The article addresses the problem of gainful employment undertaken during full-time studies. It analyzes the importance of students’ motivations to work and the selected effects of combining studies and work. It refers to areas that have not yet been investigated by other researchers. The data used in the article come from the survey conducted by the author at the Faculty of Economics, at the University of Economics in Katowice through 2014-2017. The study revealed a strong relationship between the motivations to start work during studies and the following factors: the alignment of a chosen job to the field of study, the opportunity to develop new skills and competencies valued on the labor market, the willingness to continue working for the same employer after graduation, and an employer’s intention to employ a student after graduation. Another connection was identified between the character of the work performed by students and their readiness to change if given another opportunity. The relationship, albeit relatively weak, was also confirmed between the character of the work performed and difficulties experienced by students with combining work with studies and the ability to maintain a balance between time assigned to studying, work and leisure.

The purpose of the research was to identify whether the English language e-classes that are automated and delivered through the Smart Sender platform influence the students’ attendance and procrastination rates, their motivation, time management skills, cognitive processing speed, and satisfaction. The study used qualitative and quantitative methods to monitor students’ attendance and procrastination rates, motivation and engagement, time management skills, thinking speed, and satisfaction. The questionnaire on learning motivation, engagement, and competence, the time management skills test, the mental speed test, and the course satisfaction questionnaire were used to collect data. The focus group discussion questionnaire was used to obtain verbal feedback for the participants. The Smart Sender platform proved effective as an instructional tool for teaching the English Language to students majoring in Philology, International Business, and Law. The automated delivery of the English language e-classes was effective in addressing the issues of dropouts and procrastination in distance learning through automation of the lesson delivery based on the ‘push’ factor. It increased students’ motivation, improves time management skills, and satisfaction. The quantitative findings showed that the students experienced a positive change in attendance, motivation and learning engagement, time management skills, and thinking speed due to the intervention. The students perceived the automated delivery-based approach to language teaching positively. They reported that the delivery approach content met the participants’ expectations and needs. Focus group discussion revealed that the intervention changed their learning behaviour and strategies which were considered the improvements of the quality learning outcomes.

The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one, being: type of high school, number of semesters taken, grade-point average, lockouts, dropouts and course terminations. The data treatment considered the manual removal of several characteristics that did not add value to the output of the algorithm, resulting in a package composed of 2184 instances. Thus, the logistic regression, MLP and XGBoost models developed and compared could predict a binary output of graduation or non-graduation to each student using 30% of the dataset to test and 70% to train, so that was possible to identify a relationship between the six attributes explored and achieve, with the best model, 94.15% of accuracy on its predictions.

In Austria, segregated German language support classes (GLSC) were introduced in the school year 2018/19 to intensively support students who had previously little or no contact with German, the official language of instruction. These classes have been widely criticised; however, a formal evaluation of their effects has yet to be published. In absence of this evaluation, this article describes the language support model as it currently exists in Austria and reviews existing evidence about its efficacy. The literature review synthesises findings from educational research undertaken in other contexts that offer insight into features of ‘good practice’ in language support models. The article then explores the extent to which GLSC comply with these features. As such, this review allows insights into ways of ensuring students’ language and socio-emotional development – all central aspects of academic success – in language support models. It therefore allows research-informed understanding of the effects of the newly implemented model of German support classes in Austria and makes recommendations for further development.

Academic failure and school dropout, or early school leaving, are two of the situations that most concern countries and educational institutions worldwide, because of their prevalence and also their economic and social implications. Despite this prominent role that academic failure and school dropout have in societies, there seems to be no consensus on the literature on their conceptualization, definition, and relationship. Moreover, it is frequent to observe how both concepts are confused or overlap in the scientific literature and how many authors avoid defining these constructs, using them indistinctly. Therefore, this work analyses whether educational research considers them as two different concepts or if they are used indistinctly. For this purpose, 2,051 keywords from 450 articles were subjected to a systematic review and classified into the Education Resources Information Center (ERIC) thesaurus´ descriptors. The results reveal statistically significant differences in the descriptors according to the type of paper to which they correspond (academic failure or dropout). Thus, academic failure is associated with sociocultural, personal, and academic factors, while dropout is linked to employment and educational trajectories. These differences evidence that, although academic failure and school dropout refer to closely related educational problems, there are remarkable differences between them and between the treatment given to each of them in the scientific literature. Therefore, they should be considered as two different concepts. For all this, keyword analysis has proved to be a relevant element for the study of the structure of knowledge, allowing to clearly establish the differences between the two closely related concepts.

Numerous events occur in students' educational trajectories that are linked to school success. Some of these events are related to school-related factors. Moreover, these factors alter the quality of students' engagement, generating the risk of dropping out of school. The objective of this research has been to explore, compare and understand the different events that occur in the school trajectories of at-risk youths that are related to the existing dynamics in schools. In order to achieve this objective, a narrative research based on the life stories approach was developed. For the reconstruction of the stories, the technique of in-depth interviews and mixed data analysis was used, by means of different analysis techniques. The main conclusions reached after the research have been highly relevant for studies on educational trajectories of at-risk youth. The different factors associated with schools affect the trajectory and involvement of students. There are certain dynamics that have a greater presence in some stages or others, however, all of them can positively or negatively affect the quality of student engagement. Finally, it is shown that the key lies in the way in which the different dynamics of schools develop, i.e., how the dynamics associated with certain factors develop.

This study seeks to investigate the relationship between dropout intent, the weekly work duration of student employees, and university social capital by analysing empirical evidence from three European countries, including Estonia, Lithuania, and Poland. This exploratory study utilised Eurostudent-VII survey data and employed cross-tabulation and exhaustive Chi-square Automatic Interaction Detection (CHAID) to achieve its objectives. Findings indicate that student employees who believe they get along well with their teachers and have more connections with fellow students to discuss subject-related issues are less likely to intend to drop out of university. In addition, the results show that students’ likelihood of abandoning their higher education increases in the presence of difficulties caused by an inapt academic programme. Regarding employment duration, for the Estonian and Lithuanian markets, there is no difference between working more than 20 hours per week or less than that with the intention of dropping out of university. In Poland, however, the disparity in working hours interacts with other factors related to social capital to explain dropout intent. These findings provide novel insights into the dropout literature by refreshing thoughts on the role of teacher-student and peer relations in the dropout intentions of student employees. In addition to reviving the relevance of university social capital, which has received too little attention lately, they have also sparked a recent debate on whether or not combining work and university actually affects the intention to drop out.

University students may encounter situations where they perform poorly in a course and contemplate dropping out. This intention to drop out of a course manifests not only in thoughts or ideas but also in a cognitive self-evaluation of their performance and skills, enabling them to reflect on the possibility of dropping out. In this sense, there is a shortage of instruments that evaluate the intention to drop out of a course, so the aim was to develop and validate the Course Dropout Intention Scale (CDIS). Data from two samples (N1 = 198; N2 = 675) were used; the first was for the EFA, and the second was for the CFA, GRM, and SEM. The one-factor model was derived from the EFA and confirmed in the second sample, exhibiting appropriate goodness-of-fit indices. Similarly, the GRM obtained adequate fit indices; all items discriminated adequately, and the difficulty parameter had a monotonic increase. The SEM model of the effect of satisfaction with studies on the CDIS showed a negative and statistically significant effect. Thus, it was demonstrated that the CDIS is a robust instrument in its psychometric properties and empirical evidence with other variables.

Although central governments, particularly in Latin America and the Caribbean, have defined reducing school dropout rates as a priority, and drawn policies accordingly, there are still young people who do not finish secondary education, and numbers are still alarming. Therefore, it is necessary to observe educational communities and analyze how they interpret and implement guidelines issued by the central government. The following study sought to describe the institutional and teaching practices deployed by four high schools in Valparaíso (Chile) in order to achieve student retention. A qualitative approach was employed. The management team, support professionals, teachers, students, and their families were interviewed. The information gathered was analyzed using the Grounded Theory. As a main finding, establishments use practices such as monitoring attendance, providing support to students facing problematic situations, and encouraging them during class, through a series of strategies. It is recommended that researchers implement this type of methodology for other study objectives, and that the central government consider these results to provide feedback on its policies.

It is known that the COVID-19 pandemic led to learning losses among students both domestically and internationally. Therefore, situational and casual factors were examined to discover and understand them so that learning loss could be reduced or recovered from. This research aimed to: (a) study learning loss situation; and (b) develop and examine the causal model of learning loss among lower secondary school students affected by the pandemic. The sample included 650 Grade 7-9 students selected by multi-stage random sampling. The data was collected using a self-developing questionnaire as a research instrument. The data was analyzed using descriptive statistics, independent samples t-test, ANOVA, and structural equation modeling (SEM) through the LISREL program. The findings were: (a) Lower secondary school students had an average academic achievement learning loss at the moderate level with the highest mean of learning loss in mathematics (M=3.012, SD=1.074), and an average learning characteristics learning loss at the medium level (M=2.824, SD=0.842). Several situational factors had a different effect depending on the school size with a statistical significance of .05.; and (b) the causal model showed the learning loss of grade 7-9 students was consistent with the empirical data (χ2=46.885, df=34, p= .069, GFI=0.991, AGFI=0.964, CFI=0.999, RMSEA=0.024, SRMR=0.014).