algorithmic modeling for Rhino
As computer education is considered one of the fundamental aspects of science education, the range of educational opportunities to introduce algorithms to students has been expanded. An increasing number of universities have implemented an algorithmic curriculum for non-computer science graduates, as algorithmic education is expected to promote logical thinking and general problem formulation and skills.
Typically, an algorithm course is organized in the form of classroom lectures and is accompanied by a related programming course, organized as an exercise to confirm and establish a better understanding of the content learned in the lectures, according to wowessays reviews. Thus, learning through instruction in lectures and establishing understanding through the construction of an exercise cycle.
The cycle allows students to solve problems in the formal of algorithm and understand the algorithm as the essence of the program code. However, according to our classroom experience, many students are unable to develop the learning style needed for this cycle and come to a halt. We believe that this is the result of continuing to practice programming without the proper understanding of the algorithm.
To date, several learning support systems have been developed to support students beginning to understand algorithms (Pears et al., 2007). These systems provide for how to process the program's code objects and algorithms (that is, the world of the target domain) and reproduce the behavior of the code and algorithms. The presentation of these systems in classrooms should allow students to develop a better understanding of the algorithm (Robbins et al, 2003).
However, the expansion of the range of educational opportunities has increasingly that teachers teach students with different levels of prior knowledge. Depending on the students' prior knowledge, the teacher must adjust the content or intent of the instruction, such as the point at which the student must focus on the algorithm or program, or the degree of abstraction or generalization of the instruction. Almost all current systems reject these variations in the teacher's intentions and view the target domain world with a fixed view policy.
In addition, although existing systems focus on reproducing the entire flow of algorithmic behavior, knowledge related to algorithms is also important in learning algorithms. For example, the properties of an algorithm, such as the number or changes in the classification tasks, are an important learning objective.
In this article, we describe the classroom practice of our algorithm curriculum, based on these ideas. We introduced the classroom to the learning support system developed in our previous work (Kogur et al., 2014). Our system visualizes the world of the target domain according to a policy defined by the teacher.
In addition, the content of our practice includes not only using our system to learn about the entire flow that represents the behavior of an algorithm, but also discovering learning about the properties of the algorithm.
For example, a teacher can draw a matrix object using a horizontal layout when the instruction is classified in the target matrix, while the teacher can draw it using a vertical layout for the stack.