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’ spatial behaviours in the classroom can strongly influence students’ engagement, motivation and other behaviours that shape their learning. However, classroom teaching behav-iour is ephemeral, and has largely remained opaque to computational analysis. This paper presents a library called ‘Moodoo’ that can serve to automatically model how teachers make use of the classroom space by analysing indoor positioning traces. The system automatically ex-tracts spatial metrics (e.g. teacher-student ratios, frequency of visits to students’ personal spaces, presence in classroom spaces of interest, index of dispersion and entropy), mapping from the teachers’ low-level positioning data to higher-order spatial constructs.

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.

With the advent of the Internet and wide-spread popularity of online technology-enhanced learning platforms, many pedagogical activities today involve learners in online discussions such as synchronous chat. In this study, we describe a text mining method used for analyzing teamwork from such chat dialogue of students. The steps in the text mining method such as pre-processing and classification are described and the results of our analysis are presented in this paper.

Teamwork is an important 21st century competency to be nurtured in students. In this study, we describe the first two trial cycles of a web tool “My Groupwork Buddy” that is designed to support teamwork awareness and reflection in collaborative inquiry tasks. The tool is developed using a design-based approach and was based on the Team and Self Diagnostic Learning pedagogical framework. The system was trialled with 35 Secondary School students in a blended learning environment. Qualitative feedback from student focus group discussions and questionnaires were analysed from each trial. Design changes that focused students on specific reflection questions and goals helped to improve the quality of student responses of their teamwork. Further refinement of the tool and activity designs is in progress to better support teamwork awareness and reflection to build the teamwork competency of 21st century learners.

Many pedagogical models in the field of learning analytics are implicit and do not overtly direct learner behavior. While this allows flexibility of use, this could also result in misaligned practice, and there are calls for more explicit pedagogical models in learning analytics. This paper presents an explicit pedagogical model, the Team and Self Diagnostic Learning (TSDL) framework, in the context of collaborative inquiry tasks. Key informing theories include experiential learning, collaborative learning, and the learning analytics process model. The framework was trialed through a teamwork competency awareness program for 14 year old students. A total of 272 students participated in the program. This paper foregrounds students' and teachers' evaluative accounts of the program. Findings reveal positive perceptions of the stages of the TSDL framework, despite identified challenges, which points to its potential usefulness for teaching and learning. The TSDL framework aims to provide theoretical clarity of the learning process, and foster alignment between learning analytics and the learning design. The current work provides trial outcomes of a teamwork competency awareness program that used dispositional analytics, and further efforts are underway to develop the discourse layer of the analytic engine. Future work will also be dedicated to application and refinement of the framework for other contexts and participants, both learners and teachers alike.

The increasing use of chat tools for learning and collaboration emphasizes the need for automating assessment. We propose a text mining approach to automate teamwork assessment in chat data. This supervised training approach can be extended to other domains for efficient assessment.