Programming Language Experience Research Articles

Investigating Factors in Artificial Intelligence Literacy for Korean Elementary School Students

In recent years, Artificial Intelligence (AI) has rapidly evolved due to significant improvements in computing performance, increased utilization of large datasets, and algorithm advancements, leading to widespread societal changes. These developments promise innovative applications of AI across various fields but highlight the necessity of ethical use and deep understanding of AI, underscoring the importance of AI literacy. While current research on AI literacy primarily focuses on secondary and higher education, the need for education that impacts cognitive and social development at the elementary level is increasingly emphasized. Furthermore, understanding the factors influencing AI literacy is crucial for educators and policymakers in designing and implementing effective AI education programs. This study investigated how gender, grade level, experiences related to AI, interest in AI, and programming language experience affect AI literacy among elementary students, revealing that these factors significantly impact AI literacy levels. Male students showed higher AI literacy than female students, and AI literacy improved with higher grade levels. Direct and indirect experiences related to AI positively influenced literacy improvement, and high interest in AI and experience with programming languages played essential roles. These findings provide evidence for developing effective AI education strategies for elementary students, emphasizing the importance of educational programs that meet students' diverse backgrounds and needs. These factors in AI education can enhance students' literacy levels and contribute to nurturing talents equipped with the necessary technical, ethical, and problem-solving skills for future society.

A Baseline Evaluation of Bioinformatics Capacity in Tanzania Reveals Areas for Training

Due to the insufficient human and infrastructure capacity to use novel genomics and bioinformatics technologies, Sub-Saharan Africa countries have not entirely ripped the benefits of these technologies in health and other sectors. The main objective of this study was to map out the interest and capacity for conducting bioinformatics and related research in Tanzania. The survey collected demographic information like age group, experience, seniority level, gender, number of respondents per institution, number of publications, and willingness to join the community of practice. The survey also investigated the capacity of individuals and institutions about computing infrastructure, operating system use, statistical packages in use, the basic Microsoft packages experience, programming language experience, bioinformatics tools and resources usage, and type of analyses performed. Moreover, respondents were surveyed about the challenges they faced in implementing bioinformatics and their willingness to join the bioinformatics community of practice in Tanzania. Out of 84 respondents, 50 (59.5%) were males. More than half of these 44 (52.4%) were between 26–32 years. The majority, 41 (48.8%), were master’s degree holders with at least one publication related to bioinformatics. Eighty (95.2%) were willing to join the bioinformatics network and initiative in Tanzania. The major challenge faced by 22 (26.2%) respondents was the lack of training and skills. The most used resources for bioinformatics analyses were BLAST, PubMed, and GenBank. Most respondents who performed analyses included sequence alignment and phylogenetics, which was reported by 57 (67.9%) and 42 (50%) of the respondents, respectively. The most frequently used statistical software packages were SPSS and R. A quarter of the respondents were conversant with computer programming. Early career and young scientists were the largest groups of responders engaged in bioinformatics research and activities across surveyed institutions in Tanzania. The use of bioinformatics tools for analysis is still low, including basic analysis tools such as BLAST, GenBank, sequence alignment software, Swiss-prot and TrEMBL. There is also poor access to resources and tools for bioinformatics analyses. To address the skills and resources gaps, we recommend various modes of training and capacity building of relevant bioinformatics skills and infrastructure to improve bioinformatics capacity in Tanzania.

Heuristic Algorithm for Automatic Extraction Relational Data from Spreadsheet Hierarchical Tables

Spreadsheets are contained critical information on various topics and were most broadly utilized in numerous spaces. There are a huge amount of spreadsheet clients everywhere in the world. Spreadsheets provide considerable flexibility for data structure organization. As well as it gives their makers an enormous level of opportunity to encode their data as it is simple to utilize and easy to store the data in a table format. Because of this flexibility, tables with very complex and hierarchical data structures could be generated. Thusly, such complexity makes table processing and reusing this data is a difficult task. Therefore, the expansion in volume and complexity of these tables has prompted the necessity to preserve this data and reuse it. As a result, this paper implemented a novel algorithm-based heuristic technique and cell classification strategy to automate relational data extraction from spreadsheet hierarchical tables and without need any programming language experience. Finally, the paper does experiments on 2 different real public datasets. The percentage of average accuracy using the proposed approach on the two datasets is 95 % and 94.2% respectively.

Support vector regression for predicting the productivity of higher education graduate students from individually developed software projects

Productivity prediction of a software engineer is necessary to determine whether corrective actions are needed and to identify improvement options to produce better results. It can be performed from abstraction levels such as organisation, team project, individual project, or task. Software engineering education and training has approached its efforts at individual level. In this study, the authors propose the application of a data mining technique named support vector regression (SVR) to predict the productivity of individuals (i.e. graduate students). Its prediction accuracy was compared with that of a statistical regression model, and with those of two neural networks. After applying a Wilcoxon statistical test, results suggest that an SVR with linear kernel using new and changed lines of code, and programming language experience as independent variables, could be used for predicting the individual productivity of a higher education graduate student, when software projects coded in either Java or C++ programming languages, have been developed by following a disciplined process specifically proposed for academic environments.

Relationship Analyses among High School Students’ Educational Program Understanding, Abstract Thinking Ability, and Programming Language Experience– Focusing on RUR-PLE

Received (November 24, 2015), Review Result (December 8, 2015) Accepted (December 15, 2015), Published (March 31, 2016) Dept. Computer Education, Jeju National Univ., Jejudaehak-ro 102, Jeju-do, 63243, Korea email: cjpark@jejunu.ac.kr (Corresponding Author) Dept. Management Information Systems, Jeju National Univ., Jejudaehak-ro 102, Jeju-do, 63243, Korea email: jshyun@jejunu.ac.kr Dept. Computer Education, Jeju National Univ., Jejudaehak-ro 102, Jeju-do, 63243, Korea email: ksm0032@jejunu.ac.kr Relationship Analyses among High School Students’ Educational Program Understanding, Abstract Thinking Ability, and Programming Language Experience– Focusing on RUR-PLE Copyright c 2016 HSST 242 According to the changes from industrial society to digital society, software education for ordinary people has been popular both in home and overseas. In particular, due to the revision of the curriculum of Informatics in 2015, the interest on software education has been rising. Recently, various types of instructional materials have being developed, and thus, many schools are concerned about what to teach. Typically, most teachers do not choose a programming language based on the characteristics of the language but based on the stableness or popularity of the language. Focusing the RUR-PLE, which has been taught in many schools recently, this paper surveyed high school students, who learned the RUR-PLE for a semester, on the abstract thinking ability, problem solving ability, and academic achievement. And then, this research analyzed the relationship between abstract thinking ability and problem solving ability. As a result, the student group at low construal level performed better in predicting concrete actions of the robot of the RUR-PLE unlike the C language. In addition, we examined if there exists an interaction effect between abstract thinking ability and language experience about program understanding ability. From the research results, we knew the importance of the appropriate language selection for improving the secondary school students’ thinking ability.

Automated Cell Tracking and Analysis in Phase-Contrast Videos (iTrack4U): Development of Java Software Based on Combined Mean-Shift Processes

Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects

Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment.

Algorithmic Bricks: A Tangible Robot Programming Tool for Elementary School Students

Tangible programming tools enable children to easily learn the programming process, previously considered to be difficult for them. While various tangible programming tools have been developed, there is still a lack of available tools to help students experience the general programming process. This study therefore developed a tool called Algorithmic Bricks (A-Bricks), to improve the programming language experience by considering and utilizing characteristics of procedural language. Specifically, elements such as sequence, repetition, condition, function, and parameter were used to develop A-Bricks. In addition, this study observed the benefits of A-Bricks and confirmed its potential as a tangible educational programming tool by comparing and analyzing elementary school students using A-Bricks to control groups using Scratch.

The Effect of Computer-and-Writing Instruction on Prospective English Teachers’ Attitudes Toward and Perceived Uses of Computers in Writing Instruction

AbstractIn this study the attitudes toward and perceived uses of computers of English education undergraduates were measured after an 11-week course focusing on computers and writing. The group was divided into three groups: (a) those entering with no computer experience, (b) those entering with word processing experience, and (c) those entering with both word processing and programming language experience. It was found that the computer anxiety of the entire group reduced from pre- to post-treatment. Not only did the number of computer uses they listed at the onset increase—from 94 to 143, an increase of 52%—but also the types of uses departed from the early focus on isolated skills (via drill and practice) to the later domination of writing process uses. The changes in concerns of all students went from Awareness and Personal to Consequence, Collaboration, and Refocusing—changes that reflect concerns focusing less on self and more on their prospective students and fellow teachers. In all analyses, the s...

Axiomatic definitions for data model semantics

The axiomatic method, a widely accepted technique for the precise (formal) definition of programming language semantics, is used to define data model semantics. First, a definition of the term “data model” is developed. The strong relationship between database and programming language concepts is discussed. The nature of data model formalization is described. Based on the experience in programming languages, it is argued that the formal definition of a data model aids database design, database management system implementation, semantic integrity verification and validation, and data model theory. It is further argued that precision must be weighed against understandability, and that a degree of informality can be introduced without loss of precision. Several different formal description techniques and their particular advantages are mentioned. It is argued that in order to achieve desired goals, more than one technique be used to develop consistent and complementary formal definitions of a data model. The axiomatic method is described. Axiomatic definitions are particularly appropriate for the design, analysis, and comparison of schemas, transactions, and databases. The axiomatic definition technique is demonstrated in an annotated, precise definition of the semantics of the structural aspects of a semantic data model which is based on the relational data model.