Framing higher education quality from a business perspective: setting the conditions for value co-creation

Cavallone, Mauro; Ciasullo, Maria Vincenza; Douglas, Alex; Palumbo, Rocco

doi:10.1080/03075079.2019.1672644

Cited by 39 publications

(21 citation statements)

References 60 publications

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“…However, the research on curriculum quality has not been independent and has received due attention. As we all know, curriculum plays a central role in the entire education system, and education and teaching activities are also carried out on the basis of curriculum, which is the basic embodiment of educational goals and training goals [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Higher Education Course Evaluation Based on Deep Learning Model

Zheng

2022

Wireless Communications and Mobile Computing

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Higher education has completed a revolutionary transition stage from traditional elite education to mass quality education, and the improvement of higher education quality has increasingly become the focus of social attention. Courses are the most basic unit of university teaching, and the quality of courses directly affects the quality of personnel training. The expansion of higher education has resulted in insufficient teaching resources and a decline in the quality of courses. Improving quality is the eternal theme of higher education reform. How to formulate scientific curriculum quality standards and objectively evaluate curriculum quality is the most concerned issue of the society. Therefore, in-depth analysis of the factors that affect the quality of the curriculum and the establishment of the curriculum quality evaluation system and method has certain practical significance for improving the quality of the curriculum. This work combines the evaluation of higher education courses with deep learning models and proposes a network MSACNN for quality evaluation of higher education courses. This work proposes to use an attention-based multiscale network to explicitly learn the relationship between the qualities of various higher education courses. By using parallel networks with different convolution kernels, it combines different scale features at the same spatial location to better learn the relationship between the quality features of higher education courses. This work conducts sufficient experiments on the higher education curriculum quality dataset, and the experimental results demonstrate that the multiscale network based on the attention mechanism exhibits superior performance. MSACNN surpasses other machine learning methods in both precision and recall metrics.

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Section: Introductionmentioning

confidence: 99%

Higher Education Course Evaluation Based on Deep Learning Model

Zheng

2022

Wireless Communications and Mobile Computing

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“…Community needs must always be used as a reference in the formulation of learning resources, especially those related to the presentation of learning messages and their delivery strategies. [36], [37]. Many learning messages are spread in the community, both learning messages related to cultural preservation, local wisdom-based behaviour, survival strategies, togetherness attitudes, and other social activities.…”

Section: Findings and Discussionmentioning

confidence: 99%

Utilization of Various Disruptive Community Learning Resources for the Covid-19 Period in the Perspective of Life Based Learning

Hardika

Aisyah

Listyaningrum

2021

Int. J. Interact. Mob. Technol.

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This study aims (1) to analyze the use of various disruptive community learning resources during the Covid-19 period in terms of characteristics, levels, and types of learning resources used by lecturers in lectures, and (2) to find out the differences in the use of various disruptive community learning resources during the Covid-19 period between fields of science at Universitas Negeri Malang. The research approach used quantitative with an ex post facto design. The respondents of this research were 393 students of Universitas Negeri Malang from eight faculties. The data was collected through a closed questionnaire distributed through the Google Form application technology. Data analysis was performed by using percentage description statistics and different tests of the use of learning resources between the sciences, social humanities, and engineering. The results of the analysis show that (1) the characteristics, levels, and types of disruptive community learning resources in the Covid-19 era used by lecturers in lectures vary widely, (2) there are significant differences in the use of various disruptive community learning resources during the Covid-19 period between science and social humanities students, and engineering.

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“…Hence some thirdmission activities of universities can be synergistic with the two other missions (teaching and research) of many universities, by helping student recruitment, and partnering with firms for collaborative projects (Piirainen et al, 2016). Some recent studies have examined the dynamics of educational focused U-I collaborations which may have mutual benefits for universities and industrial firms (Cavallone et al, 2019;Sin & Amaral, 2017). Hence these studies highlight some useful incentives for universities and industry when they engage in One key challenge often faced by university researchers in executing collaborations with industry, is that they may not possess previous experience of such collaborations and are comfortable with intense company interactions.…”

Section: Literature Reviewmentioning

confidence: 99%

University-industry collaborations in Pakistan: current challenges and future opportunities

Malik

Bashir

Ali³

2021

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Purpose This paper aims to identify current challenges that hinder university–industry (U-I) collaboration in Pakistan and presents future opportunities for promoting such collaborations in developing countries. Design/methodology/approach This exploratory study presents new empirical evidence obtained from Pakistan via a questionnaire survey of 24 universities, 25 interviews with key stakeholders including industry managers and outputs from a high level workshop event. Findings Although there is limited evidence of U-I engagement in Pakistan, the findings show that a lack of coordination between government, universities and industrial firms has hindered knowledge transfer between universities and industry. Such steps as utilising intermediaries to help broker effective collaborations and building trust-based relationships can help in socialising these types of scientific activities. Research limitations/implications Any overall conclusions drawn from this exploratory study can only be tentative, as the findings represent a snapshot of current U-I collaboration initiatives in Pakistan. Practical implications Less stringent policy interventions from government entities, as well as more universities willing to invite industry input on their board of studies might enable co-development of some university curriculums with industry partners. U-I collaborations could also help to boost innovation efforts in developing country firms. Originality/value This paper also offers awareness into benefits of teaching activity collaboration with industry partners, which has been an under explored area of past U-I collaboration studies. The findings should be of interest for both innovation policy and higher education policy researchers.

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Framing higher education quality from a business perspective: setting the conditions for value co-creation

Cited by 39 publications

References 60 publications

Higher Education Course Evaluation Based on Deep Learning Model

Higher Education Course Evaluation Based on Deep Learning Model

Utilization of Various Disruptive Community Learning Resources for the Covid-19 Period in the Perspective of Life Based Learning

University-industry collaborations in Pakistan: current challenges and future opportunities

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