No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Analytics and the Imperatives for Data-informed Decision Making in Higher Education
... The findings of many empirical studies by [94,95] reveal that data can be used to discover students at risk and understand the overall student population, as well as the learning environment and teaching processes. As many studies indicate, adding predictive analytics to institutional management information allows for better-informed decisions [24][25][26]95]. Identifying at-risk students enables universities, particularly program directors, to implement targeted intervention strategies to assist the students and improve success rates [95]. ...
... The use of data in decision-making has grown widespread in HEIs [61,120]. The importance of data-driven decision-making has grown with accountability, school effectiveness, and transformative learning [25,26,82,121]. Effective teachers and schools use data to inform their educational methods and systems at all levels [121]. To address the problems posed by accountability measures, school leaders are urged to utilize data to influence teaching and learning methods [19,122,123] and to direct and monitor school reform [106]. ...
... Assessment of learning outcomes has always been an internal affair of HEIs; however, conventional collegial procedures are no longer adequate. In analyzing the results of higher education students, such as employability, policymakers face a significant knowledge and data deficit [23][24][25]134]. Using data, HEIs may determine the global best practice employability models and how career services and academic departments collaborate [25,28,79,82,135]. ...
The transformative function of data-driven leadership in higher education institutions (HEIs) is becoming crucial for advancing sustainable development. By integrating data-driven decision-making with Sustainable Development Goals (SDGs), particularly SDG4 (quality education) and SDG10 (reduced inequalities), EIs can improve the efficacy, inclusivity, and employability of their graduates. To examine this influence, this study implements a systematic literature review (SLR) that adheres to the PRISMA standards and integrates empirical and theoretical insights regarding data-driven leadership in HEI governance, teaching, and learning strategies. The results indicate that combining data analytics into decision-making processes improves institutional efficacy, aligns curricula with the market demands, strengthens student outcomes, and cultivates an inclusive and sustainable academic environment. Moreover, this study introduces a conceptual model connecting sustainable development and data-driven decision-making, offering a structured framework for HEIs to navigate digital transformation responsibly. In addition, this model also emphasizes the importance of balancing technology, ethics, and human-centric leadership in developing educational institutions that are prepared for the future. Ultimately, these insights provide practical advice for academic leaders and policymakers aligning HEI strategies with global sustainability objectives. By advocating for innovative, inclusive, and data-driven leadership, HEIs can promote long-term societal transformation and higher education excellence.
... ML models excel in dynamic environments, adapting to evolving patterns in fraud detection, customer behaviour analysis, and risk assessment. For instance, gradient boosting algorithms are widely used in finance to identify credit risks (14). ...
... Tools like logistic regression and neural networks are used to identify individuals at risk of chronic conditions, such as diabetes and heart disease, based on lifestyle, genetic predispositions, and medical histories. For example, a study demonstrated that predictive models could achieve a 92% accuracy rate in identifying patients at risk of cardiovascular diseases, allowing healthcare providers to tailor treatment plans effectively (13,14). ...
... Predictive analytics is revolutionizing the manufacturing industry by enabling proactive strategies in predictive maintenance, supply chain optimization, quality control, and production efficiency. These advancements enhance operational reliability and reduce costs, positioning manufacturers for greater competitiveness in a dynamic market (14,15). ...
The advent of big data and advanced analytics has revolutionized decision-making processes across industries, enabling organizations to transition from reactive to predictive strategies. Data analytics, particularly predictive analytics, leverages vast datasets to identify patterns, forecast trends, and optimize decision-making. This paradigm shift addresses the growing need for precision, efficiency, and adaptability in dynamic environments. By harnessing data from diverse sources, organizations can anticipate challenges, uncover opportunities, and align strategies with future demands. At its core, predictive analytics integrates machine learning, artificial intelligence (AI), and statistical models to analyze historical and real-time data. Industries such as healthcare, finance, manufacturing, and retail have embraced these innovations to enhance performance. For instance, predictive models in healthcare improve patient outcomes by forecasting disease risks, while financial institutions utilize analytics to mitigate fraud and optimize investment decisions. Similarly, in manufacturing, predictive maintenance minimizes downtime by identifying potential equipment failures before they occur. Despite its benefits, implementing predictive analytics presents challenges, including data quality, integration complexities, and ethical considerations. Addressing these barriers requires robust data governance frameworks, scalable technologies, and interdisciplinary collaboration. This paper explores the transformative role of data analytics in delivering predictive insights, highlighting its applications, challenges, and future prospects. By examining real-world case studies and emerging trends, it provides actionable insights for leveraging big data innovations to advance decision-making. The findings underscore the importance of predictive analytics as a critical tool for fostering resilience, sustainability, and competitive advantage in an increasingly data-driven world.
... Learning and teaching as well as services and facilities are changed on the basis of evidence (Webber & Calderon, 2015). Increasingly, student and/or institutional data are used to inform a variety of institutional decisions, including myriad measures of student success, institutional efficiency, and staff and student satisfaction (Webber & Zheng, 2020). ...
... Across the world, calls for quality assurance and accountability (see Stensaker & Harvey, 2011) will remain, if not increase further. Institutions are awash with data (Webber & Zheng, 2020), assiduously collected and used by senior managers to effect quality improvement programmes. ...
... Advanced analyses, both traditional inferential analyses as well as predictive modeling and machine learning techniques, enable analysts to discern patterns that can be combined with contextual judgement to inform decisions. A number of recent publications are available to describe these kinds of activities under way across the world, including publications by Daniel (2017), Kahlil and Ebner (2016), Prinsloo and Slade (2015), Reidenberg & Schaub, 2018;and Webber and Zheng (2020). ...
Institutional Research associations across the world are re-imagining and redesigning their professional development and capacity building activities. This paper outlines the professional development activities of the Association for Institutional Research (AIR) in the United States (est. 1966), the European Association for Institutional Research (EAIR) (est. 1978), the Southern African Association for Institutional Research (SAAIR) (est. 1994) and the United Kingdom and Ireland Higher Education Institutional Research Network (HEIR) (est. 2008) and argues that a more sophisticated approach to IR is needed, informed by systems thinking, aimed at proactive engagement with policy-makers and managers, organisational learning, direct links to institutional strategy (‘a seat at the table’), and the analysis and use of larger volumes of data.
... Implementing analytical tools to support management decision-making is a long process that often does not run smoothly. In implementing such tools, HEIs face several technological challenges and challenges related to privacy and ethical and responsible use of data [26]. Furthermore, large datasets do not necessarily guarantee better decisions [26]. ...
... In implementing such tools, HEIs face several technological challenges and challenges related to privacy and ethical and responsible use of data [26]. Furthermore, large datasets do not necessarily guarantee better decisions [26]. The implementation process usually goes through six steps (justification, planning, business analysis, design, construction, and deployment [27]). ...
... At the end of this process, they have to integrate analytic tools as part of the HEI decision-making structure, which demands institutional strategic planning and resource allocation to reflect its rising relevance in supporting the institution's mission. The successful admission of a data-based decisionmaking culture in HEI requires trained staff, technologies for data integration, data management systems, and tools for reporting, analysis, and data visualization [26]. ...
... It takes time and often does not go well to implement analytical tools to help managerial decision-making. Using such technologies presents HEIs with several technical obstacles and issues about privacy and the moral and responsible use of data (Webber & Zheng, 2020). Large datasets also don't always translate into superior choices. ...
... Large datasets also don't always translate into superior choices. The implementation process often includes six stages: planning, business analysis, design, construction, deployment, and justification (Webber & Zheng, 2020). This process requires a detailed analysis of the current procedures, including the choice of suitable data for processing, the choice of data extraction and visualization tools, the establishment of data warehouses, the integration of relevant data sources, etc. (Chairungruang et al., 2022), (Yulianto& Kasahara, 2018). ...
This research examined the relationship of data analytics to data-driven decision-making with the academic success and the institutional efficacy of higher education students in the United Arab Emirates. The aim was to understand data analytics’ impact on student learning, retention, academic results, and institutional efficacy. For quantitative correlational research, stratified random sampling was utilized to solicit responses from 384 educators, administrators, and decision-makers representing all UAE institutions. The results indicate that data analytics improved learning outcomes and increased student retention rates. Data-driven decisions resulted in more efficient institutions. The institution's efficacy further moderates the correlation between data analytics and student academic success. Overall, the results suggest the effectiveness of data analytics in better decision-making and processes within the UAE higher education system, ultimately leading to better educational outcomes and better administration of institutions.
... Although increasingly available in support of undergraduate education, decision aid penetration into higher education institutions appears to be limited given the lack of trust in the data underlying these aids as well as by the distrust in the motives of those releasing the aids [33]. There is also evidence that higher education administrators are not well-equipped to leverage data-they are data rich but insight poor [34]-and there are concerns that administrators are pushing to mimic their peers without strong insights into the utility of the data they are gathering [35]. At our own university, the development of dashboards has been largely focused on data relevant to the undergraduate mission of the university. ...
... Given the surfeit of dashboards being developed and released by universities, administrators can be overwhelmed by the amount of data now available to them [34]. It is necessary to ensure that the right people are provided the right data at the right time and in the best format to maximize the data's impact on student progress and departmental practices. ...
Higher education is awash with data that, when refined, facilitates data-informed decisions. Such decision-making is much more prevalent in support of undergraduate education given the much larger number of undergraduates pursuing higher education in contrast to the much smaller proportion of graduate students. A simple extension of current undergraduate-focused tools to the population of graduate students risks ignoring large differences in the students, processes, and policies that are unique to graduate education. Graduate education is more decentralized, less grade-focused and more milestone-focused (e.g., passing preliminary exams, defending a thesis), graduate admissions is driven by department-level processes and criteria, expected products vary across programs (e.g., performances, journal articles, books), and curricular specialization is the norm. Consequently, local contextual variables predominate. This article describes the development of a milestones dashboard to support programs in their pursuit of graduate education excellence by creating data transparency at the student level, visualizing student progress through milestones, allowing benchmarking against other programs at the university, and empowering college and university administrators to identify trends and to ask questions when unusual data arise.
... There are many schools of thought when it comes to how data and LA should be used in education, for example the question of "data-driven" versus "data-informed" decision making in education has been widely debated for the last decade [Webber & Zheng provide a summary of the discussion in their 2020 book on big data in higher education (Webber & Zheng, 2020)]. The precise role of data and analytics in the decision making process is still debated, but there is convergence on the important opportunities presented by modern analytics to improve learning equity and outcomes (Mandinach, 2012;Park, 2018), and also the "critical importance of processes and structures such as a mission-focused data strategy and robust governance policies" (Webber & Zheng, 2020). ...
... There are many schools of thought when it comes to how data and LA should be used in education, for example the question of "data-driven" versus "data-informed" decision making in education has been widely debated for the last decade [Webber & Zheng provide a summary of the discussion in their 2020 book on big data in higher education (Webber & Zheng, 2020)]. The precise role of data and analytics in the decision making process is still debated, but there is convergence on the important opportunities presented by modern analytics to improve learning equity and outcomes (Mandinach, 2012;Park, 2018), and also the "critical importance of processes and structures such as a mission-focused data strategy and robust governance policies" (Webber & Zheng, 2020). ...
Data is fundamental to Learning Analytics research and practice. However, the ethical use of data, particularly in terms of respecting learners’ privacy rights, is a potential barrier that could hinder the widespread adoption of Learning Analytics in the education industry. Despite the policies and guidelines of privacy protection being available worldwide, this does not guarantee successful implementation in practice. It is necessary to develop practical approaches that would allow for the translation of the existing guidelines into practice. In this study, we examine an initial set of privacy-preserving mechanisms on a large-scale education dataset. The data utility is evaluated before and after privacy-preserving mechanisms are applied by fitting into commonly used Learning Analytics models, providing an evaluation of the utility loss. We further explore the balance between preserving data privacy and maintaining data utility in Learning Analytics. The results prove the compatibility between preserving learners’ privacy and Learning Analytics, providing a benchmark of utility loss to practitioners and researchers in the education sector. Our study reminds an imminent concern of data privacy and advocates that privacy-preserving can and should be an integral part of the design of any Learning Analytics technique.
... These stakeholders may hold a range of views about the utility, efficacy, optimal design, and importance of such an initiative that would potentially impact its implementation and success. When evaluating a proposed project for potential adoption, leaders and managers interested in data-informed decision making need to understand the project's value to the institution and its constituents to determine whether it warrants the resources required [3]. If stakeholders who would potentially use the analytics do not see the value of the initiative or its practicality for aiding their decision making and practice, they may not use what eventually becomes available to them. ...
... Results also emphasize the importance of having early involvement of groups who will contribute to the design phase of the implementation. The lack of consensus about the alignment with the institutional core values demonstrates the relative newness of analytics in higher education and the need to continue exploring of the value of data to inform decisions [3]. The need for ongoing stakeholder involvement exists even at an institution that (a) espouses the value of data-informed decision making, (b) seeks out ways to implement this approach, and (c) has successfully done so in the past, including with displays of institutional data using PowerBI and using analytics in the learning management system and the adaptive learning system [7]. ...
Data-driven educational decisions enabled by online technologies hold promise for improving student performance across the full range of student dis/ability, even when efforts to design for student learning requirements (such as through Universal Design for Learning) fall short and undergraduates struggle to learn course material. In this action research study, 37 institutional stakeholders evaluated the potential of prescriptive analytics to project student outcomes in different simulated worlds, comparing hypothetical future learning scenarios. The goal of these prescriptions would be to make recommendations to students about tutoring and to faculty about beneficial course redesign points. The study’s analysis focused on the alignment of resources, processes, and values for feasible institutionalization of such analytics, highlighting institutional core values. In the postpandemic mix of online and on-campus learning under increasingly constrained resources, educational leaders should explore the potential competitive advantage of leveraging data from online technologies for greater student success.
... There are many schools of thought when it comes to how data and LA should be used in education, for example the question of "data-driven" versus "data-informed" decision making in education has been widely debated for the last decade [Webber & Zheng provide a summary of the discussion in their 2020 book on big data in higher education (Webber & Zheng, 2020)]. The precise role of data and analytics in the decision making process is still debated, but there is convergence on the important opportunities presented by modern analytics to improve learning equity and outcomes (Mandinach, 2012;Park, 2018), and also the "critical importance of processes and structures such as a mission-focused data strategy and robust governance policies" (Webber & Zheng, 2020). ...
... There are many schools of thought when it comes to how data and LA should be used in education, for example the question of "data-driven" versus "data-informed" decision making in education has been widely debated for the last decade [Webber & Zheng provide a summary of the discussion in their 2020 book on big data in higher education (Webber & Zheng, 2020)]. The precise role of data and analytics in the decision making process is still debated, but there is convergence on the important opportunities presented by modern analytics to improve learning equity and outcomes (Mandinach, 2012;Park, 2018), and also the "critical importance of processes and structures such as a mission-focused data strategy and robust governance policies" (Webber & Zheng, 2020). ...
For the developers of next‐generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology‐mediated learning to gain insights into how students learn, especially for large cohorts, which was unthinkable only a few decades ago. This LA growth‐spurt coincides with a growing global “Ethical AI” movement focussed on resolving questions of personal agency, freedoms, and privacy in relation to AI and Analytics. At this time, there is a significant lack of actionable information and supporting technologies, which would enable the goals of these two communities to be aligned. This paper describes a collaborative research project that seeks to overcome the technical and procedural challenges of running a data‐driven collaborative research project within an agreed set of privacy and ethics boundaries. The result is a reference architecture for ethical research collaboration and a framework, or roadmap, for privacy‐preserving analytics which will contribute to the goals of an ethical application of learning analytics methods.
Practitioner notes
What is already known about this topic Privacy Enhancing Technologies, including a range of provable privacy risk reduction techniques (differential privacy) are effective tools for managing data privacy, though currently only pragmatically available to well‐funded early adopters.
Learning Analytics is a relatively young but evolving field of research, which is beginning to deliver tangible insights and value to the Education and EdTech industries.
A small number of procedural frameworks have been developed in the past two decades to consider data privacy and other ethical aspects of Learning Analytics.
What this paper adds This paper describes the mechanisms for integrating Learning Analytics, Data Privacy Technologies and Ethical practices into a unified operational framework for Ethical and Privacy‐Preserving Learning Analytics.
It introduces a new standardised measurement of privacy risk as a key mechanism for operationalising and automating data privacy controls within the traditional data pipeline;
It describes a repeatable framework for conducting ethical Learning Analytics.
Implications for practice and/or policy For the Learning Analytics (LA) and Education Technology communities the approach described here exemplifies a standard of ethical LA practice and data privacy protection which can and should become the norm.
The privacy risk measurement and risk reduction tools are a blueprint for how data privacy and ethics can be operationalised and automated.
The incorporation of a standardised privacy risk evaluation metric can help to define clear and measurable terms for inter‐ and intra‐organisational data sharing and usage policies and agreements (Author, Ruth Marshall, is an Expert Contributor on ISO/IEC JTC 1/SC 32/WG 6 "Data usage", due for publication in early 2022).
... In the financial sector, where precision and accuracy are critical, DDDM leverages the vast quantities of data available to drive strategies that maximize profitability and manage risks more effectively. This shift from traditional, experiencebased decisions to data-centric strategies allows companies to make informed choices that are both evidence-based and forward-looking (Webber & Zheng, 2020). ...
... Data-informed decision-making plays a critical role in addressing equity within higher education by providing institutions with the tools necessary to identify and analyze disparities in access and success among diverse demographic groups (Webber & Zheng, 2020). According to Qui et al. (2022), utilizing disaggregated data allows institutions to uncover trends and gaps that may not be apparent through aggregated statistics. ...
In today's rapidly changing higher education landscape, data-informed decision-making is crucial for institutions to remain competitive and responsive to the needs of students, faculty, and staff. This chapter overviews the importance of using ana-lytics to drive strategic management in higher education. It covers the benefits and challenges of data-informed decision-making and provides practical guidance on how to leverage data to inform institutional decisions. The chapter further shares the uses of data analysis in decision-making processes in three selected institutions of higher learning in Zimbabwe. Applying document analysis to the Strategic Plans of the three universities, the chapter reveals the realization of data as key in decision-making and improving the overall effectiveness of higher education institutions, and that institutions are instituting measures to make sure data drives decisions on strategies in the areas of teaching and learning, academic development, innovation and business development, and wider educational improvement.
... In addition, this system can also evaluate the effectiveness of policy in raising birth. All those actions can lead to making data-informed decisions for continuous improvement (Webber & Zheng, 2020). It ensures that strategies are adjusted and optimized based on real-time insights (Raji et al., 2024). ...
... Integrating new technologies into an organization's infrastructure requires thoughtful consideration of various interrelated components. As Webber and Zheng (2019) propose, four key aspects-people, process, technology, and culture-must align to ensure proper and beneficial adoption of data analytics platforms. Similarly, Chen and Popovich (2003) put forth three core pillars of platform, process, and people as critical to customer relationship management system success. ...
This chapter explores the potential of integrating conversational AI tools such as ChatGPT with data visualization (DV) tools such as Power BI in higher education settings. A brief history of chatbots is summarized and challenges and opportunities in higher education are outlined. The highlights include AI's prospects for enhancing data‐informed decision‐making while needing safeguards to mitigate risks. Through a pioneering exercise, we integrated ChatGPT's conversational capabilities with Power BI's interface via API and tested functionality. Suggestions for good practice and implications for higher education are discussed.
Practical Takeaways
The importance of data security, recognizing current limitations in accuracy, and good practices for integrating DV tools with ChatGPT. The exercise shows that AI tools have the potential to aid higher education leaders, but still face many challenges, and human judgment remains essential.
Integration of ChatGPT with Power BI in higher education aims to enable natural language interaction with data visualizations, facilitating dynamic discussions and insights for leaders.
Considerations include data security protocols, technical challenges, financial constraints, and the potential and limitations of AI. Technical challenges include limitations in ChatGPT's interpretation of complex queries, API performance issues, and data processing inefficiencies.
Affordability of enterprise AI versions poses a barrier to adoption for institutions with budget constraints despite recognizing potential benefits.
ChatGPT represents a significant advancement in AI capabilities but has limitations, including generating inaccurate information and lacking awareness of its limitations. Human oversight and critical evaluation are crucial in navigating the evolving AI landscape and leveraging its benefits effectively while mitigating risks.
... Omakhanlen et al. (2021) also corroborated these results in their investigation into the impact of financial difficulties on individual financial literacy levels. Other literature suggests that higher financial knowledge empowers employees to navigate personal finance complexities effectively, facilitating informed decisions in budgeting, debt management, and investments (Webber and Zheng, 2020). This, in turn, reduces the likelihood of encountering common spending-related problems that may jeopardize financial welfare. ...
This study investigates how financial well-being, a key factor affecting life quality, job contentment, and retirement readiness, varies among individuals. It looks at the spending habits, financial challenges, and knowledge of four generations (Baby Boomers, Generation X, Generation Y, and Generation Z) working in four state universities and colleges (SUCs) in the Philippines. The study involved 371 regular staff and academic employees who completed a modified questionnaire. The results showed that these employees generally spend cautiously and face few financial problems, yet they possess considerable financial understanding. There was a noticeable link between how they spend and the problems they face. A strong connection was observed between their financial knowledge and spending habits. However, the link between the financial issues they face and their knowledge of finances was weaker. This suggests that the employees are careful with their spending and have good financial knowledge. These insights are useful for creating specific programs and educational efforts to improve the financial well-being of staff and academics at these Philippine universities.
... Understanding these four dimensions in Figure 2 as well as categories explains how university social responsibility can support corporate social responsibility. The first dimension, that is, knowledge creation, includes three factors: (a) universities provide licenses to perform certain professional tasks through education [52], (b) universities provide the necessary toolkit, including knowledge and skills, for conducting research by using scientific sources [53], and (c) universities help individuals make informed decisions by interpreting choices and choose the most rational ones through critical thinking skills obtained through their educational experiences [54,55]. ...
The concept of university social responsibility (USR) is getting increasingly interrelated with the concept of corporate social responsibility (CSR) because universities are key institutions for promoting private sector businesses, creating social capital, and supporting innovations. Since USR is important in promoting economic development, any CSR program that is supported by USR, the effectiveness of CSR programs gets higher because of its support for the society and environment becomes more sustainable by making the support more ethical, resourceful, and responsible. Furthermore, the incorporation of USR into CSR will lead to more profitable companies because the brand images of these companies will be stronger. This chapter first introduces the concept of USR, and it then explains the relationships between USR and CSR by utilizing the comparative case study method. The case of Saudi Arabia revealed that there is a healthy USR-CSR cooperation in the country, promising a better future for the development in Saudi Arabia. On the other hand, the case for Türkiye depicted that the support for USR-CSR cooperation is decreasing, indicating the possibility of negative impacts on the society in the near future.
... Much of how data are collected is shaped by how they are intended to be used. The widespread adoption and enforcement of data-driven decision-making (DDDM) amplified the inflation of data centeredness as institutions were increasingly expected to not only use but also demonstrate the use of data to inform institutional decision-making (Cox et al., 2017;Hora et al., 2017;Taylor, 2020;Webber & Zheng, 2020). DDDM and similar data-centered administrative models are part of a precarious and predominant movement in higher education, carrying over from k-12 with roots in business and management (Sims & Sims, 1995;Birnbaum, 2000). ...
The fullness of Black students’ experiences in college has yet to be archived. The same can be said of Black people broadly, whose existence has long been reduced by and to what is observable, by systems of power and those at the helm. This is perhaps due to the structural and structural limitations of data collection efforts, or not of interest to decision-makers. Or perhaps, this is a symbol of the impossibility of capturing Black life beyond the physical. Regardless, Black life on college campuses across the globe is systematically reduced to standard institutional measures. This reduction is facilitated by the ongoing datafication of student success—the ways that students’ success has been made metric and the ways student success as a socio-political phenomenon has been increasingly governed by data-related practices and discourses. As a result, Black students’ material and data realities are (re)constructed, especially through student success data practices.
... Regarding the second question proposed for this review, AI algorithms should concentrate efforts on analyzing the entire group (Figure 9), but in the educational exercise, it is essential to have individual data for decision-making and strategy design. In a similar way, although in other technological contexts, authors such as Pastora and Fuentes (2021) and Webber and Zheng (2020) have an impact on this issue. This is reinforced by what is seen in 2022, as the interest in individual performance equaled the collective (Figure 10). ...
The diversity of topics in education makes it difficult for artificial intelligence (AI) to address them all in depth. Therefore, guiding to focus efforts on specific issues is essential. The analysis of competency development by fostering collaboration should be one of them because competencies are the way to validate that the educational exercise has been successful and because collaboration has proven to be one of the most effective strategies to improve performance outcomes. This systematic review analyzes the relationship between AI, competency development, and collaborative learning (CL). PRISMA methodology is used with data from the SCOPUS database. A total of 1,233 articles were found, and 30 passed the inclusion and exclusion criteria. The analysis of the selected articles identified three categories that deserve attention: the objects of study, the way of analyzing the results, and the types of AI that could be used. In this way, it has been possible to determine the relationship offered by the studies between skill development and CL and ideas about AI’s contributions to this field. Overall, however, the data from this systematic review suggest that, although AI has great potential to improve education, it should be approached with caution. More research is needed to fully understand its impact and how best to apply this technology in the classroom, minimizing its drawbacks, which may be relevant, and making truly effective and productive use of it.
... A popular modern definition of BI is that of Wixom and Watson [13]: "Business intelligence is a broad category of technologies, applications, and processes for gathering, storing, accessing, and analyzing data to help its users make better decisions." Although our modern views of BI have evolved greatly and extended beyond traditional business contexts (e.g., health care [14][15][16], and education [17,18]), the fundamental concept of analyzing information, often from various sources, to make an informed decision, has remained. ...
Simple Summary
Monitoring animal behavior over time is important for zoos and aquariums seeking to continually evaluate animal welfare. Although new digital tools are making behavior monitoring more accessible, analyzing behavior data in a timely manner to draw meaningful insights can be challenging. Business intelligence software has the potential to help address these challenges. Business intelligence software is a class of tools that combines the ability to integrate multiple data streams with advanced analytics and robust data visualizations. Here, I highlight features of the Microsoft Power BI platform as an example. Power BI is a leading option in business intelligence software and is freely available. To demonstrate the potential of business intelligence tools for behavior monitoring, I provide two example data dashboards of data recorded using the ZooMonitor behavior recording software. The first dashboard illustrates a simple quarterly behavior summary to track behavior changes in an ongoing manner. The second dashboard visualizes data relating to enrichment evaluation. I hope this introduction to business intelligence software and the Microsoft Power BI platform can provide researchers and managers in zoos and aquariums with new tools to support their evidence-based decision-making processes.
Abstract
Animal welfare is a dynamic process, and its evaluation must be similarly dynamic. The development of ongoing behavior monitoring programs in zoos and aquariums is a valuable tool for identifying meaningful changes in behavior and allows proactive animal management. However, analyzing observational behavior data in an ongoing manner introduces unique challenges compared with traditional hypothesis-driven studies of behavior over fixed time periods. Here, I introduce business intelligence software as a potential solution. Business intelligence software combines the ability to integrate multiple data streams with advanced analytics and robust data visualizations. As an example, I provide an overview of the Microsoft Power BI platform, a leading option in business intelligence software that is freely available. With Power BI, users can apply data cleaning and shaping in a stepwise fashion, then build dashboards using a library of visualizations through a drag-and-drop interface. I share two examples of data dashboards built with Power BI using data from the ZooMonitor behavior recording app: a quarterly behavior summary and an enrichment evaluation summary. I hope this introduction to business intelligence software and Microsoft Power BI empowers researchers and managers working in zoos and aquariums with new tools to enhance their evidence-based decision-making processes.
... This group provided an essential opportunity to share information and gather input; just as importantly, it allowed the BI community to share information with each other across units and functional areas (Canfield-Budde & Walz, 2016). In further support of the importance of expanding knowledge of BI and data-driven decision making at the institution, Webber and Zheng (2019, October), shared that: ...
... Meanwhile, DIDM is a data-driven decision-making process taking into account previous experience, user research, and other important information. DIDM as the process of organizing data resources, performing data analysis, and developing data insights to provide context and an evidence base for formulating organizational decisions (Webber & Zheng, 2019). Thus, in addition to leaders being equipped with adequate data and excellent analysis, they also need to draw on their professional experience, political acumen, ethical practice, and strategic considerations in making decisions. ...
In general, to make decisions in the discipline of information systems are divided into two, namely Decision Support System (DSS) and Data Informed Decision Making (DIDM). DIDM is a data-driven decision-making process taking into account previous experience, user research, and other important information. Many applications are categorized as data-informed for universities, one of which is a portal that contains data or information about various aspects of a university. There are not many known factors that influence leaders to use informed data as a tool for making decisions. This study applies the UTAUT (Unified Theory of Acceptance and Use of Technology) model by adding a leadership style variable as a moderating variable. Hypothesis testing using the bootstrapping technique in this study involved a number of samples (N) of 300, testing for the two-tailed hypothesis, using a significance level of 5%. Based on the test results revealed only facilitating conditions that affect use behavior. Meanwhile, the variables of performance expectancy, effort expectancy, and social influence have no effect on behavioral intention to use the application. In addition, it was also found that the moderator variable of leadership style did not affect the relationship between performance expectancy, effort expectancy, social influence, and facilitating conditions with the intention and actual use of leaders in data informed applications to make decisions.
... Simulations are tools for evaluating alternative strategies and scenarios before committing resources to a strategy [26]. As data should not drive but rather inform decision making [27], models should not drive decisions but rather be part of a process that recognizes the importance of the human judgement as one of the inputs. By design, models are imperfect and incomplete because of the numerous assumptions that must be made during model construction. ...
The COVID-19 pandemic has had a significant impact on higher education. Steering academic institutions through the pandemic is a complex and multifaceted task that can be supported with model-based scenario analysis. This article studies the short-term and long-term effects of the pandemic on the financial health of a college using scenario analysis and stress testing with a system dynamics model of a representative tuition-dependent college. We find that different combinations of the pandemic mitigation protocols have varying effects on the financial sustainability of an academic institution. By simulating six individual components of the COVID-19 shock, we learn that due to the causal complexity, nonlinear responses and delays in the system, the negative shocks can propagate widely through the college, sometimes with considerable delays and disproportionate effects. Scenario analysis shows that some pandemic mitigation choices may destabilize even financially healthy institutions. The article concludes that higher education needs new sustainable business models.
This research examines the relationship between big data analytics capability (BDAC) and competitive advantage (CA) in China’s agribusiness sector, with a specific focus on the mediating role of absorptive capacity (AC). Although BDAC has been extensively studied in other industries, its role within agribusiness remains underexplored, particularly in developing economies like China. This study addresses this gap by investigating how BDAC can be utilized to enhance competitive advantage in the unique context of agribusiness. Grounded in Resource-Based Theory (RBT), a comprehensive framework is developed to elucidate the interconnections among BDAC, CA, and AC. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) on data collected from 287 agribusiness firms in China, the findings reveal that key resources—such as data quality, infrastructure, information systems, data governance, data-driven culture, managerial expertise, and technical proficiency—significantly influence BDAC. Moreover, BDAC is shown to positively impact CA, with AC serving as a critical mediator in this relationship. These results emphasize the strategic importance of integrating BDAC and AC to enhance the competitiveness of agribusinesses, particularly amidst rapid digital transformation. This study provides valuable contributions to the literature on BDAC, enriches the theoretical foundations of RBT in agribusiness contexts, and offers practical recommendations for digital transformation strategies in the sector.
In today’s digital age, the success of higher education institutions hinges on their ability to embrace and integrate advanced technological solutions. MIS with data visualization technology offers a transformative solution. This innovative approach not only simplifies data management but also improves data interpretability and accessibility, empowering higher education institutions to make informed decisions, enhance academic and administrative processes, and ultimately improve student outcomes. This paper underscores the necessity for a system that visualizes financial allocation, tracks funding sources, monitors expenditure patterns, and forecasts budgetary needs related to resource generation management. By providing a clear, visual understanding of financial flows, the system enables decision-makers to identify potential savings, uncover new funding opportunities, and strategically support academic and research initiatives. This enhances efficiency, effectiveness, and financial sustainability of higher education institutions. By applying ISO/IEC 25010 criteria for software product quality evaluation, this study assesses the functionality, usability, and reliability of the ASSCAT RGMO Information System, demonstrating its potential to optimize resource management, streamline operations, and support informed decision-making within the university context.
AI has the potential to accelerate climate action through applications like solar power generation, heating and cooling systems, and deforestation detection. It has also been used to impede the fight against climate change. AI can analyze large amounts of data, aiding policymakers in understanding complex relationships between the environment and human behavior. AI-driven predictive analytics can predict extreme weather events, improve agricultural productivity, and enhance resource management. Integrating AI technologies into climate policies requires governance and policy frameworks.
Knowledge discovery in academic databases is vital in facilitating informed decision-making and deeper insights within the academic domain. However, traditional approaches often need to be revised regarding the accuracy, interpretability, and usability of the discovered knowledge. This paper proposes an ontology-driven approach to knowledge discovery in academic databases. By leveraging ontologies, which provide a formal representation of domain-specific knowledge, this approach enhances the precision and context awareness of the discovery process. Integrating ontologies with academic databases establishes a semantic link, enabling more accurate and interpretable queries. Additionally, ontology enriches the knowledge discovery process, facilitating the identification of hidden patterns, relationships, and insights in the data. The ontology’s concepts and relationships represent the discovered knowledge, allowing for better interpretation and understanding. In addition, visualization techniques can be employed to enhance the usability and interpretability of the knowledge. Through case studies and examples, we demonstrate the effectiveness of the ontology-driven approach in uncovering meaningful knowledge in academic databases. This approach has the potential to support evidence-based decision-making, foster collaborations, and contribute to advancements in the academic field.
The undeniable potential benefits of learning analytics (LA) in education have led to growing investment in developing and integrating LA systems into K-12 schools. Yet, the actual integration and adoption depend on levels of teachers' acceptance and usage. This study aims to propose a conceptual model to reveal the decisive factors affecting teachers' adoption of the LA system for evaluating teachers' sustainable LA usage through the integration of Information System Success and the Technology Acceptance Model. The variables used were system performance quality (SP), information quality (IQ), perceived usefulness (PU), and intention to use (IU) via the partial least square structural equation modeling (PLS-SEM). Furthermore, the moderation effect of teachers' AI literacy level (LIT) between dependent variables (SP and IQ) and PU was analyzed. The results indicated SP (sub-constructed by accessibility and human awareness) and IQ (comprised of reliability and relevancy) have statistically positive influences on PU and IU, where LIT played a moderating role between IQ and PU. This study contributes to our understanding of teachers’ perceptions regarding LA system integration through a theoretically grounded analysis of empirical data from the K-12 school education context and highlights important areas of development for better LA integration into teachers' practice.
There has been a recent shift toward using big data in the administration of educational institutions. New complex data infrastructures with human and non-human agents enable data collection, processing, and dissemination in higher education (HE), where all these aspects are characterized by political, economic, and social settings. Instead of being seen just as technical endeavors, HE data infrastructures should be regarded as practical conduits of political ambitions to revamp the educational system. Projects on data infrastructure in higher education are the focus of this paper. Businesses, industry standards, dashboards, software, and visual analytics make up the infrastructure, and their relationship to legislative demands for market change along with their role in big data infrastructure is analyzed in this paper. The findings provide light on how the marketization agenda of politics is reconfiguring higher education and how the utopian ideal is remaking a ‘smarter university’.KeywordsPedagogyCurriculumTeachingLearningEducational technologySmart campusVisualizationStandardsMarketizationInfrastructureDataDashboards
Weather warnings are critical risk communication messages because they have the potential to save lives and property during emergencies. However, making warning decisions is challenging. While there have been significant advances in technological weather forecasting, recent research suggests that social factors, including communication, influence warning meteorologists’ decisions to warn. We examine the roles of both scientific and social factors in predicting warning meteorologists’ decisions to warn on tornadoes. To do so, we conducted a cross-sectional survey of National Weather Service forecasters and members of management in the southern and the central regions of the United States, as well as conducted a retrospective data analysis of cross-sectional survey data from the central region Tornado Warning Improvement Project. Results reveal that dependency on radar velocity couplet and a variety of social factors predicted decisions to warn.
Data analytics is increasingly important to the operations and strategic growth of higher education institutions. In September 2019, three higher education professional associations issued a rare joint statement calling for the accelerated investment and intensified efforts to develop and deploy data analytics in support of campus decision making. COVID‐19 disrupted the momentum generated by the joint statement. However, during the pandemic, many universities recognized data analytics as a critical asset to their response to the crisis. In this case study of Ohio State University's COVID‐19 dashboard development process, the authors demonstrate that the principles advocated by the joint statement are exactly the reasons that a successful and nationally recognized dashboard effort was possible. The case study shows that senior leadership support, strategic investment in the right technologies, and close collaboration among campus data analytics professionals are among the key factors of success.
Problem: This article studies the short-term and long-term effects of the COVID-19 pandemic on the financial health of a college. Method: We use scenario analyses and stress testing with a system dynamics model of a representative tuition-dependent college. Findings: Different combinations of the pandemic mitigation protocols may have varying effects on the financial sustainability of an academic institution. By simulating six components of the COVID-19 shock, we show that the negative shocks can propagate widely through the college, sometimes with considerable delays and disproportionate effects. Simulations demonstrate that some pandemic mitigation protocol choices may destabilize even financially healthy institutions. Contribution: The article adds to the literature on the economics of higher education, management of the pandemic-related risk on campus, college stress-testing and model-informed decision making in higher education.
Steering academic institutions through the COVID-19 pandemic is a complex and multifaceted task that can be supported with model-based scenario analysis. This article studies the short-term and long-term economic effects of the pandemic on an academic institution using scenario analysis and stress testing with a system dynamics model of a representative tuition-dependent college. We find that different combinations of the pandemic mitigation protocols have varying effects on the economic sustainability of an academic institution. By simulating six individual components of the COVID-19 shock, we learn that due to the causal complexity, nonlinear responses and delays in the system, the negative shocks can propagate widely through the college, sometimes with considerable delays and disproportionate effects. Scenario analysis shows that some pandemic mitigation choices may destabilize even financially healthy institutions. The article adds to the literature on the economics of higher education, management of the pandemic-related risk, college stress-testing and model-informed decision making in higher education. The article concludes that higher education needs new sustainable business models.
The development of a hybrid descriptive data analytics system for human resource and development office of a University. It is a developmental evaluation type of research that focuses on the development of an online system for Human Resource office. Specifically, it only includes two major functionalities which is the Recruitment, Selection , & Placement(RSP) module and Learning & Development Module. The research introduces recommender systems, reminder system, and descriptive data analytics implementation.
Development evaluation research project. The prototype includes the four core systems of PRIME HR of Northwest Samar State University in Calbayog City, Samar. It is an online system that synchronize the transactions of the PRIME HR departments with the following significant functions: 1) Recommender system; 2) Reminder System, and; 3)Descriptive data analysis.
The COVID-19 pandemic has had a significant impact on higher education. Steering academic institutions through the pandemic is a complex and multifaceted task that can be supported with model-based scenario analysis. This article studies the short-term and long-term effects of the pandemic on the financial health of a college using scenario analyses and stress testing with a stock-and-flow consistent model of a representative tuition-dependent college. The article identifies six effects of COVID-19. By simulating six individual components of the COVID-19 shock, we learn that due to the causal complexity, nonlinear responses and delays in the system, the negative shocks can propagate widely through the college, sometimes with considerable delays and disproportionate effects. We find that different combinations of the pandemic mitigation protocols may have varying effects on the financial sustainability of an academic institution. Some pandemic mitigation choices may destabilize even financially healthy institutions. The article adds to the literature on the economics of higher education, management of the pandemic-related risk on campus, college stress-testing and model-informed decision making in higher education.
ResearchGate has not been able to resolve any references for this publication.