The Effects of Mind Maps on Computational Thinking

Mind Maps (MM) have proven to be a practical approach that promotes meaningful learning in various domains. Yet, few works exist that investigate employing MM to blend CT across curricula. In this paper, we developed a MM approach named Storyboard-tree to transform "Standard/traditional" slides (SS) to the MM structure. Storyboard-tree associates the information by creating a story that chains the data with ideas and concepts which lead from the first to next and so on. The applied materials are two models in an Introduction to Computer Science (CS) course. The study utilizes two sections: one is taught with MM, and the other with SS. The observed academic results and the acceptance rate of the students and the instructors were encouraging. MM with freshman show statically significant self-efficiency scores with an approximate 50% better performance than with SS in the Algorithm concept, while all students show a statistically similar trend in the knowledge gained as well as the fondness of the approach through the self-efficiency scores. Instructor satisfaction tends to go more towards the SS approach seeing the MM implementation as not mature enough. However, the investigation concludes that the mind map technique is a feasible way to deliver CT concepts, thereby a practical approach to integrate CT into the curriculum. Introduction Since 2006 the popularity of computational thinking (CT) skills for solving problems by adopting the theoretical concepts of computer science has been increasing substantially, leading to an increase in the amount of research and experiments on the CT method. Yet, there are limited numbers of inquiry investigate approaches to incorporated CT into a curriculum. Betül Czerkawski researched ways to integrate CT across all curriculum, through surveying instructional CT designers. She constructed the survey using the ADDIE instructional design model. One of her findings showed that the Mind Map(s) (MM) strategy can establish a better connection between CT and instructional design [1]; however, very little research existed to investigate the correlation. A MM is a form of knowledge representation about a topic. It has a straightforward organizational structure that spreads from the center, including words, lines, colors, and pictures, toward an attractive, memorable diagram [2]. Previous work has shown that Mind Maps is an effective way to promote meaningful learning in various domains, including Digital Forensics [3], Cybersecurity [4] [5], Bioinformatics [6], Education [7] and more. The primary objective of this study is to investigate the relation between MM and CT through introductory CS curriculum. Considering the nature of CS tasks, practical computing skills can be developed while nurturing an understanding of how the CT process can be applied. We propose a Storyboard-tree to associate information based on the Chain Association Method, which chains items inside a memorable story, promoting retrieval within the flow of a story. Thoughtfully planned MM efficiently direct one idea to another. Although a series of information can be reasonably remembered using only a connection between two ideas, matching the links within a story minimizes the potential of forgetting one respect and thereby omitting the rest of the list. Furthermore, Danny P. Wallace used the relationship, data-to-information-toknowledge-to-wisdom (AKA DIKW pyramid), to explain a topic. The DIKW pyramid maps data such as words, numbers, and images into sentences and concepts that hold meaning and purpose is defined as information, connecting the information to relationships leads to knowledge, and applying knowledge to make judgments and decisions without thought is wisdom [8]. Moving from one level to another, looking at the data to draw some conclusions, then looking at it from different angles analyzing it piece by piece to see how those pieces related to each other is critical thinking. MM technique through which the first three levels of the DIKW pyramid can be achieved, by using data as concepts linked by relationship to produce a highly consistent diagram. To serve the study’s purpose, MMs were integrated into a computer science course at Kansas State University. MM slides were created from the lecture materials using a Storyboard-tree, and students in the course were divided into a control group (classic method) and a research group (MM method). Study results showed success with the MM approach for juniors and first-year students with CT concepts such as algorithm and control flow. Additionally, freshmen demonstrated improved performance in decomposition and incremental and iterative concepts, showing that MM can be a practical approach for integrating CT into a curriculum. Background and Related Work Computational Thinking CT has become a mainstream but dates back to the 1980s, where Seymour Papert proposed the idea of CT in his book Mindstorms: Children, Computers, and Powerful Ideas. He developed the programming language Logo for his envisioned learning environment Mathland, in which students explore and use abstract concepts concretely [9]. His vision inspired numerous researchers and educators who thought his idea was an “alternative to the prevailing technocentric and behaviorist notions of computer-aided instruction” [10]. Then in 2006, Jeanette Wing originated a discussion about the use of CT across all disciplines. She explored some fundamental questions of what computer science is and how CT could solve human problems. She also argued that including computing into all disciplines allows researchers to uncover new approaches to problem-solving. She defines CT as “solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science” [11]. In 2011, Wing expanded her definition of CT to be ”thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can effectively be carried out by an information-processing agent” [12]. Her definition included smart agents that promote the use of computer science concepts in non-computer science fields. Consequently, CT gained increased attention from the research community, although no unified definition of CT has yet been developed. Barr and Stephenson asserted that automation could be used to solve problems in a wide range of educational settings. They claimed that CT concepts such as automation, algorithms and procedures, abstraction, problem decomposition, parallelization, simulation, and data (representation, analysis, collection) could supplement and enhance teaching methods [13]. In contrast, Selby and Woollard defined CT concepts as a mental process for problem-solving. The authors considered it to be a cognitive process for humans only, not machines, and their study focused on abstraction, decomposition, algorithmic thinking, evaluation, and generalization [14]. Brennan and Resnick defined CT from programming contexts, providing an important three-dimensional perspective in terms of CT concepts, CT perspectives, and CT practices. They further categorized CT concepts as semantic, syntactic, and strategic knowledge, while CT practices primarily related to strategic knowledge [15]. Large companies such as Microsoft and Google have also expanded CT applications by facilitating and developing projects using CT in diverse fields [16]. For example, Google designed a CT concept guide that distinguishes mental processes from tangible outcomes. It involves abstraction, algorithms, automation, data collection, data analysis, data representation, decomposition, parallelization, pattern generalization, pattern recognition, and simulation [17]. Based on the above two studies, Weese and Feldhausen, incorporated computer science principles into a preferred CT set [18]. CT lists are shown in generalization. Brennan and Resnick defined CT from programming contexts, providing an important , which we adopted for this paper. Table 1. Computational Thinking Concepts and Related Computer Science Principles.