Personalization for Location-Based E-Learning Rui Zhou and Klaus Rechert Communication Systems, Dept. of Computer Science The University of Freiburg, Germany nd International Conference and Exhibition on Next Generations Mobile Applications, Services and Technologies (NGMAST 2008), Cardiff, Wales, UK

Outline Location-based E-Learning Process of personalization Personalization techniques –Learning context modeling –Content selection and recommendations –Personalized multimedia presentation Prototypical application 2

Location-based E-Learning Learners are on the move Learners are equipped with portable devices with Internet connection Keep track of learners Provide learners with location-based contents and services Used in museums, botanical gardens, national parks, zoos… Indoors and outdoors 3

Location-based E-Learning Location-based content delivery Guiding –Predefined tours, for beginners –Tours generated on the fly, based on learners’ requirements and interests –Recommendations 4 Location determination Location- dependent content queries Compose personalized presentation Deliver the presentation

Location determination Primary prerequisite for location-based services GPS, Galileo for outdoors Wi-Fi fingerprinting for indoors – received signal strength –Accuracy down to a few meters Composite positioning –Improved accuracy, robustness and multiplicity –Sensor fusion 5

Process of personalization 6 Tailor services Facilitate work Accommodate social requirements

Learning context Personalization is based on the context Characterizes the situation and environment Information about the learner –Personal profile, goal, knowledge, interests, preferences, interaction and presentation history… Information about the environment –Location, device, time, date, weather… 7

Learning context - location Location is the most important ingredient Contents are provided according to location What other learners have learnt at the same location will be recommended Geographic coordinate or symbolic location Orientation, facing objects, velocity, confidence about location estimation 8

Learning context Personal profile –Characteristics of the learner –Name, gender, occupation, nationality… –Initial stereotype -> initial learning context model Goal –To learn a new topic –To Review an old topic –An e-course for a conventional lecture –To prepare an examination –Contents that other learners with the same goals have learnt will be recommended 9

Learning context - knowledge Most important for educational systems Adapt learning activity to learner’s knowledge Knowledge model is an overlay of domain model Domain model –Expert knowledge of the domain –A network model –Decompose to concepts/topics Learner knowledge model –(concept/topic, knowledge level) –Qualitative or numeric values 10 From: P.Brusilovsky, A.Kobsa, W.Nejdl. The Adaptive Web. Springer. 2007

Learning context - knowledge Knowledge update –Changeable: increases (learn) and decreases (forget) –Initially empty –Updated through interactions Learn a topic -> knowledge increases Self test -> knowledge estimation and update –Knowledge propagation Adaptive learning –Check knowledge level first –Present adaptively –Ranking of recommendations 11

Learning context - interest Most commonly used for tourism and learning Provide information the learner is interested in Interest model is an overlay of domain model (concept/topic, interest level) Interest update –Initially empty –Updated Learner chooses a topic -> interest increases Longer learning -> higher interest level –Interest propagation 12

Learning context - interest Adaptivity –Check interest level first –Adaptive presentation Low: brief information Intermediate: more detailed information high: full explanation –Ranking of recommendations –Contents that other learners with similar interests have learnt will be recommended 13

Learning context Preferences –About how to present –Multimedia types –Notification mode upon new presentation –Presentation mode –Provided by learners explicitly Interaction and presentation history –Series of presentations cohesive –Capture the characteristics of the learner 14

Learning context Device –Different portable devices have different OS, screen sizes… –Adapt presentation to the device –Solution Map the combinations of features to a few stereotypes Adapt presentation to the stereotype Other context elements –Time, date, season, weather… 15

Content organization Local database Support location-awareness spatial attribute point (location) or polygon (region) indexed on spatial attribute Support personalization tagged for stereotypes knowledge levels interest levels others like time and date 16

Location-dependent content query New location -> new learning material Planar point query Window query Nearest neighbor(s) query Single query or combination 17 Planar point queryWindow queryNearest neighbor query

Recommendations When –New location 1)First location-dependent content queries 2)Then recommendations –After finish learning a topic What to learn next How –Based on learning context model –Collaborative filtering: location, goal, interests 18

Ranking of recommendations Most relevant topics appear first Remove irrelevant topics Stereotype Knowledge level Interest level Preferences Context elements like time, date and weather Rank relevant topics Interest level Knowledge level Distance Learnt topics are at the bottom marked learnt 19

Content selection for presentation Select content for a topic from local database Compose to a personalized multimedia presentation Selection is based on: –Stereotype –Knowledge level –Interest level –Context elements such as time, date and weather 20

Personalized multimedia presentation 21 Modified from: A. Scherp. A component framework for personalized multimedia applications. Ph.D. dissertation, Dept. Computer Science, University of Oldenburg, Oldenburg, Germany, 2006.

Location-based Botany Guide Location-based guiding and learning in botanic gardens For biology majors and visitors Personalized multimedia information of nearby plants –Botanic description of the plant –Hyperlinks to its botanic parents and ancestors –Hyperlinks to its botanic children and planted individuals –Recommendations such as what to see next WLAN- and GPS-enabled portable device –GPS for outdoor positioning –WLAN for indoor positioning and data communication 22

Location-based Botany Guide 23

Browser-Web server architecture 24

Location-based Botany Guide Stereotype: teacher, biology major, visitor Domain model: based on botanic taxonomy Knowledge model and interest model are overlays Qualitative knowledge level –novice, intermediate, advanced –determined by scientific self test –knowledge propagation: to parents, to children, to siblings Qualitative interest level –low, intermediate, high –determined by interactions between learner and the system –interest propagation: to parents, to children, to siblings 25

Thanks! 26