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As software affects each industry, companies are increasingly trying to recruit talent. Despite the interest and investment of companies, it is difficult to find talents with technical expertise and specialization in a specific field at the same time. As a result, companies have begun to discover talents who can overcome their lack of expertise through talents with high problem-solving skills. Countries worldwide that felt the need to discover competitive talents began to show changes in education for nurturing talents. In particular, the results of the expansion and increase of programming education that cultivate problem-solving ability have begun to be seen. However, programming education is different from existing education, and many learners have difficulties with the introductory process due to the difficult debugging process. In order to analyze the difficulties of introductory learners and support their learning, a system that can collect data from the programming process and analyze behavior types is required. There are several methods for deriving the system requirements, but the interface requirements analysis method was selected in this study. We approached how to process data in the system by deriving the type of data that the system administrator wants to collect. This study laid the foundation for a system that can analyze the programming process of introductory learners by deriving the functional and non-functional requirements required by the data collection system through interface requirements analysis.

With the development of information and communication technology, countries around the world have strengthened their computer science curriculums. Korea also revised the informatics curriculum(The name of a subject related to computer science in Korea is informatics.) in 2015 with a focus on computer science. The purpose of this study was to automatically extract and analyze whether textbooks reflected the learning elements of the informatics curriculum in South Korea. Considering the forms of terms of the learning elements mainly comprised of compound words and the characteristics of Korean language, which makes natural language processing difficult due to various transformations, this study pre-processed textbook texts and the learning elements and derived their reflection status and frequencies. The terms used in the textbooks were automatically extracted by using the indexes in the textbooks and the part-of-speech compositions of the indexes. Moreover, this study analyzed the relevance between the terms by deriving confidence of other terms for each learning element used in the textbooks. As a result of the analysis, this study revealed that the textbooks did not reflect some learning elements in the forms presented in the curriculum, suggesting that the textbooks need to explain the concepts of the learning elements by using the forms presented in the curriculum at least once. This study is meaningful in that terms were automatically extracted and analyzed in Korean textbooks based on the words suggested by the curriculum. Also, the method can be applied equally to textbooks of other subjects.

The field of artificial intelligence (AI) is permeating education worldwide, reflecting societal changes driven by advancements in computing technology and the data revolution. Herein, we analyze the alignment between core AI educational curricula and textbooks to provide guidance on structuring AI knowledge. Text mining techniques using Python 3.10.3 and frame-based content analysis tailored to the computing field are employed to examine a substantial amount of text data within educational curriculum textbooks. We comprehensively examine the frequency of knowledge incorporated in AI curricula, topic structure, and practical tool utilization. The degree to which keywords are reflected in curriculum textbooks and in the textbook characteristics are determined using Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) analysis, respectively. The topic structure distribution is derived by Latent Dirichlet Allocation (LDA) topic modeling and the trained model is visualized using PyLDAvis. Furthermore, the variation in vertical content range or level is investigated by content analysis, considering the tools used to teach similar AI knowledge. Lastly, the implications for AI curriculum structure are discussed in terms of curriculum composition, knowledge construction, practical application, and curriculum utilization. This study provides practical guidance for structuring curricula that effectively foster AI competency based on a systematic research methodology.

The advancement of computing technology has led to many changes in a variety of fields, and the importance of programming education has been emphasized in many countries worldwide. Despite the importance of programming education, the cognitive burden of text programming for beginners has not been reduced. The goal of this study was to implement an environment where a text programming language is used in a block-based programming environment and to determine at which school level this learning environment affects positive perceptions of programming. To achieve this goal, we conducted programming classes targeting 128 middle school, high school, and university students for 14 weeks and analyzed the effects of the factors of “understanding of programming instructions”, “usage confidence”, and “usefulness” on “positive perceptions of programming”. The results of the analysis by school level show that “usefulness” influenced positivity toward programming for middle school students, “usefulness” and “understanding of programming instruction” for high school students, and “understanding of programming instruction” and “usage confidence” for university students. Therefore, the significance of this study confirms the need to construct the learning environment differently depending on school level, even for beginners.

A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new spiking neural network (SNN) based on conventional, biologically plausible paradigms, such as the leaky integrate-and-fire model, spike timing-dependent plasticity, and the adaptive spiking threshold, by suggesting new biological models; that is, dynamic inhibition weight change, a synaptic wiring method, and Bayesian inference. The proposed network is designed for image recognition tasks, which are frequently used to evaluate the performance of conventional deep neural networks. To manifest the bio-realistic neural architecture, the learning is unsupervised, and the inhibition weight is dynamically changed; this, in turn, affects the synaptic wiring method based on Hebbian learning and the neuronal population. In the inference phase, Bayesian inference successfully classifies the input digits by counting the spikes from the responding neurons. The experimental results demonstrate that the proposed biological model ensures a performance improvement compared with other biologically plausible SNN models.

Along with the importance of digital literacy, the need for SW(Software) education is steadily emerging. Programming education in public education targets a variety of learners from elementary school to high school. This study was conducted for the purpose of judging the proficiency of low school-age learners in programming education. To achieve the goal, a tool to collect data on the entire programming learning process was developed, and a machine learning model was implemented to judge the proficiency of learners based on the collected data. As a result of determining the proficiency of 20 learners, the model developed through this study showed an average accuracy of approximately 75%. Through the development of programming-related data collection tools and programming proficiency judging models for low school-age learners, this study is meaningful in that it presents basic data for providing learner-tailored feedback.

AIM:This study was conducted to investigate the effect of resuscitation guideline terminology on the performance of infant cardiopulmonary resuscitation (CPR). METHODS:A total of 40 intern or resident physicians conducted 2-min CPR with the two-finger technique (TFT) and two-thumb technique (TT) on a simulated infant cardiac arrest model with a 1-day interval. They were randomly assigned to Group A or B. The participants of Group A conducted CPR with the chest compression depth (CCD) target of "approximately 4 cm" and those of Group B conducted CPR with the CCD target of "at least one-third the anterior-posterior diameter of the chest". Single rescuer CPR was performed with a 15:2 compression to ventilation ratio on the floor. RESULTS:In both chest compression techniques, the average CCD of Group B was significantly deeper than that of Group A (TFT: 41.0 [range, 39.3-42.0] mm vs. 36.5 [34.0-37.9] mm, P = 0.002; TT: 42.0 [42.0-43.0] mm vs. 37.0 [35.3-38.0] mm, P < 0.001). Adequacy of CCD also showed similar results (Group B vs. A; TFT: 99% [82-100%] vs. 29% [12-58%], P = 0.001; TT: 100% [100-100%] vs. 28% [8-53%], P < 0.001). CONCLUSIONS:Using the CCD target of "at least one-third the anterior-posterior diameter of the chest" resulted in deep and adequate chest compressions during simulated infant CPR in contrast to the CCD target of "approximately 4 cm". Therefore, changes in the terminology used in the guidelines should be considered to improve the quality of CPR. TRIAL REGISTRATION:Clinical Research Information Service; cris.nih.go.kr/cris/en (Registration number: KCT0003486).

Artificial intelligence (AI) is bringing about enormous changes in everyday life and today’s society. Interest in AI is continuously increasing as many countries are creating new AI-related degrees, short-term intensive courses, and secondary school programs. This study was conducted with the aim of identifying the interrelationships among topics based on the understanding of various bodies of knowledge and to provide a foundation for topic compositions to construct an academic body of knowledge of AI. To this end, machine learning-based sentence similarity measurement models used in machine translation, chatbots, and document summarization were applied to the body of knowledge of AI. Consequently, several similar topics related to agent designing in AI, such as algorithm complexity, discrete structures, fundamentals of software development, and parallel and distributed computing were identified. The results of this study provide the knowledge necessary to cultivate talent by identifying relationships with other fields in the edutech field.

Objective: The aim of this study was to investigate the prevalence of metabolic syndrome (MS) and its relationships with socio-demographic and lifestyle-related factors in firefighters comparing it to general workers. Methods: Data from 257 Korean men career firefighters and 1064 Korean men workers were analyzed. Results: There was no significant difference in the prevalence of MS between firefighters (21.4%) and general workers (25.6%). We found that age and obesity were independent risk factors for MS in both firefighters and general workers. After adjusting for age and obesity, the risk for MS in firefighters was not significantly different from that in the most of occupational groups except sales workers. Conclusions: About one-fifth of firefighters had MS showing a similar prevalence compared with general workers. Therefore continuous monitoring and preventive programs for MS are needed in firefighters. [ABSTRACT FROM AUTHOR]

Many researches indicate that programming learning could help improve problem solving skills through algorithmic thinking. But in general, programming learning has been focused on programming language features and it also gave a heavy cognitive load to learners. Therefore, this paper proposes a programming activity process to improve novice programming learners' algorithmic thinking efficiently. An experiment was performed to measure the effectiveness of the proposed programming activity process. After the experiment, the learners' perception on programming was shown to be changed, to effective activity in improving problem solving. [ABSTRACT FROM AUTHOR]

This paper proposes a novel of a personalized Computer Assisted Language Learning (CALL) system based on learner's cognitive abilities related to foreign language proficiency. In this CALL system, a strategy of retrieval learning, a method of learning memory cycle, and a method of repeated learning are applied for effective vocabulary memorization. The system is designed to offer personalized learning based on cognitive abilities related to the human language process. For this, the proposed CALL system has a cognitive diagnosis module which can measure five types of cognitive abilities. The results of this diagnosis are used to create dynamic learning scenarios for personalized learning and to evaluate user performance in the learning. This system is also designed in order to have users be able to create learning word lists and to share them simply with various functions based on open APIs. Additionally, through experiments, it has shown that this system helps students to learn English vocabulary effectively and enhances their foreign language skills. [ABSTRACT FROM AUTHOR]