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Motivation: Timetrees depict evolutionary relationships between species and the geological times of their divergence. Hundreds of research articles containing timetrees are published in scientific journals every year. The TimeTree project has been manually locating, curating, and synthesizing timetrees from these articles for almost two decades into a TimeTree of Life, delivered through a unique, userfriendly web interface (timetree.org). The manual process of finding articles containing timetrees is becoming increasingly expensive and time-consuming. So, we have explored the effectiveness of textmining approaches and developed optimizations to find research articles containing timetrees automatically. Results: We have developed an optimized machine learning (ML) system to determine if a research article contains an evolutionary timetree appropriate for inclusion in the TimeTree resource. We found that BERT classification fine-tuned on whole-text articles achieved an F1 score of 0.67, which we increased to 0.88 by text-mining article excerpts surrounding the mentioning of figures. The new method is implemented in the TimeTreeFinder tool, TTF, which automatically processes millions of articles to discover timetree-containing articles. We estimate that the TTF tool would produce twice as many timetree-containing articles as those discovered manually, whose inclusion in the TimeTree database would potentially double the knowledge accessible to a wider community. Manual inspection showed that the precision on out-of-distribution recently-published articles is 87%. This automation will speed up the collection and curation of timetrees with much lower human and time costs. Availability: https://github.com/marija-stanojevic/time-tree-classification Contact: {marija.stanojevic, s.kumar, zoran.obradovic}@temple.edu Supplementary information: Supplementary data are available at Bioinformatics online 

In this study, an AR-based mobile learning application is proposed to assist online civil engineering course learning during the COVID-19 pandemic. A quasi-experiment has been conducted, and feedback from both the teacher and students has been analysed to examine the effectiveness of the proposed approach in terms of learning achievements. The subjects were 46 sophomores who majored in civil engineering in one class taught by one instructor in a southern U.S. state university. The quasi-experimental results showed that the proposed approach could not significantly improve the students’ online learning achievements. However, the feedbacks brought some explanation to this non-significant result. They indicated that students found this mobile AR app to be an interesting, helpful, practical, and effective approach in their online learning that helped them gain more in-depth knowledge than traditional teacher-centred classroom instruction. 

In this paper, we present a co-design study with teachers to contribute towards the development of a technology-enhanced Artificial Intelligence (AI) curriculum, focusing on modeling unstructured data. We created an initial design of a learning activity prototype and explored ways to incorporate the design into high school classes. Specifically, teachers explored text classification models with the prototype and reflected on the exploration as a user, learner, and teacher. They provided insights about learning opportunities in the activity and feedback for integrating it into their teaching. Findings from qualitative analysis demonstrate that exploring text classification models provided an accessible and comprehensive approach for integrated learning of mathematics, language arts, and computing with the potential of supporting the understanding of core AI concepts including identifying structure within unstructured data and reasoning about the roles of human insight in developing AI technologies. 

In this paper, we present a co-design study with teachers to contribute towards development of a technology-enhanced Artificial Intelligence (AI) curriculum, focusing on modeling unstructured data. We created an initial design of a learning activity prototype and explored ways to incorporate the design into high school classes. Specifically, teachers explored text classification models with the prototype and reflected on the exploration as a user, learner, and teacher. They provided insights about learning opportunities in the activity and feedback for integrating it into their teaching. Findings from qualitative analysis demonstrate that exploring text classification models provided an accessible and comprehensive approach for integrated learning of mathematics, language arts, and computing with the potential of supporting the understanding of core AI concepts including identifying structure within unstructured data and reasoning about the roles of human insight in developing AI technologies. 

For many students, trigonometry is a difficult subject because it requires strong spatial visualization abilities. A team at Jackson State University makes the teaching and learning process easer with a new learning tool for mobile phones developed using augmented reality (AR). The results indicated that AR incorporated learning tool has great potential for learning trigonometry. 

Multiphoton microscopy has emerged as the primary imaging tool for studying the structural and functional dynamics of neural circuits in brain tissue, which is highly scattering to light. Recently, three-photon microscopy has enabled high-resolution fluorescence imaging of neurons in deeper brain areas that lie beyond the reach of conventional two-photon microscopy, which is typically limited to ~ 450 µm. Three-photon imaging of neuronal calcium signals, through the genetically-encoded calcium indicator GCaMP6, has been used to successfully record neuronal activity in deeper neocortical layers and parts of the hippocampus in rodents. Bulk-loading cells in deeper cortical layers with synthetic calcium indicators could provide an alternative strategy for labelling that obviates dependence on viral tropism and promoter penetration, particularly in non-rodent species. Here we report a strategy for visualized injection of a calcium dye, Oregon Green BAPTA-1 AM (OGB-1 AM), at 500–600 µm below the surface of the mouse visual cortex in vivo. We demonstrate successful OGB-1 AM loading of cells in cortical layers 5–6 and subsequent three-photon imaging of orientation- and direction- selective visual responses from these cells.

 

Engineering design is often used to teach science, but not-yet leads to solid learning gains. We examined the relationship between science learning and engineering design using text mining. Association rule mining was applied to texts written during design to extract the relationships between solar-energy concepts and solar design performance. Findings suggest that students test concept-related factors’ effects on design outcomes to learn concepts and eliminate misconceptions. These findings have implications for future instructional design. 

As solar energy becomes increasingly affordable, many schools are considering installing new solar power systems. Can students contribute to the design, evaluation, and decision-making process in any way? Many students are familiar with solar power and energy, having researched solar energy on the internet, built solar cookers, inspected mini solar cells, gone on field trips to local solar farms, and so on. Well-informed and motivated, they are just one step away from taking responsibility for their own schools. In this article, we present Solarize Your School, an engineering project that gives students the opportunity to design and evaluate solar power solutions for their own schools. This STEM project requires students to learn and apply skills and practices related to solar energy and photovoltaic technology concepts, such as architectural measurement and modeling techniques, graphical interpretation and data analysis, budgeting and investing, scientific inquiry and engineering design, and collaboration and communication (see Next Generation Science Standards table, p. 47). Solarize Your School can be incorporated into environmental science, physical science, and engineering courses, and can be adapted to fit any curriculum scope and time frame. We suggest a 10-day sequence of learning activities. All the technologies and materials mentioned are freely available (see “On the web”).