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Embedding sustainability into pedagogical approaches is a key priority in higher education. Equipping students with knowledge, understanding, and skills, and developing the next generation of innovators and leaders, can potentially provide the change needed and create a real impact in the journey to a sustainable future. Advancement in Science, Technology, Engineering, and Mathematics (STEM), and corresponding industries can be seen as vital for the success of meeting a majority of the sustainable development goals, and hence the approaches taken to embed sustainability in learning and teaching in STEM higher education programmes can be considered significant in many ways. This paper is based on published literature over the last two decades and a semi-structured interview with 12 university academics from a developing country. Hence, the paper reviews approaches taken to embed sustainability in learning, teaching and assessments in STEM programmes. It also aims to investigate the actions taken by universities to integrate sustainability in STEM education and the remedies taken to minimise the impact of the pandemic on the effectiveness of the learning pedagogies used to integrate sustainability concepts.

Coaching and mentoring has been regarded as one of the key learning techniques in the modern learning environment in the recent past. With the swift shift in the higher education sector to a more digitally driven learning environment, limited research has evaluated the impact of coaching and mentoring on student engagement in online learning. This study aimed to evaluate the impact of coaching and mentoring on enhancing student engagement in the higher education sector. The study applied a combination of quantitative and qualitative methods of data collection, where the primary data were collected from 120 respondents via a questionnaire and 20 in-depth interviews, covering undergraduates through a simple random sampling technique. The data analysis showed that the mentor's ability to facilitate learning and encourage mentees to be focused on their goals were the most significant factors that affected student engagement in online learning. In total, 43% of the respondents extremely agreed that they are willing to recommend their colleagues to receive the service of a mentor to help them reach their optimum potential in an online learning environment. The findings of the study recommend that the higher educational institutes should administer a sound mentoring process that meets the ethical backgrounds to consistently support the continuous improvement of the students in an online learning environment to enhance their engagement in learning activities.

Globally, the number of COVID-19 cases continues to rise daily despite strict measures being adopted by many countries. Consequently, universities closed down to minimise the face-to-face contacts, and the majority of the universities are now conducting degree programmes through online delivery. Remote online delivery and assessment are novel experiences for many universities, which presents many challenges, particularly when safeguarding academic integrity. For example, invigilated assessments, often considered as more secure, are not an option given the current situation and detecting any cheating would be significantly challenging. This paper reviews assessment security in the digital domain and critically evaluates the practices from different universities in safeguarding academic integrity, including associated challenges.

A great number of universities worldwide are having their education interrupted, partially or fully, by the spread of the novel coronavirus (COVID-19). Consequently, an increasing number of universities have taken the steps necessary to transform their teaching, including laboratory workshops into an online or blended mode of delivery. Irrespective of the measures taken, universities must continue to maintain their high academic standards and provide a high-quality student experience as required for delivery of learning outcomes associated with each degree programme. This has created a challenge across the higher education landscape, where academics had to switch to remote teaching and different approaches to achieving laboratory delivery. As a result, students have not been receiving face-to-face teaching, and access to laboratory facilities has been limited or nearly impossible. This paper reviews numerous approaches taken by universities to deliver teaching and laboratory practices remotely, in consideration of the COVID-19 pandemic, whilst also considering the potential impacts on the student learning experience. This review is primarily focused on the fields of engineering, science and technology, based on published literature including books, reviewing web-based provision of selected universities, institutional and national policy documents.

The final year design project is one of the most important components of any undergraduate engineering program. Fair and efficient project allocation procedures can be vital in ensuring a great student experience and exceptional learning out of these projects, which then could contribute in shaping students' future prospects. In this paper, we review a wide range of project allocation strategies used in various universities at undergraduate levels. We then focus on the project allocations in transnational education (TNE) contexts, which inherit additional allocation challenges. We highlight these challenges and provide recommendations to solve them. We present and compare project allocation strategies adopted at two of the largest TNE programs in China. We also present the factors that influence the project allocations, particularly regarding TNE provisions. Finally, we describe the challenges associated with the project allocations in the TNE scenario, along with proposing some feasible solutions to address these challenges.

Assessment and feedback (A&F) are two major components of any educational program and must be properly in place to ensure student learning and quality of experience. However, these important components come under severe challenges of meeting student expectations in the large class size context. When the program delivery relates to a transnational educational (TNE) scenario, the additional constraints on staff-student physical interaction, regional time differences and cultural background gaps introduce additional challenges: Conducting proper assessments and provide timely and constructive feedback to the students. In this paper, the authors propose a novel assessment and feedback framework which exploits having a large student number as a positive factor by introducing staff-student partnership to implement efficient assessment and feedback strategies. Authors propose to use students for peer-review, assessment design, evaluation rubric design and tutorial-based feedback. The students also take part in preparing feedback clusters based on which the instructor provides pseudo-personalised video feedback. Through feedback clusters, authors introduce the trade-off between individual feedback and generic feedback. The results of the study are particularly promising in terms of student satisfaction and learning enhancement.

Short-term electric load forecasting is critical for power system planning and operations due to demand fluctuations driven by variable energy resources. While deep learning-based forecasting models have shown strong performance, time-sensitive applications require improvements in both accuracy and convergence speed. To address this, we propose a hybrid model that combines long short-term memory (LSTM) with a modified particle swarm optimisation (mPSO) algorithm. Although LSTM is effective for nonlinear time-series predictions, its computational complexity increases with parameter variations. To overcome this, mPSO is used for parameter tuning, ensuring accurate forecasting while avoiding local optima. Additionally, XGBoost and decision tree filtering algorithms are incorporated to reduce dimensionality and prevent overfitting. Unlike existing models that focus mainly on accuracy, our framework optimises accuracy, stability, and convergence rate simultaneously. The model was tested on real hourly load data from New South Wales and Victoria, significantly outperforming benchmark models such as ENN, LSTM, GA-LSTM, and PSO-LSTM. For NSW, the proposed model reduced MSE by 91.91%, RMSE by 94.89%, and MAPE by 74.29%. In VIC, MSE decreased by 91.33%, RMSE by 95.73%, and MAPE by 72.06%, showcasing superior performance across all metrics. [ABSTRACT FROM AUTHOR]

An automated device is designed to measure the density of a liquid material using hydrostatic pressure method. A low cost pressure sensor is calibrated and used to get highly accurate readings. The calibration is done by measuring the pressure values vs. the generated voltage signal. The calibration has been challenging due to the low accuracy of the sensor but proved to be highly effective in applications. The interface is developed using a microcontroller, motor drives, analog to digital converters and sensors. The device is designed to get several readings automatically by changing the positions of the device/liquid column heights to increase the accuracy. Also the device can be programmed to measure the real time density of a liquid continuously. The readings were analyzed and averaged by a software developed in python language. The instruments accuracy was tested against 3 liquid types, water, coconut oil, kerosene oil, and showed a low error (0.007%, 0.001%, and 0.002% respectively) compared to the readings of a standard Pycnometer. The low error percentages confirm the accuracy of the device and the effectiveness of the sensor calibrations. [ABSTRACT FROM AUTHOR]

In recent times, higher education has seen a growing concern regarding the utilisation of artificial intelligence, especially with the emergence of ChatGPT. This technology can generate written content and respond to queries at a level that is nearly indistinguishable from a human writer. This feature has drawn substantial interest from students in higher education and has led to concern that students will use ChatGPT's capabilities to cheat on written formative and summative assessments. In this paper, we will review the usage of ChatGPT in higher education assessments and investigate why students want to cheat using artificial intelligence capabilities. It also offers a critical perspective on the challenges associated with detecting ChatGPTgenerated content and its impact on academic integrity. We also consider whether artificial intelligence provides more opportunities for academics to focus on assessing higherorder thinking and strategies. [ABSTRACT FROM AUTHOR]

Contract cheating has become increasingly an issue as universities adapt to online and hybrid teaching, learning, and assessments. Due to the COVID-19 pandemic, higher education institutions began to administer examinations more frequently online, and it led to the emergence of websites and commercial service providers who offer contract cheating services globally. In this paper, we examine the key elements that lead students to turn to contract cheating as well as the elements that deter the students from engaging in such unethical behaviour. We also investigate how assessment design can encourage authentic learning, although assessment design alone cannot eliminate contract cheating. The effects of contract cheating on academic standards and quality assurance are also examined. Mainly the study results show that the act of contract cheating is a result of interrelated internal and external factors in an individual. Although a number of measures, including authentic evaluations and digital tools, have been implemented to discourage students from cheating, no strategy is strong enough to control the issue permanently. Hence, academic integrity is still not assured, highlighting the necessity of a global movement to solve the problem. [ABSTRACT FROM AUTHOR]