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Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.com/VCCRI/CIDR .

Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.

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.

Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step. Single-cell RNA sequencing (scRNA-seq) data provides information on transcriptomic heterogeneity within cell populations. Here, Risso et al develop ZINB-WaVE for low-dimensional representations of scRNA-seq data that account for zero inflation, over-dispersion, and the count nature of the data.

Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona.

Dropout students are a severe problem in higher education (HE) in many countries. Student dropout has a tremendous negative impact not only on individuals but also on universities and socioeconomic. Consequently, preventing educational dropouts is a considerable challenge for HE’s institutions. Therefore, knowing the factors influencing student dropout is an essential first step in preventing students from dropping out. This study uses a mix of qualitative and quantitative approaches. To determine what variables affect student dropout, we use a qualitative approach, after which the variables found will be validated by the public and stakeholders using a quantitative approach. Then, the next step is to classify variables using a quantitative approach. This study observes dropout students at private universities in Central Java, Indonesia. The findings reveal that personal economic factors, academic satisfaction, academic performance, and family economics are the most influential. The results of this paper are significant for universities in Indonesia, especially Central Java, to overcome the problem of student dropouts, so that they are more precise in making decisions. In addition, the results of this study are also helpful for further research as a basis for predicting students dropping out of university.

One of the most significant indicators for assessing the quality of university careers is the dropout rate between the first and second year. Both literature on the subjects and the results that emerged from numerous specific investigations into the dropouts of the university system, showed the crucial importance of this junction between the first and the second year. Reasons for dropping out can be quite varied, ranging from incorrect and/or insufficient prospective student orientation, the willingness or need to find a job as quickly as possible, to a lack of awareness of not being able to cope with a particular course of study rather than another. In this paper we focus specifically on the problem of dropouts in Italy, addressing it from a dual point of view. At an aggregate level, the analysis deals with dropout rates in Italy between the first and second year, in order to identify the main trends and dynamics at the national level. Subsequently, we analyze individual-level data from the University of Bari Aldo Moro, aiming to identify the most important contributing factors. This individual-level approach has emerged over recent years, and is generally known as 'Educational Data Mining', focused on the development of ad hoc methods that can be used to discover regularities and new information within databases from contexts related to education. Using supervised classification methods, we are able to identify retrospectively the profile of students who are most likely to dropout.

The number of adult learners who participate in online learning has rapidly grown in the last two decades due to online learning's many advantages. In spite of the growth, the high dropout rate in online learning has been of concern to many higher education institutions and organizations. The purpose of this study was to determine whether persistent learners and dropouts are different in individual characteristics (i.e., age, gender, and educational level), external factors (i.e., family and organizational supports), and internal factors (i.e., satisfaction and relevance as sub-dimensions of motivation). Quantitative data were collected from 147 learners who had dropped out of or finished one of the online courses offered from a large Midwestern university. Dropouts and persistent learners showed statistical differences in perceptions of family and organizational support, and satisfaction and relevance. It was also shown that the theoretical framework, which includes family support, organizational support, satisfaction, and relevance in addition to individual characteristics, is able to predict learners' decision to drop out or persist. Organizational support and relevance were shown to be particularly predictive. The results imply that lower dropout rates can be achieved if online program developers or instructors find ways to enhance the relevance of the course. It also implies that adult learners need to be supported by their organizations in order for them to finish online courses that they register for.

Since a high dropout rate for university students is a significant risk to local communities and countries, a dropout prediction model using machine learning is an active research domain to prevent students from dropping out. However, it is challenging to fulfill the needs of consulting institutes and the office of academic affairs. To the consulting institute, the accuracy in the prediction is of the utmost importance; to the offices of academic affairs and other offices, the reason for dropping out is essential. This paper proposes a Student Dropout Prediction (SDP) system, a hybrid model to predict the students who are about to drop out of the university. The model tries to increase the dropout precision and the dropout recall rate in predicting the dropouts. We then analyzed the reason for dropping out by compressing the feature set with PCA and applying K-means clustering to the compressed feature set. The SDP system showed a precision value of 0.963, which is 0.093 higher than the highest-precision model of the existing works. The dropout recall and F1 scores, 0.766 and 0.808, respectively, were also better than those of gradient boosting by 0.117 and 0.011, making them the highest among the existing works; Then, we classified the reasons for dropping out into four categories: “Employed”, “Did Not Register”, “Personal Issue”, and “Admitted to Other University.” The dropout precision of “Admitted to Other University” was the highest, at 0.672. In post-verification, the SDP system increased counseling efficiency by accurately predicting dropouts with high dropout precision in the “High-Risk” group while including more dropouts in total dropouts. In addition, by predicting the reasons for dropouts and presenting guidelines to each department, the students could receive personalized counseling.

In school dropout prevention programs, researchers emphasize the importance of addressing all educational components and stakeholders (e.g., Burzichelli et al., 2011). Among these, sport is recognized as a key factor for(re)integration (Siedentop et al., 2019). This article examines the impact of various specific sport programs across France on students' engagement at school. The nationwide study focuses on students' subjective perceptions of these initiatives. Although the programs differ, they share a common feature: they are based on cooperation and mainly organized by the students themselves, with support from their teachers, particularly physical education teachers. Semi-structured interviews were conducted among 19 young people aged 12 to 18. These interviews focused on their personal school and life trajectory, their perceptions of school, of sport and of the specific sport project. Collected data (speech) was then analyzed using ALCESTE lexical analysis software (Reinert, 1990, 1994).