Online textbooks with interactive content emerged as a popular medium for learning programming and other computer science topics. While the textbook component supports acquisition of programming concepts by reading, various types of ``smart'' interactive learning content such as worked examples, code animations, Parson's puzzles, and coding problems allow students to immediately practice and master the newly learned concepts. This paper attempts to automate the time-consuming manual process of augmenting textbooks with ``smart'' interactive content. We introduce an ontology-based approach that can link fragment of text with ``smart'' content activities, demonstrate its application to two practical linking cases, and present the results of its pilot evaluation.

1. Isaac Alpizar-Chacon, Jordan Barria-Pineda, Kamil Akhuseyinoglu , Sergey Sosnovsky, and Peter Brusilovsky
Integrating Textbooks with Smart Interactive
Content for Learning Programming

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25-06-2021
Motivation
• Technology enhanced learning:
• Electronic textbooks (learning-by-reading)
• “Smart” interactive systems such as Intelligent Tutoring Systems (ITS)
(learning-by-doing)
• There is complementary nature of learning-by-reading and learning-by-doing
• Integration of textbooks with interactive content

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Motivation
• Problem:
• what content to associate? - which interactive activity is the best match for a
section or text?
• Manual allocation of interactive content

4. Goal
Automatically cross the border between
text and smart interactive content for
learning computer programming
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The two systems
intextbooks P4

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intextbooks

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P4

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73 concepts
35 classes
The Python Ontology

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Two-way linking of content: approach
• 1. Knowledge model extraction and glossary unification
• 2. Glossary - ontology linking
• 3. Granular content linking
• 3.1. Textbook sub-chapters to P4
• 3.2. P4 learning activities to textbook sub-chapters

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Knowledge model extraction and glossary unification
~
~
different lexical forms
acronyms
synonyms

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Glossary - ontology linking
textual matching
stemming
abbreviations list
compound names

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Granular content linking: Textbook sub-chapters to P4
bridge
bridge

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Granular content linking: P4 learning activities to textbook sub-chapters
bridge
bridge

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Two-way linking of content: real data
5 Python textbooks from intextbooks
157 learning activities for 12 topics in P4

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Results
Knowledge model extraction and glossary
unification
•4055 different index terms
•3250 in the unified glossary

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Results
Glossary - ontology linking
•53 linked pairs
•36 Python concepts
•10 Python classes

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Results
Granular content linking
•186 index terms linked to Python
concepts in P4
•245 sub-chapters mapped to the topics
in P4
•1790 learning activities linked to sub-
chapters (avg. 65 unique per textbook)

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Validation
• Assessment of the linking for the “Python for Everybody” textbook.
• 55 sections from the textbook matched to 17 topics of the Python course
• Two domain experts with experience on teaching Python rated the match
quality
• 3: good match, 2: partial match, 1: bad match

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Validation
Good match:
45%
Partial match:
12%
Bad match: 14%
Positive
evaluation: 17%
Negative
evaluation:
12%
Agreement (Cohen’s Kappa = 0.63)
Disagreement

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Conclusion and future work
• We showed a two-way linking between textbook and smart learning content
• First step to resolving the problem of automatic linking
• Number of linked sub-chapters to topics (245) and the learning activities to
sub-chapters (1790) is promising
• Explore:
• a more “fine-grained” association of concepts (e.g., at a paragraph level)
• different domains

22. Thanks!
You can contact me at: i.alpizarchacon@uu.nl
Any questions?