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1 – 10 of 297Thao-Trang Huynh-Cam, Long-Sheng Chen and Tzu-Chuen Lu
This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct…
Abstract
Purpose
This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.
Design/methodology/approach
The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.
Findings
DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”
Practical implications
This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.
Originality/value
Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.
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Alesandra de Araújo Benevides, Alan Oliveira Sousa, Daniel Tomaz de Sousa and Francisca Zilania Mariano
Adolescent pregnancy stands as a societal challenge, compelling young individuals to prematurely discontinue their education. Conversely, an expansion of high school education can…
Abstract
Purpose
Adolescent pregnancy stands as a societal challenge, compelling young individuals to prematurely discontinue their education. Conversely, an expansion of high school education can potentially diminish rates of adolescent pregnancy, given that educational attainment stands as the foremost risk factor influencing sexual initiation, the use of contraceptive methods during initial sexual encounters and fertility. The aim of this paper is to analyze the impact of the implementation of the public educational policy introducing full-time schools (FTS) for high schools in the state of Ceará, Brazil, on early pregnancy rates.
Design/methodology/approach
Using the difference-in-differences method with multiple time periods, we measured the average effect of this staggered treatment on the treated municipalities.
Findings
The main result indicates a reduction of 0.849 percentage points in the teenage pregnancy rate. Concerning dynamic effects, the establishment of FTS in treated municipalities results in a 1.183–1.953 percentage point decrease in teenage pregnancy rates, depending on the timing of exposure. We explored heterogeneous effects within socioeconomically vulnerable municipalities, yet discerned no impact on this group. Rigorous tests confirm the robustness of the results.
Originality/value
This paper aims to contribute to: (1) the consolidation of research on the subject, given the absence of such research in Brazil to the best of our knowledge; (2) the advancement and analysis of evidence-based public policy and (3) the utilization of novel longitudinal data and methodology to evaluate adolescent pregnancy rates.
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Dritero Arifi and Ngadhnjim Brovina
This chapter delves into the education of Kosovo's Roma community, shining a light on the obstacles they encounter and the initiatives taken by the government to enhance their…
Abstract
This chapter delves into the education of Kosovo's Roma community, shining a light on the obstacles they encounter and the initiatives taken by the government to enhance their educational prospects. The Roma community represents just 0.5% of Kosovo's entire population and faces significant challenges with low literacy rates and high dropout rates, particularly at lower levels of education. Since Kosovo's independence proclamation in 2008, there have been institutional advancements and partnerships with local, central, and worldwide organisations to improve the education system for the Roma community.
This study aims to provide an in-depth understanding of the educational situation of the Roma community in Kosovo. It recognises their challenges and proposes practical solutions to accelerate their integration and improve their social, educational, and cultural welfare. Various analytical methods, including descriptive statistics and historical analysis, are utilised to conduct comparative and quantitative assessments.
The Roma community in Kosovo continues to encounter numerous challenges, such as economic hardships, lack of education opportunities, and early marriages that often result in school dropouts. However, research highlights the government's dedication to enhancing education conditions and enabling better access to the labour market for the Roma community through proper education and qualifications. Additionally, Kosovo's constitutional and legal framework safeguards the rights of all communities, including the Roma community, regarding education and employment with high standards.
Elizabeth Lapon and Leslie Buddington
The transition to college presents significant challenges for many students as they navigate new academic and social experiences. In the USA, 30% of first-year students drop out…
Abstract
Purpose
The transition to college presents significant challenges for many students as they navigate new academic and social experiences. In the USA, 30% of first-year students drop out before their second year. Research indicates that mentoring programs help students achieve social integration and likely have a positive effect on their transition to college. This research study was conducted with education students to better understand the potential impacts of peer mentorship.
Design/methodology/approach
Student mentors and mentees were matched by attributes such as their concentration within the education major, gender, sports they played and whether they were first-generation matriculants. Data collection utilized two surveys one before the peer mentoring process and one after the process.
Findings
The findings suggest that peer mentoring improved first-generation students' sense of belonging to both their major and the college. Peer mentors also experienced increased belongingness. The transfer rate among participants of 2% was a significant drop from previous years.
Originality/value
The success of the peer mentoring experience was possibly due to the intentional matching process based on certain attributes. Additionally, taking a leadership role increased a sense of belonging in the peer mentors.
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Dmitri Williams, Sukyoung Choi, Paul L. Sparks and Joo-Wha Hong
The study aims to determine the outcomes of mentorship in an online game system, as well as the characteristics of good mentors.
Abstract
Purpose
The study aims to determine the outcomes of mentorship in an online game system, as well as the characteristics of good mentors.
Design/methodology/approach
A combination of anonymized survey measures and in-game behavioral measures were used to power longitudinal analysis over an 11-month period in which protégés and non-mentored new players could be compared for their performance, social connections and retention.
Findings
Successful people were more likely to mentor others, and mentors increased protégés' skill. Protégés had significantly better retention, were more active and much more successful as players than non-protégés. Contrary to expectations, younger, less wealthy and educated people were more likely to be mentors and mentors did not transfer their longevity. Many of the qualities of the mentor remain largely irrelevant—what mattered most was the time spent together.
Research limitations/implications
This is a study of an online game, which has unknown generalizability to other games and to offline settings.
Practical implications
The results show that getting mentors to spend dedicated time with protégés matters more than their characteristics.
Social implications
Good mentorship does not require age or resources to provide real benefits.
Originality/value
This is the first study of mentorship to use survey and objective outcome measures together, over time, online.
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In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly…
Abstract
Purpose
In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly. It is not practical to directly migrate achievements obtained in English sentiment analysis to the analysis of Chinese because of the huge difference between the two languages.
Design/methodology/approach
In view of the particularity of Chinese text and the requirement of sentiment analysis, a Chinese sentiment analysis model integrating multi-granularity semantic features is proposed in this paper. This model introduces the radical and part-of-speech features based on the character and word features, with the application of bidirectional long short-term memory, attention mechanism and recurrent convolutional neural network.
Findings
The comparative experiments showed that the F1 values of this model reaches 88.28 and 84.80 per cent on the man-made dataset and the NLPECC dataset, respectively. Meanwhile, an ablation experiment was conducted to verify the effectiveness of attention mechanism, part of speech, radical, character and word factors in Chinese sentiment analysis. The performance of the proposed model exceeds that of existing models to some extent.
Originality/value
The academic contribution of this paper is as follows: first, in view of the particularity of Chinese texts and the requirement of sentiment analysis, this paper focuses on solving the deficiency problem of Chinese sentiment analysis under the big data context. Second, this paper borrows ideas from multiple interdisciplinary frontier theories and methods, such as information science, linguistics and artificial intelligence, which makes it innovative and comprehensive. Finally, this paper deeply integrates multi-granularity semantic features such as character, word, radical and part of speech, which further complements the theoretical framework and method system of Chinese sentiment analysis.
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B. Vasavi, P. Dileep and Ulligaddala Srinivasarao
Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use…
Abstract
Purpose
Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.
Design/methodology/approach
This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.
Findings
To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.
Originality/value
The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.
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This underscores individual and social implications for how mental disorders and mental well-being are constructed, conceived of and treated. Further, this paper aims to examine…
Abstract
Purpose
This underscores individual and social implications for how mental disorders and mental well-being are constructed, conceived of and treated. Further, this paper aims to examine positive psychology’s role in supporting the advancement of a broader systemic and contextual approach to mental health. With that aim, this paper connects data on mental health and well-being with peace studies to describe the systems of value and social ecologies underpinning mental disorders, using public happiness/Felicitas Publica as a possible framework to enhance public mental health while intervening at the local level (Bruni and Zamagni, 2007; Marujo and Neto, 2013, 2014, 2016, 2017, 2021; Marujo et al., 2019).
Design/methodology/approach
Theoretical foundations and data on positive peace and mental well-being are described with the intention to propose a systemic, contextual, relational, communitarian, economic and sociopolitical perspective of well-being that goes beyond individual bodies and/or brains and, instead, views mental disorder and mental health as social currency (Beck, 2020).
Findings
The interventions using dialogic, conversational and community approaches are a possible path to promote peace, mental health and public happiness.
Research limitations/implications
Examining the interplay between the fields of positive psychology, mental health and cultures of peace, this work contributes to the broadening of research and subsequent intervention topics through transdisciplinary approaches while reinforcing the role of systemic and social determinants and complementing the prevalent medical model and intraindividual perspective of mental health and well-being.
Practical implications
Adopting positive psychology to address mental health through public happiness concepts and interventions opens opportunities to respond to the ebb and flow of social challenges and life-giving opportunities. Therefore, the paper intends to articulate actor-related, relational, structural and cultural dimensions while moving away from discrete technocratic and individual models and pays attention to the way their implementations are aligned with both individual and social needs.
Social implications
The work offers an inclusive, equalitarian, politically sensitive approach to positive mental health and positive psychology, bringing forward a structural transformation and human rights-based approach perspective while rethinking the type of social and political solutions to mental health issues.
Originality/value
Creating a critically constructive debate vis-à-vis the fluidity and complexity of the social world, the paper examines mental health and positive psychology simultaneously from a “hardware” (institutions, infrastructures, services, systems, etc.) and a “software” (i.e. individuals and community/societal relations).
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Shaodan Sun, Jun Deng and Xugong Qin
This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained…
Abstract
Purpose
This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.
Design/methodology/approach
According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.
Findings
This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.
Originality/value
Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.
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