Towards the Discovery of Learner Metacognition From Reflective Writing

Gibson, Andrew; Kitto, Kirsty; Bruza, Peter

doi:10.18608/jla.2016.32.3

Cited by 43 publications

(32 citation statements)

References 19 publications

(25 reference statements)

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“…Several researchers see automated methods to analyse and assess reflective writing as a major step towards the solution to this problem [7,17,19]. Studying such methods can help explain which parts of the analysis of reflective writings can be automatised.…”

Section: Resultsmentioning

confidence: 99%

“…They can be also useful for research on rule-based and machine learning based methods for reflection detection. Rule-based approaches often combine dictionaries with rules [7,17,21]. These dictionaries are a core building block of such rule-based systems.…”

Section: Discussionmentioning

confidence: 99%

“…Often, dictionaries are used in combination with the rule-based system. There is some research regarding rule-based methods to detect reflection [7,17,21]; however, it is less researched than the dictionary-based approach. The last and least researched approach uses machine learning algorithms to detect reflection from texts [19].…”

Section: Automated Approachesmentioning

confidence: 99%

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Reflective writing analytics

Ullmann

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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Despite their importance for educational practice, reflective writings are still manually analysed and assessed, posing a constraint on the use of this educational technique. Recently, research started to investigate automated approaches for analysing reflective writing. Foundational to many automated approaches is the knowledge of words that are important for the genre. This research presents keywords that are specific to several categories of a reflective writing model. These keywords have been derived from eight datasets, which contain several thousand instances using the log-likelihood method. Both performance measures, the accuracy and the Cohen's κ, for these keywords were estimated with ten-fold cross validation. The results reached an accuracy of 0.78 on average for all eight categories and a fair to good interrater reliability for most categories even though it did not make use of any sophisticated rule-based mechanisms or machine learning approaches. This research contributes to the development of automated reflective writing analytics that are based on datadriven empirical foundations. CCS Concepts KeywordsReflective writing; natural language processing; reflective writing analytics; automated detection of reflection.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Reflective writing analytics

Ullmann

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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“…Building on and extending this work, Gibson, Kitto, and Bruza (2016) have more recently proposed a conceptual model to explain the relationships between reflection and metacognition in learning. This motivates exploratory computational analysis of undergraduate reflective writing, in which a range of textual features is mapped to the model's constructs.…”

Section: Related Approachesmentioning

confidence: 99%

Towards Reflective Writing Analytics: Rationale, Methodology and Preliminary Results

et al. 2017

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When used effectively, reflective writing tasks can deepen learners' understanding of key concepts, help them critically appraise their developing professional identity, and build qualities for lifelong learning. As such, reflective writing is attracting substantial interest from universities concerned with experiential learning, reflective practice, and developing a holistic conception of the learner. However, reflective writing is for many students a novel genre to compose in, and tutors may be inexperienced in its assessment. While these conditions set a challenging context for automated solutions, natural language processing may also help address the challenge of providing real time, formative feedback on draft writing. This paper reports progress in designing a writing analytics application, detailing the methodology by which informally expressed rubrics are modelled as formal rhetorical patterns, a capability delivered by a novel web application. Preliminary tests on an independently human-annotated corpus are encouraging, showing improvements from the first to second version, but with much scope for improvement. We discuss a range of issues: the prevalence of false positives in the tests, areas for future technical improvements, the issue of gaming the system, and the participatory design process that has enabled work across disciplinary boundaries to develop the prototype to its current state.

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“…Reflective processes in learning have most impact when they are formative and future-oriented [5]. In addition, reflection is important for meta-cognitive adaptation, when students connect their thinking to the wider world [6]. Reflection can be a purely internal form of contemplation, but making it explicit can help clarify one's own thinking, benefit fellow learners, and help a student narrate (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflective Statements

Liu

Shum

Mantzourani

et al. 2019

Lecture Notes in Computer Science

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Reflective writing is widely acknowledged to be one of the most effective learning activities for promoting students' self-reflection and critical thinking. However, manually assessing and giving feedback on reflective writing is time consuming, and known to be challenging for educators. There is little work investigating the potential of automated analysis of reflective writing, and even less on machine learning approaches which offer potential advantages over rule-based approaches. This study reports progress in developing a machine learning approach for the binary classification of pharmacy students' reflective statements about their work placements. Four common statistical classifiers were trained on a corpus of 301 statements, using emotional, cognitive and linguistic features from the Linguistic Inquiry and Word Count (LIWC) analysis, in combination with affective and rhetorical features from the Academic Writing Analytics (AWA) platform. The results showed that the Random-forest algorithm performed well (F-score=0.799) and that AWA features, such as emotional and reflective rhetorical moves, improved performance.

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Towards the Discovery of Learner Metacognition From Reflective Writing

Cited by 43 publications

References 19 publications

Reflective writing analytics

Reflective writing analytics

Towards Reflective Writing Analytics: Rationale, Methodology and Preliminary Results

Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflective Statements

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