Early research on hypermedia learning and Web-based education featured a strong stream of work on intelligent and adaptive textbooks, which combined the knowledge modeling ideas from the field of intelligent tutoring with rich linking offered by the hypermedia and the Web. However, over the next ten years from 2005 to 2015 this area was relatively quiet as the focus of research in e-learning has shifted to other topics and other creative ideas to leverage the power of Internet. A recent gradual shift of the whole publication industry from printed books to electronic books followed by a rapid growth or the volume of online books re-ignited interests to “more intelligent” textbooks. The research on the new generation of intelligent textbooks engaged a larger set of technologies and engaged scholars from a broader range of areas including machine learning, natural language understanding, social computing, etc. In my talk I will review the past and present of research on intelligent textbooks from its origins to the diverse modern work providing examples of most interesting technologies and research results.

1. The Return
of Intelligent Textbooks
Peter Brusilovsky
School of Computing and Information,
University of Pittsburgh

2. How to Structure History?
• Pre-History (before 6,000 BCE)
• Ancient history (6,000 BCE – 650 CE)
– Stone Age, Bronze Age, Classical Era…
• Post-classical history (500 – 1500)
• Modern history (1500 – present)
– Early Modern Period (1500 – 1750)
– Late Modern Period (1750 – 1945)
– Contemporary Period (1945 – present)
https://en.wikipedia.org/wiki/History_by_period

3. Pre-History: Hypertext
1945: Vannevar Bush proposes Memex in his
article "As We May Think".
1965: Ted Nelson introduces Xanadu and coins the
term hypertext.
1967: Andries van Dam develops the Hypertext
Editing System at Brown University, the first
working hypertext
1968: Doug Engelbart gives a demo of NLS, a part
of the Augment project, started in 1962.

4. Ancient History: HyperTextbooks
1987: Apple delivers HyperCard free with every Macintosh
1987: The ACM organizes the first Conference on Hypertext
• Remde, J. R., Gomez, L. M., and Landauer, T. K. (1987)
SuperBook: an automatic tool for information exploration —
hypertext? In: Proceedings of the ACM conference on
Hypertext, Hypertext ’87, pp. 175-188.
• Rada, R. (1992) Converting a textbook to hypertext. ACM
Transactions on Information Systems 10 (3), 294-315.
• Boyle, T., Gray, G., Wendl, B., and Davies, M. (1994) Taking
the plunge with CLEM: the design and evaluation of a large
scale CAL system. Computers and Education 22 (1/2), 19-26.

5. Bronze Age: Experiments with
Adaptive Textbooks (1991-1995)
• gpAdapter (Hohl, Böker, Gunzenhouser, 1991)
• Sorting page fragments and links by relevance
• Manuel Excel (de La Passardiere, Dufresne, 1992)
• Adaptive link annotation with icons
• HYPERFLEX (Kaplan, Fenwick, Chen, 1993)
• Sorting links by relevance
• MetaDoc (Boyle, Encarnacion, 1994)
• Adaptive stretch text

6. ISIS-Tutor: Adaptive ISIS Textbook
Annotations for concept states in ISIS-Tutor: not ready (neutral); ready
and new (red); seen (green); and learned (green+)
Brusilovsky,
P.
and
Pesin,
L.
(1994)
ISIS-Tutor:
An
adaptive
hypertext
learning
environment.
In:
H.
Ueno
and
V.
Stefanuk
(eds.)
Proceedings
of
JCKBSE'94,
Japanese-CIS
Symposium
on
knowledge-based
software
engineering,
Pereslavl-Zalesski,
Russia,
May
10-13,
1994,,
pp.
83-
87.

7. Early Results
• ISIS-Tutor study: Learn 10 concepts (of 64),
solve 10 tasks, check all related examples
Brusilovsky, P. and Pesin, L. (1998) Adaptive navigation support in educational hypermedia: An evaluation of
the ISIS-Tutor. Journal of Computing and Information Technology 6 (1), 27-38.

8. Classical Era: Web-Based Adaptive
Textbooks (1996-2004)
1990: The World Wide Web delivers Hypertext to
millions
• ELM-ART
– Brusilovsky, P., Schwarz, E., and Weber, G. (1996) ELM-ART: An intelligent tutoring system
on World Wide Web. In: C. Frasson, G. Gauthier and A. Lesgold (eds.) Proceedings of Third
International Conference on Intelligent Tutoring Systems, ITS-96, Montreal, Canada, June
12-14, 1996, Springer Verlag, pp. 261-269.
– Schwarz, E., Brusilovsky, P., and Weber, G. (1996) World-wide intelligent textbooks.
In: Proceedings of ED-TELECOM'96 - World Conference on Educational
Telecommunications, Boston, MA, June 17-22, 1996, AACE, pp. 302-307.
• 2L670
– De Bra, P. M. E. (1996) Teaching Hypertext and Hypermedia through the Web. Journal of
Universal Computer Science 2 (12), 797-804.
– De Bra, P. (1997) Teaching Through Adaptive Hypertext on the WWW. International Journal
of Educational Telecommunications 3 (2/3), 163-180.

9. ELM-ART: Adaptive Textbook + ITS

10. ELM-ART: Student Modeling and OLM
Weber,
G.
and
Brusilovsky,
P.
(2001)
ELM-ART:
An
adaptive
versatile
system
for
Web-based
instruction.
International
Journal
of
Artificial
Intelligence
in
Education
12
(4),
351-384.

11. 2L670
De Bra, P. (1997) Teaching Through Adaptive Hypertext on the WWW. International Journal of Educational Telecommunications 3 (2/3), 163-180.

12. More Web-based Adaptive Textbooks
• AST
– Specht, M., Weber, G., Heitmeyer, S., and Schöch, V. (1997) AST: Adaptive WWW-
Courseware for Statistics. In: Proceedings of Workshop "Adaptive Systems and User
Modeling on the World Wide Web" at 6th International Conference on User Modeling,
UM97, Chia Laguna, Sardinia, Italy, June 2, 1997, pp. 91-95
• MultiBook
– Seeberg, C., Steinacker, A., Reichenberger, K., Fischer, S., and Steinmetz, R.
(1999) Individual tables of contents in Web-based learning systems. In: Proceedings of
Tenth ACM Conference on Hypertext and hypermedia (Hypertext'99), Darmstadt, Germany,
February 21 - 25, 1999, ACM Press, pp. 167-168.
• KBS-Hyperbook
– Henze, N., Naceur, K., Nejdl, W., and Wolpers, M. (1999) Adaptive hyperbooks for
constructivist teaching. Künstliche Intelligenz 13 (4), 26-31.
• ALICE
– Kavcic, A. (2001) ALICE: Adaptive educational hypermedia on the Web. In: Proceedings of
Computer Aided Learning in Engineering, CALIE'2001, Tunis, 8-10 November, 2001, pp.
101-104.

13. KBS-HyperBook: Expandable AH
Bayesian student modeling, integrating new resources by indexing

14. ALICE: Link Recommendation

15. InterBook:
Authoring of Adaptive Textbooks
Brusilovsky,
P.,
Eklund,
J.,
and
Schwarz,
E.
(1998)
Web-
based
education
for
all:
A
tool
for
developing
adaptive
courseware.
Seventh
International
World
Wide
Web
Conference,,
Australia,
14-18
April
1998,
pp.
291-300.

16. Knowledge and Hyperspace
Chapter 1
Chapter 2
Section 1.1
Section 1.2
Section 1.2.1 Section 1.2.2
Domain model
Concept 1
Concept 2
Concept 3
Concept 4
Concept m
Concept n
Textbook
Brusilovsky, P. (2003) Developing Adaptive Educational Hypermedia Systems: From Design Models to Authoring Tools. In: T.
Murray, S. Blessing and S. Ainsworth (eds.): Authoring Tools for Advanced Technology Learning Environments: Toward cost-
effective adaptive, interactive, and intelligent educational software. Kluwer, pp. 377-409.

17. Glossary: Concept-based Navigation
Brusilovsky,
P.
and
Schwarz,
E.
(1997)
Concept-based
navigation
in
educational
hypermedia
and
its
implementation
on
WWW.
In:
T.
Müldner
and
T.
C.
Reeves
(eds.)
Proceedings
of
ED-MEDIA/ED-TELECOM'97
-
World
Conference
on
Educational
Multimedia/Hypermedia
and
World
Conference
on
Educational
Telecommunications,
Calgary,
Canada,
June
14-19,
1997,
AACE,
pp.
112-117.

18. Content Recommendation

19. Authoring Tools (1997-2004)
• AHA! (De Bra)
• NetCoach (Weber, Weibelzahl)
• ACE (Specht)
• MetaLinks (Murray)
• KBS-Hyperbook & ALICE
• INSPIRE (Grigoriadou,Papanikolaou,
Kornilakis, Magoulas)

20. MetaLinks (Murray)
Murray, T. (2003) MetaLinks: Authoring and affordances for conceptual and narrative flow in adaptive
hyperbooks. International Journal of Artificial Intelligence in Education 13 (2-4), 199-233.

21. AHA! (De Bra)
De Bra, P. and Calvi, L. (1998) AHA! An open Adaptive Hypermedia Architecture. The New Review of Hypermedia and Multimedia 4, 115-139.

22. AHA!

23. ACE (Specht and Opperman)
Specht,
M.
and
Oppermann,
R.
(1998)
ACE
-
Adaptive
Courseware
Environment.
The
New
Review
of
Hypermedia
and
Multimedia
4,
141-
161.

24. NetCoach (Weber)
Weber, G., Kuhl, H.-C., and Weibelzahl, S. (2002) Developing adaptive internet based courses with the authoring
system NetCoach. In: Hypermedia: Openness, Structural Awareness, and aptivity. Berlin: Springer-Verlag, pp. 226-238.

25. The Values of Concept-Indexed Textbooks
• Navigation support (ELM-ART, InterBook…)
• Content-level adaptation (De Bra)
• Content recommendation (InterBook, ALICE)
• Connect external content (KBS-Hyperbook)
• Concept-based navigation (InterBook)
• Generating guided tours (MultiBook)
• Constructing exercises (MediBook)

26. The End of Classic Age: 3 Bottlenecks
• Huge knowledge engineering
investment to construct a concept-
based textbook
– Experience with ACT-R textbook
– Topic-based models adapted better than concept-
based models
• Lower value of text-only personalization
– ISIS-Tutor and ELM-ART vs. InterBook
• Weak learning modeling approaches

27. Post-Classical Age: Open Corpus
Adaptive Hypermedia (2004-2014)
• Integrate external (Open Corpus) content
– KBS-Hyperbook, SIGUE, AHA!...
• Constructing Hyperspace semi-automatically
– Knowledge Sea
• Navigation support without concept model
– Knowledge Sea II (social navigation)
• Ignoring Textbooks! Focus on ”smart” learning
content
– Exploring topic-based modeling (QuizGuide)
• Automatic concept indexing of learning content
– Special case of programming- NavEx, MasteryGrids

28. Knowledge Sea Map: SOM Linking
Brusilovsky, P. and Rizzo, R. (2002) Map-based horizontal navigation in educational hypertext. Journal of Digital Information 3 (1).

29. Social Knowledge Map: Knowledge Sea II
Farzan, R. and Brusilovsky, P. (2005) Social navigation support through annotation-based group modeling.
10th International User Modeling Conference Lecture Notes in Artificial Intelligence, vol. 3538. Berlin: Springer Verl

30. Farzan, R. and Brusilovsky, P. (2005) Social navigation support through annotation-based group modeling. 10th International
User Modeling Conference, Lecture Notes in Artificial Intelligence, vol. 3538. Berlin: Springer Verlag, pp. 463-472.
Textbook Page in AnnotatEd

31. Exercises
served by
QuizPACK
List of annotated
links to all quizzes
available for a
student in the
current course
QuizGuide: Topic-Based OLM and
ANS for Smart Learning Content
Brusilovsky, P. and Sosnovsky, S.
(2005) Engaging students to work
with self-assessment questions: A
study of two approaches.
In: Proceedings of 10th Annual
Conference on Innovation and
Technology in Computer Science
Education, ITiCSE'2005, pp. 251-255.

32. QuizGuide: Adaptive Annotations
• Target-arrow abstraction:
– Number of arrows – level of
knowledge for the specific
topic (from 0 to 3).
Individual, event-based
adaptation.
– Color Intensity – learning
goal (current, prerequisite
for current, not-relevant,
not-ready). Group, time-
based adaptation.
n Topic–quiz organization:

33. Progressor: Topic-Based ANS & SNS
33
Hsiao,
I.-H.,
Bakalov,
F.,
Brusilovsky,
P.,
and
König-Ries,
B.
(2013)
Progressor:
social
navigation
support
through
open
social
student
modeling.
New
Review
of
Hypermedia
and
Multimedia
19
(2),
112-131.

34. Mastery Grids: Topic-Based OSLM
34

35. Mastery Grids: Focus on Smart Content
35

36. Automatic Concept Indexing: C & Java
• C Programming (NavEx and QuizGuide)
– C-code parser (based on UNIX lex & yacc)
– 51 concepts totally (include, void, main_func,
decl_var, etc)
• Java Programming (Mastery Grids)
– Hosseini, R. and Brusilovsky, P. (2013) JavaParser: A Fine-Grain Concept Indexing Tool for Java
Problems. In: Proceedings of The First Workshop on AI-supported Education for Computer Science
(AIEDCS) at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN,
USA, July 13, 2013, pp. 60-63.
• Topic-based models for prerequisite elicitation
– Use a subsetting approach to divide extracted
concepts into prerequisite and outcome concepts

37. Early Modern History (2013-2016)
• “Real” Electronic textbooks lead the way
• Progress in Semantic Web area and Ontologies
• Progress in Information Retrieval
– Language models
– Data-driven ranking approaches
• Progress in NLP and knowledge extraction
– Topic Models
– Key-phrase extraction

38. OOPS: Content Linking with Ontologies
38
• Topic-based model of an HTML-based
Java textbook automatically extracted
and mapped to a central ontology already
linked to a set of Java exercises
• Mapping serves as a bridge to
jointly interpret learner’s reading
and exercise attempts in terms of
ontology and adapt access to
textbook pages accordingly
Sosnovsky, S. (2013). Ontology-based Open-Corpus Personalization for E-Learning PhD thesis.
School of Information, University of Pittsburgh.

39. NLP Approaches for Content Linking
• Guerra, J., Sosnovsky, S., and Brusilovsky, P. (2013) When One
Textbook is not Enough: Linking Multiple Textbooks Using Probabilistic
Topic Models. In: D. Hernández-Leo, T. Ley, R. Klamma and A. Harrer
(eds.) Proceedings of 8th European Conference on Technology Enhanced
Learning (EC-TEL 2013), Paphos, Cypres, September 17-21, 2013, pp. 125-
138.
• Meng, R., Han, S., Huang, Y., He, D., and Brusilovsky, P. (2016)
Knowledge-Based Content Linking for Online Textbooks. In: Proceedings
of 2016 IEEE/WIC/ACM International Conference on Web Intelligence,
Omaha, Nebraska, USA, 13-16 October 2016, pp. 18-25.
• Mota, P., Coheur, L., and Eskenazi, M. (2018) Efficient Navigation in
Learning Materials: An Empirical Study on the Linking Process.
In: Proceedings of 20th International Conference on Artificial Intelligence
in Education, AIED 2018, Part 2, London, UK, June 27–30, 2018, Springer,
pp. 230-235.

40. IR Approaches for Open Corpus Links
• Liu, X. and Jia, H. (2013) Answering Academic Questions for Education
by Recommending Cyberlearning Resources. Journal of the American
Society for Information Science and Technology 64 (8), 1707-1722.
• Kokkodis, M., Kannan, A., and Kenthapadi, K. (2014) Assigning
Educational Videos at Appropriate Locations in Textbooks. In: J. Stamper,
Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th
International Conference on Educational Data Mining (EDM 2014),
London, UK, July 4-7, 2014, pp. 201-204.
• Liu, Xiaozhong, Jiang, Z., and Gao, L. (2015) Scientific Information
Understanding via Open Educational Resources (OER). In: Proceedings of
the 38th International ACM SIGIR Conference on Research and
Development in Information Retrieval, ACM, pp. 645-654.

41. Concept Extraction and Domain Modeling
• Wang, S., Liang, C., Wu, Z., Williams, K., Pursel, B., Brautigam,
B., Saul, S., Williams, H., Bowen, K., and Giles, L. (2015) Concept
Hierarchy Extraction from Textbooks. In: Proceedings of Proceedings of
the 2015 ACM Symposium on Document Engineering, Lausanne,
Switzerland, ACM, pp. 147-156.
• Liu, H., Ma, W., Yang, Y., and Carbonell, J. (2016) Learning Concept
Graphs from Online Educational Data. Journal of Artificial Intelligence
Research 55, 1059-1090.
• Wang, S., Ororbia, A., Wu, Z., Williams, K., Liang, C., Pursel, B.,
and Giles, L. (2016) Using Prerequisites to Extract Concept Maps from
Textbooks. In: Proceedings of Proceedings of the 25th ACM International
on Conference on Information and Knowledge Management, Indianapolis,
Indiana, USA, ACM, pp. 317-326.

42. NLP and ML for Prerequisite Linking
• Agrawal, R., Gollapudi, S., Kannan, A., and Kenthapadi, K. (2014) Study
Navigator: An Algorithmically Generated Aid for Learning from Electronic
Textbooks. Journal of Educational Data Mining 6 (1).
• Liang, C., Wu, Z., Huang, W., and Giles, C. L. (2015) Measuring Prerequisite
Relations Among Concepts. In: Proceedings of 2015 Conference on Empirical
Methods in Natural Language Processing, Lisbon, Portugal, September 17-21, 2015,
Association for Computational Linguistics, pp. 1668–1674.
• Chaplot, D. S., Yang, Y., Carbonell, J., and Koedinger, K. R. (2016) Data-
driven Automated Induction of Prerequisite Structure Graphs. In: T. Barnes, M. Chi
and M. Feng (eds.) Proceedings of the 9th International Conference on Educational
Data Mining (EDM 2016), Raleigh, NC, USA, June 29 - July 2, 2016, pp. 318-323.
• Labutov, I., Huang, Y., Brusilovsky, P., and He, D. (2017) Semi-Supervised
Techniques for Mining Learning Outcomes and Prerequisites. In: Proceedings of
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, Halifax, NS, Canada, ACM, pp. 907-915.

43. Example: prerequisite/outcome
separation using supervised ML
• Sources of distant supervision in textbooks
– Supervision Source 1: Unit Cohesiveness
• Our hypothesis is that the author usually explains (i.e.,
outcome) a concept in one place (e.g., a chapter or a
section)
– Supervision Source 2: Unit Titles
• Our hypothesis is that the author of a textbook is more likely
to include the concept’s name in the title of a unit (e.g.,
chapter or section) if the concept is an outcome concept
Labutov, I., Huang, Y., Brusilovsky, P., and He, D. (2017) Semi-Supervised Techniques for Mining Learning Outcomes and
Prerequisites. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax,
NS, Canada, ACM, pp. 907-915.

44. Model 1: A concept is should be an outcome
in one place (cohesiveness)
xij yij
Latent variable
denoting the unit
in which a concept
is explained
zi
concept i
unit j
Features describing
the context of concept
within the unit
Latent variable
denoting whether
concept is prerequisite
or outcome in this unit

45. Model 2: Concept’s appearance in the title
makes it more likely to be an outcome
concept i
unit j
xij yij
Concept appears
in the title of the
unit
tij

46. 85
79
76
73
73
69
83
78
76
67
Biology Anatomy Chemistry Psychol. Economics
78
74
86
78
72
66
75
74
76
74
75
74
Biology
Anatomy
Chemistry
Psychol.
Economics
83
80
78
70
72
70
89
75
82
69
72
63
74
74
70
67
89
85
75
74
75
73
72
75
83
82
93
85
Textbook
TRAINED
on Textbook TESTED
Prerequisite/Outcome
models learned are
able to generalize
across domains

47. Late Modern History (2016-2020)
• Large volume of learner data collected
• Better learned data mining
– Matrix and tensor factorization
– Sequence mining
• Progress in data-driven learner modeling
– BKT extensions
– AFM and PFA
– Deep Knowledge Tracing
• More types of data collected
– Some interaction with questions, problems, videos
– Annotations and highlighting
– Eye Tracking
• Smart Content Arrived to Online Textbooks
– CMU OLI, RuneStone, Open DSA

48. Data-Driven Student Modeling in Textbooks
• Huang, Y., Yudelson, M., Han, S., He, D., and Brusilovsky, P. (2016) A
Framework for Dynamic Knowledge Modeling in Textbook-Based Learning.
In: Proceedings of 24th Conference on User Modeling, Adaptation and Personalization
(UMAP 2016), Halifax, Canada, July 13-17, 2016, ACM Press, pp. 141-150.
• Thaker, K., Huang, Y., Brusilovsky, P., and He, D. (2018) Dynamic Knowledge
Modeling with Heterogeneous Activities for Adaptive Textbooks. In: Proceedings of the
11th International Conference on Educational Data Mining, Buffalo, USA, July 15-18, 2018,
pp. 592-595.
• Thaker, K., Carvalho, P., and Koedinger, K. (2019) Comprehension Factor Analysis:
Modeling student’s reading behaviour: Accounting for reading practice in predicting
students’ learning in MOOCs. In: Proceedings of 9th International Conference on Learning
Analytics & Knowledge (LAK’19), Tempe, AZ, USA, March 4-8, 2019, pp. 111-115.
• Hunt-Isaak, N., Cherniavsky, P., Snyder, M., and Rangwala, H. (2020) Using
online text books and in-class quizzes to predict in class performance. In: A. N. Rafferty, J.
Whitehill, V. Cavalli-Sforza and C. Romero (eds.) Proceedings of 13th International
Conference on Educational Data Mining, July 10-13, 2020, pp. 438-443.

49. Example: Student Modeling
Thaker, K., Huang, Y., Brusilovsky, P., and He, D. (2018) Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive
Textbooks. In: Proceedings of the 11th International Conference on Educational Data Mining, Buffalo, USA, July 15-18, 2018, pp. 592-595.

50. Wider Bandwidth for Student Models
• Winchell, A., Mozer, M., Lan, A., Grimaldi, P., and Pashler, H. (2018) Can
Textbook Annotations Serve as an Early Predictor of Student Learning?
In: Proceedings of the 11th International Conference on Educational Data Mining,
Buffalo, USA, pp. 431-437.
• Rajendran, R., Kumar, A., Carter, K. E., Levin, D. T., and Biswas, G. (2018)
Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior.
In: Proceedings of the 11th International Conference on Educational Data Mining,
Buffalo, USA, pp. 455-461.
• Kim, D., Winchell, A., Waters, A., Grimaldi, P., Baraniuk, R., and Mozer,
M. (2020) Inferring Student Comprehension from Highlighting Patterns in Digital
Textbooks: An Exploration in an Authentic Learning Platform. In: Proceedings of
Second Workshop on Intelligent Textbooks at 21st International Conference on
Artificial Intelligence in Education (AIED 2020), June 25, 2019, CEUR.

51. Studies of Learner Reading Behavior
• Warner, J., Doorenbos, J., Miller, B., and Guo, P. (2015) How High School,
College, and Online Students Differentially Engage with an Interactive Digital
Textbook. In: O. Santos, et al. (eds.) Proceedings of the 8th International Conference
on Educational Data Mining (EDM 2015), Madrid, S[ain, June 26-29, 2015.
• Yin, C., Yamada, M., Oi, M., Shimada, A., Okubo, F., Kojima, K., and
Ogata, H. (2018) Exploring the Relationships between Reading Behavior Patterns
and Learning Outcomes Based on Log Data from E-Books: A Human Factor
Approach. International Journal of Human–Computer Interaction.
• Mouri, K., Shimada, A., Yin, C., and Kaneko, K. (2018) Discovering Hidden
Browsing Patterns Using Non-Negative Matrix Factorization. In: Proceedings of the
11th International Conference on Educational Data Mining, Buffalo, USA, pp. 568-
571.
• Boubekki, A., Jain, S., and Brefeld, U. (2018) Mining User Trajectories in
Electronic Text Books. In: Proceedings of the 11th International Conference on
Educational Data Mining, Buffalo, USA, pp. 147-156.

52. Knowledge-Based Recommendation
• Lan, A. S. and Baraniuk, R. G. (2016) A Contextual Bandits Framework for
Personalized Learning Action Selection. In: T. Barnes, M. Chi and M. Feng (eds.)
Proceedings of the 9th International Conference on Educational Data Mining (EDM
2016), Raleigh, NC, USA, June 29 - July 2, 2016, pp. 424-429.
• Rahdari, B., Brusilovsky, P., Thaker, K., and Barria-Pineda, J. (2020)
Using Knowledge Graph for Explainable Recommendation of External Content in
Electronic Textbooks. In: Proceedings of Second Workshop on Intelligent Textbooks
at 21st International Conference on Artificial Intelligence in Education (AIED 2020),
July 6, 2020, CEUR.
• Thaker, K., Zhang, L., He, D., and Brusilovsky, P. (2020) Recommending
Remedial Readings Using Student’s Knowledge State. In: Proceedings of 13th
International Conference on Educational Data Mining, July 10-13, 2020, pp. 233-
244.

53. Example: Wikipedia Recommendation in
Reading Mirror
1 2 3
1- Relevance Bar
2- Recommendations
3- Explanations
04/17
Rahdari, B., Brusilovsky, P., Thaker, K., and Barria-Pineda, J. (2020) Using Knowledge Graph for Explainable
Recommendation of External Content in Electronic Textbooks. In: Proceedings of Second Workshop on Intelligent Textbooks at
21st International Conference on Artificial Intelligence in Education (AIED 2020), July 6, 2020, CEUR, pp. 50-61.

54. Reading Mirror- Explanations
Intermediate
Dialog
Explanation
Dialog
05/17
Driven by both – concept model and student model

55. The Knowledge Graph for Recommendation
Category
Article
Question
Section
Concept User
Has_Page
Related_to
Related_to
Belongs_to
Includes
Knows
Includes
Related_to
Has_Child
1
2
3 Student
Model
B
o
o
k
C
o
n
t
e
n
t
Wikipe
dia
Article
s
06/17

56. Contemporary History (2020-)
• Bring it all together for real impact
• Build “Smart” knowledge-enhanced textbooks
using progress in ontologies, domain modeling,
keyphrase extraction
• Empower “Smart” textbook with modern learner
modeling
• Combine knowledge-driven and social
personalization approaches
• Build “Smart” textbooks with “smart” content

57. Model extraction from PDF textbooks
57
Alpizar-Chacon, I., & Sosnovsky, S. (2020). Order out of Chaos: Construction of Knowledge Models from PDF Textbooks. In Proceedings of
DocEng’2020: The 20th ACM Symposium on Document Engineering, (Article No.: 8, pp 1–10). New York, NY, USA: ACM Press.

58. Connecting Books to Smart Content
https://intextbooks.science.uu.nl/workshop2021/

59. Connecting Smart Content to Books

60. Acknowledgements
• Joint work with
– Rosta Farzan, Sergey Sosnovsky, Sharon Hsiao, Tomek
Loboda, Sherry Sahebi, Julio Guerra, Roya Hosseini
– Yun Huang, Daqing He, Igor Labutov, Rui Meng
– Jordan Barria, Khushboo Thaker, Behnam Rahdari
• NSF Grants
– CAREER 0447083
– EHR 0310576
– IIS 0426021 with Prof. Daqing He
• ADL.net support for OSSM work

61. Visit us in Pittsburgh to Learn More!

62. … or Read our Papers
• http://www.pitt.edu/~peterb/papers.html
• https://www.researchgate.net/profile/Peter_Br
usilovsky