Reflective writing analytics for actionable feedback

Gibson, Andrew; Aitken, A. J.; Sándor, Ágnes; Shum, Simon Buckingham; Lucas, Cherie; Knight, Simon

doi:10.1145/3027385.3027436

Cited by 66 publications

(48 citation statements)

References 21 publications

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“…Artificial intelligence and data analytics (AIDA) is increasingly entering education: Standardised tests, such as the Programme for International Student Assessment: (PISA—http://www.oecd.org/pisa) evaluate student performance around the world (Sellar, Thompson & Rutkowski, ); academics are appointed and promoted based upon satisfaction scores sourced from students (Kitto, Williams, & Alderman, ); machine learning predicts which students might be at risk of failure based upon various datasets (Gašević, Dawson, Rogers, & Gasevic, ); text analysis can give students real time feedback on their writing (Gibson et al , ; Shibani, Knight, Buckingham Shum, & Ryan, ); intelligent tutoring and adaptive learning systems are leveraged to personalise content delivery (Feldstein & Hill, ); and companies and consortia such as Google, Burning Glass, Salesforce, IMS Global and ADL are marketing data, standards and suites of new tools to institutions (Kitto, O'Hara, et al , ).…”

Section: Introductionmentioning

confidence: 99%

“…However, much of this work on ethics has been completed in the abstract , independent of concrete cases. Here, as people who have been actively involved in building LA tools in a higher education context over a number of years (Gibson et al , ; Kitto, Buckingham Shum, & Gibson, ; Kitto, Cross, Waters, & Lupton, ; Kitto, Lupton, Davis, & Waters, ; Shibani et al , ), we will consider the ways in which various ethical framings inform practice in LA, highlighting gaps that those building AIDA for education are likely to encounter in attempting to adopt an ethical approach to LA. We draw attention to three tensions that we have encountered in building LA infrastructure, demonstrating that there is a gap in existing approaches through the use of three edge case scenarios which help us to explore these tensions.…”

Section: Introductionmentioning

confidence: 99%

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Practical ethics for building learning analytics

Kitto¹,

Knight²

2019

Brit J Educational Tech

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Artificial intelligence and data analysis (AIDA) are increasingly entering the field of education. Within this context, the subfield of learning analytics (LA) has, since its inception, had a strong emphasis upon ethics, with numerous checklists and frameworks proposed to ensure that student privacy is respected and potential harms avoided. Here, we draw attention to some of the assumptions that underlie previous work in ethics for LA, which we frame as three tensions. These assumptions have the potential of leading to both the overcautious underuse of AIDA as administrators seek to avoid risk, or the unbridled misuse of AIDA as practitioners fail to adhere to frameworks that provide them with little guidance upon the problems that they face in building LA for institutional adoption. We use three edge cases to draw attention to these tensions, highlighting places where existing ethical frameworks fail to inform those building LA solutions. We propose a pilot open database that lists edge cases faced by LA system builders as a method for guiding ethicists working in the field towards places where support is needed to inform their practice. This would provide a middle space where technical builders of systems could more deeply interface with those concerned with policy, law and ethics and so work towards building LA that encourages human flourishing across a lifetime of learning. What is already known about this topic Applied ethics has a number of well‐established theoretical groundings that we can use to frame the actions of ethical agents, including, deontology, consequentialism and virtue ethics. Learning analytics has developed a number of checklists, frameworks and evaluation methodologies for supporting trusted and ethical development, but these are often not adhered to by practitioners. Laws like the General Data Protection Regulation (GDPR) apply to fields like education, but the complexity of this field can make them difficult to apply. What this paper adds Evidence of tensions and gaps in existing ethical frameworks and checklists to support the ethical development and implementation of learning analytics. A set of three edge cases that demonstrate places where existing work on the ethics of AI in education has failed to provide guidance. A “practical ethics” conceptualisation that draws on virtue ethics to support practitioners in building learning analytics systems. Implications for practice and/or policy Those using AIDA in education should collect and share example edge cases to support development of practical ethics in the field. A multiplicity of ethical approaches are likely to be useful in understanding how to develop and implement learning analytics ethically in practical contexts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical ethics for building learning analytics

Kitto¹,

Knight²

2019

Brit J Educational Tech

Self Cite

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“…The second approach uses ETM to extract information to aid instructors in the elaboration of feedback from different resources (Goldin, Narciss, Foltz, & Bauer, 2017). Gibson et al (2017), Akçapınar (2015) and Hwang, Cheng, Chu, Tseng, and Hwang (2007) presents different methods to help instructors to provide feedback based on writing activities. Also, several papers extract information to support formative feedback based on data from forums interactions and essays (Woods et al, 2017;Yang, Heinrich, & Kemp, 2011).…”

Section: Student Feedbackmentioning

confidence: 99%

Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

115

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The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends. This article is categorized under: Application Areas > Education and Learning Ensemble Methods > Text Mining

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“…Similar arguments have been made by Kruse and Pongsajapan (2012), and Wise (2014), who discuss ways in which students might be encouraged towards metacognition and reflection, but few concrete working examples of how this might be achieved have been presented to date. The reflective writing analytics (RWA) that have recently been developed at University of Technology Sydney Gibson et al, 2017, provides an example of an early prototype tool where LA is used to encourage students to reflect upon analytic reports that aim to improve their writing. However, we are yet to see such processes move into the mainstream, or the development of sophisticated pedagogical approaches that might utilise them.…”

Section: Student-facing Lamentioning

confidence: 99%

Designing for student-facing learning analytics

Kitto

Lupton

Davis

et al. 2017

AJET

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Despite a narrative that sees learning analytics (LA) as a field that aims to enhance student learning, few student-facing solutions have emerged. This can make it difficult for educators to imagine how data can be used in the classroom, and in turn diminishes the promise of LA as an enabler for encouraging important skills such as sense-making, metacognition, and reflection. We propose two learning design patterns that will help educators to incorporate LA into their teaching protocols: do-analyse-change-reflect, and active learning squared. We discuss these patterns with reference to a case study utilising the Connected Learning Analytics (CLA) toolkit, in three trials run over a period of 18 months. The results demonstrate that student-facing learning analytics is not just a future possibility, but an area that is ripe for further development.

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Reflective writing analytics for actionable feedback

Cited by 66 publications

References 21 publications

Practical ethics for building learning analytics

Practical ethics for building learning analytics

Text mining in education

Designing for student-facing learning analytics

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