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Abstract: Four research questions and four null hypotheses were formulated to guide the study. A simple random sampling technique was used in selecting 34 respondents from each 6 schools, with a sample size of 204 was selected from the population of 1,518. The instrument titled LEVSLEQ was used to elicit response from the respondent. The reliability of the research instrument yielded a co-efficient of 0.82. The data was analyzed using dependent t-test and was tested at .05 level of significance. The findings indicated that environmental variable studied influence students' learning effectiveness.
Keywords: learning, environment, learning effectiveness, Students’, variables
Published in DKUM: 19.06.2025; Views: 0; Downloads: 0
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Abstract: Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this paper presents the interplay of these two research areas. In this paper, we surveyed approaches for the data mining of blockchain data, yet show several real-world applications. Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. The paper concludes with challenges for future investigations of this research area.
Keywords: anomaly detection, blockchain, distributed ledger, data mining, machine learning
Published in DKUM: 16.06.2025; Views: 0; Downloads: 0
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Abstract: The paper analyzes the positive effects of video games on teen development and on teenagers' informal learning. The participants in the research were teenagers. The analysis and interpretation of games and explanations of their pedagogical merit indicate that players recognize their value for informal learning. Latent characteristics of video games include positive elemests such as taking responsibility, teamwork, sense of belonging, creativity, multiculturalism, personal virtues and values, emotion management, learning new information and skills, motor skills development and professional orientation. In that respect, it is possible to conclude that gaming may help in the development of certain abilities important for informal as well as formal learning in today's digital age.
Keywords: digital media, informal learning, teenagers, video games, a qualitative analysis
Published in DKUM: 10.06.2025; Views: 0; Downloads: 0
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Abstract: Artificial intelligence, as a field of computer science focused on developing technologies that simulate intelligent behaviours and human cognitive functions, undoubtedly has huge potential to transform all business activities, including the process of employee learning. However, different generations have varying attitudes toward the rapid advancement of technology and the increasing possibilities offered by artificial intelligence. The general purpose of this research is to gain insights into the attitudes of Generation Z regarding the use of AI in the context of the employee learning process. Empirical research was conducted on a sample of 264 respondents from Slovenia and Bosnia and Herzegovina. In addition to descriptive statistics, Cronbach's alpha, Shapiro-Wilk, and Mann-Whitney tests were used to test hypotheses. Generally, the research findings indicate that the upcoming generation of the workforce considers artificial intelligence a significant factor in improving the employee learning process. The study contributes to human resource management literature because it brings new insights into Generation Z attitudes, whose participation in the active workforce will significantly increase in the coming years.
Keywords: learning process, Artificial Intelligence, employees, generation Z, sociodemographic characteristic
Published in DKUM: 27.05.2025; Views: 0; Downloads: 4
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Abstract: Patient-reported health data, especially patient-reported outcomes measures, are vital for improving clinical care but are often limited by memory bias, cognitive load, and inflexible questionnaires. Patients prefer conversational symptom reporting, highlighting the need for robust methods in symptom extraction and conversational intelligence. This study presents a weakly-supervised pipeline for training and evaluating medical Named Entity Recognition (NER) models across eight languages, with a focus on low-resource settings. A merged English medical corpus, annotated using the Stanza i2b2 model, was translated into German, Greek, Spanish, Italian, Portuguese, Polish, and Slovenian, preserving the entity annotations medical problems, diagnostic tests, and treatments. Data augmentation addressed the class imbalance, and the fine-tuned BERT-based models outperformed baselines consistently. The English model achieved the highest F1 score (80.07%), followed by German (78.70%), Spanish (77.61%), Portuguese (77.21%), Slovenian (75.72%), Italian (75.60%), Polish (75.56%), and Greek (69.10%). Compared to the existing baselines, our models demonstrated notable performance gains, particularly in English, Spanish, and Italian. This research underscores the feasibility and effectiveness of weakly-supervised multilingual approaches for medical entity extraction, contributing to improved information access in clinical narratives—especially in under-resourced languages.
Keywords: low-resource languages, machine translation, medical entity extraction, NER, NLP, patient-reported outcomes, weakly-supervised learning
Published in DKUM: 19.05.2025; Views: 0; Downloads: 2
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