Semantic Web Technologies for the Integration of Learning Tools and Context-aware Educational Services

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DEPTHS is an collaborative, semantic-rich learning environment for learning software design patterns.

Design patterns are solutions to the some common design problems that happens over and over again. They are very important for software engineers because they exploit previous successful experiences and knowledge of other people in solving similar problems. They relieve programmers to create a good design of his code and spare a lot of time to solve some problems.

It is possible to develop many scenarios for learning software patterns in the DEPTHS framework. Due to the limited size of this paper, we describe only one, which is based on a problem-based learning approach with collaborative learning support. In this scenario, a teacher defines a specific software problem that has to be solved in a workshop-like manner. Workshop is a peer assessment activity with a huge array of options, such as allowing students to review and assess each other’s solutions. The teacher provides an informal description of the problem, a typical scenario, a task that has to be accomplished and a set of learning resources that could help students to solve the problem. Students are typically supposed to provide a graphical representation of their solutions (i.e., the designed software models). A student can draw his own solution from scratch, use some other student’s solution, and/or use a partial solution provided by the teacher in the problem’s description. If one of the last two options is selected, an appropriate solution (often in the form of a diagram) would be loaded within the student’s modeling tool and the tool would keep track of all changes that the student would make and tag them with other color on the diagram. Based on the student’s current learning context, DEPTHS would suggest him to consult online resources that it estimated as potentially useful for the student’s current situation. It would also find and suggest other students, experts and/or teachers that could be contacted in order to get additional support. The system would do this both proactively and on the student’s request. As DEPTHS provides seamless integration of all of its tools, the student is able to send a message to or chat with peers regardless what tools of the DEPTHS framework they are using at that moment.

The LOCO-Cite ontology is based on the notion of learning context which is related to the activity that a learner or a teacher has been performed while interacting with a learning content. The LOCO-Cite ontology allows for semantic representation of the data about a student’s overall interactions with learning content and other students during different learning activities. Based on this data, DEPTHS can perform context-aware retrieval of software patterns resources from online repositories and its own repository of software artifacts; identify and draw students’ attention to the related threads in discussion forums; and identify peers that could help in a specific situation. Activities are very important part of the learning process in DEPTHS as they lead to realization of learning objectives. Examples of such activities are reading lessons, visiting online pages, participating in a workshop or doing an assignment, solving design problems, quizzing and collaborating with other participants.

We believe that this approach can be beneficial for all participants in the learning process: Teachers would spend less time developing online lessons. Instead of creating lessons for each design pattern from scratch, a teacher would (re)use the existing online resources. DEPTHS would secure the ease of locating relevant online resources for the course the teacher develops. DEPTHS will improve students’ learning effectiveness and efficiency by recommending resources from online repositories that are related to the goal students are currently working on. In addition, it will help students find the most suitable peer(s) to collaborate within the given learning context. The common ontological framework (LOCO) provides a sound platform for integration of knowledge about all learning related activities performed by any particular student and the content he/she has used or produced during those activities. This knowledge is currently scattered over different learning systems and tools for studying software patterns. This means that the overall quality of important educational aspects (e.g., adaptation and context-awareness) will be improved.

DEPTHS is an collaborative, semantic-rich learning environment for learning software design patterns.

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Semantic Web Technologies for the Integration of Learning Tools and Context-aware Educational Services - Presentation Transcript

Semantic Web Technologies for the Integration of Learning Tools and Context-aware Educational Services Zoran Jeremi ć 1 , Jelena Jova nović 1 , Dragan Gašević 2 1 University of Belgrade, Serbia 2 Athabasca University, Canada
Motivation
- Design patterns
- Integrative view to problems
Motivation
- Current learning support
Pedagogical Foundation
- Social constructivism
- Learning through design
- Engagement theory
- Genex (Generator of Excellence) framework
- Project-based learning
Learning Scenario Context-aware Proactive
Learning Context Ontology
- Learning Object Context Ontology (LOCO)
Learning Context Ontology: Extension
Domain Ontology
- Software design patterns
Moodle ArgoUML LOCO-Analyst
Semantic Annotation and Indexing
- Content
Semantic Annotation and Indexing
- Technical approach
Educational Services in DEPTHS
- Context-aware learning services
Educational Services in DEPTHS Submissions
Evaluation
- Performed in February 2009
- Perceived
Evaluation
- Students’ perception of learning with DEPTHS
- Usefulness of integrated tools and services
Not very useful Less effective Same More effective 0% 15.38% 53.85% 30.77% 30.77% 46.15% 15.38% 7.69% 0.00% Brainstorming 30.77% 46.15% 23.08% 0.00% 0.00% Stimulates control 1 2 3 4 5 Web resource finding 0.00% 0.00% 7.69% 15.38% 76.92% Internal content finding 0.00% 0.00% 15.38% 69.23% 15.38% Peer finding 0.00% 0.00% 15.38% 46.15% 38.46%
Conclusion
- DEPTHS
- Future work
Thank you! Questions?