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Resilience is the ability of ecosystems to maintain function while experiencing perturbation. Globally, forests are experiencing disturbances of unprecedented quantity, type, and magnitude that may diminish resilience. Early warning signals are statistical properties of data whose increase over time may provide insights into decreasing resilience, but there have been few applications to forests. We quantified four early warning signals (standard deviation, lag-1 autocorrelation, skewness, and kurtosis) across detrended time series of multiple ecosystem state variables at the Hubbard Brook Experimental Forest, New Hampshire, USA and analyzed how these signals have changed over time. Variables were collected over periods from 25 to 55 years from both experimentally manipulated and reference areas and were aggregated to annual timesteps for analysis. Long-term (>50 year) increases in early warning signals of stream calcium, a key biogeochemical variable at the site, illustrated declining resilience after decades of acid deposition, but only in watersheds that had previously been harvested. Trends in early warning signals of stream nitrate, a critical nutrient and water pollutant, likewise exhibited symptoms of declining resilience but in all watersheds. Temporal trends in early warning signals of some of groups of trees, insects, and birds also indicated changing resilience, but this pattern differed among, and even within, groups. Overall, ∼60% of early warning signals analyzed indicated decreasing resilience. Most of these signals occurred in skewness and kurtosis, suggesting ‘flickering’ behavior that aligns with emerging evidence of the forest transitioning into an oligotrophic condition. The other ∼40% of early warning signals indicated increasing or unchanging resilience. Interpretation of early warning signals in the context of system specific knowledge is therefore essential. They can be useful indicators for some key ecosystem variables; however, uncertainties in other variables highlight the need for further development of these tools in well-studied, long-term research sites.

 

Video analysis tools such as Tracker are used to study mechanical motion captured by photography. One can also imagine a similar tool for tracking thermal motion captured by thermography. Since its introduction to physics education, thermal imaging has been used to visualize phenomena that are invisible to the naked eye and teach a variety of physics concepts across different educational settings. But thermal cameras are still scarce in schools. Hence, videos recorded using thermal cameras such as those featured in “YouTube Physics” are suggested as alternatives. The downside is that students do not have interaction opportunities beyond playing those videos. 

Learning analytics, referring to the measurement, collection, analysis, and reporting of data about learners and their contexts in order to optimize learning and the environments in which it occurs, is proving to be a powerful approach for understanding and improving science learning. However, few studies focused on leveraging learning analytics to assess hands-on laboratory skills in K-12 science classrooms. This study demonstrated the feasibility of gauging laboratory skills based on students’ process data logged by a mobile augmented reality (AR) application for conducting science experiments. Students can use the mobile AR technology to investigate a variety of science phenomena that involve concepts central to physics understanding. Seventy-two students from a suburban middle school in the Northeastern United States participated in this study. They conducted experiments in pairs. Mining process data using Bayesian networks showed that most students who participated in this study demonstrated some degree of proficiency in laboratory skills. Also, findings indicated a positive correlation between laboratory skills and conceptual learning. The results suggested that learning analytics provides a possible solution to measure hands-on laboratory learning in real-time and at scale. 

During the COVID-19 pandemic, many students lost opportunities to explore science in labs due to school closures. Remote labs provide a possible solution to mitigate this loss. However, most remote labs to date are based on a somehow centralized model in which experts design and conduct certain types of experiments in well-equipped facilities, with a few options of manipulation provided to remote users. In this paper, we propose a distributed framework, dubbed remote labs 2.0, that offers the flexibility needed to build an open platform to support educators to create, operate, and share their own remote labs. Similar to the transformation of the Web from 1.0 to 2.0, remote labs 2.0 can greatly enrich experimental science on the Internet by allowing users to choose and contribute their subjects and topics. As a reference implementation, we developed a platform branded as Telelab. In collaboration with a high school chemistry teacher, we conducted remote chemical reaction experiments on the Telelab platform with two online classes. Pre/post-test results showed that these high school students attained significant gains (t(26)=8.76, p<0.00001) in evidence-based reasoning abilities. Student surveys revealed three key affordances of Telelab: live experiments, scientific instruments, and social interactions. All 31 respondents were engaged by one or more of these affordances. Students behaviors were characterized by analyzing their interaction data logged by the platform. These findings suggest that appropriate applications of remote labs 2.0 in distance education can, to some extent, reproduce critical effects of their local counterparts on promoting science learning. 

Mental health concerns have become a growing problem among collegiate engineering students. To date, there has been little research to understand the factors that influence student mental health within this population. Literature on engineering student mental health supports the idea that engineering students experience high levels of mental health distress, which often stems from stressors such as academic workload, maintaining a strong grade point average (GPA), and pressure from parents and/or professors. Of particular concern, distressed engineering students are less likely to seek professional help when compared to students in other majors. As a result, a comprehensive study was conducted on engineering mental health help-seeking behavior. Through secondary analysis of the data from that study, this work aims to identify common perceived stressors that may contribute to mental health distress, as well as perceived coping strategies that may be used instead of seeking professional mental health help. A diverse group of 33 engineering undergraduate students were a part of the comprehensive study on engineering mental health help-seeking behavior. For this study, qualitative data was analyzed to address two specific research questions: 1) What are the main sources of stress that engineers have experienced in their engineering training? and 2) What coping strategies have students developed as an alternative to seeking professional help? Several common perceived stressors were identified including an unsupportive and challenging engineering training environment, challenges in time management, and academic performance expectations. Perceived coping strategies identified include relationships with family, friends, and classmates and health and wellness activities such as exercise, mindfulness, and maintaining spiritual health. The results of this work will be helpful in recognizing ways to improve engineering education and increase student support. 

Augmented reality (AR) has the potential to fundamentally transform science education by making learning of abstract science ideas tangible and engaging. However, little is known about how students interacted with AR technologies and how these interactions may affect learning performance in science laboratories. This study examined high school students’ navigation patterns and science learning with a mobile AR technology, developed by the research team, in laboratory settings. The AR technology allows students to conduct hands-on laboratory experiments and interactively explore various science phenomena covering biology, chemistry, and physics concepts. In this study, seventy ninth-grade students carried out science laboratory experiments in pairs to learn thermodynamics. Our cluster analysis identified two groups of students, which differed significantly in navigation length and breadth. The two groups demonstrated unique navigation patterns that revealed students’ various ways of observing, describing, exploring, and evaluating science phenomena. These navigation patterns were associated with learning performance as measured by scores on lab reports. The results suggested the need for providing access to multiple representations and different types of interactions with these representations to support effective science learning as well as designing representations and connections between representations to cultivate scientific reasoning skills and nuanced understanding of scientific processes. 

Inflammatory pathologies caused by phagocytes lead to numerous debilitating conditions, including chronic pain and blindness due to age-related macular degeneration. Many members of the sialic acid-binding immunoglobulin-like lectin (Siglec) family are immunoinhibitory receptors whose agonism is an attractive approach for antiinflammatory therapy. Here, we show that synthetic lipid-conjugated glycopolypeptides can insert into cell membranes and engage Siglec receptors incis, leading to inhibitory signaling. Specifically, we construct acis-binding agonist of Siglec-9 and show that it modulates mitogen-activated protein kinase (MAPK) signaling in reporter cell lines, immortalized macrophage and microglial cell lines, and primary human macrophages. Thus, thesecis-binding agonists of Siglecs present a method for therapeutic suppression of immune cell reactivity.

 

In this paper, we demonstrate how machine learning could be used to quickly assess a student’s multimodal representational thinking. Multimodal representational thinking is the complex construct that encodes how students form conceptual, perceptual, graphical, or mathematical symbols in their mind. The augmented reality (AR) technology is adopted to diversify student’s representations. The AR technology utilized a low-cost, high-resolution thermal camera attached to a smartphone which allows students to explore the unseen world of thermodynamics. Ninth-grade students (N= 314) engaged in a prediction–observation–explanation (POE) inquiry cycle scaffolded to leverage the augmented observation provided by the aforementioned device. The objective is to investigate how machine learning could expedite the automated assessment of multimodal representational thinking of heat energy. Two automated text classification methods were adopted to decode different mental representations students used to explain their haptic perception, thermal imaging, and graph data collected in the lab. Since current automated assessment in science education rarely considers multilabel classification, we resorted to the help of the state-of-the-art deep learning technique—bidirectional encoder representations from transformers (BERT). The BERT model classified open-ended responses into appropriate categories with higher precision than the traditional machine learning method. The satisfactory accuracy of deep learning in assigning multiple labels is revolutionary in processing qualitative data. The complex student construct, such as multimodal representational thinking, is rarely mutually exclusive. The study avails a convenient technique to analyze qualitative data that does not satisfy the mutual-exclusiveness assumption. Implications and future studies are discussed. 

With the increasing popularity of distance education, how to engage students in online inquiry‐based laboratories remains challenging for science teachers. Current remote labs mostly adopt a centralized model with limited flexibility left for teachers' just‐in‐time instruction based on students' real‐time science practices.

The goal of this research is to investigate the impact of a non‐centralized remote lab on students' cognitive and behavioural engagement.

A mixed‐methods design was adopted. Participants were the high school students enrolled in two virtual chemistry classes. Remote labs 2.0, branded as Telelab, supports a non‐centralized model of remote inquiry that can enact more interactive hands‐on labs anywhere, anytime. Teleinquiry Instructional Model was used to guide the curriculum design. Students' clickstreams logs and instruction timestamps were analysed and visualized. Multiple regression analysis was used to determine whether engagement levels influence their conceptual learning. Behavioural engagement patterns were corroborated with survey responses.

We found approximate synchronizations between student–teacher–lab interactions in the heatmap. The guided inquiry enabled by Telelab facilitates real‐time communications between instructors and students. Students' conceptual learning is found to be impacted by varying engagement levels. Students' behavioural engagement patterns can be visualized and fed to instructors to inform learning progress and enact just‐in‐time instruction.

Telelab offers a model of remote labs 2.0 that can be easily customized to live stream hands‐on teleinquiry. It enhances engagement and gives participants a sense of telepresence. Providing a customizable teleinquiry curriculum for practitioners may better prepare them to teach inquiry‐based laboratories online.