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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)
Introduction
Structuring textbook knowledge as concept maps with explicit
prerequisite dependencies
1
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
1) PR Exploration - Dataset
3
1) PR Exploration - Techniques: concept graph
It revealed interesting insights concerning graph’s connectivity,
transitivity and topology. 4
1) PR Exploration - Techniques: matrix
It confirmed our hypothesis that PR is highly correlated with
co-occurrence and temporal order. 5
2) Algorithm Refinement - Dataset
Burst Analysis (see our paper on Friday for further details [1])
6
2) Algorithm Refinement - Techniques: Gantt Chart
It facilitates the analysis of temporal patterns.
7
2) Algorithm Refinement - Techniques: Gantt Chart
It allows to compare temporal relations with gold relations.
8
2) Algorithm Refinement - Techniques: Gantt Chart
It allows forms of textbook exploration.
9
2) Algorithm Refinement - Techniques: Allen Graph
It suggested limits of the algorithm and possible improvements. 10
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
Thank you!
12

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Visualisation Analysis For Exploring Prerequisite Relations In Textbooks

  • 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. Introduction Structuring textbook knowledge as concept maps with explicit prerequisite dependencies 1
  • 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. 1) PR Exploration - Dataset 3
  • 5. 1) PR Exploration - Techniques: concept graph It revealed interesting insights concerning graph’s connectivity, transitivity and topology. 4
  • 6. 1) PR Exploration - Techniques: matrix It confirmed our hypothesis that PR is highly correlated with co-occurrence and temporal order. 5
  • 7. 2) Algorithm Refinement - Dataset Burst Analysis (see our paper on Friday for further details [1]) 6
  • 8. 2) Algorithm Refinement - Techniques: Gantt Chart It facilitates the analysis of temporal patterns. 7
  • 9. 2) Algorithm Refinement - Techniques: Gantt Chart It allows to compare temporal relations with gold relations. 8
  • 10. 2) Algorithm Refinement - Techniques: Gantt Chart It allows forms of textbook exploration. 9
  • 11. 2) Algorithm Refinement - Techniques: Allen Graph It suggested limits of the algorithm and possible improvements. 10
  • 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