From Expert-Driven to Data-Driven Adaptive Learning

Peter Brusilovsky

School of Computing and Information, University of Pittsburgh

Adaptive (Personalized) Learning

•  Take into account student individual features (knowledge, goals, personal traits…)

•  Improve learning by providing different learning support (personalization!) to different students – Adaptive sequencing in CAI – Adaptive navigation support and adaptive

presentation in adaptive hypermedia – Mastery learning in Intelligent tutoring systems – Style-adapted Hypermedia

Brusilovsky, P. and Peylo, C. (2003) Adaptive and intelligent Web-based educational systems. International Journal of Artificial Intelligence in Education 13 (2-4), 159-172.

Exercise Sequencing in ELM-ART

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Style-adaptive Hypermedia: AES-CS

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Personalization needs knowledge

•  Knowledge-based personalization –  Domain model (network of skills and concepts - KCs) –  Mapping between learning content (problems, problem

steps, textbook pages) and KCs –  Student modeling rules (how action with content changes

student knowledge) –  Personalization (what to do given specific knowledge state)

•  Learning-style-based personalization –  How to map an individual to a specific style

(questionnaire) –  Which learning content or interface to offer to each style

(rules)

Domain and content models

Example 2 Example M

Example 1

Problem 1

Problem 2 Problem K

Concept 1

Concept 2

Concept 3

Concept 4

Concept 5

Concept N

Examples

Problems

Concepts

Expert Driven vs. Data-Driven

•  Expert-Driven Personalized Leaning – Knowledge are provided by domain and learning

science experts in several ways – Good quality – Expensive, hard to scale, subject to biases

•  Data-Driven Personalized Learning – Knowledge is extracted from data – Good potential (scalability, objectivity) – Main challenge is achieving good quality

Data-Driven Personalization

•  Where to get knowledge – Wisdom of instructors: encapsulated in content,

structure, rules developed by – Wisdom of learners: encapsulated in traces left by

past learners – Tuned by success/failure rata

•  How this knowledge could be used? – Empower humans - Visual Learning Analytics – Empower decision algorithms: Educational Data

Mining

Visual Learning Analytics

•  The idea: Present data in visual form to student administrators helping them to make better decisions about learning process

•  Support self-regulated learning •  Provide navigation support for students •  Show performance to instructors to make

decisions •  Show data to administrators to redesign process

Educational Data Mining

•  The idea: Feed data to various data mining and machine learning approaches to improve existing automated learning and discover important things for future improvements

•  Better domain modeling •  Better student modeling •  Better adaptation approaches •  Finding what works best for different groups and

students

Research at PAWS Lab, U of Pittsburgh

•  http://adapt2.sis.pitt.edu/wiki/ •  Social navigation in E-learning

–  open social learner modeling •  Data-driven individual differences

–  problem solving genome •  Domain modeling and latent concept discovery •  Content modeling

–  mining prerequisites and outcomes from textbooks •  Data-driven student modeling •  Open corpus adaptive hypermedia

Data-Driven Personalized Learning

a b

c d

•  a: social navigation & open social learner modeling •  c: problem solving genome •  b, d: mining prerequisites and outcomes from

textbooks

Teachers Students

Better Interfaces

Personalized Decision-making

Wisdom of:

Social Navigation Support

•  Students need personalized guidance (navigation support) to access right content in the right time

•  Traditional knowledge-based navigation support requires considerable knowledge engineering

•  Social navigation uses behavior of past users to guide new users

•  Can we use “wisdom” extracted from the work of a community of learners to replace knowledge-based guidance?

•  Knowledge engineering vs. data analysis

Knowledge Sea II (+ AnnotatEd)

Farzan, R. and Brusilovsky, P. (2008) AnnotatEd: A social navigation and annotation service for web-based educational resources. New Review in Hypermedia and Multimedia 14 (1), 3-32.

Open Social Student Modeling

•  Key ideas – Make traditional student models open to the users – Allow students to compare themselves with class and

peers – Social navigation based on performance data

•  Main challenge – How to design the interface to make an easy

comparison and provide social guidance and motivation

– We went through several attempts

Open Social Student Modeling

Interactive Demo

YouTube Demo

Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., Zadorozhny, V., and Durlach, P. (2016) Open Social Student Modeling for Personalized Learning. IEEE Transactions on Emerging Topics in Computing 4 (3), 450-461.

Impact of OSSM on Learning •  A study in 2 sections of Database course •  Student knowledge significantly increased in both

groups •  The mean learning gain was in OSSM group •  Students who used OSSM interface worked more

efficiently •  OSSM significantly increases engagement with all kind

of learning content (2-4 times!) •  Much higher retention in OSSM group (3 times!) •  (Why engagement and retention is important?)

Problem-Solving Genome

•  Key ideas –  Individual differences important for understanding

students and adapting learning –  "Old generation" of individual differences (i.e. learning

styles) not valuable in e-learning context –  Could we use "data-driven" science extracting individual

differences from behavior data? •  Main challenge

–  How to process the data to find and use individual differences

•  Our approach uses sequence mining and profiling based on the use of micro-sequences

Context: Parameterized Java Exercises

Some numbers change each time the exercise is loaded

Hard to game Exercise from QuizJet system

Labeling Steps (attempts)

Correctness: Success (S) or Failure (F) Time: Short (lowercase) or Long (uppercase)

– Using median of the distribution of time per exercise – Using different distributions for first attempt

label correctness time s success short S success long f failure short F failure long

Pattern mining

•  Using PexSPAM algorithm with gap = 0 •  Each possible pattern of length 2 or higher is

explored •  Support of a pattern: proportion of sequences

containing the pattern (at least once) –  Does not count multiple occurrences of the pattern within a

sequence

•  Select all patterns with minimum support of 1%

The Problem Solving Genome

•  Constructed a frequency vector over the 102 patterns (vector of size 102) for each student – Each common pattern is a gene

•  The vector represents how frequently a student uses each of the micro patterns •  The vector is an individual genome build of genes

Exploring the Genome •  Stability

– Are the patterns stable on a student?

•  Effect of complexity – Are the patterns different across

complexity levels? •  Patterns of success

– Are successful students following different patterns?

Clustering by Genome

•  Cluster students by their genomes and analyze different patterns – Between clusters – Between low and high students within each cluster

•  Spectral Clustering with k = 2 – Larger eigen-gap with k = 2

Guerra, J., Sahebi, S., Lin, Y.-R., and Brusilovsky, P. (2014) The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014) pp. 153-160.

•  Cluster 1: confirmers (repeat short successes) •  Cluster 2: non-confirmers (quitters)

Ordering patterns by difference magnitude (cluster 2 – cluster 1)

Using Cluster for Guidance •  Successful patterns in each cluster

are closer to the other cluster –  Successful confirmers tend to stop

after long success –  Successful non-confirmers (c 2) tend

to continue after hard success •  Extreme different patterns between

clusters are “harmful” •  How it could be used for

personalization? –  Identify student type –  Offer different interface or discourage

poor behavior with recommendation

_FS_

Preferred type of online learning content E – Exercise T – Text tutorial X – Example V – Video tutorial

Open Corpus Adaptive Textbook

•  Key ideas –  Extract domain model (concepts) from multiple textbooks –  Annotate each section/page with prerequisite and outcome

concepts –  Trace student reading, estimate student knowledge –  Use for reading interface, navigation support,

recommendation •  One of the challenges

–  How to leverage various indirect signals in textbooks to learn a general prerequisite/outcome model?

•  Approach: Extracting concepts from encapsulated wisdom of book authors (KDD 2017 poster)

Resolving problems: HCI vs RecSys

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

Model 1: a concept is outcome in one place (cohesiveness)

xij yij

Latent variabledenoting the unit

in which a conceptis explained

zi

concept i

unit j

Features describingthe context of concept

within the unit

Latent variabledenoting whether

concept is prerequisiteor outcome in this unit

Textbook section index

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Concept = ”conditional independence”

Ground truth

First mention of the concept is NOT

necessarily where it is explained!

Model 2: concept’s appearance in the title makes it more likely that the concept is an outcome

concept i

unit j

xij yij

Concept appearsin the title of the

unit

tij

8579

7673

7369

8378

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Biology Anatomy Chemistry Psychol. Economics

7874

8678

7266

7574

7674

7574

Biology

Anatomy

Chemistry

Psychol.

Economics

8380

7870

7270

8975

8269

7263

7474

7067

8985

7574

7573

7275

8382

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Prerequisite/Outcome models

learned are able to

generalize across domains

Leaving for the next time

•  Domain Modeling and Latent topic discovery –  Sahebi, S., Lin, Y.-R., and Brusilovsky, P. (2016) Tensor Factorization

for Student Modeling and Performance Prediction in Unstructured Domain. Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016), pp. 502-505.

•  Data-driven student modeling –  González-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2014)

General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge. Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 84-91.

•  Open Corpus modeling and personalization –  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), pp. 141-150.

Acknowledgements

•  Joint work with – Rosta Farzan, Sharon Hsiao, Tomek Loboda – Sherry Sahebi, Julio Guerra, Roya Hosseini – Yun Huang, Daqing He, Igor Labutov

•  NSF Grants – CAREER 0447083 – EHR 0310576 –  IIS 0426021

•  ADL.net support for OSSM work

Visit us in Pittsburgh to Learn More!

… or Read our Papers

•  http://www.pitt.edu/~peterb/papers.html •  https://www.researchgate.net/profile/

Peter_Brusilovsky