Transcript Learner Model`s Utilization in the e

Learner Model’s Utilization in the
e-Learning Environments
Vija Vagale, Laila Niedrite
Faculty of Computing, University of Latvia, Riga, Latvia
[email protected], [email protected]
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
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 Systems
July 8-11, 2012, Vilnius, Lithuania
2 / 19
Adaptive e-Learning system scheme
• Domain model
• Learner model (user model, student model)
• Adaptive model (interaction model)
Real people
Computer Interface
User profiling
User profile
User modeling
Abstract people
Learner Model
Adaptive Model
Domain Model
Domain Specific
Data
Adaptation Rules
Course Content
Domain Independent
Data
Instructional Rules
Delivery System
Adapting
Adaptive e-Learning system
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
3 / 19
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 Systems
July 8-11, 2012, Vilnius, Lithuania
4 / 19
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 system for 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 Systems
July 8-11, 2012, Vilnius, Lithuania
5 / 19
Learner model
• A learner model is an abstract representation of the system user.
• Learner model includes:
– profile data that gathers static information;
– specific or dynamic data obtained 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 Systems
July 8-11, 2012, Vilnius, Lithuania
6 / 19
Obtaining data for the learner model
1.
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.
2.
Indirectly – when a system creates a profile by itself by collecting
necessary information about the user from his activities.
3.
Mixed approach, when one part of information is input by the user, but
the other part of the information the system gains indirectly.
4.
By integrating data to the adaptive e-Learning environments from
other informational systems.
5.
Gaining data from ePortfolio.
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
7 / 19
In the learner model included possible
data categories
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
8 / 19
Personal data and Pedagogical data
Personal data:
Pedagogical data:
• name;
• programs;
• surname;
• topics;
• login;
• course collections;
• password;
• course sequence.
• language;
• gender;
• date of birth.
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
9 / 19
Preference data and Personality data
Preference data:
Personality data:
• language;
• learning style;
• presentation
format;
• concentration skills;
• collective work skills;
• sound value;
• relationship creating
skills;
• video speed;
• web design
personalization.
• individual features;
• attitudes.
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
10 / 19
System experience, Goal,
Cognitive data and History data
• System
experience obtained
certificates
and skills in eLearning
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 Systems
July 8-11, 2012, Vilnius, Lithuania
11 / 19
Device data and Acquired knowledge
Device Data:
Acquired knowledge:
data that characterizes
working environment
of the system user:
• hardware;
• download speed;
• screen resolution;
• Student knowledge
at the current
moment of time –
data that describes
student knowledge
gained in the
learning process.
• learner’s location;
• time;
• used devices.
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
12 / 19
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 Systems
July 8-11, 2012, Vilnius, Lithuania
13 / 19
The frequency of the learner model data
category
Data category type
[29]
Personal data
Personality data
Cognitive data/style
Pedagogical data
Preference data
History
Device
Context/Environment
Interests of user
Interests
gathered
system
Goal/Motivation
System Experience
Domain Expertise
Results of assessment
Acquired knowledge
Deadline extend
+
+
+
+
[3]
+
+
[25]
[41]
[13]
[10]
[27]
[1]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
by
+
+
+
+
+
+
+
+
+
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
+
+
+
+
+
+
+
+
+
+
+
+
14 / 19
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 Systems
July 8-11, 2012, Vilnius, Lithuania
15 / 19
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 Systems
July 8-11, 2012, Vilnius, Lithuania
16 / 19
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 Systems
July 8-11, 2012, Vilnius, Lithuania
17 / 19
Conclusions
1. Depending on the type of the adaptive system, model names included in
it may differ but their essence and tasks remain similar;
2. Each adaptive learning system must have at least three components:
(a) a domain model for keeping system-offered knowledge;
(b) a learner model (user model, student model) which describes a
person who is sitting in front of the computer and willing to learn in
an understandable way for the system;
(c) an adaptive model (interaction model) – with its help system-offered
knowledge is delivered to the learner in an understandable way.
3. 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 and
Device data.
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
18 / 19
Thanks for your attention!
10th International Baltic Conference on Databases and Information Systems
July 8-11, 2012, Vilnius, Lithuania
19 / 19