Search Results (14)

Our time is one of permacrisis, affecting the economy, the environment, and everything in between. In this context, UK higher education faces an existential crisis, where the university sector has been transformed into a marketplace, turning students into consumers and limiting the critical potential of education. In moving beyond these limits, this article explores Democracy in Action (DinA), a final-year undergraduate module offered in a UK university that creates spaces for critical and transformative education through democratic theory and practice. Grounded in traditions of transformative learning, community-based pedagogies, academic activism, and prefiguration, DinA positions students as democratic agents working in solidarity with staff and the wider community. Drawing on in-depth interviews with students, we analyse the interplay between theory and practice to understand how learning can be understood as a form of democratic participation. The article makes an original contribution to the fields of democratic education and critical university studies by offering a novel framework for integrating academic activism, community-based learning, and prefiguration in higher education. We show how students’ experiences of building community, campaign planning, and prefiguring change generate not only deep transformative learning but also new forms of civic agency and collective action. We argue that, through community organising, students embark on a process of learning that involves three key transformative moments: effecting a perspectival shift from the individual to the common, foregrounding the activist dimensions of democratic politics, and envisioning the world we want through prefiguration. This pedagogical model demonstrates that higher education can become a space of lived democratic possibility, where hope, critique, and collective transformation are not only imagined but enacted. Full article

We proposed a novel approach to investigate how problem-solving strategies, identified using response time and eye-tracking data, can impact individuals’ performance on the Object Assembly (OA) task. To conduct an integrated assessment of spatial reasoning ability and problem-solving strategy, we applied the Multimodal Joint-Hierarchical Cognitive Diagnosis Model (MJ-DINA) to analyze the performance of young students (aged 6 to 14) on 17 OA items. The MJ-DINA model consists of three sub-models: a Deterministic Inputs, Noisy “and” Gate (DINA) model for estimating spatial ability, a lognormal RT model for response time, and a Bayesian Negative Binomial (BNF) model for fixation counts. In the DINA model, we estimated five spatial cognitive attributes aligned with problem-solving processes: encoding, falsification, mental rotation, mental displacement, and intractability recognition. Our model fits the data adequately, with Gelman–Rubin convergence statistics near 1.00 and posterior predictive p-values between 0.05 and 0.95 for the DINA, Log RT, and BNF sub-models, indicating reliable parameter estimation. Our findings indicate that individuals with faster processing speeds and fewer fixation counts, which we label Reflective-Scanner, outperformed the other three identified problem-solving strategy groups. Specifically, sufficient eye movement was a key factor contributing to better performance on spatial reasoning tasks. Additionally, the most effective method for improving individuals’ spatial task performance was training them to master the falsification attribute. This research offers valuable implications for developing tailored teaching methods to improve individuals’ spatial ability, depending on various problem-solving strategies. Full article

Item quality is crucial to psychometric analyses for cognitive diagnosis. In cognitive diagnosis models (CDMs), item quality is often quantified in terms of item parameters (e.g., guessing and slipping parameters). Calibrating the item parameters with only item response data, as a common practice, could result in challenges in identifying the cause of low-quality items (e.g., the correct answer is easy to be guessed) or devising an effective plan to improve the item quality. To resolve these challenges, we propose the item explanatory CDMs where the CDM item parameters are explained with item features such that item features can serve as an additional source of information for item parameters. The utility of the proposed models is demonstrated with the Trends in International Mathematics and Science Study (TIMSS)-released items and response data: around 20 item linguistic features were extracted from the item stem with natural language processing techniques, and the item feature engineering process is elaborated in the paper. The proposed models are used to examine the relationships between the guessing/slipping item parameters of the higher-order DINA model and eight of the item features. The findings from a follow-up simulation study are presented, which corroborate the validity of the inferences drawn from the empirical data analysis. Finally, future research directions are discussed. Full article

The Tarim River Basin, China’s largest inland river basin, is renowned for its ecological fragility characterized by concurrent greening and desertification processes. Soil wind erosion emerges as a critical factor impacting the natural ecosystem of this region. This study employs a soil wind erosion model tailored to cultivated land, grassland, and desert terrains to analyze the multitemporal characteristics of and spatial variations in soil wind erosion across nine subbasins within the Tarim River Basin, utilizing observed data from 2010, 2015, and 2018. Additionally, this study investigates the influence of various factors, particularly wind speed, on the soil wind erosion dynamics. Following established standards of soil erosion classification, the intensity levels of soil erosion are assessed for each calculation grid within the study area alongside an analysis of the environmental factors influencing soil erosion. Findings indicate that approximately 38.79% of the total study area experiences soil wind erosion, with the Qarqan River Basin exhibiting the highest erosion modulus and the Aksu River Basin registering the lowest. Light and moderate erosion predominates in the Tarim River Basin, with an overall decreasing trend observed over the study period. Notably, the Qiemo River Basin, Dina River Basin, and Kaidu Kongque River Basin display relatively higher proportions of eroded area compared to their total subbasin area. Furthermore, this study underscores the substantial influence of the annual average wind speed on soil erosion within the study area, advocating for prioritizing soil and water conservation programs, particularly in the downstream regions of the Tarim River Basin, to mitigate future environmental degradation. Full article

In the field of education, cognitive diagnosis is crucial for achieving personalized learning. The widely adopted DINA (Deterministic Inputs, Noisy And gate) model uncovers students’ mastery of essential skills necessary to answer questions correctly. However, existing DINA-based approaches overlook the dependency between knowledge points, and their model training process is computationally inefficient for large datasets. In this paper, we propose a new cognitive diagnosis model called BNMI-DINA, which stands for Bayesian Network-based Multiprocess Incremental DINA. Our proposed model aims to enhance personalized learning by providing accurate and detailed assessments of students’ cognitive abilities. By incorporating a Bayesian network, BNMI-DINA establishes the dependency relationship between knowledge points, enabling more accurate evaluations of students’ mastery levels. To enhance model convergence speed, key steps of our proposed algorithm are parallelized. We also provide theoretical proof of the convergence of BNMI-DINA. Extensive experiments demonstrate that our approach effectively enhances model accuracy and reduces computational time compared to state-of-the-art cognitive diagnosis models. Full article

Remote sensing image change detection (CD) is an essential technique for analyzing surface changes from co-registered images of different time periods. The main challenge in CD is to identify the alterations that the user intends to emphasize, while excluding pseudo-changes caused by external factors. Recent advancements in deep learning and image change detection have shown remarkable performance with ConvNet-based and Transformer-based techniques. However, ConvNet-based methods are limited by the local receptive fields of convolutional kernels that cannot effectively capture the change features in spatial–temporal information, while Transformer-based CD models need to be driven by a large amount of data due to the lack of inductive biases, and at the same time need to bear the costly computational complexity brought by self-attention. To address these challenges, we propose a Transformer-based Siamese network structure called BTNIFormer. It incorporates a sparse attention mechanism called Dilated Neighborhood Attention (DiNA), which localizes the attention range of each pixel to its neighboring context. Extensive experiments conducted on two publicly available datasets demonstrate the benefits of our proposed innovation. Compared to the most competitive recent Transformer-based approaches, our method achieves a significant $12.00\%$ improvement in IoU while reducing computational costs by half. This provides a promising solution for further development of the Transformer structure in CD tasks. Full article

The cognitive diagnosis model (CDM) is an effective statistical tool for extracting the discrete attributes of individuals based on their responses to diagnostic tests. When dealing with cases that involve small sample sizes or highly correlated attributes, not all attribute profiles may be present. The standard method, which accounts for all attribute profiles, not only increases the complexity of the model but also complicates the calculation. Thus, it is important to identify the empty attribute profiles. This paper proposes an entropy-penalized likelihood method to eliminate the empty attribute profiles. In addition, the relation between attribute profiles and the parameter space of item parameters is discussed, and two modified expectation–maximization (EM) algorithms are designed to estimate the model parameters. Simulations are conducted to demonstrate the performance of the proposed method, and a real data application based on the fraction–subtraction data is presented to showcase the practical implications of the proposed method. Full article

Cognitive diagnostic models (CDMs) are increasingly being used in various assessment contexts to identify cognitive processes and provide tailored feedback. However, the most commonly used estimation method for CDMs, marginal maximum likelihood estimation with Expectation–Maximization (MMLE-EM), can present difficulties when sample sizes are small. This study compares the results of different estimation methods for CDMs under varying sample sizes using simulated and empirical data. The methods compared include MMLE-EM, Bayes modal, Markov chain Monte Carlo, a non-parametric method, and a parsimonious parametric model such as Restricted DINA. We varied the sample size, and assessed the bias in the estimation of item parameters, the precision in attribute classification, the bias in the reliability estimate, and computational cost. The findings suggest that alternative estimation methods are preferred over MMLE-EM under low sample-size conditions, whereas comparable results are obtained under large sample-size conditions. Practitioners should consider using alternative estimation methods when working with small samples to obtain more accurate estimates of CDM parameters. This study aims to maximize the potential of CDMs by providing guidance on the estimation of the parameters. Full article

Missing data are a common problem in educational assessment settings. In the implementation of cognitive diagnostic models (CDMs), the presence and/or inappropriate treatment of missingness may yield biased parameter estimates and diagnostic information. Using simulated data, this study evaluates ten approaches for handling missing data in a commonly applied CDM (the deterministic inputs, noisy “and” gate (DINA) model): treating missing data as incorrect (IN), person mean (PM) imputation, item mean (IM) imputation, two-way (TW) imputation, response function (RF) imputation, logistic regression (LR), expectation-maximization (EM) imputation, full information maximum likelihood (FIML) estimation, predictive mean matching (PMM), and random imputation (RI). Specifically, the current study investigates how the estimation accuracy of item parameters and examinees’ attribute profiles from DINA are impacted by the presence of missing data and the selection of missing data methods across conditions. While no single method was found to be superior to other methods across all conditions, the results suggest the use of FIML, PMM, LR, and EM in recovering item parameters. The selected methods, except for PM, performed similarly across conditions regarding attribute classification accuracy. Recommendations for the treatment of missing responses for CDMs are provided. Limitations and future directions are discussed. Full article

The Tarim River Basin is the largest inland river basin in China. It is located in an extremely arid region, where agriculture and animal husbandry are the main development industries. The recent rapid rise in population and land demand has intensified the competition for urban land use, making the water body ecosystem increasingly fragile. In light of these issues, it is important to comprehensively grasp regional land structure changes, improve the degree of land use, and reasonably allocate water resources to achieve the sustainable development of both the social economy and the ecological environment. This study uses the CA-Markov model, the PLUS model and the gray prediction model to simulate and validate land use/cover change (LUCC) in the Tarim River Basin, based on remote sensing data. The aim of this research is to discern the dynamic LUCC patterns and predict the evolution of future spatial and temporal patterns of land use. The study results show that grassland and barren land are currently the main land types in the Tarim River Basin. Furthermore, the significant expansion of cropland area and reduction in barren land area are the main characteristics of the changes during the study period (1992–2020), when about 1.60% of grassland and 1.36% of barren land converted to cropland. Over the next 10 years, we anticipate that land-use types in the basin will be dominated by changes in grassland and barren land, with an increasing trend in land area other than for cropland and barren land. Grassland will add 31,241.96 km², mainly in the Dina River and the lower parts of the Weigan-Kuqu, Kashgar, Kriya, and Qarqan rivers, while barren land will decline 2.77%, with significant decreases in the middle and lower reaches of the Tarim River Basin. The findings of this study will provide a solid scientific basis for future land resource planning. Full article

Cognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discussed. Full article

As in many other countries in the south, the traditional knowledge (TK) of local communities in Madagascar is facing extinction. Biocultural community protocols (BCP), introduced in Madagascar following the implementation of the Nagoya Protocol (2010) and defined by the Mo’otz Kuxtal Voluntary Guidelines as “a wide range of expressions, articulations, rules and practices produced by communities to indicate how they wish to engage in negotiations with stakeholders”, holds out hopes for TK protection. By analysing two pilot BCPs in Madagascar, one established around the Motrobe (Cinnamosma fragrans) with a view to strengthening the existing value chain (BCP in Mariarano and Betsako) and the second initially established around plant genetic resources for food and agriculture (BCP of the farmers in Analavory), this study aims to assess the place and value ascribed to TK in the overall BCP development process and to analyse whether or not the process has helped to strengthen and revitalise TK at the community level. The ethnographic studies show commonalities in both BCP, in particular their main focus on access and benefit-sharing mechanisms, this against the backdrop of an economic model which stresses the importance of financial and institutional incentives; and conversely, a relative disregard for what relates to the biocultural dimension of TK. Local taboos (fady) as well as traditional dina (social conventions), which have long allowed for the regulation of access to common resources/TK, are scarcely mentioned. Based on these findings, we conclude that in order to revitalise TK, the process of developing BCPs should recognise and give special importance to TK, considering it as a biocultural whole, bound together with the territory, local customs, and biological resources; or else, TK is likely to remain a commodity to be valued economically, or a component like any other. Full article

This study is the result of extensive investigations of leeches on the Balkan Peninsula. Our aim was to detect actual and potential (modeled) distributions of common Erpobdellidae species, and to identify their ecological differentiation with respect to the altitudinal and waterbody type gradient. Although widespread, these species rarely live together. Intense competition is avoided by preferences for different types of habitats. This was confirmed by Pearson correlation analyses that yielded negative results. Differentiation of these species was clarified by the results of logistic Gaussian regression analyses. While Erpobdella octoculata and Dina lineata have a similar distribution along the altitudinal gradient, they prefer different waterbody types. Erpobdella vilnensis prefers higher altitudes than the other two species. Its preferred habitats are smaller rivers and streams located at altitudes from 400 to 1000 m a.s.l. Although present in all waterbody types, large lowland rivers and standing waterbodies are the preferred habitats of E. octoculata. Fast-flowing springs and streams are mostly inhabited by D. lineata. While the distribution of the species overlaps to a large degree, the ecological preferences of species differ significantly and thus they can be used as confident typological descriptors and indicators of ecological status. Full article

The dipole interaction model is a classical electromagnetic theory for calculating circular dichroism (CD) resulting from the π-π* transitions of amides. The theoretical model, pioneered by J. Applequist, is assembled into a package, DInaMo, written in Fortran allowing for treatment of proteins. DInaMo reads Protein Data Bank formatted files of structures generated by molecular mechanics or reconstructed secondary structures. Crystal structures cannot be used directly with DInaMo; they either need to be rebuilt with idealized bond angles and lengths, or they need to be energy minimized to adjust bond lengths and bond angles because it is common for crystal structure geometries to have slightly short bond lengths, and DInaMo is sensitive to this. DInaMo reduces all the amide chromophores to points with anisotropic polarizability and all nonchromophoric aliphatic atoms including hydrogens to points with isotropic polarizability; all other atoms are ignored. By determining the interactions among the chromophoric and nonchromophoric parts of the molecule using empirically derived polarizabilities, the rotational and dipole strengths are determined leading to the calculation of CD. Furthermore, ignoring hydrogens bound to methyl groups is initially explored and proves to be a good approximation. Theoretical calculations on 24 proteins agree with experiment showing bands with similar morphology and maxima. Full article