This document discusses personalization in e-learning and the challenges it poses for instructional designers. It covers 5 types of personalization and challenges including understanding learner-content interactions and designing personalized learning paths. The key needs for personalization are identified as a learner model, learning object design model, ontologies, and learning analytics. Several studies are summarized that explore modeling learner characteristics, visual search performance, memory spans, navigation design, and levels of processing to better understand learners and design personalized instruction. Challenges remain around differentiating learning paths for individuals and groups.
ElectroencephalographySignalClassification based on Sub-Band Common Spatial P...IOSRJVSP
Brain-computer interface (BCI) is a communication pathway between brain and an external device. It translates human thought into commands to control the external devices.Electroencephalography (EEG) is cost effective and easier way to implement the BCI. This paper presents a novel method for classifying EEG during motor imagery by the combination of common spatial pattern (CSP) and linear discriminant analysis (LDA). In the proposed method, the EEG signal is bandpass-filtered into multiple frequency bands. The CSP features are then extracted from each of these bands. The LDA classifier is subsequently used to classify the CSP features. In this paper, experimental results are presented on a publicly available BCI competition dataset and the performance is compared with existing approaches. The experimental result shows that the proposed method yields comparatively superior cross validation accuracies compared to prevailing methods.
about process knowledge and how it is possible to enable users without any kind of IT skills to i) model processes and ii) analyze the provenance of process executions, without the intervention of software or knowledge engineers. Jose Manuel proposes the utilization of Problem Solving Methods (PSMs) as key enablers for the accomplishment of such objectives and demonstrates the solutions developed, evaluated in the contexts of Project Halo and the Provenance Challenge, respectively. Jose Manuel concludes the talk with a process-centric overview on the challenges raised by the new web-driven computing paradigm, where large amounts of data are contributed and exploited by users on the web, requiring scalable, non-monotonic reasoning techniques as well as stimulating collaboration while preserving trust.
KeepIt Course 4: Putting storage, format management and preservation planning...JISC KeepIt project
This is the opening presentation for module 4 of the 5-module course on digital preservation tools for repository managers, presented by the JISC KeepIt project. This module puts storage, format management and preservation planning in the repository, by making such functions available from within the familiar repository interface. This introduction briefly reviews the previous module, which acted as a primer on preservation workflow, formats and characterisation, as preparation for the preservation planning tools to be encountered in this module. For more on this and other presentations in this course look for the tag ’KeepIt course’ in the project blog http://blogs.ecs.soton.ac.uk/keepit/
A seminar conducted for TIE in Silicon Valley to provide perspective on how companies can do marketing on a shoestring budget in the current world of social media and interconnected business.
ElectroencephalographySignalClassification based on Sub-Band Common Spatial P...IOSRJVSP
Brain-computer interface (BCI) is a communication pathway between brain and an external device. It translates human thought into commands to control the external devices.Electroencephalography (EEG) is cost effective and easier way to implement the BCI. This paper presents a novel method for classifying EEG during motor imagery by the combination of common spatial pattern (CSP) and linear discriminant analysis (LDA). In the proposed method, the EEG signal is bandpass-filtered into multiple frequency bands. The CSP features are then extracted from each of these bands. The LDA classifier is subsequently used to classify the CSP features. In this paper, experimental results are presented on a publicly available BCI competition dataset and the performance is compared with existing approaches. The experimental result shows that the proposed method yields comparatively superior cross validation accuracies compared to prevailing methods.
about process knowledge and how it is possible to enable users without any kind of IT skills to i) model processes and ii) analyze the provenance of process executions, without the intervention of software or knowledge engineers. Jose Manuel proposes the utilization of Problem Solving Methods (PSMs) as key enablers for the accomplishment of such objectives and demonstrates the solutions developed, evaluated in the contexts of Project Halo and the Provenance Challenge, respectively. Jose Manuel concludes the talk with a process-centric overview on the challenges raised by the new web-driven computing paradigm, where large amounts of data are contributed and exploited by users on the web, requiring scalable, non-monotonic reasoning techniques as well as stimulating collaboration while preserving trust.
KeepIt Course 4: Putting storage, format management and preservation planning...JISC KeepIt project
This is the opening presentation for module 4 of the 5-module course on digital preservation tools for repository managers, presented by the JISC KeepIt project. This module puts storage, format management and preservation planning in the repository, by making such functions available from within the familiar repository interface. This introduction briefly reviews the previous module, which acted as a primer on preservation workflow, formats and characterisation, as preparation for the preservation planning tools to be encountered in this module. For more on this and other presentations in this course look for the tag ’KeepIt course’ in the project blog http://blogs.ecs.soton.ac.uk/keepit/
A seminar conducted for TIE in Silicon Valley to provide perspective on how companies can do marketing on a shoestring budget in the current world of social media and interconnected business.
Using Search Engine Marketing to Promote Niche Community College ProgramsSteve Bacher
Should community colleges use search engine marketing (SEM) channels such as Google AdWords? Is it cost effective? This presentation begins with a brief explanation of SEM, using examples from Bucks County Community College's Paragon Award-winning SEM campaign to promote medical occupational programs, both credit and non-credit. It explains the steps the Director of e-Marketing went through in the creation of the campaign, and explores which elements can be outsourced. It also explores items to consider when selecting a vendor. Finally it presents ROI data from the pilot campaign referenced above. This was presented at the NCMPR national conference in Albuquerque, NM on 3/16/10.
Spatial Thinking and Stem Education: Drawing and Mapping with New TechnologiesEduSkills OECD
The CERI OECD/National Science Foundation International Conference took place in Paris, at the OECD Headquarters on 23-24 January 2012. Here the presentation of Session 5, Informal Learning, Item 1.
Slides presented in the All Japan Computer Vision Study Group on May 15, 2022. Methods for disentangling the relationship between multimodal data are discussed.
Distilling Linguistic Context for Language Model CompressionGeonDoPark1
A computationally expensive and memory intensive neural network lies behind the recent success of language representation learning. Knowledge distillation, a major technique for deploying such a vast language model in resource-scarce environments, transfers the knowledge on individual word representations learned without restrictions. In this paper, inspired by the recent observations that language representations are relatively positioned and have more semantic knowledge as a whole, we present a new knowledge distillation objective for language representation learning that transfers the contextual knowledge via two types of relationships across representations: Word Relation and Layer Transforming Relation. Unlike other recent distillation techniques for the language models, our contextual distillation does not have any restrictions on architectural changes between teacher and student. We validate the effectiveness of our method on challenging benchmarks of language understanding tasks, not only in architectures of various sizes, but also in combination with DynaBERT, the recently proposed adaptive size pruning method.
Distilling Linguistic Context for Language Model CompressionGyeongman Kim
Abstract: A computationally expensive and memory intensive neural network lies behind the recent success of language representation learning. Knowledge distillation, a major technique for deploying such a vast language model in resource-scarce environments, transfers the knowledge on individual word representations learned without restrictions. In this paper, inspired by the recent observations that language representations are relatively positioned and have more semantic knowledge as a whole, we present a new knowledge distillation objective for language representation learning that transfers the contextual knowledge via two types of relationships across representations: Word Relation and Layer Transforming Relation. Unlike other recent distillation techniques for the language models, our contextual distillation does not have any restrictions on architectural changes between teacher and student. We validate the effectiveness of our method on challenging benchmarks of language understanding tasks, not only in architectures of various sizes, but also in combination with DynaBERT, the recently proposed adaptive size pruning method.
What Do You Want Them To Learn Today? Learning Goals and Formative AssessmentStephanie Chasteen
This is the presentation on Learning Goals for FTEP at CU-Boulder by Kathy Perkins and Stephanie Chasteen, February 22 2012.
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Students don’t always learn what it is that we intend to teach them. In several science departments, faculty are addressing this gap by collaboratively deciding on just what it is that they want students to take away from a particular course or lecture. These learning goals have been valuable as a communication tool among faculty and between faculty and students so that everybody knows what the outcomes of the course are meant to be. Once these goals are written, it’s also much easier to write exams and other assessments. But writing clear learning goals takes some practice. In this interactive workshop, you’ll get that practice – in defining goals and designing assessments that address those goals. You will work in groups with faculty from similar disciplines to generate and analyze goals and questions, and will discus how to put ongoing assessment of your students into practice. You are encouraged to work on a class you are currently teaching, so you can apply the techniques immediately.
https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
Over the last years, deep learning is rapidly advancing with impressive results obtained in several areas including computer vision, machine translation and speech recognition. Deep learning attempts to learn complex function through learning hierarchical representation of data. A deep learning model is composed of non-linear modules that each transforms the representation from lower layer to the higher more abstract one. Very complex functions can be learned using enough composition of the non-linear modules. Furthermore, the need for manual feature engineering can be obviated by learning features themselves through the representation learning. In this talk, we first explain how deep learning architecture in particular and neural networks in general are loosely inspired by mammalian visual cortex and nervous system respectively. We also discuss about the reason for big and successful comeback of neural networks with the deep learning models. Finally, we give a brief introduction of various deep structures and their applications to several domains.
References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
https://www.udacity.com/course/deep-learning--ud730
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
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