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The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

This study focuses on a regional education network in the Mid-Atlantic that aims to facilitate equitable learning practices by providing ongoing teacher/leader support, cross-sector collaboration and professional learning for educators. The authors probe networks as providing core support for systems level change and serving as precursors to the community engagement that is essential for deeper learning. Specifically, this study is driven by the hypotheses that (1) deeper learning may be supported by pathways for students and educators to meaningfully engage with the local community; (2) deeper learning is more likely to happen when educators connect to communities beyond their own school or organization and (3) education networks can help facilitate those functions.

The authors aimed to understand how participation in an education network influenced members (which include school leaders, teachers and leaders of youth programs) and how experiences might differ by level of participation. The authors conducted interviews with individuals across three groups of adults (n = 111 total): core members (n = 16), members with mid-level engagement (n = 30) and peripheral members (n = 65).

Educators who participated most intensely and deeply described the network as a vehicle for learning about and advancing equity through specific practices including individualized learning, increasing access and resource redistribution. Mid-level participants emphasized the professional network building function of the network. For peripheral or new participants, the most salient function of the network was celebration of education and educators. These findings suggest that education networks have a role in strengthening the structures that support leaders to make deeper learning happen.

More research is needed on how participants move from the periphery to more core involvement in education networks, where they may gain the full benefits of participation. Further research is also needed to explore the link between education network engagement among school leaders and the deeper learning environments in schools.

Research on education networks is limited. To the authors' knowledge, the present study is one of the largest collections and analyses of interviews with education network members to date. The authors present education network engagement as a precursor for community embedded deeper learning in schools.

To meet the needs of under-resourced, rural schools where teacher attrition is high, this case study examined how a school–university partnership strengthened teacher and leader abilities to support deeper learning for students.

This research focused on a 17-year collaborative partnership between one rural school district and a university research and outreach organization to develop deeper learning experiences for students through shared and supportive leadership and learning of teachers and leaders. The researchers utilized documents, field notes and interviews with administrators to validate the data.

The study’s findings suggest that participation in authentic, researched-based professional development through the partnership improved the skills of leaders and teachers to support deeper learning for students. This partnership heightened teacher and leader capacity to promote and support continued change and sustainability.

This case study explored how one university center collaboratively engaged with a district by sharing research and strategies to support the development of leaders and teachers to create deeper learning for students. Through these experiences, the district evolved its deeper learning system and improved its organizational effectiveness, leadership development and learning for all.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

The authors of this study examined how four leadership teams participating in a year-long deeper learning leadership academy understood and adapted key practices for change leadership, deeper learning and equity in their PK-12 schools.

This multiple-site case study used interviews, observations and documents to investigate how four school leadership teams developed deeper learning initiatives in their schools.

This study highlights how participants recast the leadership academy’s three pillars (change leadership, deeper learning and equity) as they engaged in educational reform for ambitious teaching and learning in their own contexts. Three themes emerged: transforming the culture, teaching the whole child and restructuring for collaboration. Overall, the authors found that district leaders must be fully committed to deeper learning for the culture to truly be transformed in schools under their purview.

This article contributes to the literature on supporting school leaders to enact equity-centered deeper learning initiatives using robust professional development. It is useful for understanding key deeper learning strategies and designing future training.

Although much can be learned from schools that regularly foster deeper learning, little research has been undertaken into how and why these schools have been effective or to elucidate key leadership and cultural characteristics. Moreover, there has been limited attention toward deeper learning within schools that focus on career and technical education (CTE), a major omission given the potentially elevated potential for deeper learning in these contexts. This study aims to partially rectify these issues by examining the intersections of leadership and culture at an innovative school that has demonstrated excellence whilst providing a curriculum centered on CTE.

This instrumental, insider, single-case study is focused on how leadership–cultural interactions have fostered and shaped students' opportunities to experience deeper learning. The authors take the perspective that it is largely through these leadership–cultural intersections that an organization and the work that happens within it takes on a particular meaning, direction and value. This study applies ethnographic methods, drawing upon formally and informally collected data over the past three years – e.g. from field notes, from leadership meetings and site visits; focus group interviews with students, parents, teachers, partners and school leaders; and additional artifacts.

The authors detail three interrelated features at this school, noting that it is: (1) driven by moral purpose; (2) open, collaborative and trusting; and (3) ambitious and entrepreneurial. The authors explain how/why such a culture has developed and to what effects, especially in relation to facilitating deeper learning.

Study findings meaningfully add to the literature regarding leadership for deeper learning, broadly and in relation to career and technical education and yield recommendations for educational leaders and policymakers.

This article draws upon evidence from a contemporary study of all-through schools (ATS) in three countries. ATS combine at least two stages of a child's education in a single establishment. Many admit children aged 3–19. Most children join the school at nursery or kindergarten level and continue there for their entire education before moving on to further or higher education. ATS are also called all-age, in some contexts, because they bring children of all ages together into the same school environment. Models of ATS vary internationally; hence, there is not one definition of an ATS. This article takes a comparative look at ATS in Iceland, Spain and Wales. The purpose of this article is to explore innovative pedagogies in ATS and to explore how far deeper learning occurs because of the integrated and inclusive model of schooling. The study focused on pedagogical practices in ATS and examined how far these innovative practices are considered by teachers to foster deeper learning outcomes.

The article draws upon a three-year comparative research project that explored pedagogy, leadership and well-being in ATS. The article investigates pedagogy with a cross-cutting focus on enquiry and deeper learning from the perspective of leaders and teachers. Using focus groups and lesson observations, a qualitative case-study approach was utilised to gather evidence about the teaching and learning processes adopted in ATS. Semi-structured interviews were also conducted with school leaders. The analytical approach adopted was one of constant comparison with the prime aim of eliciting common themes across the data sets. In relation to the pedagogy theme and an exploration of pedagogical innovation, research questions included (1) How far do ATS foster innovative pedagogies?, (2) What are the leadership conditions that support innovative pedagogies? and (3) To what extent do innovative pedagogies promote deeper learning?

Within and across the three education systems under investigation, the study found that all-through schooling engages students in a positive learning environment and provides innovative pedagogical processes associated with deeper learning. The article provides evidence about how deeper learning functions in ATS from different parts of the world and reflects on the way deeper learning is promoted by leaders and teachers, resulting in deeper learning for students. The evidence from this study reinforces that opportunities for pedagogical innovation and deeper learning within ATS occur because of flatter structures, more fluidity between different phases of learning and greater cross-over of teacher expertise. The study also highlights how leadership is a critical factor in creating the conditions for collective professional practices that foster pedagogical innovations to secure deeper learning. Findings suggest that leading for deeper learning is fundamentally concerned with creating the conditions for innovative learning environments that are equitable, inclusive, diverse and cross age ranges.

Contemporary empirical studies of the deeper learning environments within ATS remain relatively rare; hence, this study provides new comparative and contemporary evidence that illuminates the nature of the pedagogical innovation and the leadership practices that support pedagogical innovation in these schools. It also highlights how professional collaboration and cross-phase working are at the heart of innovative pedagogies that support deeper learning. The study outlined in this article provides critical, new insights about pedagogical innovation and deeper learning within ATS settings.

This article details findings on how leaders of deeper learning schools establish, maintain, and propel unique teaching and learning environments. In this case study, the authors present findings from data collected through interviews with 30 leaders of self-proclaimed deeper learning initiatives and site visits to those elementary and secondary schools.

Using a case study approach, the authors collected data from interviews and observations of 30 school leaders.

The study's findings indicate how leaders of schools that engage in deeper learning tend to adhere to three core practices. First, the leaders of deeper learning schools in this study intently listened to the community to ascertain needs and desires; this drove the vision. Second, leaders of deeper learning schools created learning spaces that empowered students and gave them voice, agency, and choice. Third, leaders of deeper learning schools sought to humanize the schooling experience.

This study provides actionable examples of what leaders currently do to engage kids and teachers in deeper learning. These leaders offer insights into specific actions and practices that they espoused to make the schooling experience markedly different.

Previous studies focused on the deeper learning of schools and students. This is one of the first studies to focus on the inteplay between deeper learning and school leaders.

In the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.

The data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.

The findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.

The first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.

This study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.

To the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

This study aims to examine the relationship between undergraduates' course experience and their deep learning approach and to identify areas of improvement to facilitate students' deep learning in the private higher education context.

Data were collected from 844 Malaysian undergraduate students who studied in six private higher education institutions (HEIs) in Penang and Selangor. This study used partial least squares structural equation modelling (PLS-SEM) for data analysis.

The findings revealed that good teaching and appropriate assessment have no significant relationship with deep learning. Generic skills, clear goals and standards, appropriate workload and emphasis on independence are positively related to deep learning. Generic skills and emphasis on independence are two domains that deserve attention to enhance deep learning among undergraduates.

Lecturers need to focus on to the cultivation of generic skills to facilitate students' deep learning. Student autonomy and student-centred teaching approaches should be empowered and prioritised in teaching and learning.

The current study has its originality in providing empirical findings to inform the significant relationship between dimensions of course experience and deep learning in Malaysian private HEIs. Besides, it also identifies the areas of improvement concerning teaching and learning at the private HEIs using importance-performance matrix analysis (IPMA) in a non-Western context.