Analysis of causes of early dropout of students higher education

The purpose of this document is to analyze the causes and effects of early dropout in higher education students in Ecuador, especially in those students who entered the university for the career leveling course. The work was based on a mixed approach, a longitudinal retrospective non-experimental design and with this information relevant to the results was investigated in reference to the final certificates and historical records of qualifications corresponding to the career leveling courses of the engineering areas from the Technical University of Ambato, between 2011 and 2017. The results date that, of the total enrolled, there was a considerable dropout percentage of approximately 39%, additionally dropout is more frequent in subjects such as mathematics, physics or chemistry, figures that They do not agree with the report of the National Exam for Higher Education released in 2018, especially in Mathematics. Keywords: Leveling, Mathematics, Academic dropout. References [1]M. 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Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

BackgroundDigital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant’s goal achievement.MethodsWe included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy.ResultsFrom the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69–0.73) and (0.71 95%CI 0.67–0.76), respectively, followed by cannabis (0.67 95%CI 0.59–0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success.DiscussionUsing log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention.

The Persistent Effects of Short-Term Peer Groups on Performance: Evidence from a Natural Experiment in Higher Education

This paper studies the persistent effects of short-term peer exposure on long-run performance in a college setting. I exploit the random assignment of undergraduates to peer groups during a mandatory orientation week and track the students’ performance over four years (until graduation). Assignment to orientation week groups with high levels of peer ability is associated with lower performance during the first year at college and a higher probability of early dropout. These adverse effects are driven entirely by the exposure of low-ability students to high-ability peers. Beyond the first year, exposure to higher peer ability during the orientation week negatively affects selection into the college’s most popular major (business administration) and final grade point average. Taken together, the findings suggest that the composition of short-term peer groups matters for individual choices and long-run performance outcomes. This paper was accepted by Yan Chen, decision analysis.

Early Dropout of Children and Youth from Sports – International Perspective and Societal Backgrounds

Sport and physical activities of children are essential in forming their health, personality, society and other factors which affect their future life either directly or indirectly. Their life attitudes are shaped by experience, and one of domains that can be affected in them for ever based on positive or negative experience is physical activity and relation thereto. Significance and awareness of this societal problem currently lead to activities which are to support sports and physical movement of children and youth. Efforts focused on the prevention of early dropout of children from sports are in the interest of kinanthropological research studies, national children’s sports support programmes where the issue often becomes part of political and programme statements of governments, civic and non-profit organizations and sports associations. In spite of all these efforts, however, we still face a massive dropout of children from sports, which is not replaced with an adequate alternative physical activity. Consequences of the negative, and sometimes even toxic experience with physical activity at early age lasts until adulthood, which brings a range of personal, health and social problems. Possibilities for reducing the phenomenon consist in systematic work dealing with the support and improvement of coaching procedures which will be focused more on the needs of children and diverted from the traditional perception of coaching education focused primarily on the needs of coaches, on the building of positive relationship with parents as partners in the process of physical education of children, and on extending the range of physical activities for children also in the environment of non-competitive sports.

Construction and Validation of an Arabic Questionnaire on the School Dropout Factors (QSD)

This article presents the principles of construction and validation of a questionnaire on the phenomenon of school dropout by illustrating various aspects from a measurement tool prepared to study the different factors of this reality. The standardized theoretical models were adapted and completed for the needs of the study. Educational institutions and, in particular, middle school must establish a clear vision and mission regarding the training that every social professional will demand. School has always played the role of a pupil’s education, but the fact that a significant number of secondary students are abandoning their programs without having completed their course of studies, is a specific symptom of an educational crisis that is occurring within them. For this reason, the present study aimed to validate a key tool to establish the causes, whether endogenous or exogenous as to why pupils leave college without having completed their qualification. Different aspects of validation are discussed: The acceptability by studying data concerning the description of school dropout, the validity of constructing a score on the causes of school dropout, the reliability of the components of the questionnaire, the validity of the construct of the tool. The full questionnaire, with the origin of the questions, instructions for the interviewers and coding mode are presented in the methodology. The questionnaire design, consisting to determine the reasons of early dropout Tunisian pupils. The questionnaire took into account the theoretical proposals of several scientific researches. The instrument developed was validated with a sample of 750 pupils (including 675 respondents) in Tunisian colleges with a national dropout rate of 10%. The respondents (317 girls and 358 boys) have an average age of 14.11 years. The 68 items questionnaire was designed to identify, among the population of pupils quitting their school, 7 categories of factors that potentially lead to Tunisian children dropping out, Institutional, Sociological, Economic, Personal, Family, Cultural and Geographical- with their respective subcategories. Knowing the reasons why college pupils abandon a middle school in particular will allow educational actors to analyze administrative and/or academic requirements and take mitigation measures to minimize school dropout.

Addiction Therapeutic communities: residents’ retention, early dropout, and their correlates over fourteen years

Background Long-term retention is a reliably well-studied factor associated with enhanced outcomes in addiction therapeutic communities (ATCs). Staying no less than three months is considered to be a critical time for program effectiveness. I plan to estimate retention rates of Saudi AATCs for three months, completion of therapy (stay at least six months), and early abandonment and investigate its correlations in this study. Methods A cohort retrospective study where data of all residents admitted to all Saudi ATCs since their establishment in 2000 through September 2014 were collected from their AATCs files. At the time of the study, there were five AATCs, two of them in Dammam, one in Riyadh, one in Jeddah, and the fifth one was in Taif. Date of admission, date of discharge, socio-demographic variable, and type of drug used of all of the five ACS were reported. Retention rate at 3 and 6 months and dropout in the first week were calculated. Results out of the 2050 files, 2003 of data was suitable for analysis. All of the residents were male adults. More than two-thirds of patients were younger than 40 years of age and most of them were singles (64%), unemployed (68%), and had intermediate or secondary school education (73%). Forty-six percent of patients reported Opioids use, 36% hash, 34% amphetamine, and 19% reported alcohol abuse. The retention rate for three months and six months was 45% and 28% respectively, and 8.3% dropped outs in the first week. The median duration of stay was 77 days. Residences in TC 1, TC 2, and TC 4 were less likely to stay for more than three months. Unemployment, and being students was associated with completion of treatment (stay > 6 months), while admission to TC 1 and TC-2 was associated with drop out prior completing the treatment program. Conclusion Three-month retention rates of 45% in Saudi addiction ATCs is reasonable and consistent with reported rates worldwide. In addition, treatment completion rate and drop out within the first week are at least comparable to the average rates recorded elsewhere. These rates can be considered as indicators for successful Saudi ATCs programs. However, there have been substantial variation between the different Saudi AATCs which may require further exploration to determine the factors related to these disparities.

Early Dropout Prediction Model: A Case Study of University Leveling Course Students

The school-dropout problem is a serious issue that affects both a country’s education system and its economy, given the substantial investment in education made by national governments. One strategy for counteracting the problem at an early stage is to identify students at risk of dropping out. The present study introduces a model to predict student dropout rates in the Escuela Politécnica Nacional leveling course. Data related to 2097 higher education students were analyzed; a logistic regression model and an artificial neural network model were trained using four variables, which incorporated student academic and socio-economic information. After comparing the two models, the neural network, with an experimentally defined architecture of 4–7–1 architecture and a logistic activation function, was selected as the model that should be applied to early predict dropout in the leveling course. The study findings show that students with the highest risk of dropping out are those in vulnerable situations, with low application grades, from the Costa regime, who are enrolled in the leveling course for technical degrees. This model can be used by the university authorities to identify possible dropout cases, as well as to establish policies to reduce university dropout and failure rates.