FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2361385708> ?p ?o ?g. }

• W2361385708 endingPage "71" @default.
• W2361385708 startingPage "48" @default.
• W2361385708 abstract "In this paper, we introduce a methodology to design robot-oriented generative learning objects (GLOs) that are, in fact, heterogeneous meta-programs to teach computer science (CS) topics such as programming. The methodology includes CS learning variability modelling using the feature-based approaches borrowed from the SW engineering domain. Firstly, we define the CS learning domain using the known educational framework TPACK (Technology, Pedagogy And Content Knowledge). By learning variability we mean the attributes of the framework extracted and represented as feature models with multiple values. Therefore, the CS learning variability represents the problem domain. Meta-programming is considered as a solution domain. Both are represented by feature models. The GLO design task is formulated as mapping the problem domain model on the solution domain model. Next, we present the design framework to design GLOs manually or semi-automatically. The multi-level separation of concepts, model representation and transformation forms the conceptual background. Its theoretical background includes: (a) a formal definition of feature-based models; (b) a graph-based and set-based definition of meta-programming concepts; (c) transformation rules to support the model mapping; (d) a computational Abstract State Machine model to define the processes and design tool for developing GLOs. We present the architecture and some characteristics of the tool. The tool enables to improve the GLO design process significantly (in terms of time and quality) and to achieve a higher quality and functionality of GLOs themselves (in terms of the parameter space enlargement for reuse and adaptation). We demonstrate the appropriateness of the methodology in the real teaching setting. In this paper, we present the case study that analyses three robot-oriented GLOs as the higher-level specifications. Then, using the meta-language processor, we are able to produce, from the specifications, the concrete robot control programs on demand automatically and to demonstrate teaching algorithms visually by robot's actions. We evaluate the approach from technological and pedagogical perspectives using the known structural metrics. Also, we indicate the merits and demerits of the approach. The main contribution and originality of the paper is the seamless integration of two known technologies (feature modelling and meta-programming) in designing robot-oriented GLOs and their supporting tools." @default.
• W2361385708 created "2016-06-24" @default.
• W2361385708 creator A5020757996 @default.
• W2361385708 creator A5055008635 @default.
• W2361385708 creator A5072870051 @default.
• W2361385708 creator A5076521096 @default.
• W2361385708 date "2016-11-01" @default.
• W2361385708 modified "2023-09-27" @default.
• W2361385708 title "Model-driven processes and tools to design robot-based generative learning objects for computer science education" @default.
• W2361385708 cites W1509940907 @default.
• W2361385708 cites W1965532007 @default.
• W2361385708 cites W1988063011 @default.
• W2361385708 cites W1991319391 @default.
• W2361385708 cites W1996275460 @default.
• W2361385708 cites W2001932992 @default.
• W2361385708 cites W2006924974 @default.
• W2361385708 cites W2021310992 @default.
• W2361385708 cites W2028336529 @default.
• W2361385708 cites W2033194964 @default.
• W2361385708 cites W2038152467 @default.
• W2361385708 cites W2039159340 @default.
• W2361385708 cites W2039544382 @default.
• W2361385708 cites W2050525049 @default.
• W2361385708 cites W2057405025 @default.
• W2361385708 cites W2066172099 @default.
• W2361385708 cites W2072325005 @default.
• W2361385708 cites W2073691582 @default.
• W2361385708 cites W2093810051 @default.
• W2361385708 cites W2096604710 @default.
• W2361385708 cites W2105335341 @default.
• W2361385708 cites W2106376880 @default.
• W2361385708 cites W2110323649 @default.
• W2361385708 cites W2111735684 @default.
• W2361385708 cites W2115762428 @default.
• W2361385708 cites W2116844130 @default.
• W2361385708 cites W2130876928 @default.
• W2361385708 cites W2139872812 @default.
• W2361385708 cites W2147070375 @default.
• W2361385708 cites W2147698442 @default.
• W2361385708 cites W2147903644 @default.
• W2361385708 cites W2148528560 @default.
• W2361385708 cites W2320515549 @default.
• W2361385708 cites W2397642553 @default.
• W2361385708 cites W3161441266 @default.
• W2361385708 cites W78129307 @default.
• W2361385708 doi "https://doi.org/10.1016/j.scico.2016.03.009" @default.
• W2361385708 hasPublicationYear "2016" @default.
• W2361385708 type Work @default.
• W2361385708 sameAs 2361385708 @default.
• W2361385708 citedByCount "15" @default.
• W2361385708 countsByYear W23613857082017 @default.
• W2361385708 countsByYear W23613857082018 @default.
• W2361385708 countsByYear W23613857082019 @default.
• W2361385708 countsByYear W23613857082020 @default.
• W2361385708 countsByYear W23613857082022 @default.
• W2361385708 countsByYear W23613857082023 @default.
• W2361385708 crossrefType "journal-article" @default.
• W2361385708 hasAuthorship W2361385708A5020757996 @default.
• W2361385708 hasAuthorship W2361385708A5055008635 @default.
• W2361385708 hasAuthorship W2361385708A5072870051 @default.
• W2361385708 hasAuthorship W2361385708A5076521096 @default.
• W2361385708 hasConcept C119857082 @default.
• W2361385708 hasConcept C134306372 @default.
• W2361385708 hasConcept C154945302 @default.
• W2361385708 hasConcept C162324750 @default.
• W2361385708 hasConcept C176217482 @default.
• W2361385708 hasConcept C184408114 @default.
• W2361385708 hasConcept C207685749 @default.
• W2361385708 hasConcept C21547014 @default.
• W2361385708 hasConcept C33923547 @default.
• W2361385708 hasConcept C36503486 @default.
• W2361385708 hasConcept C41008148 @default.
• W2361385708 hasConcept C92548554 @default.
• W2361385708 hasConceptScore W2361385708C119857082 @default.
• W2361385708 hasConceptScore W2361385708C134306372 @default.
• W2361385708 hasConceptScore W2361385708C154945302 @default.
• W2361385708 hasConceptScore W2361385708C162324750 @default.
• W2361385708 hasConceptScore W2361385708C176217482 @default.
• W2361385708 hasConceptScore W2361385708C184408114 @default.
• W2361385708 hasConceptScore W2361385708C207685749 @default.
• W2361385708 hasConceptScore W2361385708C21547014 @default.
• W2361385708 hasConceptScore W2361385708C33923547 @default.
• W2361385708 hasConceptScore W2361385708C36503486 @default.
• W2361385708 hasConceptScore W2361385708C41008148 @default.
• W2361385708 hasConceptScore W2361385708C92548554 @default.
• W2361385708 hasLocation W23613857081 @default.
• W2361385708 hasOpenAccess W2361385708 @default.
• W2361385708 hasPrimaryLocation W23613857081 @default.
• W2361385708 hasRelatedWork W2743342830 @default.
• W2361385708 hasRelatedWork W2961085424 @default.
• W2361385708 hasRelatedWork W3046775127 @default.
• W2361385708 hasRelatedWork W3170094116 @default.
• W2361385708 hasRelatedWork W4205958290 @default.
• W2361385708 hasRelatedWork W4285260836 @default.
• W2361385708 hasRelatedWork W4286629047 @default.
• W2361385708 hasRelatedWork W4306321456 @default.
• W2361385708 hasRelatedWork W4306674287 @default.
• W2361385708 hasRelatedWork W4224009465 @default.

• next