This paper presents an assessment designed to measure computational thinking skills of fourth grade students. The students in two public schools were given a set of Scratch programming challenges. The SOLO taxonomy was used to classify student programming responses based on the degree of understanding of the problem structure. The majority of fourth grade students from one school demonstrated the ability to synchronize the costume and motion of a single character sprite, as well as synchronize a basic conversation between multiple sprites. However, they struggled to integrate motion and costume changes into a multi-sprite conversation. The majority of fourth grade students from the second school, where reading and math comprehension scores fall below the state minimum requirements for proficiency, were unable to demonstrate a basic understanding of the programming tasks. The results suggest that fourth grade students who read at grade level are capable of understanding the relationships between multiple concerns within a single script, along with synchronizing a single concern across multiple scripts, but are challenged when synchronizing multiple concerns across multiple scripts.

We introduce the Progression of Early Computational Thinking (PECT) Model, a framework for understanding and assessing computational thinking in the primary grades (Grades 1 to 6). The model synthesizes measurable evidence from student work with broader, more abstract coding design patterns, which are then mapped onto computational thinking concepts.

In this paper we describe emergence-oriented programming (EOP), a novel, human-centric technique to engineer swarm algorithms at a higher level of complexity than those developed with simple reactive agents. The process is iterative, building modules of behavior that can be layered to produce solutions that converge faster than reactive swarms to the desired emergent goal. The layers are modular and can be independently applied, mirroring the arbitrarily nested cognitive model proposed by Baas and Emmeche. The layers are produced by external observers recognizing and reinforcing patterns within swarms that are not visible at lower levels. Each layer builds upon the previous one leading to emergence, but the entire hierarchy can be mechanically collapsed into executable if-then rules based on robot primitives. We demonstrate portions of this technique to improve on the reactive swarm approach for solving the 4-color mapping problem

This paper presents a new technique, the hypothesis swarm problem-solving technique (HSPT), that allows swarms to operate on more complex problems in a general way that produces solutions faster than traditional swarms by emerging compatible sub-solutions cooperatively. Instead of agents simply reacting to their own stimuli, they produce hypotheses about their environment to resolve discovered conflicts. As they gather supporting or refuting evidence for their hypothesis, we force a higher level of social interaction - requiring them to compare their evidence with other agents. They reward compatible hypotheses and devalue conflicting ones, encouraging acceptance of mutually supporting hypotheses. We demonstrate the effectiveness of this approach on graph-coloring and round-robin scheduling problems and also compare its efficiency with other swarm techniques including brute force, reactive swarms and ant colony optimization algorithms.

A collection of design patterns was described by E. Gamma et al. (1994). Each pattern ensures that a certain system aspect can vary over time, for example the operations that can be applied to an object or the algorithm of a method. The patterns are described by constructs such as the inheritance and reference relations, attempting to emulate more dynamic relationships. As a result, the design patterns demonstrate how awkward it is to program natural concepts of evolution when using a traditional object oriented language. We present a new relation between classes: the context relation. It directly models dynamic evolution, and it is meaningful at both the design and implementation level. At the design level we extend the Unified Modeling Language (UML) to include the context relation as a new form of arrow between classes. At the implementation level we present a small extension of Java. The context relation introduces a new form of dynamic binding that serves as a replacement to delegation. We demonstrate how the context relation can be used to easily model and program numerous design patterns.

No abstract available.

A collection of design patterns was described by Gamma, Helm, Johnson, and Vlissides in 1994. Recognizing that designs change, each pattern ensures that a certain system aspect can vary over time such as the operations that can be applied to an object or the algorithm of a method. The patterns are described by constructs such as the inheritance and reference relations, attempting to emulate more dynamic relationships. As a result, the design patterns demonstrate how awkward it is to program natural concepts of behavioral evolution when using a traditional object-oriented language.In this paper we present a new relation between classes: the context relation. It directly supports behavioral evolution, and it is meaningful at the analysis, design, and implementation level. At the design level we picture a context relation as a new form of arrow between classes. At the implementation level we use a small extension of C++. The basic idea is that if class C is context-related to a base class B, then B-objects can get their functionality dynamically altered by C-objects. Our language construct for doing this is a generalization of the method update in Abadi and Cardelli's imperative object calculus. A C-object may be explicitly attached to a B-object, or it may be implicitly attached to a group of B-objects for the duration of a method invocation. We demonstrate how the context relation can be used to easily model and program the Adapter, Bridge, Chain of Responsibility, Decorator, Iterator, Observer, State, Strategy, and Visitor patterns.