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Visualisation Analysis For Exploring Prerequisite Relations In Textbooks


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Presented at the First Workshop on Intelligent Textbooks (Chicago, IL, US; June 25, 2019)

Published in: Education
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Visualisation Analysis For Exploring Prerequisite Relations In Textbooks

  1. 1. visualisation analysis for exploring prerequisite relations in textbooks Samuele Passalacqua, Frosina Koceva, Chiara Alzetta Ilaria Torre, and Giovanni Adorni University of Genoa, Italy, Department of Informatics, Bioengineering, Robotics and Systems Engineering June 25, 2019 First Workshop on Intelligent Textbooks, 20th International Conference on Artificial Intelligence in Education (AIED 2019)
  2. 2. Introduction Structuring textbook knowledge as concept maps with explicit prerequisite dependencies 1
  3. 3. Prerequisite Relation & Information Visualisation ∙ integration in curricula for teaching and learning abstract concepts ([17]) ∙ learning dashboards with graphical representations of the learning process (e.g. Learning Analytics [7]) ∙ representation of large amount of data in Educational Data Mining ([18]) However: Information Visualisation & Prerequisite Relations is still missing in the literature. We used Information visualisation to enhance: ∙ 1) PR Exploration ∙ 2) Algorithm Refinement 2
  4. 4. 1) PR Exploration - Dataset 3
  5. 5. 1) PR Exploration - Techniques: concept graph It revealed interesting insights concerning graph’s connectivity, transitivity and topology. 4
  6. 6. 1) PR Exploration - Techniques: matrix It confirmed our hypothesis that PR is highly correlated with co-occurrence and temporal order. 5
  7. 7. 2) Algorithm Refinement - Dataset Burst Analysis (see our paper on Friday for further details [1]) 6
  8. 8. 2) Algorithm Refinement - Techniques: Gantt Chart It facilitates the analysis of temporal patterns. 7
  9. 9. 2) Algorithm Refinement - Techniques: Gantt Chart It allows to compare temporal relations with gold relations. 8
  10. 10. 2) Algorithm Refinement - Techniques: Gantt Chart It allows forms of textbook exploration. 9
  11. 11. 2) Algorithm Refinement - Techniques: Allen Graph It suggested limits of the algorithm and possible improvements. 10
  12. 12. Conclusion and Future work In this paper: ∙ better understand prerequisites in textbooks ∙ develop more powerful strategies for the automatic extraction In the future: ∙ Explore more techniques ∙ Apply them in different and larger corpora of educational texts ∙ More directly address the needs of learners and teachers 11
  13. 13. Thank you! 12