Occupational Training in High School: When Does it Pay Off?

[Excerpt] About half of all youth either do not complete high school or end their formal education with the high school diploma. Even higher proportions of minority, disadvantaged and handicapped youth do not enter postsecondary education. Should public schools offer these youth occupationally specific education and training? If so, what form should this education take? Should the goal of the occupational component of high school vocational education be occupationally specific skills, career awareness, basic skills or something else? What should be the relationship between programs providing occupationally specific training and the employers who hire their graduates

Massive Open Online Courses Temporal Profiling for Dropout Prediction

Massive Open Online Courses (MOOCs) are attracting the attention of people all over the world. Regardless the platform, numbers of registrants for online courses are impressive but in the same time, completion rates are disappointing. Understanding the mechanisms of dropping out based on the learner profile arises as a crucial task in MOOCs, since it will allow intervening at the right moment in order to assist the learner in completing the course. In this paper, the dropout behaviour of learners in a MOOC is thoroughly studied by first extracting features that describe the behavior of learners within the course and then by comparing three classifiers (Logistic Regression, Random Forest and AdaBoost) in two tasks: predicting which users will have dropped out by a certain week and predicting which users will drop out on a specific week. The former has showed to be considerably easier, with all three classifiers performing equally well. However, the accuracy for the second task is lower, and Logistic Regression tends to perform slightly better than the other two algorithms. We found that features that reflect an active attitude of the user towards the MOOC, such as submitting their assignment, posting on the Forum and filling their Profile, are strong indicators of persistence.Comment: 8 pages, ICTAI1

A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates

Offering behavioral assistance to Latino students demonstrating challenging behaviors

Challenging behaviors can significantly alter the learning environment of any classroom. Traditionally, schools have implemented practices that remove the offending student from the classroom, deliver punitive disciplinary actions, or refer the student to special education evaluation. Unfortunately, such practices have demonstrated little longitudinal effectiveness, with detrimental outcomes for the referred student, particularly students from Latino backgrounds. With enrollment projections indicating Latinos will become the majority in U.S. schools, educators are presented with the opportunity to shift away from past practices and implement evidence-based practices that concurrently assist students while addressing challenging behaviors. In this paper, the authors discuss past disciplinary practices, the adverse effects on Latino students, and offer recommendations on implementing functional behavioral assessment as a means to better meet the needs of Latino students demonstrating challenging behaviors.peer-reviewe

Predicting Dropout in Online Courses: Comparison of Classification Techniques

Due to the tremendous growth in e-learning in recent years, there is a need to address the issue of attrition in online courses. Predictive modeling can help identify students who may be “at-risk” to drop out from an online course. This study examines various categorical classification algorithms and evaluates the accuracy of logistic regression (LR), neural networks (Multilayer Perceptron), and support vector machines (SVM) models to predict dropout in online courses. The analyses with LR, MLP, and SVM indicated that current college GPA is the strongest predictor of online course completion

Explaining Student Retention: The case of the University of Aberdeen

Student retention has risen high on the political agenda in the UK as part of the government’s priorities to widen participation in higher education, in particular among groups traditionally under-represented in the sector. These concerns have been reflected in policies of the funding bodies in the UK. In turn Universities across the UK have become increasingly active in developing processes and procedures to meet the challenges of improving student retention while simultaneously widening access and participation in the context of rising student numbers overall. This has led to the desire for accurate data and reliable statistical analysis on which to inform policy at the University of Aberdeen. The purpose of this report is to answer the question: “To what extent can the probability of drop out of a student be explained by student characteristics?” Are mature students more likely to drop out? Is there an empirical distinction between younger and older mature students? Are male students more prone to dropping out? To what extent can the level of entry qualifications explain dropouts? Are there any differences in the impact of below core entry qualifications between male and female students? Do students who performed unsatisfactorily in their first year and who were allowed to repeat this first year drop out less or more often than other students? Have there been any significant trends over time? It is clear that any associations of these characteristics with drop out rates may have important policy implications for the University as it may allow the identification of those potentially “at risk” before they join the University and hence facilitate the targeting of support once students start their studies

Representative Bureaucracy, Ethnicity, and Public Schools: Examining the Link Between Representation and Performance

Demographic changes in the United States have led to challenges for public organizations that are tasked to serve shifting target populations. Many arguments exist for including greater numbers of ethnic minorities among an organization's personnel, under the guise that greater ethnic representation will result in greater competitiveness in the market or effectiveness in governance. This paper tests this proposition empirically, using data from the public education policy setting. Results show that representativeness along ethnic lines leads to gains for the organization as a whole, but some segments of the target population appear to respond more positively to representativeness than others. Working Paper 06-1

Using Prediction ML algorithm for predicting early Student Attrition in Higher Education

This research aims at using predictive models that enable us to predict students who are at risk of dropping out and identify the factors that possibly lead to this dropout. Through the results obtained, concerned stakeholders will be able to effectively develop strategies and initiatives to help decrease the percentage of students’ attrition. There are different reasons why students drop from their courses which could be related to academic issues or personal issues that stop them from being active students. Due to these many reasons of students dropping out, universities are impacted negatively in terms of the financial costs as they lose an amount of money from those students, and sometimes they lose the funds from public sponsors to major activities in universities. The proposal aims at exploring the various reasons that influence students’ decision to withdraw and what will be the best model for the prediction. I will use data from the open-source Kaggle and use Python to explore and preprocess the data. I will also use Tableau for getting visual insights from the available dataset