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Learner Model’s Utilization in the e-Learning Environments. Vija Vagale , Laila Niedrite Faculty of Computing, University of Latvia, Riga, Latvia vija.vagale@du.lv, laila.niedrite@lu.lv. Introduction.

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learner model s utilization in the e learning environments

Learner Model’s Utilization in the e-Learning Environments

Vija Vagale, Laila Niedrite

Faculty of Computing, University of Latvia, Riga, Latvia

vija.vagale@du.lv, laila.niedrite@lu.lv

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

introduction
Introduction

One of the most actual tasks for educational quality improvement is the utilization of e-learning environments.

Learning environments can be divided into:

passive systems;

active systems.

Adaptive e-lerning environments can be used:

for preschool age children;

at schools;

at universities;

for life-long education.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

adaptive e learning s ystem s cheme
Adaptive e-Learning system scheme

Domain model

Learner model (user model, student model)

Adaptive model (interaction model)

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

the a im and the tasks of research
The aim and the tasks of research

The aim of the research is to explore in the user model included data.

The work tasks are:

to explore adaptive system structure models;

to explore learner model structure;

to analyze data obtaining types for user profile;

to make an analysis of data included in user model and split into categories;

to explore the stages of the user model creation;

to analyze construction techniques of the user model of an adaptive system.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

user profile
User profile

User profile data can serve as the base for the user model creation.

User profile is created when the learner logs into the systemfor the first time.

Profile data contains learner personal data as well as data on his individual features and habits.

Profile keeps static information about the user without any additional description or interpretation.

User profile creation, modification and maintenance process is called user profiling.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

learner model
Learner model
  • A learner model is an abstract representation of the system user.
  • Learner model includes:
    • profile datathat gathers static information;
    • specific or dynamic dataobtained by the system about a certain person during the learning process.
  • The user model contains all information that the system has on the user and maintains live user accounts in the system.
  • In general, the profile concept is narrower than the user model concept.
  • In a simplified case, user profile and learner model can coincide.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

obtaining d ata for the l earner m odel
Obtaining data for the learner model

Directly - when user creates his profile on his own and data are taken from the user registration form and questionnaires – for example, birth date and gender.

Indirectly – when a system creates a profile by itself by collecting necessary information about the user from his activities.

Mixed approach, when one part of information is input by the user, but the other part of the information the system gains indirectly.

By integrating data to the adaptive e-Learning environments from other informational systems.

Gaining data from ePortfolio.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

in the l earner m odel i ncluded possible d ata categories
In the learner model included possible data categories

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

personal data and pedagogical data
Personal data and Pedagogical data

Personal data:

  • name;
  • surname;
  • login;
  • password;
  • language;
  • gender;
  • date of birth.

Pedagogical data:

  • programs;
  • topics;
  • course collections;
  • course sequence.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

preference data and personality data
Preference data and Personality data

Preference data:

  • language;
  • presentation format;
  • sound value;
  • video speed;
  • web design personalization.

Personality data:

  • learning style;
  • concentration skills;
  • collective work skills;
  • relationship creating skills;
  • individual features;
  • attitudes.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

system experience goal cognitive data and history data
System experience, Goal, Cognitive data and History data
  • System experience - obtained certificates and skills in e-Learning system utilization.
  • Goal – data about the system user long-term interests.
  • Cognitive data – data that represents reference types of the learner.
  • History data – data about all learner’s activities.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

device data and acquired knowledge
Device data and Acquired knowledge

Device Data:

data that characterizes working environment of the system user:

  • hardware;
  • download speed;
  • screen resolution;
  • learner’s location;
  • time;
  • used devices.

Acquired knowledge:

  • Student knowledge at the current moment of time – data that describes student knowledge gained in the learning process.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

analyzed articles in this research
Analyzed articles in this research
  • [29]: Nebel et.al., 2003, “A user profiling component with the aid of user ontologies”;
  • [3]: Brusilovsky, 1996, “Methods and techniques of adaptive hypermedia”;
  • [25]: Liu et.al., 2009, “A survey on user profile modeling for personalized service delivery systems”;
  • [41]: Sosnovsky & Dicheva, 2010, “Ontological technologies for user modeling”;
  • [13]: Gomes et.al., 2006, “Using Ontologies for eLearning Personalization”;
  • [10]: Frias-Martinez et.al., 2006, “Automated User Modeling for Personalized Digital Libraries”;
  • [27]: Martins et.al., 2008, “User Modeling in Adaptive Hypermedia Educational Systems”;
  • [1]: Behaz & Djoudi, 2012, “Adaption of learning resources based on the MBTI theory psychological types”.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

the frequency of the learner model data category
The frequency of the learner model data category

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

formation s tages of the l earner m odel
Formation stages of the learner model

Initialization – basic data gathering for model;

Reasoning – gaining new data about the learner from already existing data;

Updating – learner model data actualization.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

lm c onstruction t echniques
LM construction techniques

Stereotype model – is based on the system-offered stereotypes;

Overlay model – based on the user progress in the system;

Combination model – employs both of the previously mentioned models;

Differential model –similar to overlay model plus must-learn knowledge;

Perturbation model – similar to overlay model plus mal-knowledge;

Plan model – incorporates successive student actions for achieving certain goals and desires.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

user d ata m odeling m ethods
User data modeling methods

Static data elements are modeled with Attribute-Value Pairs.

Attributes are terms, concepts, variables and facts that are important for both the system and the user.

Their values can be of the following types: boolean, real or string.

Dynamic data elements are modelled using rules based on if-then logic.

To represent the relationship between data elements the hierarchy tree modeling approach or ontology are used.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

conclusions
Conclusions

Depending on the type of the adaptive system, model names included in it may differ but their essence and tasks remain similar;

Each adaptive learning system must have at least three components:

a domain model for keeping system-offered knowledge;

a learner model(user model, student model) which describes a person who is sitting in front of the computer and willing to learnin an understandable way for the system;

an adaptive model(interaction model) – with its help system-offered knowledge is delivered to the learner in an understandable way.

All data included in the learner model can be divided into some basic categories:Personal data, Personality data, Pedagogical data, Preference data, System experience, Cognitive data, History data andDevice data.

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania

thanks for your attention
Thanks for your attention!

10th International Baltic Conference on Databases and Information SystemsJuly 8-11, 2012, Vilnius, Lithuania