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Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

In January 2021, the state government of NSW, Australia, announced that all year 9 and 10 elective courses developed by schools will be phased out. This paper offers a brief historical account of school-developed board-endorsed courses (SDBECs) in NSW and a close analysis of the policy to phase them out.

I give an historical account of the meaning and place of SDBECs within the NSW school system, before situating the policy decision to phase them out within the broader historical and political context of curriculum reform in NSW. Finally, I offer an analysis of the discourses and framing of the policy both across curriculum review reports and in the government and public rhetoric, by examining policy documents, government media releases, news and blog articles at the time of the policy change.

This policy change and surrounding discourses are contextualised and analysed to show how the curriculum came to be blamed for a host of educational problems, and how the government arrived at their irrational yet politically expedient policy response by distorting the meaning of one metaphor: the crowded curriculum. I conclude with a reading of the policy as indicative of centralisation and de-legitimisation of teachers’ curriculum development work.

The convergence of state and federal discourse about curriculum as a site of cleaning up, reforming or re-organising should concern educators in Australia especially as authority over education is increasingly centralised and made vulnerable to political whim. Close studies of such minor policy decisions provide a window into how larger processes of centralisation are justified and enacted at the local level.

This paper analyzes the ethics of integrating Artificial Intelligence (AI), particularly regarding AI-generated educational content in academia. It attempts to explore how AI customization mimics human interaction and behavior in education, investigate ethical concerns in educational AI adoption, and assess ChatGPT’s ethical use for nurturing curiosity and maintaining academic integrity in education.

Fictional tales may help us think critically and creatively to uncover hidden truths. The narratives are analyzed to determine the affordances and drawbacks of Artificial Intelligence in Education (AIEd).

The study highlights the imperative for innovative, ethically grounded strategies in harnessing AI/GPT technology for education. AI can enhance learning, and human educators’ irreplaceable role is even more prominent, emphasizing the need to harmonize technology with pedagogical principles. However, ensuring the ethical integration of AI/GPT technology demands a delicate balance where the potential benefits of technology should not eclipse the essential role of human educators in the learning process.

This paper presents futuristic academic scenarios to explore critical dimensions and their impact on 21^st century learning. As AI assumes tasks once exclusive to human educators, it is essential to redefine the roles of both technology and human teachers, focusing on the future.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.

The importance of developing and implementing sustainable business practices has never been greater. Business schools are increasingly tasked with preparing students to contribute to this imperative and although progress is being made, the impact of integrating sustainability into business school curriculum has remained uncertain as studies exploring the impact have been lacking. The purpose of this multi-case study was to examine the impact of integration efforts in two distinct undergraduate business programs at Royal Roads University. The research focused on how students' understanding of sustainability and their associated attitudes and behaviors changed as they progressed throughout their programs. In addition to considering the impact of a sustainability-infused curriculum, other factors affecting sustainability orientations were also explored. The study was unique in both its comparative nature and in its investigation of the various contextual factors shaping sustainability orientations. Data were collected through semi-structured interviews and through document analysis. Findings suggest a combination of approaches to integration is most effective in impacting sustainability perspectives. While sustainability was generally understood in a multidimensional manner, there was a noticeable environmental bias and a tendency to view it within the business framework. A need for stronger and more comprehensive conceptualizations was identified. Recommendations include: (a) embed sustainability in a comprehensive manner across the curriculum, (b) move beyond a disciplinary conceptualization of sustainability and introduce stronger sustainability discourse, (c) utilize powerful experiential and place-based pedagogies, (d) pay attention to context and ensure both the formal and the informal curriculum mutually reinforce a pro-sustainability agenda.

Chat Generative Pre-trained Transformer (ChatGPT) has become everyone’s talk. It frightens many professionals, who worry about losing their jobs. ChatGPT may reconstruct some professions; some occupations may vanish while new ones may appear.

This mixed-methods study explores whether and how the use of ChatGPT impacts English is taught as a foreign language (EFL) students' social and emotional learning (SEL). The study used a questionnaire and collected perception data from 57 EFL students. A discussion with seven EFL professors was also formulated to triangulate the findings.

Results indicate that EFL students have high positive perceptions of using ChatGPT in their learning (M = 3.87). Results also showed that using ChatGPT has a moderate impact on EFL students' SEL (R = 514). This moderate effect was confirmed by the qualitative findings, which indicated that ChatGPT positively impacts EFL students' SEL by allowing them to practice conversation skills, aiding them in managing their emotional intelligence, providing them with feedback and reducing their anxiety. However, findings also indicated that ChatGPT reduces students' creativity and limits their emotional growth. Finally, the findings reported that for better use of ChatGPT, supervision is key.

This study recommends the use of ChatGPT in a way that helps students' creativity and emotional growth.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

In this chapter, we explore the ways in which media postpandemic responses communicate clearly the excessive entitlement reflected in the public discourse about teachers. During the pandemic, we noted many parent posts on social media lauding teachers. They expressed gratitude for the challenges teachers faced in teaching students on distance platforms and moving learning forward. Yet, we noted that the media reports following the pandemic were noticed a shift in the discourse following the pandemic. Thus, we became interested in exploring how teachers were represented in public discourse following the pandemic. Since the public discourse on teachers has consistently reflected a deficit orientation, given the praise of teachers during the pandemic, we wondered if this acknowledgment of teachers' sacrifice and service might shift the discourse after the pandemic to more positively represent teachers. To pursue this inquiry, we collected and analyzed narratives and examples from postpandemic media representations where teachers and teacher educators were represented as nonpersons. We also collected anecdotes and research and media reports to examine the ways in which teachers were represented. We identified three themes: lack of teachers' voices, the teacher shortage, and loss of learning. Our analysis identifies how teachers and teacher educators are positioned within society and the impact of treating teachers as nonpersons on teachers and the teaching profession. Such depictions fail to represent the vital role of teachers in the progress of society.