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
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