potential to receive study recommendations, whereas comparison with peers was amongst the least favoured elements, unless informed by qualitative information. Factors including information trust, attitudes, age, performance and academic self-confidence were found to explain these perceptions.

Educational outcomes from traditionally underrepresented groups are generally worse than for their more advantaged peers. This problem is typically known as the awarding gap (we use the term awarding gap over 'attainment gap' as attainment places the responsibility on students to attain at equal levels) and continues to pose a challenge for educational systems across the world. While Learning Analytics (LA) dashboards help identify patterns contributing to the awarding gap, they often lack stakeholder involvement, offering very little support to institutional Equality, Diversity and Inclusion (EDI) leads or educators to pinpoint and address these gaps. This paper introduces an innovative EDI LA dashboard, co‐created with diverse stakeholders. Rigorously evaluated, the dashboard provides fine‐grained insights and course‐level analysis, empowering institutions to effectively address awarding gaps and contribute to a diverse and inclusive higher education landscape. Practitioners notesWhat is already known about this topic Traditionally underrepresented groups face educational disparities, commonly known as the awarding gap.Underachievement is a complex multi‐dimensional problem and cannot be solely attributable to individual student deficiencies.LA dashboards targeting this specific problem are often not public, there is little research about them, and are frequently designed with little involvement of educational stakeholders.What this paper adds Pioneers the introduction of a dashboard specifically designed to address the awarding gap problem.Emphasises the significant data needs of educational stakeholders in tackling awarding gaps.Expands the design dimensions of Learning Analytics (LA) by introducing a specific design approach rooted in established user experience (UX) design methods.Implications for practice and/or policy Insights from this study will guide practitioners, designers, and developers in creating AI‐based educational systems to effectively target the awarding gap problem. [ABSTRACT FROM AUTHOR]

YouTube has become a popular digital media platform used by young children. However, concerns have been raised around inappropriate video content and limited quality. A lack of research and theoretical discussion exists on how best to evaluate the quality of YouTube videos made for children. In this study, we reviewed research and developed a set of design principles that informed the production of a YouTube video rubric used to evaluate the quality of YouTube videos targeted at young children 0 to 8 years old. From this, four key criteria were used to evaluate each video: Age appropriateness, Content quality, Design features and Learning objectives. These criteria demonstrated substantial inter-rater reliability between scorers. This evaluation tool has the potential to be used by educators to assess the quality of videos for early learning and guide YouTube creators in their production of educational videos for young children.

A recent report surveyed 3,154 families and showed that 80% of children age 0-7 use YouTube, and 59% use YouTube Kids. While most young children are likely using their parents' accounts, 11% have their own YouTube accounts and 16% have registered with the YouTube Kids app. On average, children spend 1.39 hours each weekday and 1.47 hours each weekend day viewing YouTube videos, which are approximately 4 to 9 minutes in length. The topics most likely to be viewed by children include: play, toys, nursery rhymes, television interests, funny videos, and animal videos. This article presents how children use YouTube, selecting quality videos, the possible benefits, and concerns.

The implementation of learning analytics may empower distance learning institutions to provide real-time feedback to students and teachers. Given the leading role of the Open University UK (OU) in research and application of learning analytics, this study aims to share the lessons learned from the experiences of 42 participants from a range of faculty, academic and professional positions, and expertise with learning analytics. Furthermore, we explored where distance learning institutions should be going next in terms of learning analytics adoption. The findings from the Learning Analytics User Stories (LAUS) workshop indicated four key areas where more work is needed: communication, personalisation, integrated design, and development of an evidence-base. The workshop outputs signalled the aspiration for an integrated analytics system transcending the entire student experience, from initial student inquiry right through to qualification completion and into life-long learning. We hope that our study will spark discussion on whether (or not) distance learning institutions should pursue the dream of an integrated, personalised, and evidence-based learning analytics system that clearly communicates useful feedback to staff and students, or whether this will become an Orwellian nightmare.

Virtual Learning Environments (VLEs) are becoming commonplace in Higher Education. Amongst the latest VLE developments is the design and use of Virtual Microscopes (VMs) that allow for viewing and manipulation of online images by multiple students. Although students are found to be generally satisfied with the use of VMs, it is yet not known what teaching and learning conditions better support their use and lead to enhanced learning outcomes. The aim of this paper is to compare the usage patterns and perceptions of two different cohorts of undergraduate students that made use of the VM in blended and online only learning conditions respectively, and draw conclusions about the pedagogy that better supports teaching and learning with VMs. Data collected from a survey with 139 students and 11 semi-structured interviews revealed that blended learning better caters for students' engagement and satisfaction due to the systematic use of the VM in course design, its complementary use with a physical microscope, and the ongoing provision of tutors' support and guidance. Equally good perceived learning gains were reported by both blended and online only students. Implications for the design of learning environments around virtual microscopy are discussed.

This study presents an advanced predictive learning analytics system, OU Analyse (OUA), and evidence from its evaluation with online teachers at a distance learning university. OUA is a predictive system that uses machine learning methods for the early identification of students at risk of not submitting (or failing) their next assignment. Teachers have access, via interactive dashboards, to weekly predictions of risk of failing for each of their students. In this study, we examined how the degree of OUA usage by 559 teachers, of which 189 were given access to OUA, related to student learning outcomes of more than 14 000 students in 15 undergraduate courses. Teachers who made "average" use of OUA, that is accessed OUA throughout the life cycle of a course presentation, and in particular between 10% and 40% of the weeks a course was running, and intervened with students flagged as at risk were found to benefit their students the most; after controlling for differences in academic performance, these students were found to have significantly better performance than their peers in the previous year's course presentation during which the same teachers made no use of predictive learning analytics. Predictive learning analytics is an innovative student's support approach in online pedagogy that, as shown in this study, can empower online teachers in effectively monitoring and intervening with their students, over and above other approaches, and result in improved learning outcomes.

By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction. The aim of this study is to evaluate whether providing teachers in a distance learning higher education institution with PLA data predicts students' performance and empowers teachers to identify and assist students at risk. Using principles of Technology Acceptance and Academic Resistance models, a university-wide, multi-methods study with 59 teachers, nine courses, and 1325 students revealed that teachers can positively affect students' performance when engaged with PLA. Follow-up semi-structured interviews illuminated teachers' actual uses of the predictive data and revealed its impact on teaching practices and intervention strategies to support students at risk.

A popular activity among young children is the use of mobile devices and apps. Yet, the impact of mobile devices on learning and development is rather underexplored. The limited studies identified explore effects on literacy development and communication and report on mixed findings. A considerable gap is observed as to how the use of mobile apps relates to young children's understanding in diverse domains including science learning, and to extend, whether and how mobile apps should be used and how in early years' settings. The aim of this paper is to shed light on this area by examining the learning effects of touch screen mobile game applications, in particular the game Angry Birds, on two groups of preschoolers 4 and 5 years old respectively. Evidence from a comparative study with 32 participants reveal significant differences between the two groups in terms of game skills and their understanding of projectile motion. Implications for educational stakeholders, parents and app designers are discussed along with future research directions.

Personalisation of learning is a recurring trend in our society, referred to in government speeches, popular media, conference and research papers and technological innovations. This latter aspect--of using personalisation in technology-enhanced learning (TEL)--has promised much but has not always lived up to the claims made. Personalisation is often perceived to be a positive phenomenon, but it is often difficult to know how to implement it effectively within educational technology. In order to address this problem, we propose a framework for the analysis and creation of personalised TEL. This article outlines and explains this framework with examples from a series of case studies. The framework serves as a valuable resource in order to change or consolidate existing practice and suggests design guidelines for effective implementations of future personalised TEL.

The authors describe the design and formative evaluation of a sensor toolkit for Android smartphones and tablets that supports inquiry-based science learning. The Sense-it app enables a user to access all the motion, environmental and position sensors available on a device, linking these to a website for shared crowd-sourced investigations. The authors describe the four investigations with the toolkit: environmental noise, sunlight levels, air pressure and rainfall, and the speed of lifts (elevators). These show a variety of methods to initiate, orchestrate and conclude inquiry-based science learning. Two of the missions are in the context of a study to develop a community of inquiry around weather and meteorology. The others are intended to engage members of the public in practical science activities. Analysis of the missions and the associated online discussions reveals that the Sense-it toolkit can be adopted for engaging science investigations, though the practical issue of calibrating sensors on personal devices needs to be addressed.

Randomised control trials (RCTs) are an evidence-based research approach which has not yet been adopted and widely used in open and distance education to inform educational policy and practice. Despite the challenges entailed in their application, RCTs hold the power to robustly evaluate the effects of educational interventions in distance learning and conclude on whether (or not) these interventions should be adopted and used extensively. The aim of this paper is to spark discussions around the use of RCTs in distance learning by illustrating their benefits and drawbacks including challenges in adopting RCTs in education. To achieve this aim, a RCT was implemented to examine whether a small-scale intervention in four language modules could improve attendance at an end-of-module speaking assessment, and in consequence, performance, completion and pass rates. Results raise the need for further research in order to identify what type of interventions should be designed and put into practice to elicit a positive impact on learners. The paper concludes with a discussion on why RCTs should be brought to the forefront as a viable method for the effective evaluation of the impact of open learning analytic interventions.