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Given declining tuition funds and government grants, Kenyan universities need to develop strategies, including increased research grants and collaborations, to diversify their income sources. Well-managed doctoral students can boost a university’s teaching and research outputs. However, numbers of students enrolled in doctoral programmes at Kenyan universities are low, and graduation rates and time-to-graduate statistics are disturbing. Research undertaken elsewhere underline the important role played by supervisors and peers in facilitating students’ sense of belonging and their success. Therefore, this study aims to investigate the influence of supervisory and peer support on PhD students’ sense of belonging and their success at Kenyan universities.

In this cross-sectional study, data were gathered through an online questionnaire from 614 students admitted to doctoral programmes at Kenyan universities between 2010 and 2018. We used multi-item scales to collect data on PhD students’ self-efficacy, supervisory and peer support and a sense of belonging.

Structural equation modelling results revealed that PhD students’ modes of study and self-efficacy were significantly associated with the quality of supervision, peer support and a sense of belonging. However, only age, a sense of belonging and the quality of supervision were directly linked to their success.

This study contributes to the literature on doctoral-level education, responding to the need for research on the influence of relationships with supervisors and peers on PhD students’ sense of belonging and their success, especially in developing countries.

The main purpose of this study is to evaluate the extent of dropout students and identify the relationship between risk factors of dropout and the survival time of students.

The Kaplan–Meier estimator (KM), also known as the product-limit technique, is a nonparametric model function that is commonly used in estimating survival function events (Kaplan and Meier, 1958). The survival function's Kaplan–Meier estimators are used to estimate and graph survival probabilities as a function of time, as well as explanatory data analysis (EDA) for the survival data, including the median survival time, and compare for two or more of the survival events. In addition, Cox proportional hazards model is employed for modelling purpose.

Results of the Kaplan–Meier curves show that male students have lower survival rates than female, researchers have found that there is a difference between the survival times of the student's school types, results show students from English-based schools are higher than Arabic-based schools as suggested by the survival curve. Similarly, there is a difference between the survival times of students aging equal or greater than 25 and students aging less than 25 and survival function estimates of dropout according to high school grade marks has huge difference. These results were confirmed using log rank test as age, school type and marks were statistically significantly different while gender is not statistically significant.

There is no study of this kind from the Somalia context about the student's dropout. Subsequent to the outbreak of civil war in 1988 and the collapse of the central government in 1991, all public social services in Somalia including education centers were severely disrupted.

The statistical methods discussed in the previous section will be applied on a real dataset obtained from different offices of the university; most of the data were extracted from faculty of economics office and admission and record office. The data set comprised of 70 students from SIMAD university, consists of full-time faculty of economics students who enrolled at the university in the academic year of 2017–2018 until two years of diploma, students either complete 24 months of diploma or leave the university and that is the event of interest.

The purpose of this study is to determine the competency characteristics required for the employment of master’s degree students in educational technology.

A combined qualitative and quantitative method was used to consult multiple experts through a modified Delphi method. Competency characteristics were extracted from Chinese recruitment apps, national recruitment websites and university training programs. Ten senior teacher experts who teach educational technology master’s students were consulted through a questionnaire consultation to validate the proposed competency model. The weights of competency characteristics were determined through a combination of the analytic hierarchy process and entropy method.

The results show that when recruiting educational technology master’s students, more emphasis is placed on operational skills. The majority of companies tend to assess practical abilities rather than theoretical knowledge. Relevant knowledge of educational technology, psychology, computer science and education is considered to be the basic knowledge components of educational technology master’s students, while professional skills are the core skills required for their positions. Therefore, universities need to focus on training, educational technology graduate students in these areas of competence. The study also found that professional qualities (such as physical and mental fitness) and personality traits (interpersonal communication and interaction) receive more attention from companies and are essential competencies for educational technology master’s students.

A competence model for educational technology master’s students is proposed, which includes aspects such as knowledge, personal skills/abilities, professional qualities and personality traits. The competence elements included in this model can serve as reference indicators for universities to cultivate the competence of educational technology master’s students, as well as reference points for recruiting units to help them select talents. This represents a new dimension in research related to the employment of educational technology master’s students. The study enriches the research objects and competence dictionary in the field of competence research.

The recruitment of undergraduate students within English universities is of vital importance to both the academic success and the financial stability of the organisation. Despite the primacy of the task, there has been a dearth of research looking at related performance and how to ensure that the process is optimised. The purpose of this study was to investigate the degree of variation both within a university and between different universities. The reliance that individual programmes and/or universities place on the Clearing process is key; given its uncertainty, resource demands and timing shortly before students take up their places.

The Nomogramma di Gandy diagrammatical approach utilises readily available data to analyse universities’ performance in recruiting students to different programmes, and the degree to which they each rely of the Clearing process. Inter-university performance was investigated on a whole-student intake basis for a sample of English universities, representative of type and region.

The study found that there were disparate patterns for the many programmes within the pilot university and also disparate patterns between different types of universities across England. Accordingly, universities should internally benchmark their programmes to inform both strategic and tactical decision-making. Similarly, Universities and Colleges Admissions Service benchmarking inter-university patterns could inform the overall sector.

The approach and findings provide lessons for analysing student recruitment which could be critical to universities’ academic and financial health, in an increasingly competitive environment.

The study is aimed at analyzing the perceptions of students and teachers regarding the effectiveness of massive open online courses (MOOCs) on learning efficiency of students and also evaluating MOOCs as an ideal tool for designing a blended model for education.

The analysis was carried out by using the data gathered from the students as well as teachers of University of Mysore, Karnataka, India. Two separate sets of questionnaires were developed for both the categories of respondents. Also, the respondents were required to have prior experience in MOOCs. Further, the collected data was analyzed using statistical package for social sciences (SPSS).

The study showed that MOOCs have a more positive influence on learning efficiency, as opined by both teachers and students. Negative views such as cheating during the assessment, lack of individual attention to students and low teacher-student ratio were also observed.

Many educational institutions view that the MOOCs do not influence learning efficiency and also do not support in achieving their vision. However, this study provides evidence that MOOCs are positively influencing the learning efficiency and also can be employed in a blended model of education so as to promote collaborative learning.

Technology is playing a pivotal role in all fields of life and the education sector is not an exception. It can be rightly said that the technology-based education models such as MOOCs are the need of the hour. This study may help higher education institutions to adopt MOOCs as part of their blended model of education, and, if already adopted, the outcome of the present study will help them to improve the effectiveness of the MOOCs they are offering.

To significantly adopt and improve indoor energy efficiency in building infrastructure in developing countries can be a challenging venture. Thus, this study aimed to assess the satisfaction of indoor environmental quality and its effect on energy use intensity and efficient among student housing.

The study is quantitative and hinged on the contrast theory. A survey of 1,078 student residents living in purpose-built student housing was contacted. Using Post-Occupancy Evaluation and Multiple Linear Regression, critical variables such as thermal comfort, visual comfort and indoor air quality and 21 indicators were assessed. Data on annual energy consumption and total square metre of the indoor area were utilised to assess energy use intensity.

The study found a direct relationship between satisfaction with indoor environmental quality and energy use intensity. The study showed that students were more satisfied with thermal comfort conditions than visual and indoor air quality. Overall, these indicators contributed to 75.9% kWh/m² minimum and 43.2% kWh/m² maximum energy use intensity in student housing in Ghana. High occupancy and small useable space in student housing resulted in high energy use intensity.

Inclusions of sustainable designs and installation of smart mechanical systems are feedback to student housing designers. Again, adaptation to retrofitting ideas can facilitate energy efficiency in the current state of student housing in Ghana.

Earlier studies have argued for and against the satisfaction of indoor environmental quality in student housing. However, these studies have neglected to examine the impact on energy use intensity. This is novel because the assessment of energy use intensity in this study has a positive influence on active design incorporation among student housing.

This study aimed to determine the expectations of students from mathematics teachers in the planning phase of lesson study (LS) in mathematics classrooms.

This study reported only a part of large-scale action research. The participants were Grade 8 students selected by the convenience sampling method. The data were obtained through open-ended questions. The content analysis method was used to analyze the data.

Four categories emerged: connection, technology-supported teaching, use of concrete materials, practice, and teacher behavior and teaching style.

This study reveals how students in a different culture and education system, such as Türkiye, want to learn mathematics in the LS process of Japanese origin. It also gives some important clues for applying LS in a different culture.

This study may attract the attention of educational stakeholders who want to implement LS in mathematics classrooms by considering student perspectives.

Due to the nature of LS, this study may emphasize teacher–student and teacher–teacher interactions. Thus, it can draw attention to the importance of social learning environments where students take responsibility and interact.

This study emphasizes the importance of listening to student voices in LS. Some ideas about mathematics teaching in Turkey should also be given. Finally, it can provide a good basis for understanding and comparing LS practices in different cultures and understandings.

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

This study aims to investigate the conditional indirect effect of academic self-efficacy in the interplay between academic motivation and academic satisfaction through academic engagement among university students.

A cross-sectional study was performed on 1,638 Vietnamese university students (31.9% males and 68.1% females) aged 16 to 36 (Mean = 20.06, SD = 1.428). The participants filled out a questionnaire with the Vietnam versions of the General Self-Efficacy Scale, Academic Motivation Scale, Academic Life Satisfaction Scale and Academic Engagement Scale. Model 4 and Model 7 in the PROCESS macro were used for the mediation analysis and the moderated mediation analysis.

Results showed that the indirect effect of academic engagement on the academic motivation-academic satisfaction link was significant. Furthermore, academic self-efficacy moderated this indirect effect. The indirect effect was stronger among students with high academic self-efficacy and weaker among students with low academic self-efficacy.

This study’s findings contribute to educational research on academic satisfaction and can be used by institutions of higher education and educators to enhance academic satisfaction among university students.

Large classes pose challenges in managing different types of skills (e.g. maths, subject-specific knowledge, writing, confidence and communication), facilitating interactions, enabling active learning and providing timely feedback. This paper shares a design of a set of assessments for a large undergraduate economics course consisting of students from diverse cultural backgrounds. The benefits, challenges and learning experiences of students are analysed.

Students worked in groups to complete an assessment with several questions which would be useful as a revision for the individual assessment, the following week. Survey questionnaires with Likert-type questions and open-ended questions were used to analyse the learning and skill development that occurred because of the group work. Responses to the open-ended survey questions were coded and analysed by identifying the themes and categorising the various issues that emerged.

This assessment design developed group working skills, created opportunities to interact and enhanced learning. The analysis of the responses found that working with peers enabled the students to generate their own feedback, clear doubts and learn to solve problems. Effective communication, planning meetings and working around the diverse group members’ strengths and weaknesses are some graduate skills that are developed in this group assessment. The challenges were arranging meetings, finalising assessments, engagement of group members and unreliable technology. However, the students found ways to overcome these challenges.

This assessment design can be useful in higher education practice by introducing a mechanism for authentic collaborative practice. This paper adds to the literature on peer interactions and group work and enables effective learning at scale.