1 / 9

TEL Seminar: Cluster IV “Formal Methods and Theories”

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

yaron
Download Presentation

TEL Seminar: Cluster IV “Formal Methods and Theories”

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 • …

More Related