{"title":"Simplifying Programming for Non-technical Students: A Hermeneutic Approach.","authors":"Andrea Valente, Emanuela Marchetti","doi":"10.1007/s13218-021-00748-0","DOIUrl":null,"url":null,"abstract":"<p><p>This paper investigates the simplification of programming for non-technical university students. Typical simplification strategies are outlined, and according to our findings CT courses for non-technical students typically address learners from different faculties, providing generic and basic knowledge, not specifically related to their major. In this study, we propose instead a hermeneutic approach to simplify programming, in which we aim at clarifying the problem-solving aspect of programming, addressing computational problems that are specific to their studies and leveraging on learners' preunderstanding of the digital media they have experienced as users. The practical counterpart of our theoretical approach is a minimalistic Python multimedia library, called Medialib, that we designed to enable university students with a non-technical profile to create visual media and games with short and readable code. We discuss the use of Medialib in two empirical case studies: a collaboration with the university of Kyushu in Fukuoka, Japan, and a coding module for Media Studies students at the University of Southern Denmark. Furthermore, we use Notional Machines to attempt a comparison of the simplicity of learning tools for programming, and to ground our claim that Medialib is \"simpler\" for learners than other popular approaches. The main contribution is a hermeneutic approach to the simplification of programming for specific contexts that combines the hermeneutic spiral and notional machines. The approach is supported by a tool, the Medialib library; the two case studies provide examples of how the approach and tool can be deployed in beginners in CT courses.</p>","PeriodicalId":45413,"journal":{"name":"Kunstliche Intelligenz","volume":"36 1","pages":"17-33"},"PeriodicalIF":2.8000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761527/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kunstliche Intelligenz","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13218-021-00748-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/17 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This paper investigates the simplification of programming for non-technical university students. Typical simplification strategies are outlined, and according to our findings CT courses for non-technical students typically address learners from different faculties, providing generic and basic knowledge, not specifically related to their major. In this study, we propose instead a hermeneutic approach to simplify programming, in which we aim at clarifying the problem-solving aspect of programming, addressing computational problems that are specific to their studies and leveraging on learners' preunderstanding of the digital media they have experienced as users. The practical counterpart of our theoretical approach is a minimalistic Python multimedia library, called Medialib, that we designed to enable university students with a non-technical profile to create visual media and games with short and readable code. We discuss the use of Medialib in two empirical case studies: a collaboration with the university of Kyushu in Fukuoka, Japan, and a coding module for Media Studies students at the University of Southern Denmark. Furthermore, we use Notional Machines to attempt a comparison of the simplicity of learning tools for programming, and to ground our claim that Medialib is "simpler" for learners than other popular approaches. The main contribution is a hermeneutic approach to the simplification of programming for specific contexts that combines the hermeneutic spiral and notional machines. The approach is supported by a tool, the Medialib library; the two case studies provide examples of how the approach and tool can be deployed in beginners in CT courses.
期刊介绍:
Artificial Intelligence has successfully established itself as a scientific discipline in research and education and has become an integral part of Computer Science with an interdisciplinary character. AI deals with both the development of information processing systems that deliver “intelligent” services and with the modeling of human cognitive skills with the help of information processing systems. Research, development and applications in the field of AI pursue the general goal of creating processes for taking in and processing information that more closely resemble human problem-solving behavior, and to subsequently use those processes to derive methods that enhance and qualitatively improve conventional information processing systems. KI – Künstliche Intelligenz is the official journal of the division for artificial intelligence within the ''Gesellschaft für Informatik e.V.'' (GI) – the German Informatics Society – with contributions from the entire field of artificial intelligence. The journal presents fundamentals and tools, their use and adaptation for scientific purposes, and applications that are implemented using AI methods – and thus provides readers with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. A highly reputed team of editors from both university and industry will ensure the scientific quality of the articles.The journal provides all members of the AI community with quick access to current topics in the field, while also promoting vital interdisciplinary interchange, it will as well serve as a media of communication between the members of the division and the parent society. The journal is published in English. Content published in this journal is peer reviewed (Double Blind).