Critical perspectives on writing analytics

Shum, Simon Buckingham; Knight, Simon; McNamara, Danielle S.; Bektik, Duygu; Crossley, Scott A.

doi:10.1145/2883851.2883854

Cited by 22 publications

(14 citation statements)

References 9 publications

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“…Lastly, writing researchers were included to identify desired indicators based on writing research and theory, and to better connect writing analytics to educational practice (cf. Buckingham Shum et al, 2016).…”

Section: Approachmentioning

confidence: 99%

How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

Conijn

Martínez-Maldonado

Knight

et al. 2020

Computer Assisted Language Learning

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Section: Approachmentioning

confidence: 99%

How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

Conijn

Martínez-Maldonado

Knight

et al. 2020

Computer Assisted Language Learning

Self Cite

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“…Much of the support provided to students is targeted at academic English for non-native speakers or ad hoc remedial support (Wingate, 2012). As a result, the potential of learning analytics to provide formative feedback to students to support their writing is a growing area of interest (see, e.g., the following series of LAK workshops: Buckingham Shum, Knight, et al, 2016;Knight, Allen, Gibson, McNamara, & Buckingham Shum, 2017;Shibani, Abel, Gibson, & Knight, 2018). The writing feedback tools build on a longer history of automated essay scoring systems, which score constructed responses in standardized assessments, and automated writing evaluation systems, which provide instructional formative feedback, typically through targeted lessons on identified writing features (Warschauer & Grimes, 2008).…”

Section: Writing: a Core Educational Aimmentioning

confidence: 99%

Are You Being Rhetorical? A Description of Rhetorical Move Annotation Tools and Open Corpus of Sample Machine-Annotated Rhetorical Moves

Knight

Abel

Shibani

et al. 2020

JLA

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Writing analytics has emerged as a sub-field of learning analytics, with applications including the provision of formative feedback to students in developing their writing capacities. Rhetorical markers in writing have become a key feature in this feedback, with a number of tools being developed across research and teaching contexts. However, there is no shared corpus of texts annotated by these tools, nor is it clear how the tool annotations compare. Thus, resources are scarce for comparing tools for both tool development and pedagogic purposes. In this paper, we conduct such a comparison and introduce a sample corpus of texts representative of the particular genres, a subset of which has been annotated using three rhetorical analysis tools (one of which has two versions). This paper aims to provide both a description of the tools and a shared dataset in order to support extensions of existing analyses and tool design in support of writing skill development. We intend the description of these tools, which share a focus on rhetorical structures, alongside the corpus, to be a preliminary step to enable further research, with regard to both tool development and tool interaction

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“…Learning analytics research has matured significantly over the past few years. In particular, the field has made significant advances in areas surrounding learner behaviour modelling (Liu, Patel, & Koedinger, 2016;Pelánek, Rihák, & Papoušek, 2016) and text analytics (Crossley, Kyle, & McNamara, 2015;Buckingham Shum et al, 2016). Despite these advances, questions of how analytics are best deployed and applied in learning and teaching practice (Wise, 2014;Wise, Vytasek, Hausknecht, & Zhao, 2016) continue to be underrepresented in the literature.…”

Section: Introductionmentioning

confidence: 99%

OnTask: Delivering Data-Informed, Personalized Learning Support Actions

et al. 2018

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The learning analytics community has matured significantly over the past few years as a middle space where technology and pedagogy combine to support learning experiences. To continue to grow and connect these perspectives, research needs to move beyond the level of basic support actions. This means exploring the use of data to prove richer forms of actions, such as personalized feedback, or hybrid approaches where instructors interpret the outputs of algorithms and select an appropriate course of action. This paper proposes the following three contributions to connect data extracted from the learning experience with such personalized student support actions: 1) a student-instructor centred conceptual model connecting a representation of the student information with a basic set of rules created by instructors to deploy Personalized Learning Support Actions (PLSAs); 2) a software architecture based on this model with six categories of functional blocks to deploy the PLSAs; and 3) a description of the implementation of this architecture as an open-source platform to promote the adoption and exploration of this area. Notes for Practice• The report draws on research findings related to the effect of personalized feedback on student satisfaction and academic performance (Pardo, Jovanović, Dawson, Gašević, & Mirriahi, 2018).• The main contribution is the description of the design and implementation of an open source platform for researchers and practitioners to connect data with personalized learning support actions.• The area of learning analytics needs tools such as the one described in this document to serve as a vehicle to exchange insights among researchers and practitioners.• This is an example of the note for practice and research

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Critical perspectives on writing analytics

Cited by 22 publications

References 9 publications

How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

Are You Being Rhetorical? A Description of Rhetorical Move Annotation Tools and Open Corpus of Sample Machine-Annotated Rhetorical Moves

OnTask: Delivering Data-Informed, Personalized Learning Support Actions

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