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The purpose of this paper is to examine a central need of students who are blind: the ability to access science curriculum content.

Agent-based modeling is a relatively new computational modeling paradigm that models complex dynamic systems. NetLogo is a widely used agent-based modeling language that enables exploration and construction of models of complex systems by programming and running the rules and behaviors. Sonification of variables and events in an agent-based NetLogo computer model of gas in a container is used to convey phenomena information. This study examined mainly two research topics: the scientific conceptual knowledge and systems reasoning that were learned as a result of interaction with the listen-to-complexity (L2C) environment as appeared in answers to the pre- and post-tests and the learning topics of kinetic molecular theory of gas in chemistry that was learned as a result of interaction with the L2C environment. The case study research focused on A., a woman who is adventitiously blind, for eight sessions.

The participant successfully completed all curricular assignments; her scientific conceptual knowledge and systems reasoning became more specific and aligned with scientific knowledge.

A practical implication of further studies is that they are likely to have an impact on the accessibility of learning materials, especially in science education for students who are blind, as equal access to low-cost learning environments that are equivalent to those used by sighted users would support their inclusion in the K-12 academic curriculum.

The innovative and low-cost learning system that is used in this research is based on transmittal of visual information of dynamic and complex systems, providing perceptual compensation by harnessing auditory feedback. For the first time the L2C system is based on sound that represents a dynamic rather than a static array. In this study, the authors explore how a combination of several auditory representations may affect cognitive learning ability.

The purpose of this paper is to examine the ability of high-functioning autistic (HFA) children to programme robotic behaviour and sought to elucidate how they describe and construct a robot’s behaviour using iconic programming software.

The robotic learning environment is based on the iPad, an iconic programming app (KinderBot), and EV3. Two case studies, of A. and N., both HFA children of average age 10.5, are the focus of this research.

The research revealed how the participants succeeded in programming the behaviour of an “other” at different programming complexity levels (from simple action to combinations of states of two binary sensors and rule with subroutine). A transformation from procedural to declarative description was also found.

This research on the ability of HFA children to programme robotic behaviour yielded results that can be implemented in K-12 education. Furthermore, learning to programme robots and understand how robotic technologies work may help HFA children to better understand other technology in their environment.

In this research, the authors present an innovative approach that for the first time enables HFA children to “design” the behaviour of smart artefacts to use their sensors to adapt in accordance with the environment. For most HFA children, this would be the first opportunity to “design” the behaviour of the other, as opposed to oneself, since in most of their experience they have been largely controlled by another person.