Similar Learning Research Articles

Behavioral trace data in an online learning environment as indicators of learning engagement in university students

Learning in asynchronous online settings (AOSs) is challenging for university students. However, the construct of learning engagement (LE) represents a possible lever to identify and reduce challenges while learning online, especially, in AOSs. Learning analytics provides a fruitful framework to analyze students' learning processes and LE via trace data. The study, therefore, addresses the questions of whether LE can be modeled with the sub-dimensions of effort, attention, and content interest and by which trace data, derived from behavior within an AOS, these facets of LE are represented in self-reports. Participants were 764 university students attending an AOS. The results of best-subset regression analysis show that a model combining multiple indicators can account for a proportion of the variance in students' LE (highly significant R2 between 0.04 and 0.13). The identified set of indicators is stable over time supporting the transferability to similar learning contexts. The results of this study can contribute to both research on learning processes in AOSs in higher education and the application of learning analytics in university teaching (e.g., modeling automated feedback).

Ranking and Combining Latent Structured Predictive Scores without Labeled Data

Combining multiple predictors obtained from distributed data sources to an accurate meta-learner is promising to achieve enhanced performance in lots of prediction problems. As the accuracy of each predictor is usually unknown, integrating the predictors to achieve better performance is challenging. Conventional ensemble learning methods assess the accuracy of predictors based on extensive labeled data. In practical applications, however, the acquisition of such labeled data can prove to be an arduous task. Furthermore, the predictors under consideration may exhibit high degrees of correlation, particularly when similar data sources or machine learning algorithms were employed during their model training. In response to these challenges, this paper introduces a novel structured unsupervised ensemble learning model (SUEL) to exploit the dependency between a set of predictors with continuous predictive scores, rank the predictors without labeled data and combine them to an ensembled score with weights. Two novel correlation-based decomposition algorithms are further proposed to estimate the SUEL model, constrained quadratic optimization (SUEL.CQO) and matrix-factorization-based (SUEL.MF) approaches. The efficacy of the proposed methods is rigorously assessed through both simulation studies and real-world application of risk genes discovery. The results compellingly demonstrate that the proposed methods can efficiently integrate the dependent predictors to an ensemble model without the need of ground truth data.

The student-teacher experience as a model for students as partners (SaP) and for enhancing student engagement among Japanese students

Education in Japan and other Asian countries advocates the traditional passive style of learning where students learn through rote memorization in a teacher-centered environment. However, this is now changing due to globalization with Japan’s Ministry of Education promoting learning strategies to involve students in the learning process actively. Many studies involving students as partners have stated that students feel more at ease when they learn with and from their peers and that working in groups makes them learn significantly better and allows them to put into practice what they have taught to others or learned from others. This case study on the Student-Teacher Experience activity aims to present the concept of students as partners to enhance active learning in Japan and its possible application in other countries and fields of study with a similar learning environment as Japan. The activity allowed students to actively engage in class and enhanced learning.

Tiers of Engagement: Achieved Learning From Business Simulations Reflected in Economics Students’ Experiences

This article investigates the impact of a business simulator in an online economics course, focusing on the relationship between student engagement and reflections on learning. Utilizing a mixed-methods approach, our study analyzes responses from 328 students who used the "Hubro Business Simulator" in a flexible online bachelor's program in economics and administration. Students provided reflections on the simulator as a learning activity, application of theoretical concepts and practical relevance. Qualitative analysis of 1640 responses further explored the depth of students' reflections, categorized into different tiers of engagement based on their responses. This analysis builds on Self-Regulated Learning (SRL) and motivational theory. The study sought to determine whether students reporting higher levels of engagement exhibited deeper metacognitive reflections compared to their less engaged peers. Findings support a link between the level of engagement and the depth of reflection. Students with higher engagement levels were more likely to reflect on specific ways in which the simulator had supported their learning and understanding of theoretical concepts. This paper discusses the limitations and implications of these findings for enhancing game-based learning interventions, emphasizing the need to foster higher engagement levels to improve educational outcomes in online learning environments. The study contributes to the literature on game-based learning by providing a large empirical study on how business simulators can foster higher-order thinking and deeper learning reflections among economics students. It also highlights the role of engagement in using business simulators as learning tools, offering insights that could improve the learning design and implementation in similar online and flexible learning program contexts.

The Competition Model and Adult Second Language Learning: Cross-Language Similarity and L2 Morphosyntax Learning

Adult second language learners arrive at the language learning situation with an already formed first language grammar system in place. The study of cross-language similarity across the first and second languages explores how the similarities and differences in the two languages make learning more or less difficult, particularly for adult learners. From a Competition Model (MacWhinney, 1997) framework, the authors and their colleagues have conducted research examining the relationship between cross-language similarity and adult second language learning and processing with actual learners and in training paradigms with naïve learners; converging methodological approaches have been used (eye tracking, event related brain potentials, self- paced reading, and grammaticality judgments). We review these studies and describe the predictions derived from the Competition Model framework as a function of whether grammatical constructions are formed similarly or differently in the two languages or are unique to the second language. We conclude with a description of Brian MacWhinney’s influence on this body of research.

Similarity learning networks uniquely identify individuals of four marine and terrestrial species

AbstractEstimating the size of animal populations plays an important role in evidence‐based conservation and management. Some methods for estimating population size rely on animals being individually identifiable. Traditionally, this has been done by marking physically captured animals, but increasingly, animals with distinctive natural markings are surveyed noninvasively using cameras. Animal reidentification from photographs is usually done manually, which is expensive, laborious, and requires considerable skill. An alternative is to develop computer vision methods that can support or replace the manual identification task. We developed an automated approach using deep learning to identify whether a pair of photographs is of the same individual or not. The core of the approach is a similarity learning network that uses paired convolutional neural networks with a triplet loss function to summarize image pairs and decide whether they are from the same individual. Prior to the main matching step, two additional convolutional neural networks perform image segmentation, cropping the animal object within the image, and orientation prediction, deciding which side of the animal was photographed. We applied the approach to four species, with images of the same individual often spanning several years: systematic surveys of bottlenose dolphins (Tursiops truncatus, 2008–2019) and harbor seals (Phoca vitulina, 2015–2019), a citizen science dataset of western leopard toads (Sclerophrys pantherina, unknown dates), and a publicly available repository of humpback whale images (Megaptera novaeangliae, unknown dates). For these species, our best‐performing models were able to identify whether a pair of images were from the same individual or different individuals in 95.8%, 94.6%, 88.2%, and 83.8% of the cases, respectively. We found that triplet loss functions outperformed binary cross‐entropy loss functions and that data augmentation and additional manual curation of training data provided small but consistent improvements in performance. These results demonstrate the potential of deep learning to replace or, more likely, support and facilitate manual individual identification efforts.

Boosting Semi-Supervised Learning with Dual-Threshold Screening and Similarity Learning

How to effectively utilize unlabeled data for training is a key problem in Semi-Supervised Learning (SSL). Existing SSL methods often consider the unlabeled data whose predictions are beyond a fixed threshold (e.g., 0.95) and discard those less than 0.95. We argue that these discarded data have a large proportion, are of hard sample, and will benefit the model training if used properly. In this article, we propose a novel method to take full advantage of the unlabeled data, termed DTS-SimL, which includes two core designs: Dual-Threshold Screening and Similarity Learning. Except for the fixed threshold, DTS-SimL extracts another class-adaptive threshold from the labeled data. Such a class-adaptive threshold can screen many unlabeled data whose predictions are lower than 0.95 but over the extracted one for model training. On the other hand, we design a new similar loss to perform similarity learning for all the highly similar unlabeled data, which can further mine the valuable information from the unlabeled data. Finally, for more effective training of DTS-SimL, we construct an overall loss function by assigning four different losses to four different types of data. Extensive experiments are conducted on five benchmark datasets, including CIFAR-10, CIFAR-100, SVHN, Mini-ImageNet, and DomainNet-Real. Experimental results show that the proposed DTS-SimL achieves state-of-the-art classification accuracy. The code is publicly available at https://github.com/GZHU-DVL/DTS-SimL .

Integrated Lab-Lecture in Introductory Biology Preferred by non-STEM Majors, but Leads to Similar Learning Outcomes

Integrated Lab-Lecture in Introductory Biology Preferred by non-STEM Majors, but Leads to Similar Learning Outcomes

Experiences of Using a Digital Guidance and Assessment Tool (the Technology-Optimized Practice Process in Nursing Application) During Clinical Practice in a Nursing Home: Focus Group Study Among Nursing Students.

Nursing students' learning during clinical practice is largely influenced by the quality of the guidance they receive from their nurse preceptors. Students that have attended placement in nursing home settings have called for more time with nurse preceptors and an opportunity for more help from the nurses for reflection and developing critical thinking skills. To strengthen students' guidance and assessment and enhance students' learning in the practice setting, it has also been recommended to improve the collaboration between faculties and nurse preceptors. This study explores first-year nursing students' experiences of using the Technology-Optimized Practice Process in Nursing (TOPP-N) application in 4 nursing homes in Norway. TOPP-N was developed to support guidance and assessment in clinical practice in nursing education. Four focus groups were conducted with 19 nursing students from 2 university campuses in Norway. The data collection and directed content analysis were based on DeLone and McLean's information system success model. Some participants had difficulties learning to use the TOPP-N tool, particularly those who had not attended the 1-hour digital course. Furthermore, participants remarked that the content of the TOPP-N guidance module could be better adjusted to the current clinical placement, level of education, and individual achievements to be more usable. Despite this, most participants liked the TOPP-N application's concept. Using the TOPP-N mobile app for guidance and assessment was found to be very flexible. The frequency and ways of using the application varied among the participants. Most participants perceived that the use of TOPP-N facilitated awareness of learning objectives and enabled continuous reflection and feedback from nurse preceptors. However, the findings indicate that the TOPP-N application's perceived usefulness was highly dependent on the preparedness and use of the app among nurse preceptors (or absence thereof). This study offers information about critical success factors perceived by nursing students related to the use of the TOPP-N application. To develop similar learning management systems that are usable and efficient, developers should focus on personalizing the content, clarifying procedures for use, and enhancing the training and motivation of users, that is, students, nurse preceptors, and educators.

Causality-Invariant Interactive Mining for Cross-Modal Similarity Learning.

In the real world, how to effectively learn consistent similarity measurement across different modalities is essential. Most of the existing similarity learning methods cannot deal well with cross-modal data due to the modality gap and have obvious performance degeneration when applied to cross-modal data. To tackle this problem, we propose a novel cross-modal similarity learning method, called Causality-Invariant Interactive Mining (CIIM), that can effectively capture informative relationships among different samples and modalities to derive the modality-consistent feature embeddings in the unified metric space. Our CIIM tackles the modality gap from two aspects, i.e., sample-wise and feature-wise. Specifically, we start from the sample-wise view and learn the single-modality and hybrid-modality proxies for exploring the cross-modal similarity with the elaborate metric losses. In this way, sample-to-sample and sample-to-proxy correlations are both taken into consideration. Furthermore, we conduct the causal intervention to eliminate the modality bias and reconstruct the invariant causal embedding in the feature-wise aspect. To this end, we force the learned embeddings to satisfy the specific properties of our causal mechanism and derive the causality-invariant feature embeddings in the unified metric space. Extensive experiments on two cross-modality tasks demonstrate the superiority of our proposed method over the state-of-the-art methods.

ANS-SCMC: A matrix completion method based on adaptive neighbourhood similarity and sparse constraints for predicting microbe-disease associations.

The use of matrix completion methods to predict the association between microbes and diseases can effectively improve treatment efficiency. However, the similarity measures used in the existing methods are often influenced by various factors such as neighbourhood size, choice of similarity metric, or multiple parameters for similarity fusion, making it challenging. Additionally, matrix completion is currently limited by the sparsity of the initial association matrix, which restricts its predictive performance. To address these problems, we propose a matrix completion method based on adaptive neighbourhood similarity and sparse constraints (ANS-SCMC) for predict microbe-disease potential associations. Adaptive neighbourhood similarity learning dynamically uses the decomposition results as effective information for the next learning iteration by simultaneously performing local manifold structure learning and decomposition. This approach effectively preserves fine local structure information and avoids the influence of weight parameters directly involved in similarity measurement. Additionally, the sparse constraint-based matrix completion approach can better handle the sparsity challenge in the association matrix. Finally, the algorithm we proposed has achieved significantly higher predictive performance in the validation compared to several commonly used prediction methods proposed to date. Furthermore, in the case study, the prediction algorithm achieved an accuracy of up to 80% for the top 10 microbes associated with type 1 diabetes and 100% for Crohn's disease respectively.

QUSL: Quantum unsupervised image similarity learning with enhanced performance

Leveraging quantum properties to enhance complex learning tasks has been proven feasible, with excellent recent achievements in the field of unsupervised learning. However, current quantum schemes neglect adaptive adjustments for unsupervised task scenarios. This work proposes a novel quantum unsupervised similarity learning method — QUSL. Firstly, QUSL uses similarity triplets for unsupervised learning, generating positive samples by perturbing anchor images, achieving a learning process independent of classical algorithms. Subsequently, combining the feature interweaving of triplets, QUSL employs metaheuristic algorithms to systematically explore high-performance mapping processes, obtaining quantum circuit architectures more suitable for unsupervised image similarity tasks. Ultimately, QUSL realizes feature learning with lower quantum resource costs. Comprehensive numerical simulations and experiments on quantum computers demonstrate that QUSL outperforms state-of-the-art quantum methods. QUSL achieves over 50% reduction in critical quantum resource utilization. QUSL improves similarity detection correlation by up to 19.5% across multiple datasets, exhibiting robustness in NISQ environments. While using fewer quantum resources, QUSL shows potential for large-scale unsupervised tasks.

Pavement Crack Detection Using Fractal Dimension and Semi-Supervised Learning

Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to the practical application of these models. This study proposes a pavement crack image detection method integrating fractal dimension analysis and semi-supervised learning. It identifies the self-similarity characteristics within the crack regions by analyzing pavement crack images and using fractal dimensions to preliminarily determine the candidate crack regions. The Crack Similarity Learning Network (CrackSL-Net) is then employed to learn the semantic similarity of crack image regions. Semi-supervised learning facilitates automatic crack detection by combining a small amount of labeled data with a large volume of unlabeled image data. Comparative experiments are conducted on two public pavement crack datasets against the HED, U-Net, and RCF models to comprehensively evaluate the performance of the proposed method. The results indicate that, with a 50% annotation ratio, the proposed method achieves high-precision crack detection, with an intersection over union (IoU) exceeding 0.84, which is close to that of U-Net. Visual analysis of the detection results confirms the method’s effectiveness in identifying cracks in complex environments.

The effects of moderate prenatal alcohol exposure on performance in hippocampal-sensitive spatial memory and anxiety tasks by adult male and female rat offspring

Moderate prenatal alcohol exposure (mPAE) results in structural alterations to the hippocampus. Previous studies have reported impairments in hippocampal-sensitive tasks, but have not compared performance between male and female animals. In the present study, performance in hippocampal-sensitive spatial memory and anxiety behavior tests were compared across adult male and female saccharin (SACC) control mPAE Long-Evans rat offspring. Two tests of spatial memory were conducted that were aimed at assessing memory for recently acquired spatial information: A delayed spatial alternation task using an M-shaped maze and a delayed match-to-place task in the Morris water task. In both tasks, rats in SACC and mPAE groups showed similar learning and retention of a spatial location even after a 2-h interval between encoding and retention. A separate group of adult male and female SACC and mPAE rat offspring were tested for anxiety-like behaviors in the elevated plus-maze paradigm. In this test, both male and female mPAE rats exhibited a significantly greater amount of time and a greater number of head dips in the open arms, while locomotion and open arm entries did not differ between groups. The results suggest that mPAE produces a reduction in anxiety-like behaviors in both male and female rats in the elevated plus-maze.

Reversal Learning in Ant Colonies

Reversal learning has been studied in many species, often as an indicator of their behavioral flexibility. Although this research typically focuses on individuals, groups of social animals, especially social insects, are often considered to have similar learning capabilities. Associative learning has been rarely studied in ant colonies and their behavioral flexibility is still to be assessed. In this study, ant colonies readily learned to discriminate between compound visuotactile cues and subsequently learned their reversal. Reversal performance was maintained after a 5-day retention interval, but not after a 10-day interval. Although this study does not differentiate learning processes at the individual vs. colony levels, it is the first demonstration of reversal learning conducted in ant colonies. These results show that the two-corridor maze can serve to assess colony-level learning in ants. This is a first step to investigate key mechanisms underlying collective learning and cognition in ants.

Reversal Learning in Ant Colonies

Reversal learning has been studied in many species, often as an indicator of their behavioral flexibility. Although this research typically focuses on individuals, groups of social animals, especially social insects, are often considered to have similar learning capabilities. Associative learning has been rarely studied in ant colonies and their behavioral flexibility is still to be assessed. In this study, ant colonies readily learned to discriminate between compound visuotactile cues and subsequently learned their reversal. Reversal performance was maintained after a 5-day retention interval, but not after a 10-day interval. Although this study does not differentiate learning processes at the individual vs. colony levels, it is the first demonstration of reversal learning conducted in ant colonies. These results show that the two-corridor maze can serve to assess colony-level learning in ants. This is a first step to investigate key mechanisms underlying collective learning and cognition in ants.

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization.

Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging high-order perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.

Inferring Taxonomic Affinities and Genetic Distances Using Morphological Features Extracted from Specimen Images: a Case Study with a Bivalve dataset.

Reconstructing the tree of life and understanding the relationships of taxa are core questions in evolutionary and systematic biology. The main advances in this field in the last decades were derived from molecular phylogenetics; however, for most species, molecular data are not available. Here, we explore the applicability of two deep learning methods - supervised classification approaches and unsupervised similarity learning - to infer organism relationships from specimen images. As a basis, we assembled an image dataset covering 4144 bivalve species belonging to 74 families across all orders and subclasses of the extant Bivalvia, with molecular phylogenetic data being available for all families and a complete taxonomic hierarchy for all species. The suitability of this dataset for deep learning experiments was evidenced by an ablation study resulting in almost 80% accuracy for identifications on the species level. Three sets of experiments were performed using our dataset. First, we included taxonomic hierarchy and genetic distances in a supervised learning approach to obtain predictions on several taxonomic levels simultaneously. Here, we stimulated the model to consider features shared between closely related taxa to be more critical for their classification than features shared with distantly related taxa, imprinting phylogenetic and taxonomic affinities into the architecture and training procedure. Second, we used transfer learning and similarity learning approaches for zero-shot experiments to identify the higher-level taxonomic affinities of test species that the models had not been trained on. The models assigned the unknown species to their respective genera with approximately 48% and 67% accuracy. Lastly, we used unsupervised similarity learning to infer the relatedness of the images without prior knowledge of their taxonomic or phylogenetic affinities. The results clearly showed similarities between visual appearance and genetic relationships at the higher taxonomic levels. The correlation was 0.6 for the most species-rich subclass (Imparidentia), ranging from 0.5 to 0.7 for the orders with the most images. Overall, the correlation between visual similarity and genetic distances at the family level was 0.78. However, fine-grained reconstructions based on these observed correlations, such as sister-taxa relationships, require further work. Overall, our results broaden the applicability of automated taxon identification systems and provide a new avenue for estimating phylogenetic relationships from specimen images.

Pairwise-Constraint-Guided Multi-View Feature Selection by Joint Sparse Regularization and Similarity Learning

Feature selection is a basic and important step in real applications, such as face recognition and image segmentation. In this paper, we propose a new weakly supervised multi-view feature selection method by utilizing pairwise constraints, i.e., the pairwise constraint-guided multi-view feature selection (PCFS for short) method. In this method, linear projections of all views and a consistent similarity graph with pairwise constraints are jointly optimized to learning discriminative projections. Meanwhile, the l2,0-norm-based row sparsity constraint is imposed on the concatenation of projections for discriminative feature selection. Then, an iterative algorithm with theoretically guaranteed convergence is developed for the optimization of PCFS. The performance of the proposed PCFS method was evaluated by comprehensive experiments on six benchmark datasets and applications on cancer clustering. The experimental results demonstrate that PCFS exhibited competitive performance in feature selection in comparison with related models.

Understanding engagement in intensive learning: From fuzzy chaotic indigestion to eupeptic clarity

This paper is framed by Nick Zepke’s, Vicki Trowler’s, and Paul Trowler’s concept of student engagement being “chaotic”, suffering from “indigestion” and “fuzziness”. This study was conducted at a UK higher education institution that recently moved to a “block and blend” delivery approach. We investigated what students and staff think engagement looks like in an intensive block and blend learning context. Data were gathered from students and staff via an online survey, which consisted of both scaled and open-ended questions. Findings are synthesised in an elemental map, providing a comparison of students and staff perceptions of engagement. Specifically, students and staff thought engagement in an intensive block and blend context entailed participation and active learning; a mindset that included enthusiasm, interest, focus, and enjoyment; timely completion of assessments; relationships with peers and tutors; doing more than required, such as completing extra readings; and accessing help and support. Participants also identified attendance as an indicator of student engagement and determined that the university has a responsibility to create learning environments to foster student engagement. Overall, the study findings point to elements of student engagement that may be designed into intensive block and blend learning environments. These approaches are also relevant to other similar intensive learning contexts.