Presentation on theme: "Learner-Space Knowledge Awareness Map in Computer Supported Ubiquitous Learning Moushir M. El-Bishouty, Hiroaki Ogata and Yoneo Yano The University of."— Presentation transcript:
Learner-Space Knowledge Awareness Map in Computer Supported Ubiquitous Learning Moushir M. El-Bishouty, Hiroaki Ogata and Yoneo Yano The University of Tokushima, Japan
Introduction While a learner is doing a new task to practice, s/he usually needs some help and instructions. How to get the appropriate knowledge immediately? Refer to educational materials which are around the learner. Ask another person (friends, experts, teachers, … ).
Introduction continue It is so difficult to know who has this knowledge even that he is at the same local area. aware The learner needs to be aware of the other learner s interests that match his request.
Introduction continue Knowledge Awareness (KA) is defined as awareness of the use of knowledge Knowledge awareness map graphically displays KA information: what kind of educational materials are available? Who can help to solve this problem? The map enhances curiosity and induce collaborative learning.
Objectives Design and implement of computer supported collaborative learning in ubiquitous computing environments. Detect the environmental objects that surround the learner. Recommend the best peer helpers. Support the learner with the knowledge awareness map (KAM) which is personalized according to his current need.
PerkamII (PERsonalized Knowledge Awareness Map). In Perkam*, the knowledge awareness map is personalized according to the learners interest and location. In PerkamII, the system uses RFID tag to detect the environmental objects that surround the learner those he uses in his practice study, and to map from the physical space to the digital space. *Moushir M. El-Bishouty, Hiroaki Ogata, Yoneo Yano, Personalized Knowledge Awareness Map in Computer Supported Ubiquitous Learning, Proc. of ICALT06, pp. 817-821, The Netherlands, 2006. (Best Paper Award)
PerkamII continue Each object in the physical space is detected, recognized and presented graphically by this system. Recommend the best learners where their interests matched the current learner s task. The learner can forward his digital space to the peer helper in order to facilitate easy understanding of his environment that augments the collaboration between them.
PerkamII continue Assume that the knowledge space contains a number of unique keywords. keywords define Environmental objects Learner s request Learner s interest. keyword item is very important to recommend the peer learners.
PerkamII Model 1-Learner Each learner s interests is characterized according to Learner Profile: -Learner explicit registration. -Learner academic level. -Learner actions during using the system.
PerkamII Model continue 2- Environmental Objects: -The available environmental objects that may surround the learner. -It may be computers, electronic parts, chemicals … etc. -A RFID tag is attached on each object to identify it. -Each object has its own keywords that specify its specification. -One object may share one or more keywords with one or more other objects.
PerkamII Model continue 3- Environmental Objects Map: -While the learner is interacting with another learner remotely, asking him for help and trying to explain to him his current environment and situation. -It may be difficult or at least need long time to describe exactly the available objects that he uses during his practice. -The role of environmental objects map is to map the physical space to a digital space, where each object in the physical space is detected, recognized and presented graphically by this system.
PerkamII Model continue -The learner can forward this digital space to the peer helper. -For example, learner1 is doing an experiment in a chemistry lab, each object surrounds learer1 is recognized, mapped into the digital space and forwarded to the peer helper. -According to the knowledge from the environmental objects map the peer helper can recognize the learner situation and can efficiently collaborate with him.
PerkamII Model continue 4- Peer Learners Map -This map displays the knowledge space of the recommended learners who are using the system and have enough knowledge about the learner s need -It represents the Level Of Interest of a peer helper, LOI = where n L =# of matched learner s interest keywords n = # of keywords of learner s query
PerkamII Implementation Detect-Location: TCP/IP stream socket client-server application Server side VC++ : listen on the network to the incoming client packets, receive client data and get the detected objects information. Client side eVC++ : read the data stored in the RFID tag and send it to the server.
PerkamII Implement. continue Search-Collaborate: Web based client-server application developed using ASP.Net and C#. Search, get recommendation and collaborate. Visualize-KA-Map Embedded flash object. KAW is dynamically designed and displayed. Developed using Macromedia Flash ActionScript.
System Usage Consider a learner (Matsuka) is doing a task PC assembling. He does not have the enough experience to complete the task PerkamIIHe uses PerkamII to detect the PC components and to build their KA map. PC Assembling Task
System Usage continue Each component is presented in a small icon (Modem, VGA, Mother board, RAM and HDD) He asks the system to recommend the suitable peer helpers whom he can interact and collaborate in order to finish his task successfully. Environmental Objects Map
System Usage continue The system recommends two peer helpers. First recommended peer helper is Sasada and the other is Yin, it is easy for Matsuaka to discover that Sasada is more familiar and expert than Yin regarding the detected components. When Matsuka moves the pointer over the Sasada s icon for example, he can get his full information (name, phone number, email … ). Peer Helpers Map
System Usage continue PerkamIIMatsuka can send him a message using PerkamII system, use chat tools, or call him by phone. Matsuka forwards to Sasada his digital KA map, takes photos for some components and sends them to him. It is easy for Sasada to recognize Matsuka problem, collaborate with him to reach to his final goal.
System evaluation An experiment is done to evaluate the system performance and the learner satisfaction. The main idea is to ask a number of students who are learning PC hardware assembling and maintenance to do a ask and measure how much the system can help them to do the task in a certain time. 12 students are involved, the are divided into two groups -Experts group, who are experts and have a strong knowledge about this problem -Learners group who are beginners.
System evaluation continue In the first phase, 5 students from the learners group were asked to do the task without using the system, and using only a web page access. 3 students completed and 2 did not. PerkamIIIn the second phase, 9 (7 + 2) students from the learners group were asked to do the task with the aid of PerkamII system and the support from 5 students who were using the system. A questionnaire is filled by all students at the end
System evaluation Result In the first phase, 2 students from 5 hardly completed the task just in time. In the second phase, all students 9 successfully completed the task. The students agree that -The system performance is fast. -It is easy to use the system. -The system is so useful as a learning assistance tool.
Future Work Design the second experiment to measure how much the learners have learnt while using the system. Integrate this environment with a location model where the system can recommend the peer helpers not only depending on their interests but also how close are their physical locations to the learner. Sharing learning experiences with videos. mLearn2006.
Another scenario: Cooking Japanese foods Situation A My situation Situation B I want to cook Japanese needle, called Udon. Its difficult to describe the situation exactly. The system tells you missing tools or ingredients I can help.
Conclusion Knowledge awareness map supports learning immediately when a problem occurs. It s a kind of a personal learning assistant (PLA, not PDA) in ubiquitous computing environments.