The gendered landscape of UK higher education: do men feel disadvantaged?

Measuring Gender Equality in Education: Lessons from Trends in 43 Countries

This study examines the general competencies and the requirements of classroom and preschool teacher candidates in terms of assessment and evaluation course. The study group consisted of 140 4th-year teacher candidates randomly selected from a total of 256 teacher candidates studying at Classroom Teaching and Preschool departments of Burdur Mehmet Akif Ersoy University Education Faculty, Turkey. In this study, a mixed research approach and a convergent design, in which quantitative and qualitative studies are used to support each other’s strengths, were used. To collect quantitative data, the “Measurement and Evaluation Common Competency Perception Scale for Teacher Candidates” (MECCPSTC), which was developed by Nartgün (2008), was used. The qualitative data of the study were obtained thorough the open-ended questions that were asked to 13 teacher candidates through focus group interview technique. In the analysis of the quantitative data, descriptive statistics, t-test and one-way ANAVO were used. Content analysis was used in the analysis of the qualitative data. The findings showed that approximately one fourth of the teacher candidates had the opinion that they were “sufficient and very sufficient” in terms of their learning in the assessment and evaluation course. The scores of three quarters of the teacher candidates obtained from the scale were at “moderate and very insufficient” interval. In addition, it was examined whether there was a significant difference between the scores of teacher candidates obtained from Basic concepts” (BC), “Measurement techniques” (MT), “Statistical analysis and Reporting” (SAT) dimensions, and total score of the scale in terms of age, gender, department and belief in appointment variables. The codes obtained from the analysis of qualitative data were grouped under themes. Each theme was arranged and interpreted in a table with the codes within the scope. The results obtained from the analysis of both quantitative and qualitative data were combined and discussed and recommendations were developed.

There are many scenarios in which it is necessary to collect data from multiple sources in order to evaluate a system, including the collection of both quantitative data - from sensors and smart devices - and qualitative data - such as observations and interview results. However, there are currently very few systems that enable both of these data types to be combined in such a way that they can be analysed side-by-side. This paper describes an end-to-end system for the collection, analysis, storage and visualisation of qualitative and quantitative data, developed using the e-Science Central cloud analytics platform. We describe the experience of developing the system, based on a case study that involved collecting data about the built environment and its users. In this case study, data is collected from older adults living in residential care. Sensors were placed throughout the care home and smart devices were issued to the residents. This sensor data is uploaded to the analytics platform and the processed results are stored in a data warehouse, where it is integrated with qualitative data collected by healthcare and architecture researchers. Visualisations are also presented which were intended to allow the data to be explored and for potential correlations between the quantitative and qualitative data to be investigated.

The main aim of this article is to discuss the factors a researcher should take into account when selecting the appropriate research design or method (i.e. qualitative, quantitative, and mixed-methods). The article also discusses sample size determination and sampling procedures in qualitative and quantitative research. Further, the paper has provided an examination of qualitative and quantitative data collection and analysis methods. The study used online desk research to collect data from online public access scholarly databases such as Google Scholar, ResearchGate, Academia, and many more freely accessible electronic books in research methods. Search terms that were used included, among many, the following: qualitative research, quantitative research, mixed-methods, qualitative and quantitative data collection, qualitative and quantitative data analysis; descriptive and inferential statistics. A wealth of relevant and timely scholarly literature was downloaded, read, and used to write this paper, and draw the conclusion provided. Keywords: Qualitative Research; Quantitative Research; Mixed-Methods Research; Qualitative Data Collection Techniques; Quantitative Data Collection Techniques; Qualitative Data Analysis Techniques; Quantitative Analysis Techniques; Descriptive Statistics; Inferential Statistics. DOI: 10.7176/JMCR/87-06 Publication date: November 30 th 2022

In the study, which was carried out to compare the perception of organizational power distance of the academic staff working at the university with the social networks they established with their colleagues at the university, the quantitative and qualitative data collection and analysis processes were carried out simultaneously using the Convergent Parallel Design, one of the mixed methods research designs, and the results of the data analysis were integrated. This research compares the organizational power distance perceptions of the faculty members working at A University, a public university in Turkey, with the social networks they have established with their colleagues at the university. In the quantitative dimension of the research carried out with the mixed method, the research population consists of 1848 academic staff working at A University, one of the public universities in Turkey, in the 2020-2021 academic year. The research sample consists of 319 academic staff. 385 academic staff from 30 academic units, 14 faculties, and 16 colleges/vocational schools, were included in the research. In the qualitative dimension of the research, 27 of 34 academic staff working in the C Department of the B Faculty of the A University were included in the study group. Convergent Parallel Design was used in the research; within this context, the quantitative and qualitative data collection and analysis processes were carried out simultaneously and the data analysis results were integrated. The quantitative data were analyzed with the SPSS 21 program, the participants' views on organizational power distance were analyzed with the MAXQDA 2022 program, and social network analysis data were analyzed with the UCINET 6.0 program. According to the research findings' conclusion, the academic staff's general social network tendencies in the quantitative dimension were high, and the participants had the highest perception of "liking to connect." It was determined that the academic staff's general organizational power distance perceptions were at a moderate level, and the participants had the highest perception of "acquiescence of power." Another conclusion was that the social network tendencies of academic staff did not differ according to the academic title variable. However, the organizational power distance differed significantly in favor of research assistants. It was revealed that there was no statistically significant relationship between the general social network tendencies of the academic staff and their perception of organizational power distance. In the qualitative dimension of the study, it was observed that the participants expressed their opinions on organizational power distance mostly in terms of accepting power and least in terms of consenting to power. Social network analysis revealed that the professional network had a denser structure than the friendship network, but the friendship network had more structured and stronger ties. In social networks, professors and research assistants were found to be at the center of the network. The study observed that the results of quantitative and qualitative data analysis confirmed each other at many points. This study is expected to contribute to the literature, policymakers in higher education management, university senior management, academics, and researchers.

The massive global growth of private sectors transforms higher education. It naturally sparks great interest and debate. But analysis lags far behind, as does cross-national documentation. Promoters glorify roles (e.g., access), while critics demonize roles (e.g., moneymaking). Policy makers tend to hold simple views of what private higher education does or what they want it to do, while participants tend to generalize from their own institution. Public discussion thus often revolves around narrow and misleading declarations. The gap is large between self-serving or ill-informed views and more complex reality. Private higher education is least understood where it has just recently become prominent—and that is in much of the world. A few decades ago, private higher education was absent or marginal in most countries. Today, it captures a major or fast-increasing portion of enrollments in Eastern and Central Europe, the Middle East and northern and sub-Saharan Africa, East and South Asia, and Latin America. Furthermore, new forms of private

Aim/Purpose: This article proposes a framework based on a sequential explanatory mixed-methods design in the learning analytics domain to enhance the models used to support the success of the learning process and the learner. The framework consists of three main phases: (1) quantitative data analysis; (2) qualitative data analysis; and (3) integration and discussion of results. Furthermore, we illustrated the application of this framework by examining the relationships between learning process metrics and academic performance in the subject of Computer Programming coupled with content analysis of the responses to a students’ perception questionnaire of their learning experiences in this subject. Background: There is a prevalence of quantitative research designs in learning analytics, which limits the understanding of students’ learning processes. This is due to the abundance and ease of collection of quantitative data in virtual environments and learning management systems compared to qualitative data. Methodology: This study uses a mixed-methods, non-experimental, research design. The quantitative phase of the framework aims to analyze the data to identify behaviors, trends, and relationships between measures using correlation or regression analysis. On the other hand, the qualitative phase of the framework focuses on conducting a content analysis of the qualitative data. This framework was applied to historical quantitative and qualitative data from students’ use of an automated feedback and evaluation platform for programming exercises in a programming course at the National University of Colombia during 2019 and 2020. The research question of this study is: How can mixed-methods research applied to learning analytics generate a better understanding of the relationships between the variables generated throughout the learning process and the academic performance of students in the subject of Computer Programming? Contribution: The main contribution of this work is the proposal of a mixed-methods learning analytics framework applicable to computer programming courses, which allows for complementing, corroborating, or refuting quantitatively evidenced results with qualitative data and generating hypotheses about possible causes or explanations for student behavior. In addition, the results provide a better understanding of the learning processes in the Computer Programming course at the National University of Colombia. Findings: A framework based on sequential explanatory mixed-methods design in the field of learning analytics has been proposed to improve the models used to support the success of the learning process and the learner. The answer to the research question posed corresponds to that the mixed methods effectively complement quantitative and qualitative data. From the analysis of the data of the application of the framework, it appears that the qualitative data, representing the perceptions of the students, generally supported and extended the quantitative data. The consistency between the two phases allowed us to generate hypotheses about the possible causes of student behavior and provide a better understanding of the learning processes in the course. Recommendations for Practitioners: We suggest implementing the proposed mixed-methods learning analytics framework in various educational contexts and populations. By doing so, practitioners can gather more diverse data and insights, which can lead to a better understanding of learning processes in different settings and with different groups of learners. Recommendation for Researchers: Researchers can use the proposed approach in their learning analytics projects, usually based exclusively on quantitative data analysis, to complement their results, find explanations for their students’ behaviors, and understand learning processes in depth thanks to the information provided by the complementary analysis of qualitative data. Impact on Society: The prevalence of exclusively quantitative research designs in learning analytics can limit our understanding of students’ learning processes. Instead, the mixed-methods approach we propose suggests a more comprehensive approach to learning analytics that includes qualitative data, which can provide deeper insight into students’ learning experiences and processes. Ultimately, this can lead to more effective interventions and improvements in teaching and learning practices. Future Research: Potential lines of research to continue the work on mixed-method learning analytics methodology include the following: first, implementing the framework on a different population sample, such as students from other universities or other knowledge areas; second, using techniques to correct unbalanced data sets in learning analytics studies; third, analyzing student interactions with the automated grading platform and their academic activities in relation with their activity grades; last, using the findings to design interventions that positively impact academic performance and evaluating the impact statistically through experimental study designs. In the context of introductory programming education, AI/large language models have the potential to revolutionize teaching by enhancing the learning experience, providing personalized support, and enabling more efficient assessment and feedback mechanisms. Future research in this area is to implement the proposed framework on data from an introductory programming course using these models.

This article discusses the place of qualitative network analysis in the strategy of mixing methods in the social sciences. We conducted a systematic review of the literature that allowed us to demonstrate examples of the use of qualitative network analysis in empirical research. There are four ways of analyzing qualitative data in network mixed studies: qualitative analysis of qualitative data, quantitative analysis of qualitative data, quantitative and qualitative analysis of qualitative data and quantitative and qualitative analysis of qualitative and quantitative data. Currently, there is a lack of a single definition of the methodology of qualitative network analysis and consensus on its implementation in practice. The main possibilities of qualitative network analysis are discussed in the article. At the level of the research object, qualitative network analysis studies the personal networks of individuals’ relationships, and also make easier an access to hard-to-reach groups of respondents. At the level of the subject of research, qualitative network analysis allows us to study the deep meanings of relationships in the network and the contexts of interaction, to describe and understand networks from the inside and outside, to focus on the activities of actors and their strategies for building a network, as well as to identify the temporality of relationships in the network. The article discusses a position that questions the existence of qualitative network analysis as an independent methodology.

Considerable prior research has indicated wide-ranging implicit and explicit biases exist regarding the association of gender with leadership and gender with scientific and engineering roles. Additionally, in higher education there is evidence that female faculty are more likely to be in teaching and advising roles than their male counterparts (Bird, Litt, & Yong, 2004; Hart & Cress, 2008). From this literature, it is unclear if these job-sorting circumstances are more so due to assignments made by faculty administrators or if men and women are self-sorting into different duties. Interest into this issue is particularly acute in engineering where known gender differences exist in interests among students and dispositions among faculty (Author, 2016). To address this question of job-sorting, the Assignment of Research, Service, and Leadership Activity (ARSLA) was designed and completed by 695 engineering faculty members from 50 colleges of engineering in the United States. ARSLA prompts respondents to pretend they are a college of engineering administrator who is recommending task assignments for faculty members in a fictitious Mechanical and Manufacturing Engineering Department. The five tasks include one research-focused position, one leadership role, and three service responsibilities related to working with students or creating a new freshman engineering course that emphasizes ethics and societal values. Respondents are also provided brief bios of five faculty members who have varying years of experience. All bios indicate strengths in both technical and interpersonal skills. Survey software randomly drove half of the respondents to an ARSLA showing five faculty members all with male names, while the other half of the respondents were shown a duplicate version with one exception. The difference was a name change of the faculty member with the middle level of experience from “Charlie” to “Cathy.” Contrary to a predicted demonstration of explicit bias in favor of assigning men to research and leadership positions, results support a bias correction theory perspective. When respondents were presented with bios that included Cathy instead of Charlie, Cathy was 2.36 times more likely to be recommended to the leadership role of co-chairing the department. Similarly, the odds of Cathy being recommended to work on a serious research project was 1.67 times higher than odds for Charlie. In absence of a female to select from, Charlie was far more often assigned to curriculum development and advising roles than Cathy. Disaggregation of data indicated male (n = 483) and female (n = 193) respondents were similarly favorable about recommending Cathy to a leadership role versus Charlie (18% and 20% for Cathy, 11% and 7% for Charlie - female and male respondents, respectively). Significant contrasts existed regarding recommending Cathy or Charlie to a serious research project. Male and female respondents both indicated a preference for Cathy versus Charlie to work on research, but the odds ratio of female respondents versus male respondents selecting Cathy to work on research was 1.64. Female respondents were twice as likely to recommend Cathy to work on research as they were to recommend Charlie (45% versus 23%).

Workplace violence, commonly known as WPV, was a prominent occurrence, particularly within the healthcare environment. The occurrence of workplace violence (WPV) targeting healthcare workers (HCWs) had witnessed a surge amidst the COVID-19 pandemic. The aim of the study was to develop triphasic methods by exploring the intended anti-workplace violence interventions among healthcare institutions, specifically hospitals in Batangas. This investigation utilized a three-phase research design that aimed to undertake an analysis of both qualitative and quantitative data to create a bespoke questionnaire pertaining to the efficacy of anti-violence measures in the healthcare workplace. An exploratory sequential design encompassed a research methodology that integrated both qualitative and quantitative methods. In this particular study design, the sequential steps involved the initial collection and analysis of qualitative data followed by the subsequent collection and analysis of quantitative data. Salvador et al. (2020) employed a three-stage exploratory sequential mixed-methods strategy. In phase one, the researchers conducted semi-structured qualitative interviews with 20 nurses. The Colaizzi approach was used to examine the data collected from these interviews. In phase two, the qualitative findings were converted into survey components, constructs, and items. The nursing process system concept was used to develop this new survey instrument. Finally, in phase three, validation was done to formally approach an extensive program tool that emphasized anti-violence. The aimed of the studied was to explore the workplace violence experienced by nurses in hospitals to provided a strategic intervention thus, this was implemented among hospitals in Batangas. Hospitals was classified according to functionality, known to acknowledged all kinds of illnesses, deformities, or diseases. The local context provided insights and statistics that was directly pertinent to the country's healthcare system and nursing staff. It assisted in addressing specific difficulties and needed faced by Filipino nurses and healthcare facilities. There had been incidents and reports of workplace violence, and knowing the precise elements that contributed to violence in this setting led to more targeted interventions and solutions. The variety of healthcare settings and the availability of resources was also factors in selecting Batangas province.

BackgroundWe report on the longitudinal quantitative and qualitative data resulting from a two-year trajectory (2008–2011) based on the DIagnosis of Sustainable Collaboration (DISC) model. This trajectory aimed to support regional coordinators of comprehensive school health promotion (CSHP) in systematically developing change management and project management to establish intersectoral collaboration.MethodsMultilevel analyses of quantitative data on the determinants of collaborations according to the DISC model were done, with 90 respondents (response 57 %) at pretest and 69 respondents (52 %) at posttest. Nvivo analyses of the qualitative data collected during the trajectory included minutes of monthly/bimonthly personal/telephone interviews (N = 65) with regional coordinators, and documents they produced about their activities.ResultsQuantitative data showed major improvements in change management and project management. There were also improvements in consensus development, commitment formation, formalization of the CSHP, and alignment of policies, although organizational problems within the collaboration increased. Content analyses of qualitative data identified five main management styles, including (1) facilitating active involvement of relevant parties; (2) informing collaborating parties; (3) controlling and (4) supporting their task accomplishment; and (5) coordinating the collaborative processes.ConclusionsWe have contributed to the fundamental understanding of the development of intersectoral collaboration by combining qualitative and quantitative data. Our results support a systematic approach to intersectoral collaboration using the DISC model. They also suggest five main management styles to improve intersectoral collaboration in the initial stage. The outcomes are useful for health professionals involved in similar ventures.

The purpose of this study is to describe the process and learning outcomes using storytelling methods that can improve the development of listening to children aged 3-4 years in PAUD Ceria Banjarrejo Lampung Timur. The data analysis techniques used in this study are qualitative and quantitative data analysis. Qualitative data analysis is used to collect data through field notes, observation sheets, interview notes, and documentation record. Quantitative data analysis is used to find out the percentage improvement in listening development of children after the action using the storytelling method. The results of this study indicate that there is an increase in the development of listening to children using the storytelling method. In the first cycle, an increase of 63% was obtained and increased to 83% in the second cycle. Based on these quantitative data, it is strengthened by qualitative data findings that the method of storytelling can improve the development of listening to children aged 3-4 years in PAUD Ceria Banjarrejo, East Lampung. The implication of the results of this study is that in the process of early childhood learning, it must apply methods and media that are appropriate to the stage of the child's age and be done in a fun way so that early childhood is able to absorb and optimize all aspects of development.

This study investigated the effects of two types of bilingual programs (two-way and transitional) on the academic performance and attitudes of fifth-grade students who entered kindergarten or first grade with different levels of English proficiency. A mixed methods design with both quantitative and qualitative data collection and analyses phases was employed. Quantitative data analyses indicated no significant differences in standardized measures of English achievement, although significant differences were found in other measures, including measures of oral language acquisition in English, Spanish-reading ability, students' attitudes, and perceived levels of proficiency in English and Spanish. Qualitative data analysis indicated that the students in two-way bilingual education programs were more likely to express positive attitudes towards bilingualism. Based on the mixed data, it is concluded that despite some similarity in the effects, each of the bilingual programs also has unique effects. Policy decisions should be made on the basis of relative importance, value, and the costs of these unique advantages and disadvantages.

The differentiation of contemporary approaches to qualitative data analysis can seem daunting even for experienced social science researchers. Especially when they move forward in the data analysis process from general analytical strategies used in qualitative research to more specific approaches for different types of qualitative data, including interviews, text, audio, images, videos, and so-called virtual data, by discovering the domain ontology of the qualitative research field, we see that there are more than twice as many different classes of data analysis methods as qualitative research methods. This article critically reflects on qualitative research and the qualitative computer data analysis process, emphasising its significance in harnessing digital opportunities and shaping collaborative work. Using our extensive analytical and research project experience, the last research results, and a literature review, we try to show the impact of new technologies and digital possibilities on our thinking. We also try to do the qualitative data analysis. The essence of this procedure is a dialectical interplay between the new world of digital technology and the classic methodology. The use of digital possibilities in qualitative research practices shapes the researcher’s identity and their analytical and research workshop. Moreover, it teaches collaborative thinking and teamwork and fosters the development of new analytical, digital, and Information Technology (IT) skills. Imagining contemporary qualitative research and data analysis in the humanities and social sciences is difficult. Opening to modern technologies in computer-based qualitative data analysis shapes our interpretation frameworks and changes the optics and perception of research problems.

Student feedback collected by higher education institutions provides an important source of data to inform decisions made at various organisational levels. Key stakeholder groups at the institutional level, responsible for planning and implementing strategies for the desired direction of a university, generally rely on insights gained from quantitative data. Whereas qualitative data from students’ perspectives tend to be overlooked, even though student comments can provide more nuanced information. This chapter describes an initiative at an Australian university, both what has been accomplished and what is currently underway, to maximise the use of student feedback in its richness and complexity, through the analyses of quantitative and qualitative data. Where feasible, the findings from student feedback are used in conjunction with other relevant data (indicators of enrolment, retention rate, achievement, etc.) for program review and accreditation. This educational analytics endeavour is supporting the decision-making and problem-solving processes in the university, by empowering summary reports for specific purposes with access to more contextual and granular information. The factors influencing institutional data analyses are common across the higher education sector. There is significant potential for text analytics being further incorporated in educational analytics, and thus influence the strategic direction of an institution or sector.