Interest-driven creator theory: towards a theory of learning design for Asia in the twenty-first century

Asian education is known for its examination-driven orientation, with the downsides of distorting the processes of learning and teaching, diminishing students’ interest in learning, and failing to nurture twenty-first century competencies among students. As a group of Asian researchers, we have been developing Interest-Driven Creator (IDC) Theory, a design theory based on three anchored concepts, namely interest, creation, and habit. Each of these anchored concepts is represented by a loop composed of three components. In the interest loop, the three components are triggering, immersing, and extending. The components of the creation loop are imitating, combining, and staging. The habit loop consists of cuing environment, routine, and harmony. These three loops are interconnected in various ways, with their characteristics revealed by the design process. We hypothesize that technology-supported learning activities that are designed with reference to IDC Theory will enable students to develop interest in learning, be immersed in the creation process, and, by repeating this process in their daily routines, strengthen habits of creation. Furthermore, students will excel in learning performance, develop twenty-first century competencies, and become lifelong interest-driven creators. To sharpen our understanding and further the development of the theory, we need more discussion and collaborative efforts in the community. Hypotheses arising from this theory can be tested, revised, or refined by setting up and investigating IDC Theory-based experimental sites. By disseminating the framework, foundations, and practices to the various countries and regions of Asia, we hope that it will bring about compelling examples and hence a form of quality education for the twenty-first century, which is an alternative to the examination-driven education system. In this paper, we present an overall introduction to IDC Theory and its history, and discuss some of the steps for advancing it in the future

Investigating Student-Generated Questioning in a Technology-Enabled Elementary Science Classroom: A Case Study

This study is aimed at providing solutions to problems in the field of science and technology education, as well as approaches to improve its effectiveness. This study’s specific goal was to ascertain how inquiry-based learning, when aided by instructional technology, raises student success and fosters their capacity for scientific inquiry. In this paper, we investigate a technology-supported intervention that facilitates students to actively generate and solve questions in a cycle of science inquiry in a primary (elementary) school. Through utilizing a question generation technology platform with a guided pedagogical framework, the teachers purposefully leveraged on students’ generated questioning to design and implement a process of creating and presenting their inquiries. The questioning-driven dialogic exchanges took place in the classroom setting, as well as during online interactions outside of the class. Our empirical study, as demonstrated by quantitative and qualitative analysis, connotes a positive causal effect of students’ generated questioning to their cognitive performances, and their noteworthy differences of attitudes towards science between the experimental and control groups. The results uphold the value of fostering students to generate questions for their inquiries and learning. We also highlight the importance of teachers’ awareness of pedagogical design and enactment, enabled by technology, in order to adapt to the profiles of students’ generated questioning for fostering productive cognitive performances

Implementing Inclusive Education through Informatization: A Case Study on Promotion of MOOCs in Western China

This paper reports on the implementation of Promoting Massive Open Online Courses (MOOCs) in Western China, an initiative over a span of 10 years aimed at promoting the MOOC education model in the Western region of China, which plays a crucial role in inclusive education, breaking down geographical barriers and empowering individuals to pursue lifelong learning and realize their full potential. By 2022, this Initiative provided 10,000 customized MOOCs to Western universities, benefited over 2300 universities nationwide, engaged 39.3 million students, and trained 250,000 teachers. The Initiative encourages collaboration and knowledge sharing among educational institutions, promoting the development of localized online course content that aligns with the needs and interests of the local community. It also facilitates partnerships between educational institutions and industry stakeholders, fostering regional innovation and entrepreneurship. The analysis focuses on how the development of MOOCs for the Western areas started a journey of inclusive education, resulting in qualitative and quantitatively scaling education opportunities. By presenting the trajectory and outcomes of this Initiative, this paper demonstrates the positive impact of MOOCs in achieving inclusive education while also highlighting the challenges and difficulties encountered in the promotion process

LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products

The remarkable success of convolutional neural networks (CNNs) in computer vision tasks is shown in large-scale datasets and high-performance computing platforms. However, it is infeasible to deploy large CNNs on resource constrained platforms, such as embedded devices, on account of the huge overhead. To recognize the label numbers of industrial black material product and deploy deep CNNs in real-world applications, this research uses an efficient method to simultaneously (a) reduce the network model size and (b) lower the amount of calculation without compromising accuracy. More specifically, the method is implemented by pruning channels and corresponding filters that are identified as having a trivial effect on the output accuracy. In this paper, we prune VGG-16 to obtain a compact network called LeanNet, which gives a 25× reduction in model size and a 4.5× reduction in float point operations (FLOPs), while the accuracy on our dataset is close to the original accuracy by retraining the network. Besides, we also find that LeanNet could achieve better performance on reductions in model size and computation compared to some lightweight networks like MobileNet and SqueezeNet, which are widely used in engineering applications. This research has good application value in the field of industrial production

Study progression in application of process analytical technologies on film coating

Film coating is an important unit operation to produce solid dosage forms, thereby, the monitoring of this process is helpful to find problems in time and improve the quality of coated products. Traditional methods adopted to monitor this process include measurement of coating weight gain, performance of disintegration and dissolution test, etc. However, not only do these methods cause destruction to the samples, but also consume time and energy. There have recently emerged the applications of process analytical technologies (PAT) on film coating, especially some novel spectroscopic and imaging technologies, which have the potential to real-time track the progress in film coating and optimize production efficiency. This article gives an overview on the application of such technologies for film coating, with the goal to provide a reference for the further researches

An Efficient Adaptive Anticollision Algorithm Based on 4-Ary Pruning Query Tree

In radio frequency identification system (RFID), the efficiency in which the reader identifies multiple tags is closely related to the methods to solve the collision of multiple tags. At present, a reasonable solution is the introduction of 4-ary query tree (or n -ary query tree) to reduce the collision time slots and additional query is used to decrease idle timeslots. The advantage of a 4-ary tree anti-collision algorithm is that it is able to reduce collision timeslots, but it also increases the idle timeslots. To reduce these excessive idle timeslots the 4-ary tree anticollision algorithm brings, an anti-collision algorithm based on adaptive 4-ary pruning query tree (A4PQT) is proposed in this paper. On the basis of the information of collision bits, some idle timeslots can be eliminated through pruning the 4-ary tree. Both theoretical analysis and simulation results support that A4PQT algorithm can significantly reduce recognition time and improve throughput of the RFID system

Anhydrous reverse micelle nanoparticles: new strategy to overcome sedimentation instability of peptide-containing pressurized metered-dose inhalers

The objective of this study was to develop a novel anhydrous reverse micelle nanoparticles (ARM-NPs) system to overcome the sedimentation instability of peptide-containing pressurized metered-dose inhalers (pMDIs). A bottom-up method was utilized to fabricate ARM-NPs. Tertiary butyl alcohol (TBA)/water system, freeze-drying and lipid inversion method were successively used to produce the ARM-NPs for pMDI. Various characteristics of ARM-NPs were investigated including particle size, morphology, secondary structure of the peptide drug, aerosolization properties and storage stability. As revealed by the results, ARM-NPs with spherical shape possessed 147.7 ± 2.0 nm of particle size with 0.152 ± 0.021 PdI. The ARM-NPs for pMDI had satisfactory fine particle fraction (FPF) value of 46.99 ± 1.33%, while the secondary structure of the peptide drug was unchanged. Stability tests showed no pronounced sedimentation instability for over 12 weeks at 4–6 °C. Furthermore, a hypothesis was raised to explain the formation mechanism of ARM-NPs, which was verified by the differential scanning calorimetry analysis. The lecithin employed in the reverse micelle vesicles could serve as a steric barrier between peptide drugs and bulk propellant, which prevented the instability of peptide drugs in hydrophobic environment. Homogenous particle size could avoid Ostwald ripening phenomenon of particles in pMDIs. It was concluded that the ARM-NPs for pMDI could successfully overcome sedimentation instability by the steric barrier effect and homogeneous particle size