Self-Adaptive Learning through Teaching Evgeny Karataev

School of Information Science University of Pittsburgh

(advisor: Vladimir Zadorozhny)

Problem: adaptive information processing in a large scale decentralized and loosely coordinated systems based on crowdsourcing and social computing. We propose to build an adaptive on-line social network to improve the process of learning. Therefore, in addition to the research contribution, our project will engage students in an active learning through teaching process.

Abstract Ways of teaching and ways of learning vary greatly. Challenge: adaptive large scale information processing.

Motivation

Approach Complex Adaptive Information System (CAIS)

Ease of sharing

and getting information

Large amount of data

Overload

“one size does not fit all”

• Design – to utilize crowdsourcing techniques, collaborative filtering and collective intelligence

• Monitor – dynamic data warehousing, data analysis and data mining techniques

• Adapt – adaptively converge to the most productive learning pathways with respect to a particular group of students and their performance profiles

Research Questions

• An agent-based simulation, to study behavior of the SALT:

• Agent: • Student (with randomly generated parameters)

• Agent’s Rules: • Create lesslet (based on agent parameters) • Take lesslet (based on lesslet and agent parameter)

• Adaptive environment: • Recommend lesslet

By controlling input parameters and experimenting with new algorithms we can study various aspects of CAISs such as their convergence properties, their sensitivity to initial conditions, and etc.

Simulation-based study

Benchmark system. SALT

Preliminary results

• Study of simulation model to analyze and find trends and patterns, as well as phase transition parameters.

• New adaptive algorithms to recommend lesslets, learning pathways or friends.

• Large graph visualization to allow easy, interactive and adaptive way of exploring topics and lesslets, as well as learning pathways and users activities.

Future work

class # of students

avg success

#of best pathways

est avg successa

UG 34 79.54% 30 97.64%

G 31 81.27% 27 95.63%

aaverage success of students if each of them follow the personal best possible pathway

• Self-Adaptive Learning through Teaching (SALT) – to gain educational knowledge via adaptive learning through teaching.

Adaptive Environment

Agents

Rules

Global structure or patterns without a central authority or external element imposing it through planning

Learning pathways

Best pathway