In this chapter, we focus on young students’ development of a systems worldview while learning-by-doing about control systems and adaptive robots. Artefacts capable of adaptive behaviours are part of our everyday environment at home, school, in- and outdoor environments, and work and leisure places. Young children encounter these artefacts in the form of, e.g., sophisticated toys, computer-controlled games and devices, kitchen appliances, elevators and automatic doors, or traffic light systems. This new “breed” of human-mind-made devices, that began populating our world only a few decades ago, is characterized by purposeful functioning capabilities, autonomous decision-making, programmability, knowledge accumulation capabilities and adaptive behaviour—challenging children’s traditional and intuitive distinctions between the living and non-living realms; between inert and behaving agents; between the natural and the artificial. In the main sections of the chapter, following a brief presentation of the background for our work, we elaborate (based on evidence collected over the years in our studies) on three main issues: (1) young children’s overall perception of adaptive systems; (2) their detailed understanding of structural, functional and behavioural features of these systems; (3) implications of our research insights for supporting children’s learning of adaptive systems concepts and skills.

Over the past 50 years, since the introduction of the programmable floor turtle, the dyad learning/programming environment underwent several transformations. Each transformation brought about new questions regarding children's understanding, learning and programming performance. This paper describes results from a preliminary study analyzing the current transformation from desktop to mobile-based learning/programming by kindergarten children. Observations of children's performance and semi structured interviews with teachers were conducted. The findings unveiled three key themes, namely, changes in programmers' perspectives, changes in foci and learning patterns in different programming modes, and changes in patterns of collaboration among peers. The preliminary findings serve as basis for planning further systematic research on the design of mobile-based programming environments for kindergarten children.

Nowadays, we are surrounded by artifacts that are capable of adaptive behavior, such as electric pots, boiler timers, automatic doors, and robots. The literature concerning human beings’ conceptions of “traditional” artifacts is vast, however, little is known about our conceptions of behaving artifacts, nor of the influence of the interaction with such artifacts on cognitive development, especially among children. Since these artifacts are provided with an artificial “mind,” it is of interest to assess whether and how children develop a Theory of Artificial Mind (ToAM) which is distinct from their Theory of Mind (ToM). The study examined a new theoretical scheme named ToAM (Theory of Artificial Mind) by means of qualitative and quantitative methodology among twenty four 5-7 year old children from central Israel. It also examined the effects of interacting with behaving artifacts (constructing versus observing the robot’s behavior) using the “RoboGan” interface on children’s development of ToAM and their ToM and looked for conceptions that evolve among children while interacting with behaving artifacts which are indicative of the acquisition of ToAM. In the quantitative analysis it was found that the interaction with behaving artifacts, whether as observers or constructors and for both age groups, brought into awareness children’s ToM as well as influenced their ability to understand that robots can behave independently and based on external and environmental conditions. In the qualitative analysis it was found that participating in the intervention influenced the children’s ToAM for both constructors and for the younger observer. Engaging in building the robot’s behavior influenced the children’s ability to explain several of the robots’ behaviors, their understanding of the robot’s script-based behavior and rule-based behavior and the children’s metacognitive development. The theoretical and practical importance of the study is discussed.

A new theoretical scheme named ToAM (Theory of Artificial Mind) was examined by means of qualitative and quantitative methodology among twenty four 5-7 year old children from centr al Israel. The study also examined the effects of interacting with behaving artifacts (constructing versus observing the robot's behavior) using the "RoboGan" interface on children's development of ToAM and ToM and looked for conceptions that evolve among children while interacting with behaving artifacts which are indicative of the acquisition of ToAM. The quantitative analysis indicated that the interaction with behaving artifacts, for both age and condition groups brought into awareness children's ToM as well as influenced their ability to understand that robots can behave independently and based on external and environmental conditions. The qualitative analysis indicated that the engagement in building the robot's behavior influenced the constructors' ability to explain several of the robots' behaviors, their understanding of the robot's script-based behavior and rule-based behavior and the children's metacognitive development. The theoretical and practical importance of the study is discussed.

RUNNING HEAD: STUDENT KNOWLEDGE-REPRESENTATIONS Abstract Students, while acquiring as well as communicatingknowledge, are regularly engaged in processes related to the organization and representation of information. However, representationalskills and processes are not explicitly taught, and the process by which students learn and apply these skills is an issue still in need of systematic study. This paper describes an exploratory study on the acquisition and use of knowledge representation skills and structures by sixth graders, supported by a computer-based learning environment. The results indicate that these symbolic structures can be successfully taught, and that students using them in the context of instructionaltasks perform at the higher band of cognitive processes; that constructing computer,knowledge,bases affected the students’ abilities to analyze, organize and represent knowledge; that the students were able to create representations of considerable structural complexity and varied nature (e.g., “taxonomic”, “encyclopedic”, and “classification”trees) and content. As a corollary a series of issues which deserve a deeper and systematic inquiry are presented (e.g., the repertoire of symbol structures or schemas which are better candidates for teaching should be defined; the learning and application process of these intellectual tools should be traced; and the refinement process of these schemas once acquired and repeatedly used should be studied, along with their

Understanding the behavior of complex systems has become a focal issue for scientists in a wide range of disciplines. Making sense of a complex system should require that a student construct a network of concepts and principles about the learning complex phenomena. This paper describes part of a project about Learning-by-Modeling (LbM). Many features of complex systems make it difficult for students to develop deep understanding. Previous research indicates that involvement with modeling scientific phenomena and complex systems can play a powerful role in science learning. Some researchers argue with this view indicating that models and modeling do not contribute to understanding complexity concepts, since these increases the cognitive load on students. In this study we investigated the effect of different modes of involvement in exploring scientific phenomena using computer simulation tools, on students' mental model from the perspective of structure, behaviour and function. Quantitative and qualitative methods are used to report about 121 freshmen students that engaged in participatory simulations about complex phenomena, showing emergent, self-organized and decentralized patterns. Results show that LbM plays a major role in students' concept formation about complexity concepts.

Articulating thought in computer‐based media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an intelligent learning environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components that are capable of generating knowledge‐based feedback and virtual characters that enhance the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed. This article presents an overview of the DynaLearn system.

In recent years, children from a kindergarten in central Israel have been exposed to learning experiences in technology as part of the implementation of a curriculum based on technological thinking, including topics related to behaving-adaptive-artifacts (e.g., robots).This study aims to unveil children's stance towards behaving artifacts: whether they perceive these as psychological or engineering entities.Hence, their explanations were analyzed looking for their use of anthropomorphic or technological language.In contrast with previous findings, which reported on kindergarten-age children's tendency to adopt animistic and psychological perspectives, we have observed that the engagement in constructing the "anthropomorphic artifacts" behavior promoted the use of technological language and indicated the early development of a technological stance.The implications of the findings for the development of technology-related learning tasks in the kindergarten are discussed.

This paper describes a pedagogical approach aiming to support students' understanding of the structural and behavioural features of complex systems. The pedagogical approach is based on the gradual progression from concept mapping to conceptual modelling of systems. Studies conducted aimed to unveil students' conceptual understanding and development of a systems worldview. Instruments and scoring guides were developed for analysing both concepts maps and qualitative computermodels constructed by the students. Results show that the integrated approach supported junior-high-school students' understanding of the structural as well as the dynamic features of systems, and their ability to explore system behaviours under changing conditions.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

This paper describes part of a study about Learning-by-Modeling (LbM).Previous research indicates that involvement with modeling scientific phenomena and complex systems can play a powerful role in science learning.Some researchers argue with this view indicating that models and modeling do not contribute to understanding complexity concepts, since these increases the cognitive load on students.This study will investigate the effect of different modes of involvement in exploring scientific phenomena using computer agent modeling tools, on students' understanding of complexity concepts.Quantitative and qualitative methods are used to report about 121 freshmen students that engaged in participatory simulations about complex phenomena, showing emergent, self organized and decentralized patterns.Results show that LbM plays a major role in students' concept formation about complexity concepts.

Project title: DynaLearn - Engaging and informed tools for learning conceptual system knowledge.

This paper describes part of a project about Learning- by-Modeling (LbM). Studying complex systems is increasingly important in teaching and learning many science domains. Many features of complex systems make it difficult for students to develop deep understanding. Previous research indicates that involvement with modeling scientific phenomena and complex systems can play a powerful role in science learning. Some researchers argue with this view indicating that models and modeling do not contribute to understanding complexity concepts, since these increases the cognitive load on students. This study will investigate the effect of different modes of involvement in exploring scientific phenomena using computer simulation tools, on students' mental model from the perspective of structure, behavior and function. Quantitative and qualitative methods are used to report about 121 freshmen students that engaged in participatory simulations about complex phenomena, showing emergent, self-organized and decentralized patterns. Results show that LbM plays a major role in students' concept formation about complexity concepts. Keywords—Complexity, Educational technology, Learning by modeling, Mental models