Dropout Process Research Articles

Why Do Students Drop Out of High School?: The Case Study of Kasi District in Lao PDR

Purpose: This study explored why high school students decided to drop out in rural area of Lao PDR and further aimed to propose possible prevention to improve the drop out situation of the countries.
 Originality: Previous research that primarily focused on specific factors and dropout rate cannot explained why students decided to leave high school in early grade. Therefore, using comprehensive framework including individual and institutional factors, this study examined the dropout factors from individual perspective in qualitative way.
 Methodology: The study uses an explanatory sequential mixed method approach that employs both a questionnaire and interviews. Total 30 participants engaged in interviews including students and their parents, teachers and governmental officials. A thematic analysis was utilized for data analysis.
 Result: The results highlight that at the individual level, such as lack of motivation, peer pressure, early marriage, and cultural norms, pose barriers to high school attendance. Low household income and negative family dynamics also contribute significantly to dropout rates, with agricultural demands and family particular situations. Insufficient school resources, including materials and extracurricular activities, along with a shortage of qualified teachers, affect student engagement. Additionally, the community's low human and social capital increase dropout rates by limiting employment opportunities.
 Conclusions and Implication: The results indicated that both individual and institutional factors played a role in the dropout decision-making process. Furthermore, they suggested essential considerations such as examining job prospects for high school graduates in the local community, and evaluating the availability of resources within the school and community to assist students at risk of dropping out.

High-stake student drop-out prediction using hidden Markov models in fully asynchronous subscription-based MOOCs

In this study, we analyze the learning behavior of 24,000 students in a fully asynchronous subscription-based MOOC platform using hidden Markov models (HMMs) to examine the relationship between learning motivation and student drop-out behavior. In contrast to previous findings, our results reveal that student drop-out is not necessarily correlated with low motivation, as students may drop out despite being highly motivated at the end of their learning journey. To design more effective student retention campaigns, educational decision-makers must consider the motivation level and target potential drop-outs with a low state of motivation. More specifically, our findings emphasize the need for early intervention to prevent students from dropping out, as it becomes challenging to stimulate motivation once it reaches its lowest state. By adopting our proposed methodology, decision-makers can gain a better understanding of the student drop-out process and make more informed student retention interventions.

Dropout predictors in the academic fields of economics and engineering in cooperative education: an observation of the first academic year using cox regression

Cooperative education programs are usually based on a partnership between companies and universities. Dropouts have a particular impact here, for example the loss of junior staff in the companies. Most dropouts in cooperative education occur in the first academic year. In this multicausal dropout process, the influence of the cooperation partner is less pronounced in research. Consequently, we shed light on perspectives of organizational commitment to the company and motivational aspects in the academic fields of economics and engineering. We analyze collected data using a cross-sectional study design and estimated cox regression analysis on 2263 first-year students at Baden-Wuerttemberg Cooperative State University (DHBW) in Germany with 149 dropouts. Our analysis presents associations between affective commitment to the company, relatedness and competence at the university, and demographic and performance control variables with dropping out. Findings are contextualized within the current state of research. Practical implications are discussed in our study.

Predictors of Romanian Psychology Students' Intention to Successfully Complete Their Courses-A Process-Based Psychology Theory Approach.

Student retention is a frequently researched issue due to the incidence of student dropout and its significance to learning outcomes. However, there are research gaps that need to be addressed in understanding the factors influencing student dropout in the context of higher education in Romania. This cross-sectional investigation aims to fill these gaps by examining the relationships between satisfaction with the specialization, self-regulation of learning behavior, students' perceived stress, perceived acceptance from family and friends, and the intention to complete studies. The study utilizes various statistical analysis techniques, including mediation analysis and correlation analysis, to analyze the collected data. An online questionnaire was administered to non-randomized students majoring in Psychology, and a total of 144 valid and consented responses were obtained. The results reveal significant influences of satisfaction with the specialization, self-regulated learning, and students' perceived stress on the intention to successfully complete courses. Furthermore, academic self-efficacy was found to fully mediate the relationship between satisfaction with the specialization and academic adjustment. These findings contribute to a better understanding of the student dropout process in the Romanian higher education system. By identifying the factors associated with student retention, this study provides insights that can inform the development of interventions aimed at improving students' retention and overall learning outcomes.

#3434 A FREE OPEN-SOURCE FRAMEWORK TO INFER PATIENT-LEVEL EGFR SLOPES FOR ASSOCIATION WITH MOLECULAR DATA

Abstract Background and Aims Unfolding over years or decades, patient kidney disease trajectories can be characterised using routinely collected longitudinal data. One frequently measured marker of kidney function is serum creatinine, used in clinical settings to estimate the glomerular filtration rate (eGFR). Change in eGFR over time has been put forward as a proxy endpoint for clinical trials, and most work on inferring eGFR slopes has been done in this context. However, 'omics-based studies of the molecular mechanisms of kidney disease have so far largely neglected to make use of longitudinal data linked to tissue samples. Here, we build on existing approaches [1, 2] and provide an open-source, R framework to infer eGFR slopes from routinely available clinical data linked to biobank samples. Method In our framework, we calculate eGFR from serum creatinine measurements using the CKD-EPI 2021 formula. To aid data adjudication, we implement criteria outlined by Hapca et al. [1] for automatic flagging of acute kidney injury (AKI) events, and extend the criteria to detect potential in-patient stays. Since the biopsy procedure may arise as a result of loss of kidney function and/or coincide with a change in treatment regime, we use a two-spline linear mixed effects model similar to that proposed by Vonesh et al. [2] to differentiate between immediate-post-biopsy (acute) and chronic eGFR slopes. Additionally, to account for non-random dropout due to the onset of dialysis or occurrence of kidney transplant, we jointly model the longitudinal and dropout processes. Our framework is made available as free, open-source software in R. Results From routinely collected longitudinal clinical data, we generate patient-specific values characterising the post-biopsy disease trajectory. These values can then be incorporated into the analysis of 'omics data to find molecular patterns associated with treatment response or prognosis. Conclusion We have developed an open-source R framework for inferring patient-level eGFR slopes from serum creatinine measurements linked to biobank samples. Leveraging this data could improve 'omics studies of the molecular mechanisms of kidney disease, allowing us to incorporate information about disease trajectory and treatment response and thus ask questions of the molecular data which have real clinical relevance.

Breast cancer prediction using gated attentive multimodal deep learning

Women are prone to breast cancer, which is a major cause of death. One out of every eight women has a lifetime risk of developing this cancer. Early diagnosis of this disease is critical and enhances the success rate of cure. It is extremely important to determine which genes are associated with the disease. However, too many features make studies on gene data challenging. In this study, an attention-based multimodal deep learning model was created by combining data from clinical, copy number alteration and gene expression sources. Attention-based deep learning models can analyze mammography images and identify subtle patterns or abnormalities that may indicate the presence of cancer. These models can also integrate patient data, such as age and family history, to improve the accuracy of predictions. The objective of this study is to help breast cancer prediction tasks and improve efficiency by incorporating attention mechanisms. Our suggested methodology employs multimodal data and generates insightful characteristics to improve the prediction of the prognosis for breast cancer. It is a two-phase model; the first phase generates the stacked features using a sigmoid gated attention convolutional neural network, and the second phase uses flatten, dense and dropout processes for bi-modal attention. Based on our findings, the proposed model produced successful results and has the potential to significantly improve breast cancer detection and diagnosis, ultimately leading to better patient outcomes.

Mediating Role of Academic Self-Efficacy Between Insufficient Self-Control and School Dropout

School dropout is a devastating problem leading to negative consequences not only for individuals but also society. Therefore, numerous preventive measures and interventions have been implemented, but the expected outcomes have not been obtained despite vigorous efforts. This may indicate that the dropout process is complicated and not limited to academic issues. Therefore, dropout should be addressed using a more comprehensive approach that integrates personnel as well as cognitive structures like schemas. Here, the relationship between dropout and its possible predictors—insufficient self-control schema and academic self-efficacy—was investigated with 365 high school students. Mediation analysis was conducted via the PROCESS macro. Academic self-efficacy was found to fully mediate the relation between insufficient self-control and dropout, and the indirect effect was also found to be significant. The findings were discussed in the framework of the literature.

Dropout of Technical School Leaving Certificate: A case study of Nepal

This study focuses on the root causes for dropping out of the Technical School Leaving Certificate and then suggests to school administration, students and their parents, stakeholders, and further research for the action they need to apply to minimize student dropout from Technical School Leaving Certificate level education. This study was carried out based on an in-depth interview to analyze the cause and process of dropout. The study found different root factors of dropout students, such as - attitude problems, mismatch of curriculum and learning objectives, high expectations, physically disabled, weakness of school administration respect and recognition, laziness, and lack of motivation. On the other hand, dropouts include increased expectations, respect and honor, learning objectives and curricula, physical disabilities, and administrative carelessness. In addition, students are affected by staff behavior, working environment, working mode, and curriculum design, which need to be improved for the school administration and industry to lower the dropout rate.

Contrasting principal stratum and hypothetical strategy estimands in multi-period crossover trials with incomplete data.

Complete case analyses of complete crossover designs provide an opportunity to make comparisons based on patients who can tolerate all treatments. It is argued that this provides a means of estimating a principal stratum strategy estimand, something which is difficult to do in parallel group trials. While some trial users will consider this a relevant aim, others may be interested in hypothetical strategy estimands, that is, the effect that would be found if all patients completed the trial. Whether these estimands differ importantly is a question of interest to the different users of the trial results. This paper derives the difference between principal stratum strategy and hypothetical strategy estimands, where the former is estimated by a complete-case analysis of the crossover design, and a model for the dropout process is assumed. Complete crossover designs, that is, those where all treatments appear in all sequences, and which compare t treatments over p periods with respect to a continuous outcome are considered. Numerical results are presented for Williams designs with four and six periods. Results from a trial of obstructive sleep apnoea-hypopnoea (TOMADO) are also used for illustration. The results demonstrate that the percentage difference between the estimands is modest, exceeding 5% only when the trial has been severely affected by dropouts or if the within-subject correlation islow.

A Case Study on University Dropout: Perspectives from Education Faculty Students and Academicians

This case study investigated the reasons for students dropping out of a university and the experiences associated with the dropout process of individuals who dropped out of the faculty of education in Turkey. We collected data from 10 individuals who dropped out and 8 academic staff members who worked in the same faculty of a state university in the Aegean region in Turkey between 2008 and 2018 using the criterion sampling technique. We collected data using semi-structured interview forms and conducted content analysis. The results revealed that pre-admission factors (I), which are the factors of guidance, personality, system, family, career, and city play decisive roles in the admission process of individuals. These factors can shape their intrinsic and extrinsic motivation before the faculty admission process. After admission, the university processes factors (II) such as psychological condition, academic condition, social condition, organizational situation, appointment factors, military service, family situation, and financial situation are determinant factors on individuals’ intrinsic and extrinsic motivations in their university experience. After the dropout decision, individuals either drop out of the system or change department/university. Findings point out that the reasons for and process of the dropout are interdependent and divergent. Recommendations for future investigations and practices are presented based on our findings.

Observer-based guaranteed cost control for networked control systems with packet dropout and nonlinear disturbance

In this article, the observer-based guaranteed cost control method is investigated for the networked control systems (NCSs) with packet dropout and nonlinear disturbance involving event-triggered mechanism (ETM). The packet dropout processes, which appear in both the sensor-to-controller and the controller-to-actuator links, are represented by two mutually independent Bernoulli distributions, and the nonlinear disturbance is supposed to satisfy the Lipschitz condition. Since the systems states are unmeasurable, a state observer is designed to estimate the values of system state. The sufficient condition for the stability of the closed-loop system is addressed by applying the Lyapunov theorem, and a guaranteed cost controller is obtained based on the stability condition with the satisfaction of some given specified value of cost function. Furthermore, the controller design issue can be formulated as a convex optimization problem that is addressed by the linear matrix inequality (LMI) technique using the cone complementarity linearization algorithm. Finally, the availability of above method is proved by a simulation example.

Optimal Energy-Centric Resource Allocation and Offloading Scheme for Green Internet of Things Using Machine Learning

Resource allocation and offloading in green Internet of Things (IoT) relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.

Study environment factors associated with retention in higher education

ABSTRACT There is a substantial body of research concerned with student retention in higher education. However, in regard to factors related to study environment, existing research is described as incomplete. Based on Tinto’s institutional departure model and a literature review of recent international articles on dropout in higher education, this article suggests a revised model for understanding dropout processes. The model envisages study environment as a concept consisting of overlapping domains of a social system, an academic system, and teaching. The article distinguishes between factors related to each of these three systems and discusses how they can be used to understand and prevent dropouts.

Attachment and School Completion: Understanding Young People Who Have Dropped Out of High School and Important Factors in Their Re-Enrollment

When students drop out of high school, this is often negative for their development as well as for society, as those who drop out have an increased risk of unemployment, health problems, and social problems. The aim of the present study was to synthesize knowledge regarding processes related to school dropout in general and school re-enrollment in particular. We performed a narrative review of the literature, focusing on Norwegian and Nordic studies, but we also included studies from other countries when relevant. We discussed the findings in relation to attachment theory and our own research on the topic. As a result, we identified five main challenges to upholding education-related goals in long-term dropout processes: lack of relatedness, overchallenged self-regulation capacity, compensating for a history of failure, wounded learner identities, and coping with prolonged stress. In conclusion, the identified challenges converged on the importance of belonging and social support. The prerequisite for addressing the challenges seemed to be the establishment of a trustful relationship between the students who have dropped out and at least one teacher, and preferably also with other supportive adults. These relationships may provide sufficient social support and aid the students’ motivation to complete school.

Towards Event-Based State Estimation for Neuromorphic Event Cameras

In this work, a dynamic information extraction problem for neuromorphic event cameras is investigated from a state estimation perspective. The ego-motion pose estimation task of an event camera is formulated as a state estimation problem for a finite-state hidden Markov model subject to a special event-triggering mechanism. We model the threshold mismatch and the bandwidth limit of the event camera output generalization process as a stochastic event-triggering condition equipped with a state-dependent packet dropout process. For this problem, the recursive expression of the system state conditioned on the event-triggered measurement information is constructed under a suitably designed reference probability measure, based on which the event-based MMSE estimate for the considered estimation problem is further obtained. The effectiveness of proposed results is illustrated by numerical analysis and comparative evaluation of an ego-motion pose estimation example.

Development of a Methodology and Software Package for Predicting the Formation of Organic Deposits Based on the Results of Laboratory Studies

One of the main problems in the oil industry is the fallout of asphaltene–resin–paraffin deposits (ARPDs) during oil production and transportation. The formation of organic deposits leads to reduced equipment life and reduced production. Currently, there is no single methodology for the numerical simulation of the ARPD dropout process. The aim of our work was to obtain a correlation dependence characterizing the rate of wax growth over time for oils in the Perm Krai, depending on temperature, pressure, and speed conditions. Experimental data for 20 oil samples were obtained using a Wax Flow Loop installation that simulates fluid movement in tubing. The developed correlation was tested in 154 wells. The results of numerical modeling of the paraffin precipitation process made it possible to correct the inter-treatment period of scraping for 109 wells (71%), indicating the high accuracy of the developed approach.

Shared parameter and copula models for analysis of semicontinuous longitudinal data with nonrandom dropout and informative censoring.

Analysis of longitudinal semicontinuous data characterized by subjects' attrition triggered by nonrandom dropout is complex and requires accounting for the within-subject correlation, and modeling of the dropout process. While methods that address the within-subject correlation and missing data are available, approaches that incorporate the nonrandom dropout, also referred to informative right censoring, in the modeling step are scarce due to the computational intensity and possible intractable integration needed for its implementation. Appreciating the complexity of this problem and the need for a new methodology that is feasible for implementation, we propose to extend a framework of likelihood-based marginalized two-part models to account for informative right censoring. The censoring process is modeled using two approaches: (1) Poisson censoring for the count of visits before dropout and (2) survival time to dropout. Novel consideration was given to the proposed joint modeling approaches for the semicontinuous and censoring components of the likelihood function which included (1) shared parameter, and (2) Clayton copula. The cross-part and within-part correlations were accounted for through a complex random effect structure that models correlated random intercepts and slopes. Feasibility of implementation, and accuracy of these approaches were investigated using extensive simulation studies and clinical application.

Measurement of hybrid rocket solid fuel regression rate for a slab burner using deep learning

This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is designed to verify mechanistic models of reacting boundary layer combustion in hybrid rockets by the measurement of fuel regression rates. A DSLR camera with a high intensity flash is used to capture images throughout the burn and the images are then used to find the fuel boundary to calculate the regression rate. A U-net convolutional neural network architecture is explored to segment the fuel from the experimental images. A Monte-Carlo Dropout process is used to quantify the regression rate uncertainty produced from the network. The U-net computed regression rates are compared with values from other techniques from literature and show error less than 10%. An oxidizer flux dependency study is performed and shows the U-net predictions of regression rates are accurate and independent of the oxidizer flux, when the images in the training set are not over-saturated. Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise. The network is superior at filtering out noise introduced by soot, pitting, and wax deposition on the chamber glass as well as the flame when compared to traditional image processing techniques, such as threshold binary conversion and spatial filtering. U-net consistently provides low error image segmentations to allow accurate computation of the regression rate of the fuel.

A longitudinal analysis of the reciprocal relationship between academic procrastination, study satisfaction, and dropout intentions in higher education

Student dropout is a multi-causal process. Different theoretical models on student dropout consider dysfunctional study behavior (e.g., academic procrastination) and low study satisfaction as possible determinants of students’ dropout intentions during their university studies. However, these models neglect contemporary conceptualizations that assume reverse relationships between dropout intentions and other determinants of the dropout process. Until now, empirical evidence on these assumptions is scant. The present three-wave longitudinal study explored the reciprocal relationships between academic procrastination, study satisfaction, and dropout intentions over one semester. To this end, we used data of N = 326 undergraduate students enrolled in mathematics and law. Our latent cross-lagged panel model replicated existing empirical cross-sectional findings between the variables (i.e., academic procrastination, study satisfaction, and dropout intentions). Regarding the longitudinal relations, as expected, the cross-lagged effects showed that higher dropout intentions significantly related to subsequent higher academic procrastination and lower study satisfaction. Unexpectedly, academic procrastination did not significantly relate to subsequent dropout intentions. Additionally, higher study satisfaction significantly associated with subsequent higher dropout intentions—possibly due to unfulfilled expectations. Further, higher study satisfaction significantly related to subsequent higher procrastination—possibly due to more confidence among satisfied students. Our results broaden the view on dropout intentions as part of the dynamic interplay of student dropout determinants and the need to refine dropout models’ assumptions accordingly. Practically, realistic expectations seem important to reduce dropout intentions. Further, student counselors should have a closer look at the reasons for academic procrastination to develop individual solutions for this dysfunctional behavior.

Measuring Stressors Among Adolescents: Validation of the Positive and Negative Adolescent Life Experiences Scale

This article develops and validates the Positive and Negative Adolescent Life Experiences (PANALE) scale, a 11-item tool that enables researchers to measure adolescent stressors in a more valid and efficient manner than existing adolescent stressors scales. The PANALE presents good psychometric qualities, in terms of construct validity (factorial structure, measurement invariance, and composite reliability) and concurrent validity with regard to eustress and distress symptoms in a French sample of high school adolescents ( N = 1513). Overall, the PANALE provides a viable alternative to existing methods for conducting quantitative research on adolescent stress processes, notably those related to school disengagement and dropout processes.