Do Institutions Matter in Student Engagement? A Comparison in Engagement Between Two Universities in Different Contexts

Academic engagement has been studied for several years because of its influence on student attrition. According to Tinto, engagement is the most important predictor for student dropout, which makes it relevant to understand how the environment influences engagement. Yet very few studies have addressed this relationship outside higher income countries. The results of a 2 × 2 factorial multivariate analysis of variance (MANOVA) suggest significant differences in engagement means between students from one American and one Bolivian university.

Predicting student performance in a blended learning environment using learning management system interaction data

PurposeStudent attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.Design/methodology/approachThis study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.FindingsIdentifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.Originality/valueThe best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

Faktorok, magyarázatok a sikeres és sikertelen felsőoktatási tanulmányi teljesítmény hátterében

CélkitűzésAz utóbbi évtizedek egyik legjelentősebb felsőoktatási problémája a nagyarányú hallgatói lemorzsolódás vagy a diploma nélküli kilépés. A jelenség megértéséhez többféle elméleti és módszertani megközelítést használtak a kutatók. Tanulmányunk célja, hogy áttekintést nyújtson a lemorzsolódás témában megjelenő cikkek szemléletmódjáról, fókuszpontjáról és néhány eredményéről. A különböző megközelítésmódok bemutatása segítheti a meglévő ismeretek integrálását, a további kutatások megtervezését és a beavatkozás programjainak kialakítását.MódszertanA lemorzsolódást vizsgáló magyar és angol nyelvű tanulmányok kiválasztása online adatbázisokból történt maghatározott kulcsszavak alkalmazásával. A tanulmányokat a kutatási módszerek (változó és személyorientált módszer) és a vizsgálatok fókuszában álló kérdések (lemorzsolódás okai, működési modellek, változók csoportjainak súlya, hallgatók alkalmazkodási mintázata) alapján soroltuk csoportokba. Eredmények: A témával foglalkozó szakirodalmi anyag áttekintése során négyféle csoport rajzolódott ki. Ezek közül három a változóorientált elemzést alkalmazta, míg a negyedik a személyorientált vizsgálatok csoportját alkotta. A tanulmány részletesen bemutatja e négy témacsoportot: 1. Befolyásoló vagy okozó faktorok csoportosítása; 2. A lemorzsolódást magyarázó modellek; 3. Pszichológiai változók és ezek súlyának azonosítása; 4. Különböző változók mintázata alapján kialakított hallgatói csoportok tanulmányi alkalmazkodása.KövetkeztetésekA megvitatás kiemeli az egyes megközelítésmódok előnyeit és gyenge pontjait a lemorzsolódás jelenségével kapcsolatban. A tanulmány utolsó fejezete azokat az új szempontokat emeli ki, amelyek a további lemorzsolódáskutatásban és az intervenciós programokban is alkalmazhatók.GoalsThe most significant challenge of higher education is dropout from college or leaving universities without degree in the last decades. In order to understand dropout phenomenon different theoretical and methodological approaches have been applied. The aim of our study is to overview the approaches, the focuses and the results of different studies concerning the student attrition. The description of different approaches can help to integrate existing information, to plan research proposals and to design intervention programs focusing on dropuot.MethodsThe studies about dropout in English and Hungarian languages have been selected from online database using relevant keywords. Papers were grouped according to their methods (variable- or person-oriented), topics and issues (causes of dropout, models, weight of variables, adjustment patters of students). Results: Four groups have been formed based on the approaches of articles during the review. Three of them use variable-oriented analysis, and the fourth group consists of studies applying person-oriented approach. The four topic groups are analysed in detail: 1. Grouping of causal and influential factors, 2. Explanatory models of dropout, 3. Identifying psychological variables and its weight, 4. Academic adjustment of student groups formed by patterns of different variables.ConclusionsThe discussion highlights the advantages and weaknesses of different approaches concerning the dropout phenomenon. The last chapter of the paper emphasizes those new aspects which can be applied in further research on dropout and intervention programs.

Back out or hang on? An event history analysis of withdrawal from doctoral education in Germany

Despite the benefits of a PhD for degree-holders as well for society as a whole, doctoral student attrition is a common phenomenon. Unfortunately, the empirical literature on dropout from doctoral education is scant, especially for non-US countries—an omission we address in the current study. Building on Tinto’s model of student attrition and rational choice theory, the study empirically assesses the association of different individual, institutional, and external factors with the propensity to leave doctoral studies. Unlike most studies in the field, it draws on longitudinal data using event history modelling, observing doctoral students in multiple disciplines and a wide range of universities. The key results can be summarized as follows: In Germany, women are more likely to dropout than men. The probability of dropping out strongly depends on the discipline and the availability of a scholarship. A close contact with the supervisor and exchange with other PhDs are associated with a lower dropout probability. Moreover, having children increases dropout rates. The study findings provide first empirical guidance for interventions that can possibly help reducing dropout.

Prediction of student attrition risk using machine learning

PurposeThe prediction of student attrition is critical to facilitate retention mechanisms. This study aims to focus on implementing a method to predict student attrition in the upper years of a physiotherapy program.Design/methodology/approachMachine learning is a computer tool that can recognize patterns and generate predictive models. Using a quantitative research methodology, a database of 336 university students in their upper-year courses was accessed. The participant's data were collected from the Financial Academic Management and Administration System and a platform of Universidad Autónoma de Chile. Five quantitative and 11 qualitative variables were chosen, associated with university student attrition. With this database, 23 classifiers were tested based on supervised machine learning.FindingsAbout 23.58% of males and 17.39% of females were among the attrition student group. The mean accuracy of the classifiers increased based on the number of variables used for the training. The best accuracy level was obtained using the “Subspace KNN” algorithm (86.3%). The classifier “RUSboosted trees” yielded the lowest number of false negatives and the higher sensitivity of the algorithms used (78%) as well as a specificity of 86%.Practical implicationsThis predictive method identifies attrition students in the university program and could be used to improve student retention in higher grades.Originality/valueThe study has developed a novel predictive model of student attrition from upper-year courses, useful for unbalanced databases with a lower number of attrition students.

Administrative data analysis of student attrition in Hungarian Medical Training

Background Even though dropout is a well-researched topic in tertiary education, it is still not clear which variables have an impact on it beyond individual attributes. There is numerous empirical evidence supporting that college students studying in STEM fields are characterized by a higher risk of attrition than their peers. Even though medicine is not traditionally considered to be part of STEM disciplines, some suggest to include it, as the field of medicine is an important area in research focusing on student attrition. Since Hungarian medical training attracts more and more international students every year, the issue of attrition in this field of study can have a global impact too. Methods In our study we examined the dropout behavior of all medical students who started their studies in 2010 in Hungary (N = 977) by analyzing longitudinal administrative data of the students between 2010 and 2017, which unlike self-reported questionnaires made it possible for us to analyse data that without any kind of distortion. Since we analyzed the data of all students studying medicine in this period in Hungary, we conducted descriptive statistics and revealed the risk and protective factors of drouput using bonary logistic regression. Results Our results indicate that the risk of dropout can be increased by a low number of credits and passive semesters and the tuition-based forms of finance, although dormitory placement can serve as a protective factor. Conclusions Relieving the rigidity of the training network, more educational attention, targeted mentoring in the case of learning difficulties and dormitory placement in support of learning communities can be formulated as a policy proposal.

Online Student Support: A Framework for Embedding Support Interventions into the Online Learning Cycle

Support is one of the vital elements of online students’ success. Although many support strategies have been documented in the past, less is known at what stages of the learning cycle suggested interventions can be best embedded into the online learning curriculum. This paper aims to address this gap. First, it offers a systematic review of the empirical research on effective support interventions, as well as analyses recommendations for student support retrieved from the research on online student attrition and retention. Secondly, by utilizing an Inclusive Student Services Process Model, this paper indicates areas where considered strategies can be embedded into the online learning cycle. The analysis suggests that support strategies and services offered at different stages of students learning cycle and embrace all the aspects of the university experience, including administrative and pastoral, hold a great potential for ensuring online student success. Yet, there is a need for the development of the criteria of quality and effectiveness of existing support interventions.

The Indelible Challenges of Student Retention in Higher Education: Imperatives for a Closer Scrutiny

Student attrition remains a serious challenge for universities across the globe despite the extended attention it continues to attract. Given the meagre research available in the Global South and particularly in Africa, this study was conducted to assess the status of student attrition in 15 Ethiopian public universities. The study examined the scope, nature and causes of student attrition at the level of institutions, programs and gender wise. The findings revealed that the attrition rate at Ethiopian public universities manifests a high level of waste that goes against the national ambition of expanding higher education through wider participation and student success. Universities were also found deficient in terms of tracking the progress of their students, hampering their potential for follow up and early interventions. It is argued that closer scrutiny and robust responses are needed both at policy and institutional levels in order to bring about the improvements sought.

I Graduated. . . . Now What?

This chapter is a reflection upon the author's journey from completing a professional degree program until present day. It is the intent of the author to share lived experiences of a professional who has completed the doctoral degree with emerging completers, and those new to the profession. Having a relationship with multiple mentors can significantly enhance development in early adulthood and in the mid-career stage of the more experienced person. Existing research tends to focus on how mentoring can influence graduate student attrition rates. However, there is little evidence that researchers have approached the issue of navigating career placement after the doctoral degree. The aim of this chapter is to provide an overview of how new doctoral completers can be supported in post-doctoral career placement.