Learning Motivation

The objective of this study is to be able to find ways or the best strategies in teaching that will motivate students to exert effort in their studies, considering the present conditions in this pandemic period. A quantitative method was used to define the study's objective, where two sections of Psychology students who took up Science 101 and Science 104 were chosen as respondents to answer the survey questionnaire through google forms. The purposive sampling technique was also employed since these students can appropriately answer the queries sent to them. The Likert scale method was used to measure the students' level of agreement, with the 5-point range, where the results were collated and analyzed. The research result finds several strategic activities such as report enhancement, online debates, virtual experiments, discussion and updating of recent findings, and the creation of infomercials that truly captured their interest and attention. This study made use of several references and tables in order to support the results obtained. The researcher recommends that there should be effective and efficient motivational activities to sustain student engagement. The researchers, who are also educators, have agreed to continuously upgrade the newfound motivational activities to encourage more students to study well even in an online setting, thus giving more opportunities for better achievement in education.

A novel aerial manipulator system compensation control based on ADRC and backstepping

This paper proposes a fully-actuated control method for a novel aerial manipulation system (AMS). A customized carbon frame structure supports the servo actuators, on which eight propellers group into pairs located. We present kinematics and dynamics modeling of the AMS based on Craig parameter method and recursive Newton–Euler equation, respectively. Then, an Active disturbance rejection control (ADRC)—Backstepping—Compensation controller is designed to control the exact position and orientation of the manipulator in the Cartesian space. Finally, the performance of the system is demonstrated through simulations and virtual experiments.

The effectiveness of combining e-learning, peer teaching and flipped classroom for delivering physiology laboratory course to nursing students

The Internet and 5G era make e-learning a vital part of modern education, and extensive evidence has shown that peer teaching and flipped classroom contribute to increased success in medical teaching. However, the applicability of these pedagogies in laboratory courses remains largely unexplored. This study aimed to evaluate the academic performance, proficiency in procedural skills and perception of nursing students in physiology laboratory classes delivered using non-traditional classroom (NTC) pedagogies comprising the combination of e-learning, peer teaching and flipped classroom. Each class was sub-divided into two equal halves by successive student identification (ID) and randomly assigned to control or NTC group. Compared to control class, NTC teaching significantly enhanced mean score of six pre-class tests (67.77 ± 9.83 vs. 62.94 ± 9.70), with "B" graders increased obviously, suggesting pre-class e-learning was more effective than textbook-based preview, especially for general grasp of the topic. Similarly, average scores on post-class quizzes in NTC group was improved (79.40 ± 9.12 vs. 74.43 ± 8.88). Lesser time-cost and higher success rates were observed in trachea, artery and heart catheterizations in NTC group , although no disparities were found in ureteral intubation . Majority of (~74%) students supported the reform and shared positive experiences in NTC methodology. They reported virtual experiments and self-paced procedural skill videos affected pre- or in-class learning outcomes most, respectively. These findings indicated NTC pedagogy was workable to improve students' subject scores and proficiency in complicated and direct-viewing procedural skills, and was favorable by students.

Bioethics as an Anthropological Challenge

The subject of the proposed paper is the disclosure of prerequisites and determinants of the implementation of the soteriological concept of studying the interdisciplinary course of bioethics, the structural model and synectic algorithm of its comprehension and epistemological map of formation of soteriological competence of the future specialist during the study of bioethics. The methodological basis for the implementation of the proposed project is the theory of research-oriented professional education (Inquiry Based Science Education) and, thereafter, technology of advanced learning (Technology Enhanced Learning), which provide wide opportunities to study the course of bioethics in the form of observation of remote virtual experiments on the portal Go-Lab; GRAASP environment for the implementation of author's developments and pedagogically structured programs in the process of performative interaction between teacher and students. The scientific novelty of the material is to substantiate the content of anthropological competencies of future specialists in biomedical specialties and the strategies of obtaining them on the basis of the principles of hermeneutic didactics. Conclusions. Today, obviously, the task of bioethics is not only to protect nature and human life, but also to participate in the realization of the high mission of Salvation. This defines the research field of bioethics as a space of secret dialogue, in which it is desirable to find a place not only for the voice of clinicians, pharmacists, engineers, philosophers, public figures, but also the pastoral voice of the Church.

Virtual experimentations by deep learning on tangible materials

Artificial intelligence relying on structure-property databases is an emerging powerful tool to discover new materials with targeted properties. However, this approach cannot be easily applied to tangible structures, such as plastic composites and fabrics, because of their high structural complexity. Here, we propose a deep learning computational framework that can implement virtual experiments on tangible structures. Structural representations of complex carbon nanotube films were conducted by multiple generative adversarial networks of scanning electron microscope images at four levels of magnifications, enabling a deep learning prediction of multiple properties such as electrical conductivity and surface area. 1716 virtual experiments were completed within an hour, a task that would take years for real experiments. The data can be used as a versatile database for material science, in analogy to databases of molecules and solids used in cheminformatics. Useful examples are the investigation of correlations between electrical conductivity, specific surface area, wall number phase diagrams, economic performance, and inversely designed supercapacitors.