In this research, we analysed exploratory behaviour trace data for students engaging in learning tasks in a technology-enhanced data analytics course as the first step towards modelling curiosity in learning.Curiosity is a complex phenomenon that is not amenable to direct modelling, but it can be understood through related behaviours like exploration, which is critical to effective learning.We analysed trace data from 40 students using visualisation and network analysis techniques, focusing on their interactions with learning tasks within the JupyterLab environment.Our analysis found that providing sufficient exploration time before explicit instruction or answer revelation, and designing learning tasks that embrace errors as opportunities, encouraged behaviours associated with curiosity-driven learning.These findings highlight the importance of designing learning environments that foster curiosity and promote active exploration.

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.

This perspectives paper proposes a conceptualization of trust that does not require a predefined feature space, but rather is dynamically formed at the point of information interaction through a cognitive predicting mechanism. Trust is a significant issue in the current information context due to fake news, echo chambers, filter bubbles, and confirmation biases which can result in a disconnect between human trust expectations and information trustworthiness, making it difficult to establish a feature space within which trust might be modeled. In response to this, we present our Cognitive Predicting Theory of Trust (CPTT) which allows trust to be modeled without the requirement of a predefined feature space. Drawn from the cognitive theory of Predictive Processing, CPTT describes how people form trust judgments based on cognitive predictions within a system of information interactions. We outline how this CPTT view of trust might be modeled using complex systems and provide examples showing how curation of the information interaction environment can affect the trust associated with the system. We conclude by proposing that our perspective opens up two avenues for exploration in Computer Human Information Interaction and Retrieval: (1) the need for alternative models, and (2) the value of curating the information environment.

Interactive home based rehabilitation therapy is a promising treatment development for stroke survivors. As the impairment characteristics of each stroke survivor are unique, interactive rehabilitation systems need to be customized to the functional and movement quality outcome goals of the patient, and adaptable over time as therapy progresses. In this paper, we present our iterative co-design process creating a set of modular therapy objects and a rehabilitation protocol for upper extremity stroke survivors. Our objects and training protocol are adaptable components within a computer vision based interactive system that captures and analyzes stroke survivors completing rehabilitation activities. We report on findings from a pilot study with nine stroke survivors and a workshop with five physiotherapists where we highlight challenges in designing objects for impaired grasps, opportunities for aligning objects with activities of everyday living, and the responsibility of design sensitivity.

It is arguable that the chief aim of Learning Analytics is to use analytics for meaningful purposes in learning and teaching contexts, and that research in the field should advance this cause. However the field does not present a single clear understanding of what constitutes quality in Learning Analytics research.

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.

Reflective writing can provide a powerful way for students to integrate professional experience and academic learning. However, writing reflectively requires high quality actionable feedback, which is time-consuming to provide at scale. This paper reports progress on the design, implementation, and validation of a Reflective Writing Analytics platform to provide actionable feedback within a tertiary authentic assessment context. The contributions are: (1) a new conceptual framework for reflective writing; (2) a computational approach to modelling reflective writing, deriving analytics, and providing feedback; (3) the pedagogical and user experience rationale for platform design decisions; and (4) a pilot in a student learning context, with preliminary data on educator and student acceptance, and the extent to which we can evidence that the software provided actionable feedback for reflective writing.

There is untapped potential in achieving the full impact of learning analytics through the integration of tools into practical pedagogic contexts. To meet this potential, more work must be conducted to support educators in developing learning analytics literacy. The proposed workshop addresses this need by building capacity in the learning analytics community and developing an approach to resourcing for building 'writing analytics literacy'.

There is an increasing interest in the analysis of both student's writing and the temporal aspects of learning data. The analysis of higher-level learning features in writing contexts requires analyses of data that could be characterised in terms of the sequences and processes of textual features present. This paper (1) discusses the extant literature on sequential and process analyses of writing; and, based on this and our own first-hand experience on sequential analysis, (2) proposes a number of approaches to both pre-process and analyse sequences in whole-texts. We illustrate how the approaches could be applied to examples drawn from our own datasets of 'rhetorical moves' in written texts, and the potential each approach holds for providing insight into that data. Work is in progress to apply this model to provide empirical insights. Although, similar sequence or process mining techniques have not yet been applied to student writing, techniques applied to event data could readily be operationalised to undercover patterns in texts.

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.

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.

Abstract Automation in science is increasingly marked by the use of workflow technology. Thesharingof workflows through repositories supports the verifiability, reproducibility and extensibility of computational experiments. However, the subsequentdiscoveryof workflows remains a challenge, both from a sociological and technological viewpoint. Based on a survey with participants from 19 laboratories, we investigate the current practices in workflow sharing, re‐use and discovery among life scientists chiefly using the Taverna workflow management system. To address their perceived lack of effective workflow discovery tools, we go on to develop benchmarks for the evaluation of discovery tools, drawing on a series of practical exercises. We demonstrate the value of the benchmarks on two tools: one using graph matching and the other relying on text clustering. Copyright © 2009 John Wiley & Sons, Ltd.

Workflows systems are steadily finding their way into the work practices of scientists. This is particularly true in the in silico science of bioinformatics, where biological data can be processed by Web services. In this paper we investigate the potential of evolving the users' interaction with workflow environments so that it more closely relates to the mode in which their day to day work is carried out. We present the Data Playground, an environment designed to encourage the uptake of workflow systems in bioinformatics through more intuitive interaction by focusing the user on their data rather than on the processes. We implement a prototype plug-in for the Taverna workflow environment and show how this can promote the creation of workflow fragments by automatically converting the users' interactions with data and Web services into a more conventional workflow specification.