Based on a smart home system model abstracted from the perspective of implementing users' household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly.
Learners understand the structure of the world by observing logical data structures visualized automatically by the system based on the provided algorithm or program code.
Many existing systems such as Jeliot 3 (Moreno, Myller, Sutinen, & Ben-Ari, 2004; Ben-Ari et al., 2011), NoobLab (Neve, Hunter, Livingstone, & Orwell, 2012), and LEPA (Yamashita et al., 2016) reproduce the behaviors of programs by visualizing logical data structures processed by target program and their changes made by statement execution.
Program visualization systems support novice learners to overcome these difficulties by visualizing the logical data structures processed by target programs.
The previous methodological steps of transition are followed by the transformation of the logical data model into a physical database.
A part of our logical data warehouse schema is depicted in Fig. 1.
TRAKRA 2 reproduces algorithm behaviors by learner's GUI manipulations on visualized logical data structures, and hence, it is expected to have an effect to understand algorithms.
Both TTO and SG methods yielded logical data (i.e., utilities in a reasonable range, with logical discrimination among health states).
Supervaluationism is also haunted by a logical analogy.
Moreover, inhibiting influences are represented by a logical 'NOT'NOT
