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This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

This paper aims to provide a parsimonious but rigorous model to assist decision-makers to determine critical factors which can lead to higher graduation rates amongst higher education institution (HEI) participants. It predicts the odds of dropout amongst university students, using HEI data from a developing country. This is used as a basis for a Student Retention Predictive (SRP) Model to inform HEI administrators about predicted risks of attrition amongst cohorts.

A classification tool, the Logistic Regression Model, is fitted to the data set for a particular HEI in a developing country. The model is used to predict significant factors for student dropout and to create a base model for predicted risks by various student demographic variables.

To reduce dropout and to ensure higher graduation rates, the model suggests that variables such as age group, faculty, academic standing and cumulative GPA are significant. These indicative results can drive intervention strategies to improve student retention in HEIs and lessen the gap between graduates and non-graduates, with the goal of reducing socio-economic inequalities in society.

This research employs risk bands (low, medium and high) to classify students at risk of attrition or drop out. This provides invaluable insights to HEI administrators in the development of intervention strategies to reduce dropout and increase graduation rates to impact the wider public policy issue of socio-economic inequities.

This study aims at developing an instrument to measure the latent trait propensity to drop out in face-to-face higher education.

Based on Tinto's student integration theory, a 27-item scale was created to measure student propensity to drop out of undergraduate programs. Item response theory was used to evaluate the psychometric analysis of the items. Furthermore, different methodologies were used to evaluate and provide evidence for content validity, response process validity, internal structure validity and criterion-related validity.

With the support of specialists in the construct, the interpretation of the scores for the use of the scale was defined in four levels of propensity: high, moderate, low and very low.

The latent trait propensity to drop out in face-to-face higher education allows the inclusion of new items and aspects in the instrument. Thus, it can be adapted to distance education.

The students' propensity to drop out score can be useful for researchers and administration units in colleges and universities in the planning of permanent institutional actions and programs to take preventive measures.

Minimize dropout in order to raise the educational level of the population and make better use of the resources invested in education.

This study points out when, why and how propensity to drop out can be measured and how scores can be interpreted in the context of the problem.

Recognizing the effects of dropping out of high school on society, several questions still remain: (a) Why do urban, African American male students drop out of high school? and (b) What makes these students more prone to dropping out than their counterparts who remain in school? In an attempt to better understand the lives and circumstances of African American males, this study uses a comparative case method to examine similarities and differences in the life histories of a matched sample of African American male high school graduates and dropouts. This study, specifically, focuses on how the interrelated factors of family, neighborhood, peers, and education may shape these youths’ perspective on school. Findings reveal that, while both groups experience high levels of risk factors, high school dropouts had significantly more risk experiences in the family, community, and criminal justice domains. The dropouts also had fewer protective factors in these domain areas. Based on the findings, recommendations are offered.

The high dropout rate among online learning students is a serious issue. Using the theory of planned behavior as a framework, this study investigates what effect attitude, opinion of others and perceived ease of online learning technologies leave on Pakistani online students' persistence intentions.

The sample of this study comprises 320 students enrolled at a distance learning university in Pakistan. Online questionnaires are used to gather data for the study. Correlations and regression analysis are run to figure out the effect of independent variables on the dependent variable of the study.

The findings of the study show that 51% variance in online students’ persistence intentions can be explained by personal attitude, subjective norms and perceived behavioral control.

The use of a non-random sampling technique along with a cross-sectional design form the major limitations of the study.

The outcome of the study may help online education providers as well as policymakers to design programs and initiatives to improve students’ retention in online study programs.

The study contributed to the extant literature by finding out Pakistani online students’ persistence behavior is affected by their attitude, subjective norms and perceived ease of online learning. The study also found that the opinion of people closely related to students influences their study persistence decisions.

The purpose of this study is to identify the learner characteristics attributable to the likelihood and the duration of programme completion in the Bachelor of Science (BSc) and Bachelor of Technology Honours in Engineering (BTech) degree programmes of the Open University of Sri Lanka (OUSL).

Data were gathered from the re-registrants for the degree programmes in the academic year 2020/2021, using a questionnaire developed as a Google form. The sample consisted of 301 and 516 re-registrants from the BTech and BSc programmes respectively. Influential factors were identified using Kruskal Wallis test (for duration of completion), binary logistic regression (for likelihood of completion) and Chi-squared test (associations between presage and process factors).

Entry qualification, age and time management skills at entry had significant effects on duration of completion. Attendance at academic activities, organizing time for self-studies and the competency in English at enrolment had significant effects on the likelihood of completion. Prior open and distance learning (ODL) experience had no significant effect on any of the product factors considered.

Inaccessibility of dropouts and using only the responses from the first administration of the questionnaire are limitations. Active learners are more likely to respond, in the first administration and may bias the results.

Findings are useful for designing future studies to identify at-risk students and thereby enhance the programme completion and reduce prolonged time for completion.

Effective strategies to control the identified factors will uplift programme completion and reduce drop-out rates.

Decision making using inferential techniques makes the study distinct among studies undertaken on the same population. The study enriches the limited current research on factors affecting programme completion in ODL mode.

The purpose of this paper is to reveal how support services for open and distance students are designed, developed and implemented to ensure successful learning to take place, with specific references to the Open University of Sri Lanka (OUSL) and Universitas Terbuka (UT) Indonesia. Success in distance learning is one major challenge for open universities to respond to expectations of students and stakeholders. This study focuses on the strategies of student support services in OUSL and UT, investigating related factors including instructional design and development, learning engagement and motivation, policy and strategy in reducing dropouts, use of OER/MOOCs, and quality assurance.

A qualitative study was employed involving analyses of documents; interviews and focus group discussion with senior administrators, academic staff, students; and on-site observation in locations of teaching and learning.

This research is exploratory in nature. Findings of the study are expected to improve our understanding of student support in distance learning, in which analysis is based on good practices, challenges and rooms for improvement of both OUSL and UT.

Findings of this study reveal practices and lessons learnt that may be useful as reference to open universities, taking into considerations the fact that each open university has been established to address specific challenges in its own unique circumstances.

This research may be adopted as baseline framework for analysis of student support for open universities. Further in-depth study is needed to understand how various aspects of student support contribute to success in open and distance learning.

The purpose of the study is to determine the impact that the crucial determinants have on the behavioural intention (BI) of undergraduates to adopt Massive Open Online Courses (MOOCs), addressing two perspectives that are students' technical experience and psychosocial factors.

This is a questionnaire study, and all the respondents are undergraduates in Barcelona, Spain. A total of 188 students are selected as survey subjects. Data collection applies the five-point Likert scale, and the collected information refers to demographic information and constructs related to the technology acceptance model (TAM) and theory of planned behaviour (TPB). Confirmatory factor analysis (CFA) and partial least square (PLS) are used to examine the measurement model's convergent and discriminant validity and test the hypotheses implementing the bootstrapping method.

The results show perceived usefulness (PU) and perceived ease of use (PEOU) are influential in forming an attitude (ATT), and PU could directly impact BI significantly. Additionally, the mediating role of ATT in the relationships amongst PU, PEOU and BI are investigated. Besides, perceived behavioural control (PBC) is found a critical variable and significantly impacts BI, whereas subjective norms (SN) is not found statistically significant in explaining BI in this study.

This study has confirmed that ATT has a positive mediation effect on PU, PEOU and BI. This study has also confirmed the validity and model fit of the incorporated model of TAM and TPB, which sufficiently accounts for the planned behavioural process of undergraduates to accept a MOOC and contributes to validating literature of the incorporated model of TAM and TPB. Besides, this study provides MOOCs managers with practical suggestions. First, ease-of-use interfaces should be designed and intensified. Second, more channels should be explored to satisfy students' needs. Third, managers should intensify the leverage of the systems and introduce the free trial period to new learners. Fourth, a bonus mechanism could be adopted to motivate students. Last, manager should intensify the construction of oral advertisements amongst students.

Student dropout rates have been a source of contention throughout many Universities worldwide. Universities recognize this fact and many have acted to improve their induction process for students joining the University in the first year. However, student dropout rates continue to be higher than acceptable and it is in this context that this project has been undertaken. However, quite often there is no additional support provided to overseas students arriving on the campus. Frequently, these students are taken into final year and miss all the inductions aimed at year 1 students. The purpose of this research is to investigate the feasibility of designing an information portal in the Chinese language to assist new arrivals on campus.

The authors started with the premise that any additional information that can make life easier for these foreign language students is to be welcomed. Mobile communications is a continually growing sector in industry and a wide variety of visual services such as video‐on‐demand have been created which are limited by low‐bandwidth network infrastructures. The distinction between mobile phones and personal device assistants has already become blurred with pervasive computing being the term coined to describe the tendency to integrate computing and communication into everyday life.

The authors believe that placing location based campus information on a mobile device outlining key information concerning the University of Ulster at the Magee campus in the Chinese language will assist overseas students greatly in the crucial early days of arrival at the University.

This paper outlines the unique implementation of a multilingual Chinese–English Campus information portal for mobile devices which assists fresh arrivals in locating important information about the university. The research finds that students are assisted by mobile portals created in their native language directing them to important sources of information on campus.

The purpose of this paper is to analyse data on first-year students’ needs regarding academic support services and reasons for their intention to leave the institution prior to degree completion. On the basis of the findings, a digital badge outline is proposed which could contribute to improved communication of academic requirements in order to help students to better adapt to higher education demands. Digital badges might also serve as an indicator for students’ needing additional academic support services.

An online-questionnaire was conducted with 730 first-year students at a German university. Participants’ responses to open-ended questions were coded and categorised. On the basis on these findings, an outline for a digital badge programme is proposed.

Participants seek the most institutional support regarding research skills and organisational aspects. Main reasons for participants’ intention to withdraw from the institution include difficulties with their programme choice.

These findings may enable higher education institutions to provide targeted support services that meet first-year students’ needs. On the basis of the findings, higher education institutions can create digital badge programmes, which may improve communication of academic requirements and may also serve as a platform for a staff-student conversation about expectations and demands for a successful first-year experience. Besides, further research and discussion may address using digital badges for learning analytics algorithms to even better identify students’ strengths and needs for targeted academic support services and enhanced student success in higher education.

Little is known about first-year students’ needs for institutional support and reasons for thinking about dropout in Germany. Understanding the student perspective is crucial for enhancing student retention. Digital badges are an emerging educational technology in higher education and they have the potential to target academic requirements, which may guide first-year students and help them to better adjust to universities’ demands.