From the literature, several approaches have been used to evaluate teaching in educational environments. For example, (Boyle and Cook 2023) says that evaluation takes place in four different areas of social practice: systemic/sector-wide (Accountability, Management, Comparison, and Auditing); programmatic/government-initiated program (Impact, Effects, and Value for money); institutional/university-wide (Quality assurance, Quality standards, Enhancement); and self/Course, subject, unit, or module (Value, Impact, Enhancement). However, the emphasis in each of these exercises is on the learners' levels of knowledge (cognitive), attitude (behavioral), and capabilities. From a behaviorist perspective, learning is defined as the modification of a behavioral tendency by experience and as knowledge or skill acquired by instruction (Merriam-Webster, 2018). In the classroom environment, (Simonson et al. 2022) argue that the majority of institutional methods for assessing instruction are insufficient, imprecise, and do not immediately enhance instruction or provide incentives for it. This is frequently because evaluating effective instruction is challenging, and the majority of tools fail to do so sufficiently or effectively and frequently lack defined standards (Simonson et al., 2022).

The traditional methods of evaluating teaching are student course evaluations. Generally speaking, student course evaluations are heavily overused, and in many situations, they serve as the only source of data used to assess instruction (Miller and Seldin, 2014). Although recent research indicates that the use of student course evaluations, also known as “student satisfaction surveys,” is questionable, they can give students a voice in the evaluation process and allow them to perceive how their teachers affect them (McCabe and Layne, 2012; Flaherty, 2015; Richmond et al., 2014; Esarey and Valdes, 2020). Class observations have also been used for course evaluations. Although observations in the classroom might shed light on instructional strategies, using this information just for assessment or evaluation has disadvantages. First, large-scale classroom observations take a lot of time and effort and require a lot of resources (Durham et al., 2018; Teoh et al., 2016). Second, the type and caliber of information derived from the observations are significantly impacted by the person making them (Durham et al., 2018; Teoh et al., 2016). Furthermore, working in a system that necessitates reaching a consensus on nearly all choices, particularly those about job performance and job security, makes evaluating teachers even more difficult (Gray, 2016). In higher education, the tenure and promotion procedures and policies also have a direct impact on job security and performance. As such, these rules frequently place a higher priority on research than instruction, which results in inadequate teaching since faculty members devote most of their time to research projects and disregard teaching-related duties (Miller and Seldin, 2014). (Simonson et al. 2022) proposed that the four main areas of an assessment that should be considered when deciding which aspects of teaching and learning to assess are: Course design, Scholarly teaching, Learner-centered teaching and reflective teaching techniques to promote ongoing teaching improvement. Besides this, (Lu and Wu 2018) created an assessment model of teaching and learning using various methodologies. It consists of three primary conceptual components: instruction, learning, and learning support. Earlier on, (Chalmers and Gardiner 2015) had also reported that the following were the most pertinent elements of teaching: (a) the variety of teaching preparation program types and goals; (b) the efficacy of these programs; (c) the influence of programs on teaching and learning; (d) the influence of institutional culture; (e) methods of measurement; and (f) impact indicators.

Contributing to the creation of new knowledge and innovations to improve practice is the main motivation for research in any higher education institution. As such, success in these areas determines research impact and performance, which are the two main dimensions of assessing research. The product of research, impact as a result of the community's ongoing output, quality as a benefit of research results, and utility, or the potential exploitation of the research, are the four main components of research performance, according to (Hermanu et al. 2023a). (Alomary 2020) notes that some academics cluster research performance indicators into two, that is, quantitative and qualitative indicators, further subdividing them into four categories: input, output, outcome, and process. Quantitative indicators include inputs and outputs, whereas qualitative indicators include processes and results. The evaluation of research performance, then, takes into account several input and output variables. Furthermore, Çakir et al. (2015) say that research performance can be explained by output indicators (research outputs), procedures (academic governance), curriculum evaluation at the institution, inputs (research money), and institutional nature (capacity and capability unique to the institution). Therefore, research effectiveness is determined by actions that provide wider outputs, outcomes, and impacts. Accordingly, the data on inputs, research procedures and outputs, outcomes, and impacts are all used to assess if a certain research project is meeting a specific need, reaching a specific beneficiary, or accomplishing a specific goal (Hinrichs-Krapels and Grant, 2016).

Research impact is a reporting service in the scholarly impact domain that generates impact reports for individual academics, schools, and faculties for a range of reasons using different bibliometric methods to demonstrate scholarly impact. Author-level, article-level, journal-level, and institution-level metrics are a few of these bibliometrics. One metric used to evaluate the influence of research at the journal level is the impact factor. (Kolle 2022) states that the average number of citations received by papers published in a specific publication over a specified period is the impact factor. It is computed to evaluate the journal's performance. Here, the two most important factors are the number of papers published in the journal and the number of citations to those articles in a certain year. The h-index is the widely used author-level statistic. The author's quality and productivity are the focus of the h-index. The author must publish additional works to increase their h-index, and the papers must receive citations at the same time. Metrics at the article level make it easier to understand how a given article affects other scholars and how it advances the field of study. The number of times a specific article is mentioned and cited in a peer-reviewed publication, typically in journals that are indexed by Web of Science and Scopus, can be used to measure the article's influence (Kolle, 2022). (Iorwerth 2005) also suggested an alternative set of standards for assessing university research, which included the following standards: Appropriateness (Was it the proper course of action?), Economic (Has it turned out to be less expensive than anticipated?), Effectiveness (Has it performed as expected?), Efficiency (How much does the investment yield?), Efficacy (How does the Return on investment stack up against projections?), Process (Is it functioning properly?), Quality (What is the level of output quality?), Impact (How has it affected things?), Additionality (What has transpired beyond what would have occurred in any case?), Displacement (What would have occurred in its absence but hasn't?), Process Improvement (What are some ways to improve it?) and Strategy (What ought to happen next?).

College students, business players, and communities aim to use and maximize the potential of natural and human resources, as well as local culture, to improve their products through lectures and community service projects. They want their products to become instantly identifiable, accessible, and fiercely competitive. With the help of university professors, the potential of exceptional items found in different places might be further explored. Furthermore, by enlisting instructors from other universities who possess the necessary skills, collaboration between universities in community service could be promoted. Aligning research with community needs is one aspect of the mutually reinforcing process that involves institutions. By encouraging social welfare, creativity, entrepreneurial skills, and human resource development, universities impart knowledge to the community (Di Berardino and Corsi, 2018). As a proactive measure to enhance knowledge transfer and respond to community requirements, community service is incorporated into teaching and research at many institutions (Hermanu et al., 2022a,b). According to (Jadhav and Suhalka 2016), community service in higher education is a commitment to social development and a win-win collaboration between academic institutions, businesses, and the government (Kruss et al., 2012). By encouraging innovation, social welfare, entrepreneurial skills, and human resource development, universities impart knowledge to the community (Di Berardino and Corsi, 2018; Mora et al., 2015). In this situation, action research is required to support an investigative process of community-based development to create self-sufficient individuals. But as concerns about universities' social roles have grown (Leten et al., 2014; Musselin, 2013), some of them have been trying to better explain how they affect their communities and society at large (Lendel, 2015; Marcovitch, 2019; Martin, 2012). According to (Johansen and Arano 2016), universities benefit the areas in which they are located by giving local youth educational options and jobs for professionals in the area. By supporting local development, encouraging culture, and improving the region's reputation, university research programs can also improve community services (Leten et al., 2014). (Filho and Junior 2020), based on bibliographical research, distinguished three categories of impacts: socioeconomic, scientific and technological, and on the region's culture and image. These three types of impact have been integrated into a conceptual model that can be used as a basis for a practical model to be employed to assess the impact of a university on the region in which it is located, and this conceptual model is shown in Figure 2 below.

(Lobo and Samaranayake 2020) define innovation as a new technology, service, or procedure that aims to enhance the performance of both individuals and organizations. One of the most important aspects of innovation, according to (Smith 2003), is “ideation.” Ideation lends legitimacy to best-practice research, which is based on both ideation and data-driven implementation. (Kostoff 2005) believes that innovation reflects the metamorphosis from present practice to some new, hopefully, better practice. As action researchers, teachers can thus find new teaching, curriculum, and management techniques that will work well for their classes and can be shared with other educators. (Goldsmith and Foxall 2003) have also imposed the notion of newness on the definition of innovation, proposing three distinct attributes of newness: resemblance, originality, and recency. Some studies have examined innovation from the standpoint of new product creation. New product development and commercialization have resulted from innovation and incubation at numerous businesses. In their research of a case study of the incubator associated with the Swedish medical university Karolinska Institute, (Baraldi and Havenvid 2016) relied on seven essential elements of incubation: time, place, sources, resources, control/governance, activities/services, and outcomes. In the instance of the Karolinska Institute incubator, they found six distinct strategic drivers of business incubation: globalization, cooperation/competition, revenue model, risk-taking/time perspective, governance/control, and location in the value chain. Science parks, incubators, and accelerators (technology-based incubator mechanisms that offer vital value-added inputs necessary for the establishment and growth of innovative technology-based firms) are examples of frequently utilized incubation programs. According to (Nandal et al. 2020), five different kinds of inventions emerge from innovations. These include:

(Ferreras-Garcia et al. 2021) and (Nandal et al. 2020) divided innovation competencies into three categories: network, interpersonal, and individual competencies. The ability to relate to others in a goal-oriented manner that facilitates teamwork is sometimes referred to as interpersonal competence. This has a lot to do with the person's ability to network and build professional ties. The following Figure 3 below displays this framework:

The following metrics are used to quantify individual competencies: goal orientation, system thinking, and creative problem-solving. Additionally, (Ferreras-Garcia et al. 2021) developed interpersonal dimensions of innovations, which include oral and written communication, teamwork, and leadership. The network dimension concentrates on a person's ability to collaborate in a multicultural, multinational, and multidisciplinary setting as well as their ability to work within networks. (Ferreras-Garcia et al. 2021) list the following individual dimensions: initiative, creativity, risk-taking, persistence, personal attitudes, idea creation, change management, results orientation, decision-making, problem-solving, and critical thinking. Based on research, a set of metrics—divided into three categories: input, process, and output metrics—has been constructed from these aspects to create a framework for evaluating innovation. Financial resources are typically used to measure input metrics. Time management, innovation pipeline management, new project performance forecasts, and personnel concerning the plan are all included in process metrics. Metrics, including the number of new ideas created, the number of projects in the pipeline, the number of concepts that receive funding, the total estimated net present value of the projects in the pipeline, and the scope of the idea generation process, are also part of managing and monitoring the innovation pipeline. The following metrics make up the scope of the idea-generated process itself: the number of projects that achieve the predetermined goals, the accuracy of the new product forecast, and projected vs. actual performance. (Nandal et al. 2020) output metrics are: New product revenue as a percentage, New product profit as a percentage, and New product revenue increase as a percentage. Some non-financial metrics may also be used to measure innovation, and these include patents generated and the number of new products launched. For institutions of higher learning, their innovative contributions to the economic development of nations have attracted the attention of some researchers, such as Jonkers et al. (2018), who then proposed an aggregation model showing outputs and their impacts on the economic development of nations as shown in Figure 4 below.

Globally, institutions of higher learning have been crucial to a country's industrial development. For instance, according to (Bonvillian 2022), American higher education institutions have actively participated in industry research and development (R&D) to support production technologies in industries through technology development, with a particular focus on artificial intelligence, advanced materials, and information technology (IT). This has allowed them to participate in the Fourth Industrial Revolution (4IR). Among these is Carnegie Mellon University, which has actively participated in robotics applications research with a group of businesses called Advanced Robotics for Manufacturing (ARM). According to (Kwandayi 2021), the major elements of industrialization in which universities must play a role across nations are:

For this to be achieved, however, (Kwandayi 2021) made the following recommendations:

The research community has produced several frameworks for assessing educational programs, but it is frequently unclear who these frameworks are meant for and why. Teaching, research, community services, innovation, and industrialization are the five components of Education 5.0 that the researcher proposes to be independently evaluated in the Zimbabwean setting. Therefore, the suggested framework takes into account the important components that come out of studies on the assessment of teaching and learning, as well as evaluations of the quality of instruction. To assess teaching and learning in Education 5.0, this review utilized factors from (Chalmers and Gardiner 2015) and (Simonson et al. 2022). While (Filho and Junior 2020) offered the primary characteristics for evaluating community services, (Hermanu et al. 2023b) and (Iorwerth 2005) also gave variables on research evaluation. Figures 2–4 provide some variables that measure the various dimensions of Education 5.0, and these are authors such as (Ferreras-Garcia et al. 2021) and (Baraldi and Havenvid 2016), and these are then shown in the comprehensive framework in Figure 5 below.

The researcher proposes a framework that may be applied to evaluate Education 5.0's variables: Teaching, Research, Community service, Innovation and Industrialization. In Figure 4 below, the author argues that these variables are influenced by the status of digital infrastructure available in an institution, and the author proposes that the dominant technologies in Education 5.0 are artificial intelligence, the Internet of Things, Cloud computing, and blockchain technology. However, beneficial usage of these technologies is influenced by six types of digital literacy that have been reported before, and these are media literacy, computer literacy, information literacy, visual literacy, technological literacy and communication literacy.

In this framework, as shown in figure above, innovation is influenced by three innovation competencies, namely: individual competencies, interpersonal competencies and networking competencies (Nandal et al., 2020; Ferreras-Garcia et al., 2021). Additionally, assessment criteria for the major components of Education 5.0 are shown numbered 1–18 in Figure 5.