Advanced decision-making in higher education: learning analytics research and key performance indicators

Lytras, Miltiadis D.; Aljohani, Naif Radi; Visvizi, Anna; Pablos, Patricia Ordóñez de; Gašević, Dragan

doi:10.1080/0144929x.2018.1512940

Cited by 29 publications

(14 citation statements)

References 6 publications

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“…In future research, key performance indicators (KPIs) will be codified in the most appropriate category as shown by the analysis performed and further discussed through in-depth interviews with higher education administrators and faculty to further validate them and consider measuring them. These KPIs are one of the learning analytic aspects which have received growing attention in recent years [21]. The methods and analysis results of learning analytics will directly affect decision-making and strategy in higher education [22].…”

Section: Discussionmentioning

confidence: 99%

Academics’ Perceptions on Quality in Higher Education Shaping Key Performance Indicators

2018

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Institutions in higher education (HE) continuously strive to develop and deliver impactful educational programs. At the same time, they should continue to fulfill their mission to educate students in basic applied subjects and in parallel respond to the need to equip students with new skills. For this reason, higher education institutions (HEI) perform periodical curricular reviews adhering to internal and external quality assurance systems. The subsequent curricular reforms are of a transformative nature, preparing graduates to tackle the challenges of globalization, unemployment and vanishing professions. For these reforms to lead to sustainable curricula, the integration of quality into educational programs is instrumental. A suggested way of achieving a transformative stance is to provide the context for the application and diffusion of quality metrics in teaching and learning. This research intends to provide a discussion of key performance indicators (KPIs) related to quality. This paper presents the second round of qualitative interviews with higher education administrators and professors as a promising vehicle for advancing towards the formulation of KPIs based on their understanding of the different independent dimensions of the quality construct. These KPIs will provide valuable insights into improving teaching, learning and assessment and will eventually lead to sustainable curricula. Research findings outline the significance of the time invested to design and update a course, indicate that technology-enhanced learning solutions are perceived as key quality drivers, and point out the need to align courses with industry requirements and real-world problems. Additionally, findings indicate that the quality and impact of teaching and learning is promoted by the multi/inter-disciplinary character of a course, the engagement of students in interactive discussions and student research as part of summative assessment. The main contribution of this research is an analytic discussion of perceptions of higher education administrators and professors about quality, leading to a significant enrichment of the relevant literature. A set of innovative generic KPIs which can be used in multidimensional quality assessment in higher education is eventually proposed.

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Section: Discussionmentioning

confidence: 99%

Academics’ Perceptions on Quality in Higher Education Shaping Key Performance Indicators

2018

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“…Key Interpretation Impact on Our Research Model [21][22][23][24] Learning analytics and KPIs serve as a measurement tool for enhanced decision making…”

Section: Literature Review Author(s) Learning Analytics Metaphorsmentioning

confidence: 99%

“…We are interested, though, in the future, in utilizing a newly established big data ecosystem towards this direction. [21,22,26,27] Learning analytics as a key approach for real-time monitoring tool of learning process…”

Section: Literature Review Author(s) Learning Analytics Metaphorsmentioning

confidence: 99%

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Evaluation of Key Performance Indicators (KPIs) for Sustainable Postgraduate Medical Training: An Opportunity for Implementing an Innovative Approach to Advance the Quality of Training Programs at the Saudi Commission for Health Specialties (SCFHS)

et al. 2020

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The Kingdom of Saudi Arabia is undergoing a major transformation in response to a revolutionary vision of 2030, given that healthcare reform is one of the top priorities. With the objective of improving healthcare and allied professional performance in the Kingdom to meet the international standards, the Saudi Commission for Health Specialties (SCFHS) has recently developed a strategic plan that focuses on expanding training programs’ capacity to align with the increasing demand for the country’s healthcare workforce, providing comprehensive quality assurance and control to ensure training programs uphold high quality standards, and providing advanced training programs benchmarked against international standards. In this research paper, we describe our attempt for developing a general framework for key performance indicators (KPIs) and the related metrics, with the aim of contributing to developing new strategies for better medical training compatible with the future. We present the results of a survey conducted in the Kingdom of Saudi Arabia (KSA), for the enhancement of quality of postgraduate medical training. The recent developments in the field of learning analytics present an opportunity for utilizing big data and artificial intelligence in the design and implementation of socio-technical systems with significant potential social impact. We summarize the key aspects of the Training Quality Assurance Initiative and suggest a new approach for designing a new data and services ecosystem for personalized health professionals training in the KSA. The study also contributes to the theoretical knowledge on the integration of sustainability and medical training and education by proposing a framework that can enhance future initiatives from various health organizations.

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“…Some scholars have discussed the impact of AI on scientific research, the expansion of scientific discoveries, and the economic impact of AI in last few years [42,43]. Visvizi, Lytras, and co-workers conducted research on smart cities, big data, education, knowledge management, and AI, explored the comprehensive impact of related technology and service, and discussed the relationship between policy-making and academic research, focusing on existing research and technological innovation [44][45][46][47]. In recent years, AI has gradually become a national strategy in China, and relevant policies and designs at the central and local levels have been introduced.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

2019

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In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China's international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009-2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China's AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009)(2010)(2011)(2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013)(2014)(2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016)(2017)(2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China's artificial intelligence.abstract symbols using logical rules) was the most productive way to pursue a computer that could "think"; however, as originally unimagined by Turing and others, the strategy of constructing an inference engine did not achieve the initial cognitive tasks, and it seemed that the theoretically possible concepts had not produced many feasible applications in practice [1]. Li and Wang gave five common definitions of artificial intelligences (AI) in their book Artificial Intelligence: (1) AI is a computer program which makes people feel inconceivable; (2) AI is a computer program similar to human thinking;(3) AI is a computer program similar to human behavior; (4) AI is a computer program that can learn; (5) AI is a computer ...

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Advanced decision-making in higher education: learning analytics research and key performance indicators

Cited by 29 publications

References 6 publications

Academics’ Perceptions on Quality in Higher Education Shaping Key Performance Indicators

Academics’ Perceptions on Quality in Higher Education Shaping Key Performance Indicators

Evaluation of Key Performance Indicators (KPIs) for Sustainable Postgraduate Medical Training: An Opportunity for Implementing an Innovative Approach to Advance the Quality of Training Programs at the Saudi Commission for Health Specialties (SCFHS)

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

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