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The primary objective of this chapter is to synthesize and organize prevailing theoretical perspectives on metacognition into a framework that can enhance understanding of metacognitive phenomena, with the aim of stimulating future research in the field of organizational behavior and human resources management (OBHRM). The author starts with a review of the history of metacognition research, distinguishing it from related theoretical constructs such as cognition, executive function, and self-regulation. Following this, the author outlines five constituent elements of metacognition – metacognitive knowledge, metacognitive experiences, metacognitive monitoring, a dynamic mental model, and metacognitive control – with discussions on their interrelationships and respective functions. Two approaches to metacognition, a process approach and an individual-difference approach, are then presented, summarizing key questions and findings from each. Finally, three broad directions for future research in OBHRM are proposed: examining metacognitive processes, considering mechanisms beyond learning to explain the effects of metacognition, and exploring both domain-specific and general metacognitive knowledge and skills. The implications of these research directions for personnel and human resources management practices are discussed.

This paper outlines the active learning methods used to develop and deliver a sustainable business course to undergraduate students. Moreover, the paper aims to investigate the effect of the sustainable business course on the students’ engagement in sustainable consumption.

From a pedagogical perspective, the paper describes the active learning methods applied in a newly introduced sustainable business course via numerous distinctive assessment techniques. On the empirical front, the research investigates the impact of the sustainable business course on the students’ engagement in sustainable consumption. To test such impact, an online survey was distributed among students who completed the sustainable business course, and as a control group for comparison, students who did not take the course.

The research results indicate a positive relationship between completion of the sustainable business course and engagement in sustainable consumption. Furthermore, the data revealed that female students exhibited more engagement in sustainable consumption than male students.

The study contributes to the literature on student-centered pedagogy, active learning techniques and the relationship between sustainable business education and engagement with sustainable consumption.

The study contributes to the literature on student-centered pedagogy, active learning techniques and the relationship between sustainable business education and engagement with sustainable consumption. Pedagogically, the nonconventional course curricula and assessment methods described in this study can be used as a reference by instructors aiming to integrate active and experiential teaching methods into their sustainable business curricula. For decision makers in higher education who are working in line with the global direction to achieve sustainability, this research provides preliminary evidence that students’ engagement with sustainability is influenced by their course curricula.

By depicting innovative approaches to teaching sustainability in business, the research enriches the field of sustainable business pedagogy which remains under-researched in many countries. Furthermore, the research goes further by investigating the effect of the course on students’ sustainable consumption. This acts as evidence of the effectiveness of teaching sustainable business in changing future leaders’ perspectives and priorities to include environmental and social aspects, which has become a global goal. Accordingly, the research has the potential to encourage more business schools to make sustainable business education mandatory.

The paper follows Jason Cope's (2011) vision of a holistic perspective on the failure-based learning process. By analyzing the research since Cope's first attempt, which is often fragmentary in nature, and providing novel empirical insights, the paper aims to draw a new comprehensive picture of all five phases of entrepreneurial learning and their interplay.

The study features an interpretative phenomenological analysis of in-depth interviews with 18 failed entrepreneurs. Findings are presented and discussed in line with experiential learning theory and Cope's conceptual framework of five interrelated learning timeframes spanning from the descent into failure until re-emergence.

The study reveals different patterns of how entrepreneurs experience failure, ranging from abrupt to gradual descent paths, different management and coping behaviors, and varying learning effects depending on the new professional setting (entrepreneurial vs non-entrepreneurial). Analyzing the entrepreneurs' experiences throughout the process shows different paths and connections between individual phases. Findings indicate that the learning timeframes may overlap, appear in different orders, loop, or (partly) stay absent, indicating that the individual learning process is even more dynamic and heterogeneous than hitherto known.

The paper contributes to the field of entrepreneurial learning from failure, advancing Cope's seminal work on the learning process and -contents by providing novel empirical insights and discussing them in the light of recent scientific findings. Since entrepreneurial learning from failure is a complex and dynamic process, using a holistic lens in the analysis contributes to a better understanding of this phenomenon as an integrated whole.

Having defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.

Conceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.

The predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.

Better understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

因此,就了解經濟週期和經濟發展而言,流動資金是一個極其重要的概念。本研究擬在安全資產的價格預測方面取得進步。安全資產代表了經濟的實際情況,特別是美國的十年期國債。

流動資金的定義上面已說明了; 為進一步了解經濟波動,本研究擬對流動資金代表性變量的預測能力進行評估。

研究使用作為流動資金代表的概念變項去規劃預測。各機器學習模型的結果會作比較,這會帶來對流動資金變量的預測值的深思,而深思的目的是確定其選擇。

只要在私營部門內流動資金的數據比公營部門的流動資金數據、在預測經濟波動方面貢獻更大時,我們發現、模型的預測能力也會依賴流動資金的來源而存在差異。國際流動資金被視為一個晦澀的概念,而它的定義的標準化,或許應是未來學術研究的焦點。當應用最先進的機器學習模型時,標桿分析法的步驟也施行了。

若我們對有關的變量加深認識,我們就可更深入地理解金融市場的運作。流動資金,雖是金融市場中一個極其重要的變量,但在現存的學術文獻裏,不但沒有明確的定義,而且也沒有被標準化; 就此而言,未來的研究或許可在這方面作進一步的探討。因此,本研究為富有新穎思維的應用研究,研究使用了現代數據科學技術,這可為探討金融市場提供一個全新的視角。

Flow control has a great potential to contribute to a sustainable society through mitigation of environmental burden. However, the high dimensional and nonlinear nature of fluid flows poses challenges in designing efficient control laws using the control theory. This paper aims to propose a hybrid method (i.e. machine learning and control theory) for feedback control of fluid flows, by which the flow is mapped to the latent space in such a way that the linear control theory can be applied therein.

The authors propose a partially nonlinear linear system extraction autoencoder (pn-LEAE), which consists of convolutional neural networks-based autoencoder (CNN-AE) and a custom layer to extract low-dimensional latent dynamics from fluid velocity field data. This pn-LEAE is designed to extract a linear dynamical system so that the modern control theory can easily be applied, while a nonlinear compression is done with the autoencoder (AE) part so that the latent dynamics conform to that linear system. The key technique is to train this pn-LEAE with the ground truths at two consecutive time instants, whereby the AE part retains its capability as the AE, and the weights in the linear dynamical system are trained simultaneously.

The authors demonstrate the effectiveness of the linear system extracted by the pn-LEAE, as well as the designed control law’s effectiveness for a flow around a circular cylinder at the Reynolds number of Re_D = 100. When the control law derived in the latent space was applied to the direct numerical simulation, the lift fluctuations were suppressed over 50%.

To the best of the authors’ knowledge, this is the first attempt using CNN-AE for linearization of fluid flows involving transient development to design a feedback control law.

Student engagement evaluation is considered to be connected to many aspects of the management of higher education, but outside Western higher education, research and evaluation on student engagement and experiences has been limited so far. Our study focuses on the underexplored aspects of Palestinian higher education with the aim of gaining an actionable understanding from the overall student engagement situation to enhance the management and development of local teaching and learning practices.

A quantitatively oriented, sequential mixed-methods design was adopted. With the applied and validated engagement measurement we collected 946 engagement questionnaire responses from Palestinian university students. Quantitative data were analysed using structural equation modelling, K-means cluster analysis and chi-squared tests. Inductive and deductive thematic analysis was employed for the open answers.

With the three validated student engagement dimensions, the applied cluster analysis allowed three different engagement profile groups to be distinguished: strongly, moderately and loosely engaged. In the subsequent statistical and qualitative thematic analyses, these three engagement clusters differ in the degree to which they had a clear vision of a future profession or in their academic engagement with their studies. Moreover, qualitative analysis brought up many shared concerns regarding theoretically oriented studies and uncertain professional and career prospects in the Palestinian higher education context.

This study is one of the first attempts to develop tools for student engagement management in Palestinian higher education. The study findings are particularly significant for developing micro- and meso-level management practices in Palestinian higher education institutions.

Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).

This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.

The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.

This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.

This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

Reflective practice (RP) is a key skill for developing one’s professional practice. It has, however, not been unanimously prioritised in public health (PH) competency and education frameworks. Reflection activities are often unstructured in higher education. There is also a dearth of literature on the RPs of undergraduate PH students. This study aims to explore in greater depth how RP helps undergraduate PH students explore their own learning in internships.

Reflection prompts were designed using the DEAL model. 124 written reflection entries from 32 students were collected and analysed thematically using a deductive-inductive approach. The conceptual framework of internship learning goals by Ash and Clayton (2009) was used to guide the deductive analysis.

Three themes were identified: initial engagement with reflective learning; gradual integration of reflective learning, and a transformative phase involving professional development, personal growth, civic learning, growth through struggle, being confronted with differences in expectations, and skill acquisition.

This study extends the limited evidence regarding RP in undergraduate non-medical PH education, and contributes toward informing the revision of undergraduate PH programmes, for example, by integrating structured reflection earlier in the curricula, and establishing/supporting mentorship programmes between institutions. The findings call for PH educators to be more intentional in creating opportunities to nurture RP among budding PH professionals.

Many studies have examined pre-diploma students' backgrounds and academic performance with results showing that some did not achieve the expected level of competence. This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning.

Secondary data analysis was used in this study, which involved collecting information about 1,052 pre-diploma students enrolled at Universiti Teknologi MARA (UiTM) Pahang Branch between 2017 and 2021. The research procedure was divided into two parts: data collecting and pre-processing, and building the machine learning algorithm, pre-training and testing.

Gender, family income, region and achievement in the national secondary school examination (Sijil Pelajaran Malaysia [SPM]) predict academic performance. Female students were 1.2 times more likely to succeed academically. Central region students performed better with a value of 1.26. M40-income students were more likely to excel with an odds ratio of 2.809. Students who excelled in SPM English and Mathematics had a better likelihood of succeeding in higher education.

This research was limited to pre-diploma students from UiTM Pahang Branch. For better generalizability of the results, future research should include pre-diploma students from other UiTM branches that offer this programme.

This study is expected to offer insights for policymakers, particularly, the Ministry of Higher Education, in developing a comprehensive policy to improve the tertiary education system by focusing on the fourth Sustainable Development Goal.

These pre-diploma students were found to originate mainly from low- or middle-income families; hence, the programme may help them acquire better jobs and improve their standard of living. Most students enrolling on the pre-diploma performed below excellent at the secondary school level and were therefore given the opportunity to continue studying at a higher level.

This predictive model contributes to guidelines on the minimum requirements for pre-diploma students to gain admission into higher education institutions by ensuring the efficient distribution of resources and equal access to higher education among all communities.