Predicting Learners’ Agility and Readiness for Future Learning Ecosystem

Abstract

Agility and future readiness are fundamental 21st-century skills that could guide university students globally to thriving and benefiting from a VUCA—volatile, uncertain, complex, and ambiguous—world. The ability to respond flexibly, make informed decisions, and adapt to rapid change reflects future-readiness capabilities. However, little is known about the empirical role of the university curriculum, learning ecosystem, and learning experience as perceived by university students in developing these skills. Therefore, we analysed data collected from 209 Malaysian university students from 16 universities to assess whether these three pertinent factors impact the students’ learning agility and determine how well learning agility predicts learners’ future readiness. The present study empirically assessed a theoretical model using a partial least squares structural equation modelling (PLS-SEM) approach. The analysis supported all the hypotheses proposed in this study, which implies that the extended model could effectively predict learners’ agility and future readiness. The results revealed that the university learning experience, ecosystem, and curriculum positively, directly, and significantly affected learning agility and future readiness. Furthermore, the findings showed that student agility significantly mediated the relationships between the student learning experience, university learning ecosystem, and curriculum and student future readiness. Taken together, these results highlight the importance of a future-ready education nurtured by a vibrant learning ecosystem that delivers lasting values and experiences for students and communities to appreciate the opportunities for a challenging yet exciting future offered by a VUCA environment. The established empirical model describing the empirical interplays between these correlates could, in turn, aid better evidence-based policy making in higher education.

1. Introduction

In 2021, The Malaysian Higher Education Ministry officially launched The Experiential Learning and Competency Based Education Landscape (EXCEL) framework developed by the Ministry together with the Malaysian Qualifications Agency, government agencies, industries, and public universities to support a holistic student learning experience at Malaysian universities (Department of Higher Education, Malaysia, 2021). The framework outlined four experiential learning thrusts, namely Industry-Driven Experiential Learning (IDEAL), Community Resilience Experiential Learning (CARE), Research-Infused Experiential Learning (REAL), and Personalised Experiential Learning (POISE). IDEAL provides an immersive learning experience for learners to work and study within an industrial context, whereas CARE brings students to learn with the community, fundamentally enhancing their roles within a society. REAL nurtures learners to be explorative and research-minded via undertaking research projects with scientists and other stakeholders. POISE will enable learners to personalise their self-designed learning curriculum according to their passions and interests, nurturing them to become active lifelong learners. As a result, it is necessary to assess students’ readiness in terms of agility.

Learning agility research has shed new light on the study of the education of the future and has inspired the development of a new body of research, both of which have contributed to deeper comprehension of learning agility and its key role in the future education ecosystem [1,2,3]. In today’s inter-related and complicated world, having a better understanding of learning agility is critical. The only way for an organization to stay ahead of the curve is to acquire knowledge more quickly and effectively than its rivals do. Educational sectors and individuals must work harder than ever to keep up in today’s ever-changing technological landscape. Educational institutions all over the world are incorporating a learning agility culture into their daily operations to deal with the ever-changing dynamics of business, come up with novel solutions to problems, strengthen resilience, and prepare for the future. It is now one of the foundational principles that encourages a more welcoming and creative work environment for everyone. The same holds true for individuals, who must adapt to the new realities of information and communication technologies. As a result, these new technologies have the potential to improve learners’ agility. The biggest challenge that higher education leaders, educators, and learners must overcome is the rapid change offered by the VUCA environment. To successfully lead and manage learning in these times of rapid change, leaders, educators, and learners must master agility [4,5]. Agile leaders, educators, and learners are adaptive, flexible, and keenly aware of their surroundings. The significance and relevance of learning agility in Malaysian future citizens cannot be overstated. However, there is still a dearth of understanding regarding learning agility, especially in the Malaysian higher education context. A vast number of educational researchers, policy makers, principals, instructors, and administrators are researching, evaluating, and implementing various solutions and strategies to strengthen higher education, whether on their own campuses or across the industry. However, there is one factor that is essential to adjusting in times of uncertainty, and that factor is learning agility. As a result, new information about the higher education ecosystem, like it is for any other industry, is critical as the area of higher education continues to change dramatically. In today’s constantly changing world, learning agility is a vital indicator of higher education success that must be assessed, analysed, and evaluated. The purpose of this study is to look into Malaysian students’ behaviours regarding future education, their agility, and how students perceive the future learning experience, curriculum, and the overarching higher education ecosystem. Although there are several research studies on future teaching and learning that explain factors underlying students’ experiences in the current educational system in general and higher education in particular, limited research has looked into the relationships between curriculum, experience, and ecosystem of future education using a structural equation model.

2. Literature Review

2.1. Learning Agility

Learning agility is a relatively new term drawn from the field of organisational behaviour that refers to the desire and capacity to enhance experiential learning and later adapt that learning to new settings [6,7]. Learning agility refers to an individual’s ability to learn from experience. Lombardo and Eichinger [7] defined learning agility as “willingness and ability to learn new competencies to perform under first time, tough, or difficult conditions” (p. 323). Learning agility has been discovered to be a strong predictor of success and high performance. According to different research findings, learning agility is a powerful predictor of future success since it stresses the capacity to adapt, adjust, and expand individuals’ learning styles to deal with new circumstances [6,8]. In a study conducted by Lombardo and Eichinger (2000), learning agility was found to predict the extent to which people performed well in their current job and had the potential to progress to the next one. According to Lombardo and Eichinger [7], learning agility is classified into four categories: people agility, mental agility, change agility, and results agility. Each one has unique abilities and a distinct description that distinguishes it from the others (see

Table 1. Different aspects of learning agility (Adapted from Ref. [7], p. 324).

	Agility Element	Description
1	People	Describes people who know themselves well, learn from experience, treat others constructively, and are cool and resilient under the pressures of change.
2	Results	Describes people who get results under tough conditions, inspire others to perform beyond normal, and exhibit the sort of presence that builds confidence in others.
3	Mental	Describes people who think through problems from a fresh point of view and are comfortable with complexity, ambiguity, and explaining their thinking to others.
4	Change	Describes people who are curious, have a passion for ideas, like to experiment with test cases, and engage in skill-building activities.

).

According to Lovell et al. [9], considerable attention is paid to the factors that impact students’ learning agility and how agility enhances students’ future readiness. Some of the factors known to influence learning agility are experience, curriculum, and ecosystem. In addition, previous studies confirm that learning agility might improve future readiness [10,11]. Thus, the current study looks at these factors that impact students’ learning agility and determines how well learning agility predicts learners’ future readiness in Malaysian tertiary students. Based on these insights, the following hypothesis is proposed:

Hypothesis 1

: Student learning agility positively affects student future readiness.

2.2. Learning Experience

Scholars in teaching and learning have indicated that learning occurs via interactions between individuals and/or groups wherein experiences and ideas are exchanged and implemented to develop new knowledge [12,13]. In education theories, individuals are described as active learners who grow via lived experience [14,15,16]. The capacity of people to learn through experience allows them to succeed at a skill in a world where change is regarded as a need and learning is becoming an increasingly critical aspect of success [17,18]. As mentioned earlier, learning agility focuses on the capacity to learn new activities and the ability to learn from experience to perform well in new settings. Learning agility is a notion that helps us understand how people learn from their experiences in ways that improve their performance. As a result, personal experiences provide valuable learning opportunities.

Furthermore, although learning agility is thought to be predictable by learning from experiences, earlier studies on the relationship between learning experiences and learning agility yielded contradictory results. As a result, the impact of learning experiences on improving students’ learning agility is still unclear. Understanding the effects of learning experience on learning agility could have practical and theoretical implications. First, educational sectors and organisations will be able to target development opportunities for individuals who are more likely to use what they have learned in previous experiences in the future. Second, researchers will be able to better understand the relationship between learning experience and learning agility. Therefore, the following hypothesis is posited:

Hypothesis 2

: Learning experiences positively affect students’ learning agility.

2.3. Future Curriculum

A future-focused curriculum, according to Sinnema and Stoll [19], “presents ambitious goals for reach impacts and the kinds of learning that would indicate success” (p. 12). Ornstein [20] stated that, with regards to a future curriculum, many significant factors are predicted to influence curriculum development. Lifelong learning, international collaboration, and communication are among them. Research reveals that learning must be collaborative as well as collective, with new paths for knowledge advancement [21,22,23]. In terms of curriculum, learning must concentrate on curriculum change, recognise what the curriculum changes are and what they signify, understand how to respond to curriculum changes, and finally develop a practice to meet the curriculum objectives [19]. Despite the importance of future curricula in students’ learning agility and future readiness, research on the impact on learning agility in higher-education contexts is limited. Furthermore, demonstrating that a person’s future readiness results from the relationship between the future curriculum and learning agility would add to the significance of learning agility in higher education. Therefore, the present study will examine the effects of the future curriculum on learning agility and future readiness in Malaysian tertiary education. Based on these insights, the following hypotheses are proposed:

Hypothesis 3

: Future curriculum positively affects students’ learning agility.

Hypothesis 4

: Future curriculum positively affects students’ future readiness.

2.4. Future Learning Ecosystem

The learning ecosystem concept helps organisations in understanding how learning occurs as a dynamic process of interaction between a range of components across location and time [24]. A comprehensive learning ecosystem is a collection of all learning solutions and materials that can help educational institutions promote learners’ development. Therefore, educational institutions must ensure that all students have access to learning opportunities, which contributes to the institution’s learning ecosystem. The influence of the ecosystem on students’ learning highlights the need to reconsider the future classroom’s structure to make them more flexible and adaptable [25,26]. The readiness of learners to make the transition from the current ecosystem to the future one should be a primary consideration during the design phase of future ecosystems. Future ecosystems will rely heavily on communication and information technology. Intelligent technologies and smart devices can assist students in developing their skills in future learning environments. Future educational ecosystems will benefit greatly from the smart learning environment’s potential to facilitate the development of novel pedagogical approaches. As a result, creating learning ecosystems that leverage smart learning to improve students’ educational experiences will be a significant challenge. The education industry can determine which models are the most effective by observing students’ unique characteristics, academic progress, and personal development, as well as experimenting with new instructional methods. To tackle the challenges of the future ecosystem, educational institutions must reimagine their teaching and learning approaches. With the appropriate adaptations, future classrooms might be effective in meeting ever-increasing student demands and ensuring satisfying learning results. Research suggests that future learning ecosystems will include the most recent stage of development, solutions, equipment, and technology that will prepare students for the future (future readiness). In addition, future learning ecosystems must address both theoretical and methodological perspectives, tackling how future ecosystems expand to new ways of approaching the complicated subject of environmental issues that emerge in dynamic ways through time and space. In order to gain a competitive advantage, future learning ecosystems should embrace the use of technology in teaching and learning. Although the future ecosystem plays a vital role in students’ learning, there has been little research into the role of the future ecosystem on learning agility and future readiness in higher education contexts, which is an important feature that requires further investigation. Therefore, the current study proposes that the future ecosystem improves students’ learning agility and makes them future-ready. Consequently, the hypotheses regarding the future ecosystem are stated below:

Hypothesis 5

: The ecosystem positively affects students’ learning agility.

Hypothesis 6

: The ecosystem positively affects students’ future readiness.

2.5. Future Readiness

It is self-evident that education must prepare individuals for future employment and life. According to recent research, “future-readiness demands cultural sensitivity, adaptability and the ability to think across multiple disciplines to find innovative solutions” [27] (p. 10). The development of a learner’s future readiness needs a creative mindset and the agility to evolve and adapt constantly [28]. Tibbetts and Leeper [29] claim that education’s future lies outside the traditional classroom, so various and related activities should be considered regarding technology immersion in educational settings. Therefore, it can be stated that in the education sector, the inclusion of the most recent technologies into the learning environment is critical to success. According to Clem and Junco [30], the “classroom of the future will look like an engaging social space, bringing forth vigorous conversation and debate while using technologies to help students collaborate, communicate, and build a sense of classroom community” (p. 526). As a result, future-ready learners should master new skills, develop a body of knowledge, and be eager to learn new skills [27,31]. This study argues that the key to future readiness is strongly reliant on students’ learning experiences, future curriculum, future ecosystem, and learning agility. It also proposes that student learning agility mediates the impact of experience, curriculum, and ecosystem on students’ future readiness. As a result, the following hypotheses are proposed:

Hypothesis 7

: Students’ learning agility mediates the relationship between the ecosystem and students’ future readiness.

Hypothesis 8

: Students’ learning agility mediates the relationship between the curriculum and students’ future readiness.

Hypothesis 9

: Students’ learning agility mediates the relationship between learning experience and students’ future readiness.

3. Research Methodology

The survey research method was used to collect data in this study. The survey was conducted using the SurveyMonkey questionnaire (https://www.surveymonkey.com/ (accessed on 1 August 2022)). The questionnaire was distributed to university students in Malaysia. All participants were drawn at random and voluntarily from public and private universities in Malaysia. Participants were informed of the purpose of the study as well as the strict confidentiality and anonymity of the survey. Prior to filling out the instrument, all participants were given a consent form.

A total of 209 valid questionnaires were obtained, forming the dataset for data analysis. In terms of educational level, 78.5% (n = 164) of the participants were undergraduates, 12.9% (n = 27) were graduate students, and the remainder were enrolled in diploma programmes. Regarding the participants’ academic performance, survey respondents were asked to rank their level of performance in the classroom. The results indicated that only 1.4% of the participants rated their academic performance as “below average,” while 74.6% rated it as “average.” In contrast, nearly 17.2% of students rated themselves among the “top ten students” in their classroom. However, 6.7% of respondents claimed to be among the “top five students” in their classroom. The data for this study were gathered from students from various fields of study. At the time of the study, the majority of participants 64 (30.6%) were studying in the field of “Business Studies and Management Sciences,” while 35 (16.7%) were studying in the field of “Social Sciences,” and 28 (13.4%) were studying in the field of “Education and Training.” The demographic information of the respondents is shown in

Table 2. Demographic information of participants (n = 209).

Profile of respondents		Frequency	Percentage (%)
Education	Undergraduate Postgraduate Diploma	164 27 18	78.5 12.9 8.6
Academic Performance	Below average Average Among top ten students Among top five students	3 156 36 14	1.4 74.6 17.3 6.7
Field of Study	Business and Management Social Sciences Education and Training Others	64 35 28 82	30.6 16.7 13.4 39.3

.

4. Data Analysis and Results

The current study used the Partial Least Square-Structural Equation Modelling (PLS-SEM) technique with Smart PLS 3.0. to evaluate the proposed model and corresponding hypotheses. Partial least squares (PLS), a component-based SEM technique, is considered an alternative to covariance-based SEM. The component-based SEM technique addresses some of the constraints of covariance-based SEM, such as sample size requirements, conditional multivariate normality, and model complexity [32]. Furthermore, unlike covariance-based SEM, which is solely concerned with explanatory model testing, PLS-SEM allows for evaluating a model’s prediction capabilities [33].

A two-stage procedure was used for data analysis and interpretation, as Hair et al. [33] recommended. First, the measurement model was examined to verify the proposed model’s reliability and validity, and then the structural model was analysed to test the research hypotheses empirically.

4.1. Measurement Model Assessment

Reflective measurement models need to be evaluated in terms of their reliability and validity. Following Hair et al. [32] recommendations, four criteria were used to assess the measurement model: indicator reliability, construct reliability, convergent validity, and discriminant validity. First, the indicator reliability was assessed using standardised factor loading of latent variable indicators. When all indicator factor loadings surpass the cut-off value of 0.70, item reliability is regarded as satisfactory. The measurement model assessment results showed that all construct indicator loadings were above the thresholds, suggesting adequate item dependability (see Table 1). Second, the reliability of the latent variables is considered acceptable when Cronbach’s alpha, Dijkstra-rho Henseler’s (ρA), and composite reliability (CR) exceed 0.70. The measurement model assessment results revealed that all variables had values greater than 0.70, indicating construct reliability (see Table 1). Third, researchers need to examine the average variance extracted (AVE) and indicator loading for convergent validity. Indicators of a latent variable are said to have convergent validity when the AVE value of the latent variable is higher than 0.50 and all indicators load substantially on their respective latent variables with item loading of 0.7 or above (factor loading). The results revealed that all latent variables’ AVE values and item loadings exceeded the recommended threshold values, signifying a satisfactory degree of convergent validity (see

Table 3. Measurement model results.

Constructs	Loadings	Alpha	ρA	CR	AVE
Future Ecosystem		0.856	0.859	0.893	0.547
Ecosystem1	0.783
Ecosystem2	0.776
Ecosystem3	0.761
Ecosystem4	0.774
Ecosystem5	0.723
Ecosystem6	0.756
Future Curriculum		0.872	0.887	0.914	0.727
Curriculum1	0.903
Curriculum2	0.910
Curriculum3	0.862
Curriculum4	0.721
Learning Experience		0.876	0.880	0.915	0.730
Experience1	0.883
Experience2	0.832
Experience3	0.855
Experience4	0.846
Learning Agility		0.861	0.864	0.894	0.547
Agility1	0.707
Agility2	0.760
Agility3	0.716
Agility4	0.799
Agility5	0.756
Agility6	0.713
Agility7	0.719
Future Ready		0.872	0.873	0.907	0.662
Future1	0.782
Future2	0.814
Future3	0.804
Future4	0.847
Future5	0.821

).

Fourth, the discriminant validity of the study’s constructs was tested using the Fornell and Larcker [34] criterion and the heterotrait–monotrait (HTMT) ratio method (Henseler et al., 2015). According to Fornell and Larcker [34], the square root of AVE for each latent variable should be greater than the construct’s highest correlation with any other latent construct. Furthermore, HTMT was also employed to examine the discriminant validity since it has been found to be more reliable in uncovering discriminant validity in PLS-SEM [35]. According to Henseler et al. [35], an HTMT value greater than 0.90 indicates a lack of discriminant validity. The findings indicate that the discriminant validity of the latent variables was confirmed because the Fornell–Larcker criterion was satisfied and all HTMT values were less than 0.90 (see

Table 4. Discriminant validity.

	Fornell–Larcker Criterion					Heterotrait–Monotrait Ratio (HTMT)
Constructs	1	2	3	4	5	1	2	3	4	5
1. Agility	0.739
2. Curriculum	0.463	0.853				0.527
3. Ecosystem	0.461	0.678	0.762			0.529	0.771
4. Experience	0.411	0.309	0.358	0.854		0.471	0.354	0.408
5. Future ready	0.476	0.469	0.490	0.782	0.814	0.548	0.539	0.564	0.893

The diagonal (in bold) represents the square root of AVE.

).

4.2. Structural Model Assessment

Following the assessment of the measurement model, an analysis of the structural model was performed to determine the explanatory power, predictive relevance of this study’s proposed model, as well as the size of the path coefficients and the significance of the hypothesised relationships. The structural model was evaluated using the following criteria: coefficient of determination (R²), predictive relevance (Q²), and path coefficient.

First, the proposed model’s explanatory power was evaluated by R². The R² value represents the percentage of variation in the endogenous latent constructs explained by the model. R² values range from 0 to 1, with higher R² values indicating stronger explanatory power. According to Chin [36], R² values of 0.67, 0.33, and 0.19 can be considered substantial, moderate, and weak. According to Hair et al. [32], R² values are interpreted based on model complexity and research contexts. The study’s findings demonstrate that the proposed model could explain up to 33.5% of the total variation in participants’ future readiness technology for learning, as shown in Figure 1 and Table 4. Furthermore, the proposed model could explain more than thirty percent of students’ learning agility through the influence of the variables ecosystem, curriculum, and experience. Therefore, the proposed model could be assumed to sufficiently reflect learners’ future readiness technology for learning. Second, the Stone-Geisser’s Q² Test [37,38] was used to confirm the predictive relevance of the model through the blindfolding procedure. Values of Q² greater than zero for a specific endogenous construct indicate the predictive power of the structural model for that construct under consideration [39]. As shown in

Table 5. R² and Q² of endogenous variables.

Endogenous Variable	Explained Variance (R2)	Prediction Relevance (Q2)
Future Ready	0.335	0.213
Agility	0.314	0.164

, the Q² values were greater than zero, establishing the model’s predictive capability.

Third, to test the hypotheses and investigate the significance of the path coefficients, the structural model was assessed through a bootstrapping procedure that used 5000 subsamples [32]. The findings in

Table 6. Results of the structural model.

Relationship	Path Coefficient	t-Value	p-Value	95% Confidence Interval	Decision
Direct effect
Ecosystem → Agility	0.199	2.343	0.019	[0.030; 0.369]	Supported
Ecosystem → Future	0.240	3.446	0.001	[0.110; 0.381]	Supported
Curriculum → Agility	0.247	3.058	0.002	[0.087; 0.401]	Supported
Curriculum → Future	0.175	2.137	0.033	[0.006; 0.329]	Supported
Experience → Agility	0.264	4.320	0.000	[0.151; 0.388]	Supported
Agility → Future	0.285	5.369	0.000	[0.185; 0.391]	Supported
Mediating effect
Ecosystem → Agility → Future	0.057	2.242	0.025	[0.008; 0.110]	Supported
Curriculum → Agility → Future	0.70	2.625	0.009	[0.024; 0.129]	Supported
Experience → Agility → Future	0.075	2.588	0.010	[0.030; 0.141]	Supported

illustrate that ecosystem had a positive and significant effect on learning agility (β = 0.199, t = 2.343, p = 0.019) and a positive and significant effect on students’ future readiness (β = 0.240, t = 3.446, p = 0.001). Moreover, the study also has found a significant relationship between curriculum and agility (β = 0.244, t = 3.058, p = 0.002) and future readiness (β = 0.175, t = 2.137, p = 0.033). In addition, the relationship between experience and agility was positive and significant (β = 0.264, t = 4.320, p = 0.000). Additionally, agility exerted a positive effect on future readiness (β = 0.285, t = 5.369, p = 0.000). Thus, all the direct relationships hypothesised in H1 to H6 were supported. Next, using the approach by Sarstedt et al. [40], the study examined the mediating effect of learning agility on the relationship between the ecosystem, curriculum, experience, and future readiness. A mediation analysis was run using bias-corrected bootstrapping with 5000 iterations and a confidence interval of 95%. Bootstrapping is preferable to the classic Sobel Test of mediation because it works well with small samples, minimises Type I error rates, and does not assume normal distributions (Hayes, 2017). Hypothesis 7 posits that student learning agility mediates the relationship between the ecosystem and student future readiness. The result showed that the indirect effect of the ecosystem on future readiness via student learning agility was significant (β = 0.057, t = 2.242, p = 0.025), supporting Hypothesis 7. Hypothesis 8 assumes that student learning agility mediates the relationship between curriculum and students’ future readiness. The results were statistically significant (β = 0.070, t = 2.625, p = 0.009), supporting Hypothesis 8. Hypothesis 9, which assumes that students’ learning agility mediates the relationship between experience and students’ future readiness, was also tested. The results were statistically significant (β = 0.075, t = 2.588, p = 0.010), supporting Hypothesis 9. Thus, H7, H8, and H9 were also supported. Figure 1 illustrates the path coefficients and R² values of the structural model.

5. Discussion

This study highlights the importance of future-ready education in nurturing a vibrant ecosystem that delivers lasting value for students and communities. With diverse learning experiences and a future-ready curriculum, universities empower their students to thrive and be agile in an environment where creativity and innovation are valued. Universities are required to continue to innovate by rethinking and reshaping students’ learning experiences, crafting future curriculum frameworks and ecosystems to produce graduates with the skillsets they need to succeed in their lives and careers. Hence, future-ready education is a system that is agile and relevant to contemporary learners and needs to be founded on the quality and variety of learning opportunities and informed by a partnership with industry, governments, and alumni.

This study also concludes that learning agility is an essential soft skill that enables students to find themselves and succeed in a situation where they have never been before. It helps them have a broader repertoire of things they do in their lives, and this energy of power will allow them to move quickly and flexibly through known and unknown situations. Therefore, universities must start responding to learners’ needs by assisting them in discovering and living their purpose for a successful and fulfilling life and career. The university needs to be a place for students to create more significant impacts by solving the world’s problems guided by the learners’ purpose and passion in life. To do so, universities must create a future ecosystem that provides a personalised and meaningful learning experience to students, and a future-ready curriculum needs to represent the students’ identity and provide a learning experience that is flexible and aligned with their purpose and passion. In addition, future-ready education needs to provide a platform for students to take time to reflect, seeking to understand why things happen, in addition to what happened, as well as developing them to remain present in challenging situations and open to new experiences.

The McKinsey Global Institute (MGI) estimates that globally, about half the work people are paid to do today could be automated by existing technology by 2030. Meanwhile, there will be strong growth in global employment due to new jobs created by worldwide investment in technologies. Tapping into the opportunities brought by technologies, universities must embrace change and have strategic insights to anticipate VUCA conditions and counter them with strategies, processes, and crisis management plans to mitigate difficult times successfully. As change agents, educators must also learn to anticipate and embrace change to facilitate their students to stay relevant in a rapidly changing world. This could be done by creating the flexibility of learning that works best for their students. It creates a comfortable environment for students to choose their own learning pace and lead their learning while changing the routine from “teacher-centred” to “self-centred” learning. All these initiatives will eventually contribute to producing a future generation of learners ready in the face of uncertainty.

6. Implications

This study suggested that a university’s curriculum, learning ecosystem, and learning experience would influence learning agility, and that learning agility would predict future readiness. The findings revealed strong positive connections between these three variables and learning agility, as well as a significant positive relationship between learning agility and future readiness. In other words, individuals with higher levels of learning agility displayed higher levels of readiness to evolve and adapt constantly. Thus, the results supported the tested framework, demonstrating significant relationships between a university’s curriculum, learning ecosystem, learning experience, learning agility, and future readiness.

This study contributes significantly to the learning agility literature by providing support for the relationship between the university curriculum, learning ecosystem, learning experience, and learning agility, as well as extending previous research connecting learning agility to students’ future readiness. In addition to theoretical contributions, this study provides a substantial contribution to the practical application of learning agility. It is essential for policymakers and university principles to be able and willing to adapt to new environments in a highly complex and constantly evolving educational environment. Universities are vital to the establishment of educational ecosystems focused on innovation. Universities can actively encourage innovation ecosystem growth and renewal in ways that support the universities’ other main goals of educating students and carrying out research. The university must be a part of an innovation ecosystem, which includes institutions, activities, and culture that encourage technological and pedagogical innovation and are assisted by the resources of the university.

The consequences of decisions made by governments, universities, and policymakers that have an effect on the sustainability of an innovation ecosystem ought to pique the interest of a wide range of individuals. In addition, the learning process is significantly influenced by the learner’s prior experiences. One of the pillars of understanding the components of student agility is the significance of the experience. Learning is a lifelong process founded on experience. As a result, institutions can improve students’ agility by redesigning the student experience in order to facilitate enhanced learning. To improve learning agility, students must be allowed to experience new innovations so that they can understand the flexibility of the many different learning techniques. Furthermore, the study reminds policymakers and curriculum designers of the importance of the curriculum in students’ agility and, as a result, their future readiness. The curriculum can be viewed as the primary source of students’ learning and future readiness. Policymakers must provide students with more up-to-date and relevant content to boost their learning agility and future readiness. As a result, the study’s findings have important implications for policymakers, curriculum designers, and higher education institutions, who can use them to foster continuous improvement conditions, thereby promoting future student growth and development. Universities and colleges should strive to provide their students with the most up-to-date and relevant curriculum, a modern learning ecosystem, and an enhanced student experience in order to increase their learning agility and future readiness.

7. Conclusions

As the EXCEL framework in Malaysia is still at its infancy stage and needs further exploration in terms of its effectiveness, this study has proactively approached the research topic of predicting students’ agility and future readiness for educational purposes by completing a confirmatory analysis of an established theoretical and empirical model. It predicts the challenges and potentials of the EXCEL framework in Malaysia and proposes a bigger and clearer picture of an alternate exploratory model that better captured the relationship between learning experience, future curriculum and future ecosystem, and their influence on learning agility and future readiness. An important contribution made by this study includes the demonstration that an established empirical model describing the attributes interrelates with attributes of future readiness in terms of agility and views of the future.