Authoring Tools to Specialize and Adapt Smart LOs

The main distinguishing feature of the smart LO approach is the realization of the concept of producing and adapting the teaching content semi-automatically, or even automatically. The automation never comes for free. On the other hand, the use of the SLO design tool enables us to develop the highly reusable entities. At the development stage, for example, we are able to ensure reusability due to the use of the design paradigm known as design for and design with (see Chap. 9). In this chapter, design with reuse can be technologically interpreted as the SLO problem. As the designed SLO, in fact, is the context-driven meta-specification implementing a wide scale of learning variability, indeed there is a large space for adaptation. In the pure technological sense, the adaptation is a specific transformation process. In the case of using SLO specification, we are able to carry out adaptation through refactoring or specialization (see the discussion on the term issues in Sect. 7.2).

[1]  Euiho Suh,et al.  Context-aware systems: A literature review and classification , 2009, Expert Syst. Appl..

[2]  Paul Dourish,et al.  What we talk about when we talk about context , 2004, Personal and Ubiquitous Computing.

[3]  BettiniClaudio,et al.  A survey of context modelling and reasoning techniques , 2010 .

[4]  Tom Mens,et al.  A survey of software refactoring , 2004, IEEE Transactions on Software Engineering.

[5]  Vytautas Stuikys,et al.  Refactoring of Heterogeneous Meta-Program into k-stage Meta-Program , 2014, Inf. Technol. Control..

[6]  Manju Bhaskar,et al.  Context Aware E-Learning System with Dynamically Composable Learning Objects , 2010 .

[7]  Doo-Kwon Baik,et al.  An Approach to Analyzing Commonality and Variability of Features using Ontology in a Software Product Line Engineering , 2007, 5th ACIS International Conference on Software Engineering Research, Management & Applications (SERA 2007).

[8]  Dave A. Thomas Refactoring as Meta Programming , 2005, J. Object Technol..

[9]  Qun Jin,et al.  Putting adaptive granularity and rich context into learning objects , 2010, 2010 9th International Conference on Information Technology Based Higher Education and Training (ITHET).

[10]  Erik Duval,et al.  Context-Aware Recommender Systems for Learning: A Survey and Future Challenges , 2012, IEEE Transactions on Learning Technologies.

[11]  Lizhen Liu,et al.  Construction of a student model in contextually aware pervasive learning , 2009, 2009 Joint Conferences on Pervasive Computing (JCPC).

[12]  Dragan Gasevic,et al.  Ontologies for Effective Use of Context in e-Learning Settings , 2007, J. Educ. Technol. Soc..

[13]  Pasi Silander,et al.  Criteria for pedagogical reusability of learning objects enabling adaptation and individualised learning processes , 2004, IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings..

[14]  Alexander Egyed,et al.  From Requirements to Features: An Exploratory Study of Feature-Oriented Refactoring , 2011, 2011 15th International Software Product Line Conference.

[15]  Vincent P. Wade,et al.  Supporting Users in Creating Pedagogically Sound Personalised Learning Objects , 2008, AH.

[16]  Jann E. Freed,et al.  Learner-Centered Assessment on College Campuses: Shifting the Focus from Teaching to Learning , 1999 .

[17]  Jadwiga Indulska,et al.  A survey of context modelling and reasoning techniques , 2010, Pervasive Mob. Comput..

[18]  Walid Taha,et al.  A Gentle Introduction to Multi-stage Programming , 2003, Domain-Specific Program Generation.

[19]  Marija Katić,et al.  Towards an appropriate software refactoring tool support , 2009 .

[20]  Mark Harman,et al.  Why Source Code Analysis and Manipulation Will Always be Important , 2010, 2010 10th IEEE Working Conference on Source Code Analysis and Manipulation.

[21]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[22]  Tom Mens,et al.  Analysing refactoring dependencies using graph transformation , 2007, Software & Systems Modeling.

[23]  Ivan Porres,et al.  Rule-based update transformations and their application to model refactorings , 2005, Software & Systems Modeling.

[24]  Uwe Aßmann,et al.  Role-based generic model refactoring , 2010, MODELS'10.

[25]  Olga C. Santos,et al.  A Standards-based Modelling Approach for Dynamic Generation of Adaptive Learning Scenarios , 2008, J. Univers. Comput. Sci..

[26]  Erik Duval,et al.  User Context and Personalized Learning: a Federation of Contextualized Attention Metadata , 2010, J. Univers. Comput. Sci..

[27]  Wolfgang Roller,et al.  Using learning maps for visualization of adaptive learning path components , 2012 .

[28]  Don S. Batory,et al.  Automating Feature-Oriented Refactoring of Legacy Applications , 2007, WRT.

[29]  Walid Taha,et al.  Multi-Stage Programming: Its Theory and Applications , 1999 .

[30]  Andreas Zimmermann,et al.  An Operational Definition of Context , 2007, CONTEXT.

[31]  Terry Winograd,et al.  Architectures for Context , 2001, Hum. Comput. Interact..

[32]  Alois Ferscha,et al.  Context aware systems , 2006, MSWiM '06.

[33]  Egon Börger,et al.  High Level System Design and Analysis Using Abstract State Machines , 1998, FM-Trends.

[34]  Kyo Chul Kang,et al.  Usage Context as Key Driver for Feature Selection , 2010, SPLC.

[35]  Tom Boyle,et al.  Context and deep learning design , 2012, Comput. Educ..

[36]  Robertas Damasevicius,et al.  Meta-Programming and Model-Driven Meta-Program Development , 2012, Advanced Information and Knowledge Processing.

[37]  Oscar Díaz,et al.  Feature refactoring a multi-representation program into a product line , 2006, GPCE '06.

[38]  Ming-Wen Tong,et al.  A service context model based on ontology for content adaptation in E-learning , 2010, 2010 IEEE Frontiers in Education Conference (FIE).