Erni Munastiwi, Ali Murfi, Sri Sumarni, Sigit Purnama, Naimah Naimah, Istiningsih Istiningsih and Annisa Dian Arini
The research aimed to explore the issues in the implementation of online education practice in elementary school, to study teachers' coping strategy to the online education issues…
Abstract
Purpose
The research aimed to explore the issues in the implementation of online education practice in elementary school, to study teachers' coping strategy to the online education issues and to evaluate teachers' problem-solving skill in online learning practice during the Covid-19 pandemic.
Design/methodology/approach
An exploratory research focused on identifying the obstacles in teaching practice faced by elementary school teachers as well as their coping strategy with eight convenience sampled schools.
Findings
Online education practice faced unpreparedness and competency issues. Unpreparedness was found in terms of social, technical and cultural factors, while competency issue was related to online education competency and digital competency. Teachers’ struggle to cope with the issue in online education practice was focused on the performing conventional education in the online manner, suggesting teachers' lack of competency in encouraging learning success. Teachers neglected the development of students' readiness and competencies to engage in online learning. Moreover, teachers’ struggle had the least impact on the development of their online teaching competency and digital competency that are required for carrying out online teaching. In general, teachers' problem-solving skill was below the expected level. These findings suggested that improvement of teachers' competencies is important in order to cope with the issues such as in online education practice during Covid-19 pandemic and to face future challenges in education.
Originality/value
This study evaluated the gap between actual action and expected action of elementary school teachers in coping with the issues regarding online education practice.
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Vartika Bisht, Priya, Sanjay Taneja and Amar Johri
Purpose: Health insurance and big data analytics have become increasingly intertwined in recent years, offering both opportunities and challenges for the industry. Thus, the…
Abstract
Purpose: Health insurance and big data analytics have become increasingly intertwined in recent years, offering both opportunities and challenges for the industry. Thus, the primary aim is to utilize bibliometric analysis for comprehensive literature reviews in health insurance and big data analytics.
Design/methodology/approach: Scopus, chosen for its broad coverage, is utilized to extract 493 manuscripts meeting the inclusion criteria set (year and language) for a 25-year period. The tools employed in the study include VOSViewer and Biblioshiny package (R-programming).
Findings: An emerging trend has been observed in the field of health insurance and big data analytics for 25 years. The US has been observed as the topmost leading country to contribute to the subject under study. The Ministry of Science and Technology of Taiwan is at the top first rank of top leading institutions contributing 20 documents to the field of health insurance and big data analytics. Moreover, thematic mapping and word cloud is done to find the most relevant keywords in the study. Furthermore, co-occurrence analysis revealed the relationship of keywords for health insurance and big data mining.
Implications: The implications of the research extend beyond academic insights and have practical implications for stakeholders involved in healthcare policy, practice, and research.
Originality/Value/Implications: The novelty in the manuscript has been brought in by focusing on one of the many types of insurance, i.e., health. Moreover, big data analytics in relation to health insurance for such a range of time period serves as the original presentation of the work with regards to the matter under study.
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Femi Emmanuel Ayo, Olusegun Folorunso, Friday Thomas Ibharalu and Idowu Ademola Osinuga
Hate speech is an expression of intense hatred. Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors. Hate speech detection with…
Abstract
Purpose
Hate speech is an expression of intense hatred. Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors. Hate speech detection with social media data has witnessed special research attention in recent studies, hence, the need to design a generic metadata architecture and efficient feature extraction technique to enhance hate speech detection.
Design/methodology/approach
This study proposes a hybrid embeddings enhanced with a topic inference method and an improved cuckoo search neural network for hate speech detection in Twitter data. The proposed method uses a hybrid embeddings technique that includes Term Frequency-Inverse Document Frequency (TF-IDF) for word-level feature extraction and Long Short Term Memory (LSTM) which is a variant of recurrent neural networks architecture for sentence-level feature extraction. The extracted features from the hybrid embeddings then serve as input into the improved cuckoo search neural network for the prediction of a tweet as hate speech, offensive language or neither.
Findings
The proposed method showed better results when tested on the collected Twitter datasets compared to other related methods. In order to validate the performances of the proposed method, t-test and post hoc multiple comparisons were used to compare the significance and means of the proposed method with other related methods for hate speech detection. Furthermore, Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related methods.
Research limitations/implications
Finally, the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of 91.3.
Originality/value
The main novelty of this study is the use of an automatic topic spotting measure based on naïve Bayes model to improve features representation.
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Haonan Hou, Chao Zhang, Fanghui Lu and Panna Lu
Three-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of…
Abstract
Purpose
Three-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of decision-making. They are proposed to assist decision-makers in better managing incomplete or imprecise information under conditions of uncertainty or fuzziness. However, it is easy to cause decision losses and the personal thresholds of decision-makers cannot be taken into account. To solve this problem, this paper combines picture fuzzy (PF) multi-granularity (MG) with 3WD and establishes the notion of PF MG 3WD.
Design/methodology/approach
An effective incomplete model based on PF MG 3WD is designed in this paper. First, the form of PF MG incomplete information systems (IISs) is established to reasonably record the uncertain information. On this basis, the PF conditional probability is established by using PF similarity relations, and the concept of adjustable PF MG PRSs is proposed by using the PF conditional probability to fuse data. Then, a comprehensive PF multi-attribute group decision-making (MAGDM) scheme is formed by the adjustable PF MG PRSs and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, an actual breast cancer data set is used to reveal the validity of the constructed method.
Findings
The experimental results confirm the effectiveness of PF MG 3WD in predicting breast cancer. Compared with existing models, PF MG 3WD has better robustness and generalization performance. This is mainly due to the incomplete PF MG 3WD proposed in this paper, which effectively reduces the influence of unreasonable outliers and threshold settings.
Originality/value
The model employs the VIKOR method for optimal granularity selections, which takes into account both group utility maximization and individual regret minimization, while incorporating decision-makers' subjective preferences as well. This ensures that the experiment maintains higher exclusion stability and reliability, enhancing the robustness of the decision results.