In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers, while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.

research-article Share on Questioning learning analytics? Cultivating critical engagement as student automated feedback literacy Authors: Antonette Shibani University of Technology, Sydney, Australia University of Technology, Sydney, AustraliaView Profile , Simon Knight University of Technology, Sydney, Australia University of Technology, Sydney, AustraliaView Profile , Simon Buckingham Shum University of Technology, Sydney, Australia University of Technology, Sydney, AustraliaView Profile Authors Info & Claims LAK22: LAK22: 12th International Learning Analytics and Knowledge ConferenceMarch 2022 Pages 326–335https://doi.org/10.1145/3506860.3506912Online:21 March 2022Publication History 0citation152DownloadsMetricsTotal Citations0Total Downloads152Last 12 Months152Last 6 weeks26 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Teachers are increasingly being encouraged to embrace evidence-based practices. Learning analytics (LA) offer great promise in supporting these by providing evidence for teachers and learners to make informed decisions and transform the educational experience. However, LA limitations and their uptake by educators are coming under critical scrutiny. This is in part due to the lack of involvement of teachers and learners in the design of LA tools. In this paper, we propose a human-centred approach to generate understanding of teachers' data needs through the lens of three key principles of translucence: visibility, awareness and accountability. We illustrate our approach through a participatory design sprint to identify how teachers talk about classroom data. We describe teachers' perspectives on the evidence they need for making better-informed decisions and discuss the implications of our approach for the design of human-centred LA in the next years.

A major promise of learning analytics is that through the collection of large amounts of data we can derive insights from authentic learning environments, and impact many learners at scale. However, the context in which the learning occurs is important for educational innovations to impact student learning. In particular, for student-facing learning analytics systems like feedback tools to work effectively, they have to be integrated with pedagogical approaches and the learning design. This paper proposes a conceptual model to strike a balance between the concepts of generalizable scalable support and contextualized specific support by clarifying key elements that help to contextualize student-facing learning analytics tools. We demonstrate an implementation of the model using a writing analytics example, where the features, feedback and learning activities around the automated writing feedback tool are tuned for the pedagogical context and the assessment regime in hand, by co-designing them with the subject experts. The model can be employed for learning analytics to move from generalized support to meaningful contextualized support for enhancing learning.

As a human-centred educational practice and field of research, learning analytics must account for key stakeholders in teaching and learning. The focus of this paper is on the role of institutions to support teachers to incorporate learning analytics into their practice by understanding the confluence of internal and external factors that influence what they do. In this paper, we reconceptualise `teaching as design' for `analytics-enabled teaching as design' to shape this discussion to allow for the consideration of external factors, such as professional learning or ethical considerations of student data, as well as personal considerations, such as data literacy and teacher beliefs and identities. In order to address the real-world challenges of progressing teachers' efficacy and capacity toward analytics-enabled teaching as design, we have placed the teacher - as a cognitive, social, and emotional being - at the center. In so doing, we discuss potential directions towards research for practice in elucidating underpinning factors of teacher inquiry in the process of authentic design.

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'.

We discuss the application of a hand-drawn self-visualization approach to learner-data, to draw attention to the space of representational possibilities, the power of representation interactions, and the performativity of information representation.

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.

Interest in temporal analytics---analytics that probe temporal aspects of learning so as to gain insights into the processes through which learning occurs---continues to grow. The relationships of temporal patterns to learning outcomes is a central area of interest. However, while the literature on temporal analyses is developing, there has been less consideration of the methods by which temporal analyses might be translated to actionable insights and thus, put into use in educational practice. Emerging temporal analysis techniques present both theoretical and practical challenges for producing and interpreting results. Synergetic actions are needed in order to support practitioners.

Writing Analytics focuses on the measurement and analysis of written texts for the purpose of understanding writing processes and products, in their educational contexts, and improving the teaching and learning of writing. This workshop adopts a critical, holistic perspective in which the definition of "the system" and "success" is not restricted to IR metrics such as precision and recall, but recognizes the many wider issues that aid or obstruct analytics adoption in educational settings, such as theoretical and pedagogical grounding, usability, user experience, stakeholder design engagement, practitioner development, organizational infrastructure, policy and ethics.

The LAK15 theme "shifts the focus from data to impact", noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach--the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory.

Interest in analyses that probe the temporal aspects of learning continues to grow. The study of common and consequential sequences of events (such as learners accessing resources, interacting with other learners and engaging in self-regulatory activities) and how these are associated with learning outcomes, as well as the ways in which knowledge and skills grow or evolve over time are both core areas of interest. Learning analytics datasets are replete with fine-grained temporal data: click streams; chat logs; document edit histories (e.g. wikis, etherpads); motion tracking (e.g. eye-tracking, Microsoft Kinect), and so on. However, the emerging area of temporal analysis presents both technical and theoretical challenges in appropriating suitable techniques and interpreting results in the context of learning. The learning analytics community offers a productive focal ground for exploring and furthering efforts to address these challenges. This workshop, the fourth in a series on temporal analysis of learning, provides a focal point for analytics researchers to consider issues around and approaches to temporality in learning analytics.

There is a well-established literature examining the relationships between epistemology (the nature of knowledge), pedagogy (the nature of learning and teaching), and assessment. Learning Analytics (LA) is a new assessment technology and should engage with this literature since it has implications for when and why different LA tools might be deployed. This paper discusses these issues, relating them to an example construct, epistemic beliefs -- beliefs about the nature of knowledge -- for which analytics grounded in pragmatic, sociocultural theory might be well placed to explore. This example is particularly interesting given the role of epistemic beliefs in the everyday knowledge judgements students make in their information processing. Traditional psychological approaches to measuring epistemic beliefs have parallels with high stakes testing regimes; this paper outlines an alternative LA for epistemic beliefs which might be readily applied to other areas of interest. Such sociocultural approaches afford opportunity for engaging LA directly in high quality pedagogy.