Computational Thinking (CT) is seen as an important competence that is required in order to adapt to the future. However, educators, especially K-12 teachers and researchers, have not clearly ...identified how to teach it. In this study, a meta-review of the studies published in academic journals from 2006 to 2017 was conducted to analyze application courses, adopted learning strategies, participants, teaching tools, programming languages, and course categories of CT education. From the review results, it was found that the promotion of CT in education has made great progress in the last decade. In addition to the increasing number of CT studies in different countries, the subjects, research issues, and teaching tools have also become more diverse in recent years. It was also found that CT has mainly been applied to the activities of program design and computer science, while some studies are related to other subjects. Meanwhile, most of the studies adopted Project-Based Learning, Problem-Based Learning, Cooperative Learning, and Game-based Learning in the CT activities. In other words, such activities as aesthetic experience, design-based learning, and storytelling have been relatively less frequently adopted. Most of the studies focused on programming skills training and mathematical computing, while some adopted a cross-domain teaching mode to enable students to manage and analyze materials of various domains by computing. In addition, since the cognitive ability of students of different ages varies, the CT ability cultivation methods and content criteria should vary accordingly. Moreover, most studies reported the learners' CT performance and perspectives, while their information society ability was seldom trained. Accordingly, the research trends and potential research issues of CT are proposed as a reference for researchers, instructors, and policy makers.
•A meta-review of selected SSCI/SCI journal CT studies for 2006–2017 is reported.•The number of CT studies shows an obviously increasing trend in recent years.•Program design was the most common subject used to convey CT instruction.•Visual programming languages were the most common instruments of CT instruction.•PBL, CL, and GBL were the top strategies used for CT instruction.
Science and technology are driving people’s life changes, including education and the environment. Many scholars have attempted to import technology into the classroom to help students learn in ...different subjects. However, students often need assistance with unfamiliar learning approaches and learning environments. This study proposed a non‐immersive virtual reality (VR) guidance system combined with a two‐tier strategy to help students learn geology knowledge. Two groups of students used different learning approaches: the experimental group students were guided by the two‐tier test VR guidance system, and the control group students learned with the conventional VR guidance system. According to the experimental result, the two‐tier test VR guidance system not only improved the students’ learning achievement in natural science, but also enhanced their learning motivation. In addition, according to the sequential results, we found that VR learning materials or environmental resources can help students answer questions and solve problems more effectively.
This study developed a peer assessment approach incorporated into virtual reality (VR) design activities for fifth-grade students to learn knowledge about a geological park in their natural science ...course. All the students were asked to design a VR project after they had learned the geological knowledge, so as to raise their environmental awareness and cultivate their earth science knowledge. In order to evaluate the learning performance and perceptions of students in two groups, one with peer assessment and the other with teacher feedback, we collected the learning achievements, learning motivation, self-efficacy, critical thinking tendency, creativity tendency, and cognitive load of learners before and after the activities. The results indicated that students performing the VR design activity with the peer assessment learning approach had higher learning effectiveness. They also had higher self-efficacy and critical thinking tendencies than those using the VR design system with conventional teacher feedback. In other words, the peer assessment approach not only improves students’ learning achievement, but also enhances their self-efficacy and critical thinking tendencies.
•This study developed a peer assessment mechanism incorporated into virtual reality design activities.•Students design a virtual reality project after natural science course.•The learning achievements, motivation, critical thinking, creativity, and cognitive loads were investigated.•Effectiveness of peer assessment incorporated into virtual reality design activities was proved.•Provide a reference of peer assessment activity and virtual reality activity for future study.
This study utilized unplugged computational thinking learning material named Robot City as the instructional material. The board game corresponds to structural programming, including sequential ...structure, conditional structure, repetitive structure, and the modeling concept of calling a procedure in programming languages. According to the different task assignment methods, the aim of playing the board game is to help instruct the seventh-grade students in computational thinking, and to explore its impact on students’ learning achievements of computational thinking and the behavioral patterns of computational participation. The results revealed that the learning achievements of the students who worked together to solve the clear-ended task objectives were significantly higher than those of the students who cooperated within their groups to solve the open-ended competitive tasks. When the target task is not gained in advance, the students had to compete with other groups and vie for their target task, resulting in students’ logical thinking constantly changing and being interrupted. From the behavioral pattern analysis, it was found that the students continued to discuss the problems during the game. The pattern of collaborative analysis was iterative itself, indicating that the board game can deepen students’ interaction and enhance their higher level thinking. The results also showed that collaborative creation was observed (RO) by others, proving that the game can increase students’ desire to learn, and thus improve their learning achievement.
Abstract
Alternative Lengthening of Telomeres (ALT) utilizes a recombination mechanism and break-induced DNA synthesis to maintain telomere length without telomerase, but it is unclear how cells ...initiate ALT. TERRA, telomeric repeat-containing RNA, forms RNA:DNA hybrids (R-loops) at ALT telomeres. We show that depleting TERRA using an RNA-targeting Cas9 system reduces ALT-associated PML bodies, telomere clustering, and telomere lengthening. TERRA interactome reveals that TERRA interacts with an extensive subset of DNA repair proteins in ALT cells. One of TERRA interacting proteins, the endonuclease XPF, is highly enriched at ALT telomeres and recruited by telomeric R-loops to induce DNA damage response (DDR) independent of CSB and SLX4, and thus triggers break-induced telomere synthesis and lengthening. The attraction of BRCA1 and RAD51 at telomeres requires XPF in FANCM-deficient cells that accumulate telomeric R-loops. Our results suggest that telomeric R-loops activate DDR via XPF to promote homologous recombination and telomere replication to drive ALT.
The high rate of aquatic mortality incidents recorded in Taiwan and worldwide is creating an urgent demand for more accurate fish mortality prediction. Present study innovatively integrated air and ...water quality data to measure water quality degradation, and utilized deep learning methods to predict accidental fish mortality from the data. Keras library was used to build multilayer perceptron and long short‐term memory models for training purposes, and the models’ accuracies in fish mortality prediction were compared with that of the naïve Bayesian classifier. Environmental data from the 5 days before a fish mortality event proved to be the most important data for effective model training. Multilayer perceptron model reached an accuracy of 93.4%, with a loss function of 0.01, when meteorological and water quality data were jointly considered. It was found that meteorological conditions were not the sole contributors to fish mortality. Predicted fish mortality rate of 4.7% closely corresponded to the true number of fish mortality events during the study period, that is, four. A significant surge in fish mortality, from 20% to 50%, was noted when the river pollution index increased from 5.36 to 6.5. Moreover, the probability of fish mortality increased when the concentration of dissolved oxygen dropped below 2 mg/L. To mitigate fish mortality, ammonia nitrogen concentrations should be capped at 5 mg/L. Dissolved oxygen concentration was found to be the paramount factor influencing fish mortality, followed by the river pollution index and meteorological data. Results of the present study are expected to aid progress toward achieving the Sustainable Development Goals and to increase the profitability of water resources.
Core Ideas
The probabilities of fish mortality events were successfully predicted using deep learning models.
The environmental data from the 5 days before a fish mortality event were important for model training.
Meteorological conditions were not the sole contributors to fish mortality.
To mitigate fish mortality, ammonia nitrogen concentrations should be capped at 5 mg/L.
The dissolved oxygen concentration was found to be the paramount factor influencing fish mortality.
With the continuous development and innovation of information technology, virtual reality (VR) has become an important topic of education technology in recent years. VR is not only applied in many ...industries, but is also used by scholars for education applications as it enables students to have an immersive learning experience to enhance their learning effectiveness and motivation. Therefore, this study combined the spherical video-based virtual reality (SVVR) and a hands-on activity to help fifth-grade students learn natural geomorphological knowledge. The experimental group students used the hands-on approach to design a SVVR system to learn natural geomorphological knowledge; the control group used the conventional SVVR guiding system to learn. From the experimental results, there were non-significant differences in the learning achievement and learning motivation of students in the experimental and control groups. However, the students in the experimental group had better achievement on the in-depth knowledge test. In other words, the experimental group students needed to understand more about the learning content of natural geomorphological knowledge to design the VR system. Moreover, the hands-on approach cultivated their problem-solving and metacognitive skills.
In traditional instruction, teachers generally deliver the content of textbooks to students via lectures, making teaching activities lack vibrancy. Moreover, in such a one-to-many teaching mode, the ...teacher is usually unable to check on individual students' learning status or to provide immediate feedback to resolve their learning problems. Chatbots provide an opportunity to address this problem. However, conventional chatbots generally serve as information providers (i.e., providing relevant information by matching keywords in a conversation) rather than as decision-making advisors (i.e., using a knowledge-base with a decision-making mechanism to help users solve problems). Thus, this study proposes an expert decision-making-based chatbot to facilitate individual students' construction of knowledge during the learning process. A quasi-experiment was conducted to compare the differences in the performances and perceptions of students using the expert decision-making-based chatbot (EDM-chatbot) and the conventional chatbot (C-chatbot) in the activities of a geography course. One class of 35 students was the experimental group, using the EDM-chatbot. The other class of 35 students was the control group, using the C-chatbot. The results of the study showed that the EDM-chatbot combined with expert decision-making knowledge significantly improved students' learning achievement and learning enjoyment as well as reducing their learning anxiety, showing the value of the proposed approach.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Elemental analyses via calibration-free laser-induced breakdown spectroscopy (LIBS) usually suffer low sensitivity due to continuum emission generated by the plasma in local thermodynamic ...equilibrium. Here we propose a two-step measurement procedure that enables improved sensitivity of calibration-free LIBS. The method consists of recording two emission spectra with different delays between the laser pulse and the detector gate. The short delay is used to probe the plasma in conditions of full local thermodynamic equilibrium in order to measure major and minor element concentrations. To evaluate the concentrations of trace elements, a second measurement with improved limits of detection is performed with a larger gate delay. In that condition, the partial equilibrium state of the plasma enables the quantification of minor and trace elements. Demonstrated via analyses of lyophilized seafood samples, the presented method is suitable for all types of food, and more general for organic materials and all materials that include elements such as carbon, oxygen, nitrogen and hydrogen, for which the equilibrium state is hardly achieved.
Display omitted
•Presentation of a method for improved calibration-free LIBS•Increase of sensitivity for trace element measurements•Plasma modeling including chemical reactions•Quantitative compositional analyses of food including major, minor and trace elements
In today’s rapidly evolving landscape, conventional construction techniques like masonry and concrete pouring are falling short in meeting the evolving needs of contemporary designs that demand ...versatile and adaptable mechanical solutions. However, the established methods for crafting such mechanisms are time-intensive and frequently fall short in achieving the desired trajectory outcomes. In light of this challenge, this paper takes up the mantle of presenting a streamlined and accurate approach for devising trajectory-mechanism designs. This is achieved through the synergistic integration of multiple deep-learning prediction models, which serve to unveil the intricate interplay between the components of a linkage mechanism and the intended trajectory. To establish a robust foundation, a ground truth generation system is crafted using Rhino 3D modeling software. This system lays the groundwork for producing essential data to be harnessed in the training and testing of the models. A comprehensive series of experiments is then conducted to unearth solutions that can generate predictive trajectories aligned with stringent design requisites. The efficacy of the proposed framework is tested on the intricate StrandBeest structure to gauge its adaptability. The ensuing quantitative and qualitative analyses offer both empirical evidence and valuable insights into the potency of the proposed methodology. In short, the proposed method introduces an innovative paradigm for fashioning trajectory-mechanism designs in a more resourceful and swift manner when juxtaposed with prior methodologies. This endeavor not only addresses current limitations but also represents a step forward in ushering efficiency and effectiveness into this realm of research and application.
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