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This study aims to propose and test a model of educational quality in marketing-management by incorporating resource-capability variables that are linked to learning outcomes for students and the competitive positioning of universities.

Drawing on the resource-dependence theory, this study develops a comprehensive model for measuring educational quality. A sample comprising Spanish university teachers has been used to test the hypothesised relationships by using a two-stage least squares regression analysis while controlling for the possible effect of the public/private nature of the university.

The results validate the model and show that educational capabilities are reliable variables for predicting the educational quality of marketing-management programmes at Spanish universities.

Similar to all educational research studies, certain problems have been acknowledged with respect to the data and the theoretical constructs that are used in the study. Future studies can replicate this study’s model by using more direct objective measures of the theoretical constructs and extend the study to other countries with different educational contexts.

The results provide guidance to marketing teachers at a university in designing high-quality marketing-management educational programmes and in developing self-diagnostic tools that can determine a university’s likelihood of competitive success.

This study is one of the few studies to apply the resource-dependence theory to the analysis of the variables associated with the quality of marketing-management education. In doing so, the study presents original multiitem scales to improve the measurement of model constructs.

Educational policy is crucial to society. Its process is related to political, economic and cultural variables. Nevertheless, there is a paucity of research in the field of applied social sciences, about how educational policies help to achieve societal objectives and welfare. This study aims to assess the concept and features of school education in Egypt during 1990-2017.

Secondary data were collected using governmental reports and educational institutional reports and assessed through specialized focus groups.

Results showed that, despite the multiplicity of strategies to reform the educational system, achievements and outcomes of educational processes are modest, and the developmental status of Egypt is lower than that of other countries. Studying educational outcomes indicated that school-education suffered from the predominance of quantity over quality and a serious inability to meet requirements of new knowledge era.

A novel future-oriented proposal for context, ethos and reforming aspects of educational policy will be suggested.

The purpose of this paper is to explore how actors subjected to public performance evaluations may “contest commensuration,” i.e. may seek to influence how such ratings and rankings will be construed among important stakeholders.

A qualitative study of press releases, and interviews with department heads, is used as a basis for the analysis.

The empirically derived taxonomy of public responses to a state-initiated performance evaluation of educational programs shows that actors may mobilize an array of commensuration management tactics so as to maintain or improve one’s relative positional status. Such tactics may have at least three different foci, namely, on the comparison object (i.e. on the new grouping of actors), the comparison dimension (i.e. the standardized format for comparison) and the comparison rate (i.e. the rate received), respectively. The authors also find that not only are threats to positional status likely to spur commensuration management tactics, but also the opportunity to exploit a good rate.

The paper augments recent research that has problematized the so-called “reactive conformance thesis” by focusing on how evaluated organizations may directly try to influence external stakeholders through public responses. The study is also one of the first that analytically disentangles how they may skillfully exploit different forms of “plasticity” that are inherent in any type of commensuration.

This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education.

The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020.

As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics.

The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.

The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement.

A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.

The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy.

A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

The purpose of this paper is to provide an overview of the literature regarding the academic online learning experience under the lens of broad personality traits, in the transition from traditional to online learning due to global coronavirus pandemic (COVID-19).

The systematic literature review is based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) method and includes indexed empirical studies in academic institutes during the period of COVID-19 outbreak.

Electronic sources identified 103 references; while after the elimination of duplicates and irrelevant titles, 42 papers were forwarded for abstract screening and later full-text assessment. Of these, 14 met the eligibility criteria. Finally, nine studies were included in the literature review profiling and in the qualitative analysis.

The research insights provided in this study are useful in terms of enhancing the view that link broad personality traits and various learning outcomes, during the necessitated transition to online learning by the public health emergency of the COVID-19 pandemic.

The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education.

Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education.

The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process.

Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes.

This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

This study employs survey methods to statistically examine the internationalization of quality assurance (IQA) in Taiwanese higher education. The data collected were analyzed to assess the associations between administrators' opinions of the importance of IQA and their evaluations of its implementation, as well as the relationship between implementation and opinions on seven measures of international quality. The study also explores the mediating effect of implementation assessments on the relationship between opinions of the importance of IQA and opinions of international quality.

This study targeted higher education administrators from universities in Taiwan, including presidents, vice presidents, deans, section chiefs, directors, and heads of schools in various departments. Using systematic sampling methods, 80 universities were selected from a population of 159 higher education institutions in Taiwan, with 17-40 potential participants each in 2015. A total of 2,377 questionnaires were distributed to all the administrators of those institutions, and ultimately, 65 institutions and 337 valid questionnaires were analyzed.

The importance of IQA directly and positively influenced implementation of it on higher education institutions. The implementation directly and positively influenced the level of international quality of the institutions and the importance of IQA had an indirect positive influence on international quality through implementation. The aggregated institution-level results were similar to but much stronger than the individual-level results.

This study examined the IQA of higher education in Taiwan, which is increasingly important to institutions' competitiveness in the global higher education market. The data were analyzed using multilevel structural equation modeling at the individual-level and the aggregate-level. The analysis revealed direct and indirect associations between opinions about IQA and institutional quality. This study makes a significant contribution to the literature because it clarifies the role of administrators (individually and collectively) regarding their institutions' educational quality, and it provides useful information that institutions could apply to improve their international competitiveness.

This study aims to properly and objectively assess the students’ study progress in bachelor programmes by applying statistical process control (SPC). Specifically, the authors focused their analysis on the variation in performance rates in business studies courses taught at a Spanish University.

A qualitative methodology was used, using an action-based case study developed in a public university. Previous research and theoretical issues related to quality indicators of the training programmes were discussed, followed by the application of SPC to assess these outputs.

The evaluation of the performance rate of the courses that comprised the training programs through the SPC revealed significant differences with respect to the evaluations obtained through traditional evaluation procedures. Similarly, the results show differences in the control parameters (central line and control interval), depending on the adopted approach (by programmes, by academic year and by department).

This study has inherent limitations linked to both the methodology and selection of data sources.

The SPC approach provides a framework to properly and objectively assess the quality indicators involved in quality assurance processes in higher education.

This paper contributes to the discourse on the importance of a robust and effective assessment of quality indicators of the academic curriculum in the higher education context through the application of quality control tools such as SPC.

This article informs school leaders and staffs about existing research findings on the use of data-driven decision-making in creating class rosters. Given that teachers are the most important school-based educational resource, decisions regarding the assignment of students to particular classes and teachers are highly impactful for student learning. Classroom compositions of peers can also influence student learning.

A literature review was conducted on the use of data-driven decision-making in the rostering process. The review addressed the merits of using various quantitative metrics in the rostering process.

Findings revealed that, despite often being purposeful about rostering, school leaders and staffs have generally not engaged in data-driven decision-making in creating class rosters. Using data-driven rostering may have benefits, such as limiting the questionable practice of assigning the least effective teachers in the school to the youngest or lowest performing students. School leaders and staffs may also work to minimize negative peer effects due to concentrating low-achieving, low-income, or disruptive students in any one class. Any data-driven system used in rostering, however, would need to be adequately complex to account for multiple influences on student learning. Based on the research reviewed, quantitative data alone may not be sufficient for effective rostering decisions.

Given the rich data available to school leaders and staffs, data-driven decision-making could inform rostering and contribute to more efficacious and equitable classroom assignments.

This article is the first to summarize relevant research across multiple bodies of literature on the opportunities for and challenges of using data-driven decision-making in creating class rosters.