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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 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.

This study aims to advance the understanding on individual variations in PhD candidates’ personal interest in their doctorate and supervisory and research community support, and several individual and structural attributes potentially having an impact on the profiles.

The authors explored the interrelationship between personal interest – social support profiles, and nationality, gender, research group and study status and the risk of dropping out. A total of 768 PhD candidates from a research-intensive university in Finland responded to a modified version of the cross-cultural doctoral experience survey. Latent profile analysis was used to explore the individual variations in PhD candidates’ interest and support from the supervisor and research community.

Three distinctive PhD interest-social support profiles were detected; the high interest–high support profile (74.4%, n = 570), the high interest–moderate support profile (18.2%, n = 140) and the moderate interest–moderate support profile (7.4%, n = 56). The profiles exhibited high to moderate levels of research, development and instrumental interest. Individuals in the high interest–moderate support and in the moderate interest–moderate support profiles were more prone to consider dropping out from their PhD than in the high interest–high support profile.

The results indicate that by cultivating PhD candidates’ interest and providing sufficient supervisory and the research community offers a means for preventing candidates from discontinuing their doctorate. Hence, building a supportive learning environment that cultivates a PhD candidate’s personal interest is likely to reduce high dropout rates among the candidates.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

This study examined the psychometric properties of intention to drop out and study satisfaction measures for first-year South African students. The factorial validity, item bias, measurement invariance and reliability were tested.

A cross-sectional design was used. For the study on intention to drop out, 1,820 first-year students participated, whilst 780 first-year students participated in the study on satisfaction with studies. Confirmatory factor analysis (CFA), differential item functioning (DIF), measurement invariance and internal consistency were used to test the scales.

A one-factor structure was confirmed for both scales. For the intention to drop out scale, Items 3 and 4 were identified with statistically significant item bias; however, these differences had no practical impact. Except for scalar invariance for language, sufficient measurement invariance was established. No problematic items were identified for the study satisfaction scale.

In essence, this study provides evidence of two short measures that are culturally sensitive that could be used as short and valid measures across contextual boundaries as practically valuable tools to measure intention to drop out and study satisfaction in diverse and multicultural contexts.

This study contributes to limited research on bias and invariance analyses for scales that can be used in interventions to identify students at risk of leaving the university and utilising psychometric analyses to ensure the applicability of these two scales in diverse and multicultural settings.

This study aims to determine how school-based management (SBM) capacity developed through international cooperation functioned to overcome challenges during the coronavirus disease 2019 (COVID-19) pandemic in Nepal.

The research structure was designed based on the success case method. The researchers conducted a questionnaire survey of head teachers to understand schools’ responses during the COVID-19 pandemic period, identified likely success-case schools, held workshops at the likely success-case schools and conducted in-depth interviews with head teachers and school management committee (SMC) members to understand how SBM functioned and contributed to the success cases.

Storytelling from the success-case schools provided reliable evidence that the localised approaches of SBM are effective for planning and implementing suitable responses at school. The reviews of the head teachers showed that both head teachers and teachers had strong leadership and understood the importance of collaboration with teachers, SMC members, Parent Teacher Association (PTA), guardians and students. Although the research could not show clear evidence of a causal relationship between their achievement and Japan’s project input, the success-case schools clearly benefited from the head teachers’ appropriate execution of SBM with their strong leadership as well as the collaborative efforts of the stakeholders.

The research tries to clarify the influence of the effects of SBM capacity development projects by analysing the changes of head teachers and teachers through the storytelling aspect of the success case method with in-depth consideration of actual school responses during the emergency period of the COVID-19 pandemic.

This paper aims to develop indicators of happiness in learning of the Thai open university (TOU)'s undergraduate students.

Sampling for the study was comprised of two groups. Group I comprised eight lecturers who are experts in their disciplines and six students who were purposively sampled. The focus group was used to validate the appropriateness of the indicators. In Group II, 332 students were engaged in a multistage sampling process. The responses were analyzed using descriptive statistics, coefficient correlation, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

The indicators of happiness in learning of undergraduate students of TOU were classified in six categories. These included satisfaction with learning environment (five indicators), learning anxiety (five indicators), satisfaction with learning (five indicators), enthusiasm to learn (six indicators), self-satisfaction (six indicators) and readiness to learn (seven indicators). The six categories explained happiness in learning of undergraduate students of TOU at the 65% and fit empirical data.

The TOU can use the indicators for the assessment of happiness in learning of its students as well as guidelines for the improvement of its student learning environments.

There have been very few studies on indicators of happiness in learning of TOU students. Most were done at the basic education level. This study disclosed the six factors affecting happiness in learning of TOU students; therefore, it should inspire and draw attention of many in the field of higher education distance learning.

The purpose of this paper is to share the lessons learned in implementing specific design patterns within the “Dr Internet” massive open online course (MOOC).

MOOCs are boasting considerable participant numbers, but also suffer from declining participant activity and low completion rates. Learning analytics results from earlier xMOOCs indicate that this might be alleviated by certain instructional design patterns – critical aspects include shorter course duration, narrative structures with suspense peaks, and a course schedule that is diversified and stimulating. To evaluate their impact on retention, the authors have tried to implement these patterns in the design of the “Dr Internet” MOOC.

Statistical results from the first run of the case study MOOC do not indicate any strong influences of these design patterns on the retention rate.

With inconclusive statistical results from this case study, more research with higher participant numbers is needed to gain insight on the effectiveness of these design patterns in MOOCs. When interpreting retention outcomes, other influencing factors (course content, pacing, timing, etc.) need to be taken into account.

This publication reports about a case study MOOC and gives practical hints for further research.