Transcript ppt

Trends and challenges in
technology enhanced learning*
Živana Komlenov
Department of Mathematics and Informatics
Faculty of Science
University of Novi Sad
*Partly inspired by presentations at Online Educa 2011
and STELLAR report indicating Grand Challenges
Where to? How?
2/19
Motivation through new mobile and
ubiquitous tools
► Widespread
penetration of broadband Internet,
WIFI infrastructure, fast mobile data networks,
smart phones, tablets, and personal computers
► New opportunities for personal driven education,
yet clear lack of tools to inspire people to learn
► What is needed?
 Socio-technical tools that inspire and motivate people to
collaboratively and individually construct, create, and
aggregate information into new knowledge/artefacts
 Working across stakeholders to develop personally
meaningful environments
3/19
Providing one technology-supported
tutor per learner
► Human
tutors assisted by technology help
learners:
 become more competent
 meet the demands of knowledge-driven society
► Combining
agents and human tutors to provide
high quality tutoring to every learner
► What is needed?
 Predictive models which target predicting performance
based on traces
 Learning analytics that provide intuitive graphical user
interfaces that foster quick performance understanding
 Provision of feedback to support analysis in real-time
 Pedagogically sound user interfaces
4/19
Harnessing the power of emotions for
learning with TEL
► The
way we learn is influenced by the emotions
we experience
 Raised levels of stress and anxiety can lead to poorer
performance
 Low levels of arousal or engagement can affect our
concentration and have a negative impact on how we
take on new knowledge
► What
is needed?
 Technological developments offer new and often
unanticipated ways to improve teaching/learning
► e.g.
bio- sensors can offer insights into the physiological
impacts of learning or act as aids for using bio-feedback to
reinforce learning or identify problems areas
► Usage – self-monitoring or exploring reactions between
teaching events and emotional responses
5/19
Empowering learners to collaborate
online
► Online
collaboration is not often sustained
► Learners engage in the collaborative activity only
to the extent that is required by the task or
activity they are set
► Many learners use social networking tools in their
everyday lives
► How can formal education draw on the power of
social networking in order to optimise online
collaborative activity for learning?
6/19
Empowering learners to collaborate
online
► What
is needed?
 Innovations regarding the structuring or scripting the
collaborative activities
► Over-structuring
tends to leave students unmotivated
► Under-structuring
tends to leave students overwhelmed
► Exploring
how the structuring devices used are assimilated into
student activity systems
► Following
the 4T model, which structures online collaborative
learning activity within: task requirements, timing, team
organization and the technology used
 Ethnographic studies to investigate the use of
collaborative tools in the everyday life of learners
7/19
Assessment and automated feedback
► Breaking
current limitations in terms of:
 learning domains
 attention to summative assessment in current
educational practices
 limitation to focus on traditional question-formats
► The
final aim is to change the perception of
assessment
 From a judging instrument to a support mechanism for
learning
► What
is needed?
 Wide-scale development, evaluation and implementation
of new formative assessment scenarios
► Including
the technologies that make intensive use of text- and
data mining or natural language processing approaches
8/19
New forms of evaluation for informal
learning
► By
removing the pre-specified design objectives
we also remove traditional benchmarks against
which we evaluate
 Such as measures of cognitive learning
► What
is needed?
 Looking beyond short-term cognitive gains into mediumto longer-term attitudinal, psychomotor, affective,
motivational, emotional and behavioural gains
 Finding ways to chart changes in the emotional
intelligence of students and their effects on learning
 Educating learners in the ethical appropriation of TEL
9/19
Improve learning and course completion
through recommender technologies
► High
drop-out rates, especially in online and
distance education settings
► What is needed?
 Customize existing recommendation algorithms for
learning
 Employment of recommender systems in real-life
scenarios
 Different support systems for teachers and students to
offer relevant information at the right time
► For
instance drop-out analyzers that inform the tutor of a
course which learners are likely to drop-out
 Developing suitable evaluation criteria for different kinds
of recommender systems
10/19
Recommender systems deciding which
representation fits learning needs best
► This

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choice is very complex because of:
the nature of the content
variety of the available resources
constraints on the communication
learners’ competences and needs
► What
is needed?
 A high level collaboration of computer scientists, with
researchers having a specific expertise in semantic,
learning science, semiotic and epistemology
 A prototype of such a tool in a well-defined and not too
complex domain
11/19
Recommender systems deciding which
representation fits learning needs best
► Possible
complementary features:
 Indicators to recognize the right moment/time to
provide non-intrusive feedback/scaffolding to learners
 Indicators on when, how, and what kind structuring the
learning process should be provided in a personalized
way
 Criteria for choosing the effective order of
representation type (self-constructed created vs. preconstructed given)
► Depending
on the expected processing and conceptual
understanding of the learner
12/19
Improving efficiency and reducing costs
through improved information retrieval
► Decreasing
number of teachers and the request to
increase the number of high-educated students in
a short time period
 => Less time available for the individual support of
students
► =>
Teaching quality decreases
► Combination
of educational data and information
retrieval techniques, i.e. Learning Analytics (LA)
 Will become a powerful means in educational practice
and student guidance
 Promises to reduce delivery costs, create more effective
learning environments and experiences, accelerate
competence development, and increase collaboration 13/19
Improving efficiency and reducing costs
through improved information retrieval
► Barriers
and limitations of LA:
 Issues of privacy and data protection
 Data surveillance and its ethical implications
► What
is needed?
 A new vocabulary in order to discuss privacy, data
protection and surveillance issues
 Research on how existing privacy and transparency
solutions can be integrated in practice
 Data awareness education for society
 New policies defined to avoid unethical data mining
research
14/19
Collecting and sharing teaching practices
with new technologies
► Learner-centric
approaches foster competence
development beyond immediate domain skills
► Capturing and analyzing the interactions of a
learners with their environments
 which can be characterised as (adhoc) networks of
actors, artefacts, tools, activities, and communities
► Personal
learning environments (PLEs)
 Empowering learners to design their own environments
and to connect to learner networks and collaborate
► Standardization
movements aim at making
learning objects, learning designs, and educational
scripts accessible for others
 To foster sharing and reusability of resources
15/19
Collecting and sharing teaching practices
with new technologies
► What
is needed?
 Reaching a certain level of scale in variability and the
capacity for variability
► As
a precondition for a flexibly changing of learning
environment
 Facilities for sharing a different (individual) practices
supported by differing technology arrangements
► May
or may not base on broad socio-technical movements such
as social media and Web 2.0
 Understanding, building, and sustaining networks of
teachers, including ad-hoc formation and dissolution of
such cliques
 Large tool repositories such as widget- and app-stores 16/19
Shared datasets for recommender
systems training and development
► Educational
datasets (educational data stored
automatically by e-learning environments) offer an
unused opportunity for:
 Evaluation of learning theories
 Student support
 Development of future learning applications
► They
extend the methodological and empirical
approaches to analyze TEL
 Dominated by design-based research approaches,
simulations, and field studies
► Challenging
data ownership and access rights
17/19
Shared datasets for recommender
systems training and development
► What
is needed?
 Defining and promoting a common generic
infrastructure for sharing, analyzing and reusing
learning resources and learning activity logs
 Data policies (licenses) that regulate how different users
can use, share, and reference certain datasets
 Common dataset formats like from the CEN PT Social
data group
 Standardized documentation of datasets so that others
can make proper use of it
 Methods to anonymise and pre-process data according
to privacy and legal protection rights
18/19
Where to? How?
19/19