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Visualizing Topic Flow in Students’ Essays

Visualizing Topic Flow in Students’ Essays. Presenter : Wu, Min-Cong Authors : Stephen T. O’Rourke , Rafael A. Calvo and Danielle S. McNamara 2011, EST. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Visualizing Topic Flow in Students’ Essays

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  1. Visualizing Topic Flow in Students’ Essays Presenter : Wu, Min-Cong Authors : Stephen T. O’Rourke , Rafael A. Calvo and Danielle S. McNamara2011, EST

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Writing is an important learning activity, essays Visualizing is important that can help people assess and improve the quality of essays.

  4. Objectives • This paper presents a novel document visualization technique and a measure of quality based on the average semantic distance between parts of a document.

  5. Methodology-Mathematical Framework In order to Visualization, so need to reduce dimension : Visualizing Topic Flow term-by- paragraphs matrix topic model is created topic model is projected visualization of the document’s paragraphs stop-words low frequency words stemming is applied 2-dimensional space Use NMF Quantifying Topic Flow identify features in the topic model of the document. term-by- sentence matrix topic model is created

  6. Methodology-Visualizing Topic Flow(term-by- paragraphs matrix) j(paragraphs) i(term) Term’s frequency In paragraphs If Log-Entropy is large, this word is more import Term’s Entropy in document

  7. Methodology-Visualizing Topic Flow(NMF dimensionality reduction technique) ≈ Term-by-paragraphs martix (m*n) Term-by-topic martix(m*r) Topic-by-paragraphs martix(r*n) Ex.X(6,2)=w(6,3)*H(3,2) number of latent topics which can be approximated by minimizing the squared error of the Frobenius norm of X−WH.

  8. Methodology-Visualizing Topic Flow(2-dimensional representation) paragraph-paragraph triangular distance table iterative majorization algorithm (least-squares) minimize a loss function (Stress) Multidimensional Scaling use in Similarity comparison between the vector dissimilarities approximated distances in the low dimensional

  9. Methodology-Visualizing Topic Flow(Visualizing Flow ) Paragraphs the diameter of the grid equal to the maximum possible distance between any two paragraphs node-link Next paragraphs conclusion The degree of deviate from a circle introduction High grade, Why? Because: 1. paragraphs appear close, 2. ‘introduction’ and‘conclusion’ is similar Low grade

  10. Methodology-Quantifying Topic Flow Semantic distances between consecutive pairs of sentences or paragraphs Double average over all the pairs of sentences or paragraphs DI <=0, indicates a random topic flow DI> 0, indicates the presence of topic flow.

  11. Experiment-Evaluation 1: Flow and Grades(Experiment Dataset) Dataset:120 essays written for assignments by undergraduate students at Mississippi State University Essay grades :1-6 level Subset:High:67(1-3)Low:53(3.2-6) k(number of topic):5

  12. Experiment-Evaluation 1: Flow and Grades(Measuring Topic Flow ) less present using either of the dimensionality reduction techniques P<0.05 P>0.05

  13. Experiment-Evaluation 1: Flow and Grades(Measuring Topic Flow ) Measure the correlation

  14. Experiment-Evaluation 2: Supporting Assessment(Methodology) 1.inter-rater agreement that the tutors had with two expert raters. 2. The two tutors independently marked assignments with map and no map hypothesized : Essay’s agreement can be subjectively assessed faster, more accurately, and more consistently with map. answer

  15. Experiment-Evaluation 2: Supporting Assessment(Essay Subset Preparation ) The 40 essays remaining were divided into two subsets of 20 essays each according to the MASUS procedure to assess subest1 subest2

  16. Experiment-Evaluation 2: Supporting Assessment(Results) Rater1:native English speaker Rater2: non-native English speaker In order to eliminate the effect of essay length

  17. Conclusions • Tutors assess the essays faster and more accurately and consistently with the aid of topic flow visualization.

  18. Comments • Advantages • effectively discover market intelligence (MI) for supporting decision-makers. • Applications • Document visualizations.

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