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Adaptive Systems Intelligence: Formal Methods & Theories Seminar

Dive into item response theory, knowledge tracing, Bayesian networks, & educational data mining in adaptive intelligence systems. Explore the evaluation of e-learning systems with advanced statistical techniques.

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Adaptive Systems Intelligence: Formal Methods & Theories Seminar

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  1. TEL Seminar: Cluster IV“Formal Methods and Theories” Sergey Sosnovsky

  2. Summary • Requires some knowledge of probability theory, machine learning, statistics • This is the core of adaptive systems’ intelligence • Well defined approaches • List of Topics: • Item Response Theory • Knowledge Tracing • Performance Factor Analysis • Bayesian Networks for Adaptive e-Learning • Educational Data Mining • Evaluation of e-Learning Systems

  3. Item Response Theory (IRT) • The core technology behind adaptive testing • Is used in such standardized tests as GRE, GMAT, TOEFL • Allows to assess the ability of a test taker with better precision and fewer questions (than classic test theory) • The math apparatus was developed in the 1950-1960s, but it became popular only in the 1980s • Allows to estimate not only theability of a student, but alsothe parameters of the questions • Sigmoid curve: Seite/Page 7 Saarbrücken, 08.10.2010

  4. Knowledge Tracing • Bayesian Knowledge Tracing: developed by Corbet 1995 (and Atkinson in 1972 ;-) • Probabilistic model for modeling student’s knowledge • Helps to estimate the probability of skills acquisition by a student solving problems based on the history of attempts • Advanced the fields of student modeling and educational data mining • Markov chain:

  5. Performance Factor Analysis • Recent (2009) addition to the toolbox of probabilistic student modeling techniques • Based on two other models: Learning Factor Analysis and the simplest of IRT models: Rasch model • Helps to resolve some of the problems of earlier models (e.g. multiple evidence from a single event) • Seems to outperform classic KT

  6. Bayesian Networks for Adaptive e-Learning • A Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependences via a directed acyclic graph. • Allows to estimate the probabilities of unobservable parameters from the observable events • Has been successfully applied in many ITSs: • For modelingstudents • For representingcomplex exercises

  7. Educational Data Mining • Massive collections of data • Trend towards data-driven intelligence • Discovery if hidden patterns and hidden features • Identification of malfunctioning system components, pieces of content, etc. • Detection of critical patterns of students behavior • Detection of important characteristics that define a category og users • etc… • Comparison of different models, components, systems • Aggregation of log-data to present it in ameaningful way Seite/Page 7 Saarbrücken, 08.10.2010

  8. Evaluation of e-Learning Systems • Virtually no paper these days can miss the evaluation part • Evaluation is the way to test your hypotheses • Kinds of evaluation: • Layered vs. Holistic • Controlled vs. Longitudinal • Test-based vs. Questionnaire-based vs. Observation-based • Statistical tests • Between-subject vs. Within-subject • …

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