Issue Of Dropout Research Articles

Preventing School Dropout In Maluku: Synergy Between Family, School, And Customary Values

This article aims to examine the collaboration between family, school, and customary values in efforts to prevent school dropouts in Maluku. This research uses a qualitative approach with a case study method on several schools in the Maluku region. Data were obtained through in-depth interviews, observation, and documentation related to the interaction between families, schools, and indigenous communities. The results of the study show that families play a crucial role in addressing the issue of school dropouts through effective communication and motivation for children to continue their education to higher levels. On the other hand, schools contribute by building strong relationships with parents, providing counseling guidance, identifying at-risk students, and offering remedial programs and scholarships. Maluku’s customary values, such as masohi, pela, and the philosophy of Potong di Kuku, Rasa di Daging, make significant contributions in emphasizing solidarity and togetherness, which function as social mechanisms to maintain the continuity of education. These findings affirm the importance of synergy between family, school, and customary values in creating an inclusive and sustainable educational ecosystem. The novelty of this research lies in revealing how local customary values can be effectively integrated into educational policies to prevent school dropouts. The recommendation of this study is the importance of strengthening the role of families, schools, and indigenous communities in shaping educational policies based on local wisdom to support social and humanitarian development.

Varadinum Summer School: Between perception and motivation

The Romanian society is currently facing numerous challenges, among which the issue of school dropout is becoming increasingly prominent. The purpose of this study is to establish the relationship between the perception and motivation of the target group (students in higher grades at risk of school dropout) to participate in the Varadinum Summer School, organized under the auspices of the University of Oradea - Faculty of Geography, Tourism, and Sport, and funded through the ROSE financing program. In this context, the organization of summer schools represents a serious alternative with beneficial effects in curbing school dropout. The obtained results will be utilized to optimize the selection process for the future target group and enhance the organizational management of the 2024 edition of the Varadinum Summer School.

Machine learning predicts upper secondary education dropout as early as the end of primary school

Education plays a pivotal role in alleviating poverty, driving economic growth, and empowering individuals, thereby significantly influencing societal and personal development. However, the persistent issue of school dropout poses a significant challenge, with its effects extending beyond the individual. While previous research has employed machine learning for dropout classification, these studies often suffer from a short-term focus, relying on data collected only a few years into the study period. This study expanded the modeling horizon by utilizing a 13-year longitudinal dataset, encompassing data from kindergarten to Grade 9. Our methodology incorporated a comprehensive range of parameters, including students’ academic and cognitive skills, motivation, behavior, well-being, and officially recorded dropout data. The machine learning models developed in this study demonstrated notable classification ability, achieving a mean area under the curve (AUC) of 0.61 with data up to Grade 6 and an improved AUC of 0.65 with data up to Grade 9. Further data collection and independent correlational and causal analyses are crucial. In future iterations, such models may have the potential to proactively support educators’ processes and existing protocols for identifying at-risk students, thereby potentially aiding in the reinvention of student retention and success strategies and ultimately contributing to improved educational outcomes.

The socioeconomic determinants of University dropouts: The case of Greece

This research paper aims to explore the issue of university dropouts in Greece, which has become a growing concern in recent years due to its impact on individuals, educational institutions, and society as a whole. One of the main contributing factors to students discontinuing their higher education in Greece is the choice of the wrong faculty. Financial challenges, unrelated to tuition fees, also significantly affect students’ ability to pursue their studies. Family background plays a crucial role, with students from families with higher educational attainment exhibiting greater persistence. The study found that gender, age, and academic performance can influence students’ perceptions of dropout factors, which can change as they progress through their educational journey. To address these challenges, the research proposes a multifaceted approach, including early intervention programs, expanded financial support, improved mental health and counseling services, flexible learning options, and strong academic advising.

Analyzing the Abandonment Rates in Romanian HEIs

Abstract The advancement of technology, the changing and competitive business environment has an impact also on higher education. Potential students are faced with a multitude of choices, without always having the proper guidance to choose a path that is suitable for each of them. Their sometimes-uninformed decision, along with other factors may lead to the phenomenon of dropout. This has a negative impact on universities environment in terms of programs, to society and economy, all being linked in a way. The issue of dropout in higher education is a complex one, a challenging one these days and therefore research on this subject to address it properly are essential. In this article it is presented the current situation on Romanian universities and their dropout rates. Also, an analysis of scientific papers and sources is carried out to identify and document the key factors that contribute to the student’s choice of abandoning their university studies.

지방대학의 중도 탈락에 영향을 미치는 대학 요인 분석

Objectives This study aims to analyze the determinants of dropout rates at a rural public university systematically. The study aims to contribute to the formulation of effective policies to reduce dropout rates. Methods Lasso regression was chosen as the research method for this study because it is suitable for selecting important variables in complex data sets. Results The analysis of the results indicates that the most decisive factor significantly influencing the dropout rate is being ‘not enrolled in a university.’ Additionally, research funding per full-time faculty member, which reflects the research environment of the university, was also found to be an important factor. Conclusions To address the issue of student dropout, the study recommends that universities create policies to encourage students to persist in their studies and improve the quality of the research environment. The study also provides practical directions for policy makers and educational planners to reduce dropout rates by enhancing stu-dent support and improving the quality of education.

ESVFL: Efficient and secure verifiable federated learning with privacy-preserving

Federated learning has been widely applied as a distributed machine learning method in various fields, allowing a global model to be trained by sharing local gradients instead of raw data. However, direct sharing of local gradients still carries the risk of privacy data leakage, and the malicious server might falsify aggregated result to disrupt model updates. To address these issues, a lot of privacy-preserving and verifiable federated learning schemes have been proposed. However, existing schemes suffer from significant computation overhead in either encryption or verification. In this paper, we present ESVFL, an efficient and secure verifiable federated learning scheme with privacy-preserving. This scheme can simultaneously achieve low computation overhead for encryption and verification on the user side. We design an efficient privacy-preserving method to encrypt the users’ local gradients. Using this method, the computation and communication overheads of encryption on the user side is independent of the number of users. Users can efficiently verify the correctness of aggregated results returned by the cloud servers using cross-verification. During the verification process, there is no interaction among users and no additional computation is required. Furthermore, we also construct an efficient method to address the issue of user dropout. When some users drop out, online users do not incur any additional computation and communication overheads, while guaranteeing the correctness of the aggregated result of online users’ encrypted gradients. The security analysis and the performance evaluation demonstrate that ESVFL is secure and can achieve efficient encryption and verification.

CR-LCRP: Course recommendation based on Learner–Course Relation Prediction with data augmentation in a heterogeneous view

Course recommendation aims to offer suitable courses for learners and alleviate dropout issue in Massive Open Online Course (MOOC) learning scenarios. The present studies focus on learning semantic representations by modeling the learner’s personal information and the courses’ teaching attributes. However, with massive courses offered and various learners involved, the data sparsity and cold start are still two dominant challenges hindering the booming of online learning, which roughly results from the scarce exploitation on various relationships between learners and courses. To alleviate the above issues, we leverage the strong ability of Heterogeneous Information Network (HIN) in modeling heterogeneous data types and complex structural characteristics, and propose a Course Recommendation method based on Learner–Course Relation Prediction (CR-LCRP) with multi-granularity data augmentation strategy. Specifically, the multi-source interactive data on courses, the learner–learner similarity and the course–course similarity are integrated in building a heterogeneous information network. With both explicit and implicit features extracted and structural information from meta-paths enhanced, the CR-LCRP method learns high-quality representations of learners and courses, and eventually performs learner–course relation prediction in MOOC learning scenarios. Finally, extensive experiments are conducted on real-world MOOCs datasets and the experimental results demonstrate that the proposed CR-LCRP method outperforms the state-of-the-art baseline models with superior performance on several widely used evaluation metrics.

Secure and efficient federated learning via novel multi-party computation and compressed sensing

Federated learning (FL) enables the full utilization of decentralized training without raw data. However, various attacks still threaten the training process of FL. To address these concerns, differential privacy (DP) and secure multi-party computation (SMC) are applied, but these methods may result in low accuracy and heavy training load. Moreover, the high communication consumption of FL in resource-constrained devices is also a challenging problem. In this paper, we propose a novel SMC algorithm for the FL (FL-IPFE) to protect the local gradients. It does not require a trusted third party (TTP) and is more suitable for FL. Furthermore, we propose a secure and efficient FL algorithm (SEFL), which applies compressed sensing (CS) and all-or-nothing transform (AONT) to minimize the number of transmitted and encrypted model updates. Additionally, our FL-IPFE is used to encrypt the last element of the preprocessed parameters for guaranteeing the security of the entire local model updates. Meanwhile, the issue of participant dropouts is also taken into account. Theoretical analyses demonstrate that our proposed algorithms can aggregate model updates with high security. Finally, experimental evaluation reveals that our SEFL possesses higher efficiency compared to other state-of-the-art works, while providing comparable model accuracy and strong privacy guarantees.

Combating school dropout with Artificial Intelligence in brazilian higher education

This research explores the use of Artificial Intelligence (AI) as an innovative strategy to combat school dropout in Brazilian higher education. Faced with the increasing challenge of student retention in universities, the study focuses on evaluating the effectiveness of AI in this context, analyzing both its capabilities and limitations. The main objective is to understand how AI can be integrated into higher education, assessing its contributions to personalized learning and student engagement. The methodology consists of an extensive literature review, including the collection and detailed analysis of relevant studies on the use of AI in the educational field. The findings highlight that AI offers significant opportunities to tailor education to individual student needs, enhancing engagement and retention. Practical examples from the Brazilian context illustrate how AI implementation has contributed to reducing school dropout rates. However, the study also identifies key challenges, such as the need for adequate technological infrastructure and ongoing training for educators to effectively use AI tools. The conclusion is that AI holds substantial potential to mitigate dropout issues in higher education, but its successful implementation requires careful consideration. It is essential to take into account legal, ethical aspects, and institutional specifics to ensure effective integration of AI into the Brazilian educational system. This study provides valuable insights for strategic decision-making and the development of more efficient and contemporary educational policies.

Statistical identification of predictors of dropout in secondary education: evidence from Malaysia

The issue of school dropout has been long rooted in Malaysia. To address this issue effectively, further insight into the predictors of the school dropout and the early statistical identification of dropouts are crucial. Nevertheless, uncertainty has been persistent on these two aspects. This paper aims to identify the predictors and assess the feasibility of early identification of school dropouts. Using a school administrative database that comprised data on more than 100,000 students, the school dropout predictors, such as gender, academic achievement, and household income were identified. The estimated model correctly identified early on the 20.83% of the school dropouts. This is substantially higher than the overall sample mean of school dropouts (5%). Thus, it is feasible to identify early on the school dropout statistically. The findings have provided insightful inputs that could strengthen our intervention strategies and policies to help alleviate the problem of school dropout in Malaysia.

Strategies for Addressing Indigenous Education Dropout

The issue of indigenous education dropout among the indigenous community has deep roots, despite various efforts being made. These efforts include educational assistance such as transportation allowances, pocket money for primary and secondary school students, dormitory personal needs, fees, and school uniforms for Standard 1 and Form 1 students. Similarly, there are initiatives like the Education Incentive Scheme (SBGP) managed in collaboration with the Department of Orang Asli Development (JAKOA), aimed at encouraging indigenous students to pursue higher education. However, the education dropout issue among the indigenous at the secondary school level not only hinders educational progression but also makes it difficult for the younger generation of this community to secure better employment due to the lack of required minimum qualifications. Therefore, this qualitative study aims to explore strategies for addressing indigenous education dropout in Sungai Siput, Perak. Through semi-structured interviews and purposive sampling, the researcher conducted interviews with several teachers, parents, and indigenous community members in the district of Sungai Siput, Perak. Additionally, direct observations were made in schools, homes, and indigenous villages to reinforce the interview data. The study findings outlined strategies in the context of efforts needed by various parties to address indigenous educational dropout. The results indicate that addressing indigenous education dropout requires the involvement of various parties, establishing culturally responsive education, developing indigenous community educational recovery, and enhancing teacher training and professional development in indigenous schools. This study has important implications for addressing the issue of educational dropout among indigenous students from various angles.

비대면 교육환경에서의 대학생의 대학 몰입 예측 요인 연구: 교육 실재감을 중심으로

This study aimed to explore the factors predicting college students’ institutional commitment in the online learning environment, focusing on educational presence (social presence, cognitive presence, and teaching presence). At the beginning of the COVID-19 pandemic, the issue of student dropout emerged in Korean higher education. Against this background, this study adopted Tinto’s student integration model and the concept of educational presence (Garrison, Anderson, & Archer, 2000) as the theoretical framework. In addition, this study investigated the moderating effects of “the COVID-19 cohort,” who started their college life during the pandemic, on the relationship between educational presence and institutional commitment. Data were collected in October 2021 via an online survey with 864 students at “A” university located in Seoul, Korea. The main findings of this study are as follows: Cognitive presence, teaching presence, subjective perception of socioeconomic status, non-face-to-face interactions among students, student perception of organizational communication from university, student perception of support for the online learning management system (LMS), and student perception of support for online services and programs were found to predict institutional commitment. However, there were no moderating effects of the COVID-19 cohort on the relationship between educational presence and institutional commitment. Accordingly, this study suggests practical implications to improve institutional commitment in the online learning environment, including support for cognitive presence and teaching presence.

Postgraduate Dropout Issues of a Public University in Malaysia

Postgraduate Dropout Issues of a Public University in Malaysia

Personally Meaningful Design: Sound Making to Foster Engineering Practices with Artifacts from Home

Early engineering experiences can provide young people with experiences that can contribute to developing longer-term interest in the field and addressing dropout issues faced in engineering internationally. One way to engage young people with engineering is through the creation of personally meaningful sound-making projects with everyday materials and electronic kits. Sound making can make it possible for people to connect to their personal experiences and to represent these personal experiences through artistic means while also performing engineering practices, like asking questions, defining and delimiting problems, and developing and optimizing solutions with physical materials that produce sounds. Such design processes are referred to as engaging in the design of personally meaningful projects. However, it remains underspecified what personally meaningful means and, therefore, what aspects to integrate into engineering educational activity and technology designs to foster personally meaningful design opportunities. Building on constructionist perspectives on learning, this qualitative research investigated engineering practices as middle-school-aged youth used electronic construction kits and personal tangible material objects to create sounds. Iterative and thematic analysis of engineering practices of semi-structured interviews and video-recorded youth workshops showed that sound making with personal objects and electronic construction kits is a context for engineering design practices. This study also showed that integrating personal tangible projects that materialize personal histories can foster engineering practices. The findings contribute to our understanding of the theoretical idea of personally meaningful design in constructionism by presenting the importance of integrating personal histories through the design of personal projects with tangible material objects of a person’s life.

Causes and Prevention of Dropouts of Students in Higher Secondary Schools of Quetta

Education is an essential facet of any country's development, which can have a direct influence on the growth,development, and progress of its citizens. A cause for concern in this study is that education is a key factor in any country'sgrowth & development. The incapability to complete the academic program leads to negative impacts on the students'lives in terms of prospects and overall development. Therefore, it has to be investigated the main dropout reasons in thecountry as well as the implementing strategies to overcome these issues. Regrettably, the dropout rate of students at highersecondary levels is higher, especially in the Quetta City of Pakistan, as most of the students are unable to continue theirhigher studies. So, the current study aimed to examine the causes and prevention plans in terms of teachers' perceptionsat higher secondary levels in Quetta City. The data was collected from 78 teachers from higher secondary schools withthe help of 20 questions using a 5-point Likert scale. The data collected was analyzed using descriptive statistics, includingpercentages and the chi-square method, with the help of the 'Statistical Package of Social Sciences' (SPSS) software.Multiple reasons have been found for dropout at higher secondary levels including lack of community support, socio-economic factors, absence of extra co-curricular activities, family-related issues, a weak relationship between teachers &students, teacher absences, and most importantly the school has no concern regarding these issues. Furthermore, thesehigher secondary schools don't have any prevention plans for that. Although multiple NGOs, UNICEF (ALP), and otherorganizations are working on this issue, there is still no single prevention plan for dropouts. The study also attempted toidentify geographic and societal factors contributing to the high dropout rate and suggest practical solutions.Recommendations; Raising awareness about the issue of school dropouts and prevention strategies is crucial amongparents, teachers, stakeholders, and heads of educational institutions in Quetta's higher secondary schools to decrease thedropout rate and related prevention strategies

Photonic-crystal-based high-performance ring resonator using a topological interface state: design and analysis.

We propose a photonic crystal ring resonator for the enhancement of quality factor that supports two-dimensionally bounded topological edge states. Crystal parameters are obtained through finite-difference time-domain numerical simulation to get the enhanced quality factor using the topological properties of the photonic crystal. Topological edge states are created when two regions with dissimilar band topologies come together at an interface and are contained within a slab of dielectric material. These edge states can move along sharp edges without backscattering. The transmission dropout issue arises whenever the quality factor is enhanced in a conventional photonic system and is eliminated remarkably by employing the present approach. Such nanoscale photonic crystal structures promote robust interactions between quantum emitters and photonic edge states.

ScENT for Revealing Gene Clusters From Single-Cell RNA-Seq Data.

Recently, the fast development of single-cell RNA-seq (scRNA-seq) techniques has enabled high-resolution transcriptomic statistical analysis of individual cells in heterogeneous tissues, which can help researchers to explore the relationship between genes and human diseases. The emerging scRNA-seq data results in new analysis methods aiming to identify cell-level clustering and annotations. However, there are few methods developed to gain insights into the gene-level clusters with biological significance. This study proposes a new deep learning-based framework, scENT (single cell gENe clusTer), to identify significant gene clusters from single-cell RNA-seq data. We started with clustering the scRNA-seq data into multiple optimal groups, followed by a gene set enrichment analysis to identify classes of over-represented genes. Considering high-dimensional data with extensive zeros and dropout issues, scENT integrates perturbation in the learning process of clustering scRNA-seq data to improve its robustness and performance. Experimental results show that scENT outperformed other benchmarking methods on simulation data. To validate the biological insights of scENT, we applied it to the public experimental scRNA-seq data profiled from patients with Alzheimer's disease and brain metastasis. scENT successfully identified novel functional gene clusters and associated functions, facilitating the discovery of prospective mechanisms and the understanding of related diseases.

ScBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means.

Advances in single-cell RNA sequencing (scRNA-seq) technologies allow researchers to analyze the genome-wide transcription profile and to solve biological problems at the individual-cell resolution. However, existing clustering methods on scRNA-seq suffer from high dropout rate and curse of dimensionality in the data. Here, we propose a novel pipeline, scBKAP, the cornerstone of which is a single-cell bisecting K-means clustering method based on an autoencoder network and a dimensionality reduction model MPDR. Specially, scBKAP utilizes an autoencoder network to reconstruct gene expression values from scRNA-seq data to alleviate the dropout issue, and the MPDR model composed of the M3Drop feature selection algorithm and the PHATE dimensionality reduction algorithm to reduce the dimensions of reconstructed data. The dimensionality-reduced data are then fed into the bisecting K-means clustering algorithm to identify the clusters of cells. Comprehensive experiments demonstrate scBKAP's superior performance over nine state-of-the-art single-cell clustering methods on 21 public scRNA-seq datasets and simulated datasets. The source codes and datasets are available at https://github.com/YuBinLab-QUST/scBKAP/ and https://doi.org/10.24433/CO.4592131.v1.

Ethnic Minorities’ Dropout Decisions in Higher Education

The objective of this paper is to examine theoretical views on minority dropout decisions in higher education. This integrative review demonstrates that the dropout issue is highly complex and is comprised of different definitions and characteristics. It is required that educators clearly define the term. The review’s findings also show that minority students’ decisions to drop out from higher education involve factors of three different dimensions: psychological, physical, and environmental factors. Institutional support is necessary to assist minority students in their educational attainment. The review concludes that future research will be key in further exploring the issue of minority dropout.