Dashboards are increasingly used in education to provide teachers and students with insights into learning. Yet, existing dashboards are often criticised for their failure to provide the contextual information or explanations necessary to help students interpret these data. Data Storytelling (DS) is emerging as an alternative way to communicate insights providing guidance and context to facilitate students’ interpretations. However, while data stories have proven effective in prompting students’ reflections, to date, it has been necessary for researchers to craft the stories rather than enabling teachers to do this by themselves. This can make this approach more feasible and scalable while also respecting teachers’ agency. Based on the notion of DS, this paper presents a DS editor for teachers. A study was conducted in two universities to examine whether the editor could enable teachers to create stories adapted to their learning designs. Results showed that teachers appreciated how the tool enabled them to contextualise automated feedback to their teaching needs, generating data stories to support student reflection.

Responding to recent questioning of Learning Analytics (LA) as a field that is achieving its aim of understanding and optimising learning and the environments in which it occurs, this paper argues that there is a need to genuinely embrace the complexity of learning when considering the impact of LA. Rather than focusing upon ‘optimisation’, we propose that LA should seek to understand and improve the complex socio-technical system in which it operates. We adopt a framework from systems theory to propose 12 different intervention points for learning systems, and apply it to two case studies. We conclude with an invitation to the community to critique and extend this proposed framework.

Lifelong personalised learning is often described as the holy grail of the educational data sciences, but work on the topic is sporadic and we are yet to achieve this goal in a meaningful form. In the wake of the skills shortages arising from national responses to COVID-19 this problem has again become a topic of interest. A number of proposals have emerged that some sort of a skills passport would help individuals, educational institutions, and employers to identify training and recruitment needs according to identified skills gaps. And yet, we are a long way from achieving a skills passport that could support lifelong learning despite more than 25 years of work on the topic. This paper draws attention to two of the critical socio-technical challenges facing skills passports, and lifelong learner models in general. This leads to a proposal for how we might move towards a useful skills passport that can cross the “skills sector border”.

Content analysis (CA) is a method frequently used in the learning sciences and so increasingly applied in learning analytics (LA). Despite this ubiquity, CA is a subtle method, with many complexities and decision points affecting the outcomes it generates. Although appearing to be a neutral quantitative approach, coding CA constructs requires an attention to decision making and context that aligns it with a more subjective, qualitative interpretation of data. Despite these challenges, we increasingly see the labels in CA-derived datasets used as training sets for machine learning (ML) methods in LA. However, the scarcity of widely shareable datasets means research groups usually work independently to generate labelled data, with few attempts made to compare practice and results across groups. A risk is emerging that different groups are coding constructs in different ways, leading to results that will not prove replicable. We report on two replication studies using a previously reported construct. A failure to achieve high inter-rater reliability suggests that coding of this scheme is not currently replicable across different research groups. We point to potential dangers in this result for those who would use ML to automate the detection of various educationally relevant constructs in LA.

Learning Analytics (LA) is a bricolage field that requires a concerted effort to ensure that all stakeholders it affects are able to contribute to its development in a meaningful manner. We need mechanisms that support collaborative sense-making. This paper argues that graphical causal models can help us to span the disciplinary divide, providing a new apparatus to help educators understand, and potentially challenge, the technical models developed by LA practitioners as they form. We briefly introduce causal modelling, highlighting its potential benefits in helping the field to move from associations to causal claims, and illustrate how graphical causal models can help us to reason about complex statistical models. The approach is illustrated by applying it to the well known problem of at-risk modelling.

Learning Analytics (LA) dashboards have become a popular medium for communicating to teachers analytical insights obtained from student data. However, recent research indicates that LA dashboards can be complex to interpret, are often not grounded in educational theory, and frequently provide little or no guidance on how to interpret them. Despite these acknowledged problems, few suggestions have been made as to how we might improve the visual design of LA tools to support richer and alternative ways to communicate student data insights. In this paper, we explore three design alternatives to represent student multimodal data insights by combining data visualisation, narratives and storytelling principles. Based on foundations in data storytelling, three visual-narrative interfaces were designed with teachers: i) visual data slices, ii) a tabular visualisation, and iii) a written report. These were validated as a part of an authentic study where teachers explored activity logs and physiological data from co-located collaborative learning classes in the context of healthcare education. Results suggest that alternatives to LA dashboards can be considered as effective tools to support teachers’ reflection, and that LA designers should identify the representation type that best fits teachers’ needs.

In nursing education through team simulations, students must learn to position themselves correctly in coordination with colleagues. However, with multiple student teams in action, it is difficult for teachers to give detailed, timely feedback on these spatial behaviours to each team. Indoor-positioning technologies can now capture student spatial behaviours, but relatively little work has focused on giving meaning to student activity traces, transforming low-level x/y coordinates into language that makes sense to teachers. Even less research has investigated if teachers can make sense of that feedback. This paper therefore makes two contributions. (1) Methodologically, we document the use of Epistemic Network Analysis (ENA) as an approach to model and visualise students’ movements. To our knowledge, this is the first application of ENA to analyse human movement. (2) We evaluated teachers’ responses to ENA diagrams through qualitative analysis of video-recorded sessions. Teachers constructed consistent narratives about ENA diagrams’ meaning, and valued the new insights ENA offered. However, ENA’s abstract visualisation of spatial behaviours was not intuitive, and caused some confusions. We propose, therefore, that the power of ENA modelling can be combined with other spatial representations such as a classroom map, by overlaying annotations to create a more intuitive user experience.

In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, decision making, and and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.

Effective teamwork is critical to improve patient outcomes in healthcare. However, achieving this capabilityrequires that pre-service nurses develop the spatial abilities they will require in their clinical placements, suchas: learning when to remain close to the patient and to other team members; positioning themselves correctlyat the right time; and deciding on specific team formations (e.g. face-to-face or side-by-side) to enable effectiveinteraction or avoid disrupting clinical procedures. However, positioning dynamics are ephemeral and caneasily become occluded by the multiple tasks nurses have to accomplish. Digital traces automatically capturedby indoor positioning sensors can be used to address this problem for the purpose of improving nurses' reflection, learning and professional development. This paper presents; i) a qualitative study that illustrateshow to elicit spatial behaviours from educators' pedagogical expectations, and ii) a modelling approachthat transforms nurses' low-level position traces into higher-order proxemics constructs, informed by sucheducatos' expectations, in the context of simulation-based teamwork training. To illustrate our modellingapproach, we conducted an in-the-wild study with 55 undergraduate students and five educators from whompositioning traces were captured in eleven authentic nursing education classes. Low-levelx-ydata was usedto model three proxemic constructs: i) co-presence in interactional spaces, ii) socio-spatial formations (i.e.f-formations), and ii) presence in spaces of interest. Through a number of vignettes, we illustrate how indoorpositioning analytics can be used to address questions that educators and researchers have about teamwork inhealthcare simulation settings.

In an era that will increasingly depend upon lifelong learning, the LA community will need to facilitate the movement and sharing of data and information across institutional and geographic boundaries. This will help us to recognise prior learning (RPL) and to personalise the learner experience. Here, we explore the utility of skills-based curriculum analytics and how it might facilitate the process of awarding RPL between two institutions. We explore the potential utility of combining natural language processing and skills taxonomies to map between subject descriptions for these two different institutions, presenting two algorithms we have developed to facilitate RPL and evaluating their performance. We draw attention to some of the issues that arise, listing areas that we consider ripe for future work in a surprisingly underexplored area.

Learning Analytics (LA) sits at the confluence of many contributing disciplines, which brings the risk of hidden assumptions inherited from those fields. Here, we consider a hidden assumption derived from computer science, namely, that improving computational accuracy in classification is always a worthy goal. We demonstrate that this assumption is unlikely to hold in some important educational contexts, and argue that embracing computational "imperfection" can improve outcomes for those scenarios. Specifically, we show that learner-facing approaches aimed at "learning how to learn" require more holistic validation strategies. We consider what information must be provided in order to reasonably evaluate algorithmic tools in LA, to facilitate transparency and realistic performance comparisons.

The 2nd LAK Failathon will build on the successful event in 2016 and extend the workshop beyond discussing individual experiences of failure to exploring how the field can improve, particularly regarding the creation and use of evidence.

This poster will be a chance for a wider LAK audience to engage with the 2nd LAK Failathon workshop. Both of these will build on the successful Failathon event in 2016 and extend beyond discussing individual experiences of failure to exploring how the field can improve, particularly regarding the creation and use of evidence.

The hackathon is intended to be a practical hands-on workshop involving participants from academia and commercial organizations with both technical and practitioner expertise. It will consider the outstanding challenge of visualizations which are effective for the intended audience: informing action, not likely to be misinterpreted, and embodying contextual appropriacy, etc. It will surface particular issues as workshop challenges and explore responses to these challenges as visualizations resting upon interoperability standards and API-oriented open architectures.

While help seeking has been extensively studied using self report survey data and models, there is a lack of content analysis techniques that can be directly applied to classify help seeking behaviour. In this preliminary work we propose a coding scheme which is then applied to an open dataset that we have created by carefully selecting sub groups from two popular discussion sites (Reddit and StackExchange). We then explore the possibility for automatically classifying help seeking behaviour using machine learning models. A preliminary model provides good initial results, suggesting that it may indeed be possible to construct student support systems that build off of an accurate classifier.

An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content-related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.

This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

It is of high relevance to the LAK community to explore blended learning scenarios where students can interact at diverse digital and physical learning spaces. This workshop aims to gather the sub-community of LAK researchers, learning scientists and researchers from other communities, interested in ubiquitous, mobile and/or face-to-face learning analytics. An overarching concern is how to integrate and coordinate learning analytics to provide continued support to learning across digital and physical spaces. The goals of the workshop are to share approaches and identify a set of guidelines to design and connect Learning Analytics solutions according to the pedagogical needs and contextual constraints to provide support across digital and physical learning spaces.

Reflective writing is an important learning task to help foster reflective practice, but even when assessed it is rarely analysed or critically reviewed due to its subjective and affective nature. We propose a process for capturing subjective and affective analytics based on the identification and recontextualisation of anomalous features within reflective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Recontextualisation process for Learning Analytics.

We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. A number of implementation issues are discussed, and a mapping that will enable the consistent storage and then analysis of xAPI verb/object/activity statements across different social media and online environments is introduced. A set of example learning activities are proposed, each facilitated by the Learning Analytics beyond the LMS that the toolkit enables.

Incorporating a learner's level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom's taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.

As computers approach the physical limits of information storable in memory, new methods will be needed to further improve information storage and retrieval. We propose a quantum inspired vector based approach, which offers a contextually dependent mapping from the subsymbolic to the symbolic representations of information. If implemented computationally, this approach would provide exceptionally high density of information storage, without the traditionally required physical increase in storage capacity. The approach is inspired by the structure of human memory and incorporates elements of Gärdenfors' Conceptual Space approach and Humphreys et al.'s matrix model of memory.