Topic modeling for evaluating students' reflective writing

Chen, Ye; Yu, Bei; Zhang, Xuewei; Yu, Yihan

doi:10.1145/2883851.2883951

Cited by 48 publications

(37 citation statements)

References 13 publications

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“…It can be seen that the top ten features include LIWC linguistic features (LIWC.Quant/Compare/Adj), emotional features, cognitive features (AWA.Self-Critique and LIWC.Differ), reflective rhetorical moves (AWA.Context/Self-Critique), authentic (LIWC.Authentic) and space features (LIWC.Space). These results confirm earlier reports that LIWC provides good classification indicators of reflective writing [15,20]. Some of these features are significantly correlated to the level of reflection (the average rating scores of our human experts, as described in section 3.1).…”

Section: Feature Importance Analysis and Discussionsupporting

confidence: 91%

“…differentiating reflective and non-reflective sentences) by using both rule-based and machine learning approach [13,18,19]. Chen et al [20] adapted topic modelling to analyze pre-service teachers' reflection journals, but topic models focus on the content rather than quality and depth of reflection. Extending the work of Buckingham Shum et al [4], Gibson et al [3] proposed a concept-matching rhetorical analysis framework [21] to automatically detect sentences performing three key reflective rhetorical functions summarized as Context, Challenge and Change.…”

Section: Reflective Writing Analyticsmentioning

confidence: 99%

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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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Section: Feature Importance Analysis and Discussionsupporting

confidence: 91%

Section: Reflective Writing Analyticsmentioning

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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“…Topic modelling has potential as an analytic tool to help teachers assess reflective thoughts in written journals (N=80) [18]. An analytics dashboard designed for users of interactive e-books could potentially be used by teachers [41].…”

Section: Evidence With Potential To Support Teachingmentioning

confidence: 99%

Where is the evidence?

Ferguson

Clow

2017

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

108

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Where is the evidence for learning analytics? In particular, where is the evidence that it improves learning in practice? Can we rely on it? Currently, there are vigorous debates about the quality of research evidence in medicine and psychology, with particular issues around statistical good practice, the 'file drawer effect', and ways in which incentives for stakeholders in the research process reward the quantity of research produced rather than the quality. In this paper, we present the Learning Analytics Community Exchange (LACE) project's Evidence Hub, an effort to relate research evidence in learning analytics to four propositions about learning analytics: whether they support learning, support teaching, are deployed widely, and are used ethically. Surprisingly little evidence in this strong, specific sense was found, and very little was negative (7%, N=123), suggesting that learning analytics is not immune from the pressures in other areas. We explore the evidence in one particular area in detail (whether learning analytics improve teaching and learners support in the university sector), and set out some of the weaknesses of the evidence available. We conclude that there is considerable scope for improving the evidence base for learning analytics, and set out some suggestions of ways for various stakeholders to achieve this.

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“…FLSA supposes that the list of documents and their embodied words can be fuzzy clustered where each cluster can be represented by a certain topic. LDA and similar unsupervised techniques have been widely used in several modelling applications [37][38][39][40][41][42].…”

Section: Sbd Incorporating Machine Learning For Data Classification Amentioning

confidence: 99%

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Abu-Salih

Bremie

Wongthongtham

et al. 2019

Advances in Intelligent Systems and Computing

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The wealth of Social Big Data(SBD) represents a unique opportunity for organisations to obtain the excessive use of such data abundance to increase their revenues. Hence, there is an imperative need to capture, load, store, process, analyse, transform, interpret, and visualise such manifold social datasets to develop meaningful insights that are specific to an application's domain. This paper lays the theoretical background by introducing the state-ofthe-art literature review of the research topic. This is associated with a critical evaluation of the current approaches, and fortified with certain recommendations indicated to bridge the research gap.

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Topic modeling for evaluating students' reflective writing

Cited by 48 publications

References 13 publications

Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflective Statements

Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflective Statements

Where is the evidence?

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

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