Trends and challenges in technology enhanced learning *

1. 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 

2. Where to? How? 2/19

3. Motivation through new mobile and ubiquitous tools • Widespread penetration of broadband Internet,WIFI infrastructure, fast mobile data networks, smartphones,tablets,andpersonalcomputers • Newopportunitiesforpersonaldriveneducation, yet clearlackoftoolstoinspirepeopletolearn • What is needed? • Socio-technicaltoolsthat inspireandmotivatepeopletocollaborativelyandindividuallyconstruct,create,andaggregateinformationintonewknowledge/artefacts • Workingacross stakeholders to develop personally meaningful environments 3/19

4. Providing one technology-supported tutor per learner • Human tutors assisted by technologyhelp learners: • becomemorecompetent • meetthedemandsofknowledge-drivensociety • Combiningagents andhumantutorstoprovidehighqualitytutoringtoevery 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

5. 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 concentrationand have a negative impact on how we take on new knowledge • What is needed? • Technological developments offer new and often unanticipatedwaystoimprove teaching/learning • e.g. bio- sensors can offer insights into the physiological impacts of learning or actasaids for using bio-feedbacktoreinforcelearningoridentifyproblemsareas • Usage – self-monitoring or exploring reactions between teaching events and emotional responses 5/19

6. 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 intheir everyday lives • How can formal education draw on thepower of social networking in order to optimise online collaborativeactivity for learning? 6/19

7. 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

8. Assessment and automated feedback • Breaking current limitations in terms of: • learning domains • attentiontosummativeassessmentincurrenteducationalpractices • limitationtofocusontraditionalquestion-formats • Thefinalaim istochangetheperceptionofassessment • Fromajudginginstrumenttoasupportmechanismforlearning • What is needed? • Wide-scale development, evaluation and implementation of new formative assessment scenarios • Including the technologiesthat make intensive use of text- and data mining ornatural language processing approaches 8/19

9. 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 medium- to 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

10. 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

11. 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 specificexpertiseinsemantic,learningscience, semioticandepistemology • A prototypeofsuchatoolina well-definedandnottoocomplexdomain 11/19

12. Recommender systems deciding which representation fits learning needs best • Possible complementary features: • Indicators to recognize the right moment/time to providenon-intrusive feedback/scaffolding to learners • Indicators on when, how, and what kind structuring thelearning process should be provided in a personalized way • Criteria for choosing the effective order of representationtype (self-constructed created vs. pre-constructed given) • Depending on the expected processing and conceptual understandingofthelearner 12/19

13. Improving efficiency and reducing costs through improved information retrieval • Decreasing number ofteachers and the request to increase the number of high-educatedstudents in a short time period • => Less timeavailable for the individualsupport of students • =>Teachingquality decreases • Combinationofeducationaldataandinformationretrievaltechniques, i.e. LearningAnalytics(LA) • Will becomea powerfulmeansineducationalpracticeandstudentguidance • Promises to reduce delivery costs,create more effective learning environments and experiences, acceleratecompetencedevelopment,andincreasecollaboration 13/19

14. Improving efficiency and reducing costs through improved information retrieval • Barriers and limitations of LA: • Issues ofprivacy and data protection • Data surveillance and its ethical implications • What is needed? • A new vocabulary in order todiscuss 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 miningresearch 14/19

15. Collecting and sharing teaching practices with new technologies • Learner-centric approaches fostercompetence development beyond immediate domain skills • Capturing and analyzing the interactions of alearners with their environments • which can be characterised as(adhoc)networksofactors,artefacts,tools,activities,andcommunities • Personal learning environments(PLEs) • Empowering learnerstodesigntheirownenvironmentsandtoconnecttolearnernetworks 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

16. Collecting and sharing teaching practices with new technologies • What is needed? • Reaching a certain level of scale in variabilityand the capacity for variability • As a precondition for aflexiblychanging of learning environment • Facilities for sharing a different (individual)practicessupportedbydifferingtechnologyarrangements • May or may not base on broad socio-technical movementssuchassocialmediaand Web2.0 • Understanding, building, and sustaining networks of teachers, including ad-hoc formation and dissolution of suchcliques • Large tool repositories such as widget- and app-stores 16/19

17. 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

18. 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

19. Where to? How? 19/19