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We focus in particular on how such situations can be detected and presented to the user. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. This survey should be equally helpful for language engineering researchers and language engineers alike. Likewise, we focused on identifying the main components of block-based environments, how they are engineered, and how they are used. In particular, we provide a helpful inventory of block-based editors for end-users on different topics and domains. So we conducted a systematic literature review on block-based environments by studying 152 papers published between 20, and a non-systematic tool review of 32 block-based environments. However, it is unclear how these types of programming environments are developed and used in general.

As a result they are often associated with tools for children or young learners.

Part of the current popularity of block-based environments is thanks to Scratch. The ease of use comes thanks to their intuitive graphical representation and structural metaphors (jigsaw-like puzzles) to display valid combinations of language constructs to the users. We discuss the implications of the findings within the framework of the distributed cognition theory and point out directions for future research.īlock-based environments are visual programming environments, which are becoming more and more popular because of their ease of use.

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On the other hand, textual programming takes an upper hand when it comes to providing an authentic learning experience as well as supporting efficient source code management in collaborative development. They could quickly develop functional apps with complex features.

It allowed beginners to focus on the logic of apps without facing the barrier of too many technical details. Our findings show that a visual programming environment was useful even for university students. The analysis is based on instructors’ reflection as well as feedback from students.

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This empirical study compares the merits and demerits of using textual programming environments (e.g., Android Studio) and visual programming environments (e.g., MIT App Inventor) in an Android app development course targeting first year university students in a faculty of engineering. While the benefits of using visual programming environments at the level of K-12 education have been well-studied, little is known about the plausibility of using visual programming in higher education settings. Linguistic evidence implies that this power is more than necessary to compose the meanings of sentences in natural language from those of their grammatical constituents. This rearrangement uses both the siding and part of the track as working memories, shuffling cars between them, and so it relies on the power of a linear-bounded computer. Kolmogorov complexity predicts the relative difficulty of abducing such programs – for easy rearrangements, such as reversing the order of the cars, to difficult ones, such as splitting a train in two and interleaving the two resulting halves (equivalent to a faro shuffle). They rely on such loops to compute recursive functions for rearranging the order of cars in trains on a track with a siding. Participants in our experiments spontaneously simulate loops of instructions in kinematic mental models. Our theory aims to explain how naive individuals devise informal programs in natural language, and is itself implemented in a computer program that creates programs. Some of these programs are semi-circular too, but they needn’t be, because repeated loops of instructions can compute any recursive function. Any function that is computable – and many are not – can be computed in an infinite number of distinct programs. In the logic of computability, a function maps one or more sets to another, and it can have a recursive definition that is semi-circular, i.e., referring in part to the function itself. This article presents a theory of recursion in thinking and language.