Computer Science Problem Solving Research Articles

Development and Implementation of Visualization of CPU Components using AR

Augmented Reality (AR) technology presents innovative solutions for visualizing complex concepts and systems. This paper investigates the utilization of AR in the visualization of CPU (Central Processing Unit) components, with the objective of augmenting comprehension and learning within computer architecture. By developing an AR application, CPU components are depicted in three dimensions, enabling users to interactively explore and comprehend their functionalities and interconnections. The paper delineates the design and execution of the AR system and discusses its potential advantages for education, training, and technical problem-solving in computer science and engineering. Keywords: Augmented Reality, CPU Components, Visualization, Computer Architecture.

Empowering Problem-solving in Computer Science: A Need Analysis for a Computational Thinking Mobile Learning Application

The advancement of digital technology has revolutionized education across all levels, giving rise to digital learning as a novel pedagogical environment. Among the subjects at the forefront of this transformation is computer science, introduced in matriculation colleges to supplant traditional information technology courses. However, computer science poses inherent complexity, demanding abstract thinking and diverse problem-solving methodologies. Computational thinking (CT) emerges as a promising approach to address these challenges, recognized as a vital skillset for fostering innovation in digital technology among students. The ubiquity of smartphones and mobile internet facilitates the adoption of mobile learning, offering students the flexibility to access educational content anytime, anywhere. Consequently, this need analysis study aims to assess current teaching practices in computer science and identify the need for mobile learning applications that integrate CT as a problem-solving technique among matriculation college students. Interviews with three computer science lecturers revealed a reliance on conventional pedagogy with limited blended learning approaches through college portals. Notably, specific techniques for imparting programming problem-solving skills were lacking. Nonetheless, it is noteworthy that all instructors recognized the considerable potential of mobile learning applications in effectively engaging students and facilitating the development of problem-solving proficiency.

There’s an App for That, and I Made It

The Thunkable online platform is an easy-to-use resource for creating apps for mobile devices. Computational thinking is at the heart of problem solving in computer science, and research suggests students’ computational thinking improves when they use simple block coding systems similar to the format used for Thunkable.

Empirical validation and application of the computing attitudes survey

Student attitudes play an important role in shaping learning experiences. However, few validated instruments exist for measuring student attitude development in a discipline-specific way. In this paper, we present the design, development, and validation of the computing attitudes survey (CAS). The CAS is an extension of the Colorado Learning Attitudes about Science Survey and measures novice to expert attitude shifts about the nature of knowledge and problem solving in computer science. Factor analysis with a large, multi-institutional data-set identified and confirmed five subscales on the CAS related to different facets of attitudes measured on the survey. We then used the CAS in a pre–post format to demonstrate its usefulness in studying attitude shifts during CS1 courses and its responsiveness to varying instructional conditions. The most recent version of the CAS is provided in its entirety along with a discussion of the conditions under which its validity has been demonstrated.

Ant Colony Optimization: A Swarm Intelligence based Technique

The growing complexity of real-world problems has motivated computer scientists to search for efficient problemsolving methods. Divide and conquer techniques are one way to solve large and difficult problems. Division of large work into smaller parts and combining the solution of small problems to get the solution of large one has been a practice in computer research since long time. Swarm also exhibits the behavior of division of work and cooperation to achieve difficult tasks. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples which show that nature has been an unending source of inspiration. Artificial Swarm/Ant foraging utilizes various forms of indirect communication, involving the implicit transfer of information from agent to agent through modification of the environment. Using this approach, one can design efficient searching methods that can find solution to complex optimization problems. Over times, several algorithms have been designed and used that are inspired by the foraging behavior of real ants colonies to find solutions to difficult problems. In this paper the idea of Ant Colonies is presented with brief introduction to its applications in different areas of problem solving in computer science.

On varying perspectives of problem decomposition

The most common decomposition perspective in computer science problem-solving is 'top-down', in which the problem at hand is divided into 'smaller' sub-problems. Yet there are more decomposition perspectives. In this paper we illuminate three additional perspectives and demonstrate their didactic value. The presentation is displayed in an apprenticeship manner, through different approaches for solving an intriguing algorithmic challenge - the problem of finding majority. Each of the three perspectives is tied to a variety of algorithmic problems and solutions, and elaborated as a pedagogical tool for teaching algorithms.

How mathematical thinking enchances computer science problem solving

There are deep connections between algorithmic and mathematical thinking. Both construct "systems" --- computing systems in the algorithmic case, intellectual ones in mathematics --- from simple primitives. As Knuth notes in the preface to The Art of Computer Programming, "The construction of a computer program from a set of basic instructions is very similar to the construction of a mathematical proof from a set of axioms" [1]. Other connections include similar ways of organizing primitives into larger structures ( e.g., recursion in algorithms, recursion and induction in math; conditionals in algorithms, definition in cases and proof by cases in math), similar ways of using abstraction to manage complexity, and an underlying reliance on logic. In short, mathematics is not merely a tool for limited areas of computer science, it is a mindset that fundamentally improves one's ability to devise and implement algorithms. Computer science students therefore need to exercise their mathematical as well as their computational abilities, and computer science educators need to help students use both ways of thinking to solve computing problems.This panel illustrates specific ways in which mathematical reasoning enhances algorithmic problem solving, and provides educators with concrete examples and resources to use in their own teaching. Each panelist will present an exercise, classroom example, or similar item, from their own experience, and will demonstrate ways in which mathematical reasoning helps one solve and/or understand it. The audience will be invited to contribute their own examples and to comment further on the role of mathematical thinking in computer science problem solving.The panelists' and audience members' examples will be collected on a Web page for continuing reference. A prototype of this page is at http://www.cs.geneseo.edu/~baldwin/math-thinking/examples.html.