1. Ontologies for Personalization:
A new challenge for instructional
designers
IETC 2012
Prof. Dr. Arif ALTUN
Hacettepe University / Ankara-Turkey
Keynote Presentation at IETC 2012 Taiwan
2.
3.
4. Personalization
• Personalization is described as adapting learning
experiences to different learners by analyzing
individuals’ knowledge, skills and learning
preferences (Devedzic, 2006).
• …tailors instructional materials for each learner’s
constantly changing needs and skills (Sampson,
Karagiannidis & Kinshuk, 2002).
5. Five types of personalization
1. Name based personalization
2. Self-described personalization
3. Segmented personalization
4. Cognitive personalization
5. Whole-person personalization
(Martinez, 2000).
6. Some of the Challenges for ID
• Paradigm shift: From “one design for one
learner” to “many designs for one learner ”
• Better understanding the nature and the
outcomes of the interaction between learners
and content.
• Designing learning objects
• Designing navigational paths
• Monitoning and analyzing the learning progress
• But, how should we proceed?
7. In order to make an e-learning environment personalized,
– Regular and constant data monitoring and analysis
tools (Learning Analitics),
– Determining cognitive and non-cognitive personal
characteristics accurately, (Learner characteristics)
– Learners’ interaction with –designed- medium: i.e.,
learning outcomes (Learning & Instruction)
– Tools to diagnose and/or guide learners with study
or navigational paths (Ontology and Designing
Navigational Paths).
8. What we need is
1. A learner model
2. A learning object design model
3. Ontolog(ies)
4. Learning analytics
10. A Learner Model for Learning Object Based
Personalized Learning Environments
• What will be modeled about learners?
• How will it be modeled? And,
• How the sustainability of the model would be
maintained?
16. What do results imply?
• ECRT– no correlation was observed between
computerized and P&P tests (r= -.09; p>.05)
• Significant correlation was observed in LOT (r= .61;
p<.05)
• ECRT– P&P test scores are higher than ( M= 46.07; SD=
2.127 ) computerized one (M = 40.12; SD= 5.099).
• LOT– P&P test scores are higher than (M= 22.76; SD =
4.314) computerized one (M= 19.58; SD= 4.933).
• ECRT and LOT: Time spent in P&P tests is much longer
than the computerized one.
17. Visual Search
• Sönmez, D., Altun, A. & Mazman, S. G. (2012). How
Prior Knowledge and Colour Contrast Interfere Visual
Search Processes in Novice Learners: An Eye Tracking
Study. Under Review.
• The effect of persons’ prior knowledge and
experiences on their visual search performances.
• A visual search task on identifying the phases of
mitosis from a microscope view with two different
background contrasts.
18. Low level prior knowledge High level prior knowledge
Prior exposure No Prior exposure Prior exposure No Prior exposure
(n=10) (n=10) (n=10) (n=10)
M 1.46 1.29 2.79 2.81
Blue (High
Contrast)
Fix.Dur.
Sd .806 .764 1.27 1.94
M 7.04 7.52 4.66 3.98
T_FirstFix.
Sd 5.2 5.07 3.61 4.33
M .818 .946 1.28 .889
Yellow (Low
Contrast)
Fix.Dur.
Sd .728 .813 .852 .697
M 4.93 3.52 3.84 5.18
T_FirstFix
Sd 3.24 2.87 2.31 5.22
19. Short-term memory spans and
attention design
• Different STM spans (High - medium - low) undergraduate
students in two different attention design types: (Focused-
divided)
• Dependent variable : recall performance
• Time spent in focused one is longer than in divided design
• Recall performance is affected across modalities: Low STM <
High STM and Meed STM < high STM
• Low STM group spent more time in the environment than the
High STM group
20. Spatial Location Memory and
Navigation Environment
• Different location memory groups
• Dependent variable: Recall performance
• Environment: 2-D vs 3-D environments.
21.
22. Findings
• Overall, participants had higher recall scores in 2D.
• Once controlled their location memory, however, results
indicate that higher LM group had higher recall scores in 2D,
but did not change for low LM group.
• Male participants were advantageous over females in 3-D.
23. Levels of Processing and
Navigation design
Dependent Variables: Recall and retention (free recall, heading recognition, and
location memory)
26. • Left side navigation menu yielded better results
in free recall, heading recognition, and location
memory
• Deep level of processing yields better recall
performances
• Memory performances are affected depending
on the design of the given instruction (levels of
processing).
27. Challenges
• More research is needed across age groups,
gender, and in culturally different settings.
• How much time is needed?
• How to differentiate the learning paths for
individuals and/or group of learners?
29. Some definitions to start with…
• A learning object is defined as “…any entity,
digital or non-digital, that may be used for
learning, education or training” (IEEE
Learning Technology Standards Committee,
2001).
• “...a Learning Object... [is] ‘any digital resource
that can be reused to support learning” Wiley
(2002).
30. Common Characteristics of LOs
• All learning objects need to have an
instructional purpose to be re-used within
different instructional settings.
• Each LO should appropriately support learning
through the possible inclusion of educational
objectives, content, resources, and
assessment.
32. Fundemental Questions for IDs
• How to store each learning object so that they
can further become accessible through
different digital learning and/or content
management systems or different delivery
modes
• What should be the size of the learning object
(granuality)
• How can the context be modeled?
33. Learning Space Model
Aşkar & Altun (2010)
• Proposes a separation of learning expectations
as concepts and skills based on their
ontological relations in a specific domain;
34. Ontology based representation of
A Learning Object
Concept Space Skill Space
Adjusted Weight
Adjustable
via Intelligent Bot
Relation
Raw
Content Content
Content
n
1 n 1 4 n 1 44
44 2 3 2 3
2 3
Calculated LC Calculated LC
LC
(pre-defined) (or pre-defined)
Relation via Relation via
Intelligent Bot Intelligent Bot
35. Ontology-based Learning Space
Skills
Adjusted Weight
Learning Space (LS) Concepts
Learning Container (LC)
Learning Objects (LO)
Assets
38. Challenges
• Reusable,
• With reasonable granuality,
• Capable of handling learning contexts,
• Interoprable, and
• LO development tools (designed with an
instructionally sound design approach) are
needed.
40. An ontology is …
• an explicit specification of a conceptualization
(Gruber, 1995) or a model (Musen, 1998),
which is used for structuring and modeling of
a particular domain that is shared by a group
of people in an organization (O’Leary, 1998).
• Domain ontologies provide explicit and formal
descriptions of concepts in a domain of
discourse, their properties, relationships
among concepts and axioms (Guarino, 1995)
41. Semantic Web
– Well defined meanings (semantics)
– Common and shared standards and technologies
Tim Berners-Lee
42. The challenge is…
• By using the capabilities of semantic web,
World Wide Web led the interchange of
information about data (e.i., metadata) as well
as documents.
• Such capabilities also indicated a new kind of
challenge for instructional designers to design
a common framework that allows content to
be shared and reused within and across
applications.
43. Ontology as a Design &
Development Process
Stage 1: Identifying the concepts
Stage 2: Determining class and class hierarchies
Stage 3: Determining the attributes within classes and their
relationships
Stage 4: Determining instances
Stage 5: Setting up axioms / rules
(adapted from McGuiness, 1999)
44. PoleONTO: Modeling the K-12 curricula by using
ontology
Expectation
PoleONTO
Personalized
Expectation ..n
Expectation
Expectation 2
Ontological
Learning
Concept Skill
Environments
S1
C1
S2
C2
Sn
Cn
45. • CogSkillNet is an ontology of skills exists in the
curriculum of K-12 education.
• In POLEonto context, skill is defined as the interaction
and any processes between persons and concepts. For
example, the concept of “square” is envisioned in one’s
mind; yet, they can define it, they can extend square
into some other thing (i.e., a table or a flower-stand),
which is creative thinking. The square can be
manipulated to approach a problem by using its types
and functions, which requires problem solving.
46. • Expectations in K-12
curricula Identifying the concepts
• Cognitive action verbs in class and class hierarchies
attributes within classes and
curricula their relationships
– Put, show, etc. Determining instances
– Summarize, generalize, Setting up axioms / rules
etc.
– Critical thinking, problem
solving, etc.
47. Identifying the concepts
class and class hierarchies
attributes within classes and
their relationships
Determining instances
Setting up axioms / rules
48. • Y: is an instance of
• X: is a class of
• C: is a superClass of
Identifying the concepts
• A: is a subClass of
class and class hierarchies
• K: is a process_component of attributes within classes and
• T: has process_component of their relationships
Determining instances
Setting up axioms / rules
Skills Relation Skills
Integrated Skill X Analyze
Analyze Y Integrated Skill
Analyze T Determine Relationship
Determine K Analyze
relationship
Basic Skill C Encapsulated Skill
Encapsulated Skill A Basic Skill
49. Identifying the concepts
class and class hierarchies
attributes within classes and
their relationships
Determining instances
Setting up axioms / rules
50. • Each act can be acted upon.
• Each action can include sub-actions. Identifying the concepts
• All actions can call others while being executed. class and class hierarchies
• All actions start with an input and produces an attributes within classes and
output. their relationships
• An Output can be an input for another action. Determining instances
• Inputs and outputs can be null, single or multiple. Setting up axioms / rules
56. Design and Application of Apothegm
Ontology
• 90 apothegmes were selected
• 281 concepts with 113 action verbs
• Relations:
– hasMeaning (isMeaningOf),
– hasComponent (isComponentOf),
– hasMeaningValue (isMeaningValueOf)
57. Visualizing the ontology
• A web based navigation tool is designed
• Apothegmes were presented on screen, users
navigate by selecting an apothegm and
reaches its components, meaning, and type.
• In addition, users are provided an interface in
order to add new statements and relations to
the ontology.
61. To conclude…
• Personalization can be a valuable tool to
facilitate lifelong learning with just-in-time
and on-the-job training, as well.
• Different frameworks and learner (and group)
characteristics will drive the method of
personalization
• Personalization can be expensive and time-
consuming if properly developed and
maintained
62. Last but not the least…
Davie & Inskip (1992) once emphasized
“good instructional design is more important than the
specific technology”
and, Ana Donaldson puts it well
“ online courses are demanding further considerations”
…thus, we need to “know our learners well”
Thank you for your patience…
Hacettepe University , Computer Education and Instructional Technologies
63. Thank you...
For the list of references, see
http://www.ontolab.hacettepe.edu.tr
and/or
http://www.ontolab.hacettepe.edu.tr/en
