Keynote taiwan

Prof. Dr. Arif ALTUNHacettepe University / Ankara-Turkey

Keynote Presentation at IETC 2012 Taiwan

Ontologies for Personalization: A new challenge for instructional designers

IETC 2012

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).

Five types of personalization

1. Name based personalization2. Self-described personalization3. Segmented personalization4. Cognitive personalization5. Whole-person personalization

(Martinez, 2000).

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?

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).

What we need is

1. A learner model2. A learning object design model3. Ontolog(ies)4. Learning analytics

International Conference Cognition and Exploratory Learning in Digital Age CELDA 2010

www.ontolab.hacettepe.edu.tr/en

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?

Kaya & Altun, 2011

Neuropsychological Assessment

• Determining the strengths and weaknesses in one’s cognitive functions (such as, memory types, attention levels, language ability etc.)

• Paper-pencil tests vs computerized tests

Line Orientation Test

Enhanced Cued Recall Test

Test Environment

• 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.

What do results imply?

• 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.

Visual Search

Low level prior knowledge High level prior knowledge Prior exposure

(n=10)No Prior exposure

(n=10)Prior exposure

(n=10)No Prior exposure

(n=10)

Blue (High Contrast)

Fix.Dur.M 1.46 1.29 2.79 2.81Sd .806 .764 1.27 1.94

T_FirstFix.M 7.04 7.52 4.66 3.98Sd 5.2 5.07 3.61 4.33

Yellow (Low

Contrast)

Fix.Dur.M .818 .946 1.28 .889Sd .728 .813 .852 .697

T_FirstFixM 4.93 3.52 3.84 5.18Sd 3.24 2.87 2.31 5.22

• 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

Short-term memory spans and attention design

• Different location memory groups• Dependent variable: Recall performance• Environment: 2-D vs 3-D environments.

Spatial Location Memory and Navigation Environment

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.

Dependent Variables: Recall and retention (free recall, heading recognition, and location memory)

Levels of Processing and Navigation design

Heading recognition task

Location memory task

• 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).

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?

Learning Objects

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).

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.

Common Metaphors• Lego (i.e., Hodgins & Conner, 2000)• Learning Atom & Learning Crystal (Wiley, 2001)• Luggage (Dawnes, 2002)

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?

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;

Ontology based representation of A Learning Object

Adjustable Relation

Concept Space Skill Space

1

Raw Content

LC

2 3 4 n

Adjusted Weight via Intelligent Bot

Content

LC

2 3 44 n1

Calculated (or pre-defined) Relation via Intelligent Bot

Content

LC

2 3 44 n

Calculated (pre-defined) Relation via Intelligent Bot

1

Ontology-based Learning Space

ConceptsLearning Space (LS)

Learning Container (LC)

Learning Objects (LO)

Assets

Adjusted Weight

Skills

Representation of skills and concepts in ontology space

21

22

Representation of skills and concepts in ontology space

Challenges

• Reusable,• With reasonable granuality,• Capable of handling learning contexts, • Interoprable, and• LO development tools (designed with an

instructionally sound design approach) are needed.

ONTOLOGY

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)

Semantic Web– Well defined meanings (semantics)– Common and shared standards and technologies

Tim Berners-Lee

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.

Stage 1: Identifying the conceptsStage 2: Determining class and class hierarchies Stage 3: Determining the attributes within classes and their relationshipsStage 4: Determining instancesStage 5: Setting up axioms / rules

(adapted from McGuiness, 1999)

Ontology as a Design & Development Process

PoleONTO: Modeling the K-12 curricula by using ontology

PoleONTO Personalized Ontological

Learning Environments

Expectation

Expectation 2

Expectation ..n

Concept

C1

C n

C2

Skill

S1

S n

S2

Expectation

• 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.

• Expectations in K-12 curricula

• Cognitive action verbs in curricula– Put, show, etc.– Summarize, generalize,

etc.– Critical thinking, problem

solving, etc.

Identifying the concepts class and class hierarchies attributes within classes and

their relationships Determining instances Setting up axioms / rules

Identifying the concepts class and class hierarchies attributes within classes and

their relationships Determining instances Setting up axioms / rules

• Y: is an instance of • X: is a class of• C: is a superClass of• A: is a subClass of • K: is a process_component of• T: has process_component of

Skills Relation Skills

Integrated Skill X Analyze

Analyze Y Integrated Skill

Analyze T Determine RelationshipDetermine

relationshipK Analyze

Basic Skill C Encapsulated SkillEncapsulated Skill A Basic Skill

Identifying the concepts class and class hierarchies attributes within classes and

their relationships Determining instances Setting up axioms / rules

Identifying the concepts class and class hierarchies attributes within classes and

their relationships Determining instances Setting up axioms / rules

• Each act can be acted upon.• Each action can include sub-actions.• All actions can call others while being executed.• All actions start with an input and produces an

output.• An Output can be an input for another action.• Inputs and outputs can be null, single or multiple.

Identifying the concepts class and class hierarchies attributes within classes and

their relationships Determining instances Setting up axioms / rules

Taxonomic View of CogSkillNet

From taxonomy to ontology

Some Screenshots

Design and Application of Apothegm Ontology

• 90 apothegmes were selected • 281 concepts with 113 action verbs• Relations:

– hasMeaning (isMeaningOf),– hasComponent (isComponentOf),– hasMeaningValue (isMeaningValueOf)

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.

Apothemes.owl

Semantic web tools

UI

Ontology

Visual Representation

Compenents when selected an apothegm

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

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

Thank you...

For the list of references, see http://www.ontolab.hacettepe.edu.tr and/orhttp://www.ontolab.hacettepe.edu.tr/en