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This study explored the effects of directed and undirected online peer feedback types on students’ peer feedback performance, argumentative essay writing skills, and acquisition of domain-specific knowledge. The study used a pre-test and post-test design with four conditions (feedback, feedforward, a combination of feedback and feedforward, and undirected feedback). In this exploratory study, 221 undergraduate students, who were randomly assigned to dyads, engaged in discussions about the pros and cons of “Genetically Modified Organisms (GMOs)”, provided feedback to peers, and wrote an argumentative essay regarding the topic. Results indicated significant differences among the conditions in terms of the quality of provided feedback. This implies that the peer feedback quality can be enhanced or diminished depending on its type. Results also revealed a significant improvement in students’ argumentative essay performance and domain-specific knowledge acquisition without significant differences among conditions. We discuss how the such increase in the quality of essays and learning outcomes might be related to the power of peer feedback regardless of the feedback type. We also discuss why using multiple instructional scaffolds may result in over-scripting that may diminish the power of peer feedback and the effects of the scaffolds themselves in online learning environments.

Use of theory within a field of research provides the foundation for designing effective research programs and establishing a deeper understanding of the results obtained. This, together with the emergence of domain-specific theory, is often taken as an indicator of the maturity of any research area. This article explores the development and subsequent usage of domain-specific theories and theoretical constructs (TCs) in computing education research (CER). All TCs found in 878 papers published in three major CER publication venues over the period 2005–2020 were identified and assessed to determine the nature and purpose of the constructs found. We focused more closely on areas related to learning, studying, and progression, where our analysis found 80 new TCs that had been developed, based on multiple epistemological perspectives. Several existing frameworks were used to categorize the areas of CER focus in which TCs were found, the methodology by which they were developed, and the nature and purpose of the TCs. A citation analysis was undertaken, with 1,727 citing papers accessed to determine to what extent and in what ways TCs had been used and developed to inform subsequent work, also considering whether these aspects vary according to different focus areas within computing education. We noted which TCs were used most often and least often, and we present several brief case studies that demonstrate progressive development of domain-specific theory. The exploration provides insights into trends in theory development and suggests areas in which further work might be called for. Our findings indicate a general interest in the development of TCs during the period studied, and we show examples of how different approaches to theory development have been used. We present a framework suggesting how strategies for developing new TCs in CER might be structured and discuss the nature of theory development in relation to the field of CER.

Today breast cancer is the leading type of cancer among women undergoing cancer screening. A slight delay in detecting and diagnosing this disease may result in irreversible convolutions. Histopathological images from the biopsy examination present a large amount of structural information that can significantly improve the prognosis for breast cancer. The pathological analysis, which involves the microscopic examination of the histopathological slides, is a challenging task. An automated computer-aided detection (CAD) procedure is inevitable, as it may decrease the pathologist examination time and help detect the disease at an early stage. Lately, deep learning methods using artificial neural networks are consistently in use to improve the performance of CAD methods. A common practice among recent studies is to use the transfer learning approach of training deep neural network architectures. Transfer Learning is an established learning approach that facilitates a deep neural network to train quickly on a specific dataset and resolve an interdisciplinary problem. Deep Learning methods employing the transfer learning approach have provided highly competitive results on the datasets consisting of the whole slide images, which are captured generally at high resolutions. However, the performance is not remarkably appreciable on the small and low-resolution image datasets, in particular the datasets that include patch samples. In this direction, the present study proposes a novel domain-specific learning strategy, Breast Histo-Fusion, which aims to detect breast cancer even from images of low resolution and small size. Further, four state-of-the-art deep CNNs (AlexNet, VGG19, ResNet, and DenseNet) are trained using both learning approaches: Transfer Learning and Histo-Fusion on the IDC dataset. The proposed Histo-Fusion learning approach has improved the discriminating abilities and performance of each deep CNN by providing better results of (AlexNet – 95.75%, VGG19–95.96%, ResNet34–96.17%, ResNet50–96.67%, and DenseNet121–97.49%) compared to (AlexNet – 90.41, VGG19–90.51%, ResNet34–90.83%, ResNet50–92.27%, and DenseNet121–93.05%) using the transfer learning strategy. As a result, the procedure can help expert pathologists to perform accurate diagnoses and reduce false-positive rates.

A deep neural network, while avoiding its complex process of feature selection, requires sufficient training samples to learn those connection weights of adjacent layers. However, in many real applications, not enough training samples are available in all cases. This paper suggests a universal-to-domain-specific learning method based on recurrent neural network for cross-domain sentiment classifi- cation and activity recognition. In the situation of having only a small amount of training samples that is in available, the structure of its network model can be adjusted flexibly according to the needs of a target domain classification or recognition. Where there are two points of our concern as follows: 1) the fine-tune and regular constraints can increase its training efficiency by updating in a small local area, namely, sharing these parameters between input and hidden layers with a target domain and 2) then, a linear output network moves on its implementing amelioration from subtlety as an exploration or exploitation in order to mitigate the phenomenon of over-fitting. Aiming at an actual situation, this domain-specific learning model with a slide window of instances and features is designed and implemented for a good long-short term memory. Finally, the two strategies are applied into IMDB reviews, Amazon product reviews, and human activities recognition collected by the built-in gyroscope sensors data, and the experimental results verify their validity.

Most conceptions of giftedness have been based on intellectual and academic potential or aptitudes for high-level performance in a valued human endeavor. Individual characteristics have been used as markers for acknowledging advanced development at each stage of development. Moreover, these conceptions have driven our responses to advanced development in schools, in the work world, and in the larger society. As a society in general, we have chosen to ignore positive individual differences in favor of focusing on individual pathologies. Yet, some pockets of our society have always acknowledged the need for talent development through selective admission to mentorships and tutorials, advanced education, and work settings including the military, sports, and political spheres.

The main aim of this paper is to test the apparently unquestionable assumption that there is a strong correlation between learners’ exposure to a wide diversity of source materials exemplifying the use of the syntactic structure in question and the likelihood of learners’ successful learning and understanding the structure’s formal, semantic and pragmatic aspects. The authors put under scrutiny the absolute adequacy of this hypothesis by an attempt to teach all types of English conditionals (zero, first, second, third, mixed) without ever crossing the boundaries of the following, comparatively restricted, conceptual domain: ‘Martin Luther King and his legacy’. Authentic if clauses, mainly extracted from MLK’s speeches are supposed to provide thematically coherent contextualisation. The clarity and exactness of the chronological framework of the African American Civil Rights Movement and MLK’s biography are expected to enhance the students’ understanding of time relations between the conditions (expressed within various protases) and consequences (expressed by the corresponding apodoses). Apart from the teaching materials specifically designed for this purpose, this paper presents the results of a student perception survey that was carried out with the first-year undergraduate students of Political Science and Journalism at the University of Zagreb subsequently to the teaching process.

Artificial grammar learning depends on acquisition of abstract structural representations rather than domain-specific representational constraints, or so many studies tell us. Using an artificial grammar task, we compared learning performance in two stimulus domains in which respondents have differing tacit prior knowledge. We found that despite grammatically identical sequence structures, learning was better for harmonically related chord sequences than for letter name sequences or harmonically unrelated chord sequences. We also found transfer effects within the musical and letter name tasks, but not across the domains. We conclude that knowledge acquired in implicit learning depends not only on abstract features of structured stimuli, but that the learning of regularities is in some respects domain-specific and strongly linked to particular features of the stimulus domain.

Biometric recognition of newborn babies is an opportunity for the realization of several useful applications, such as improved security against swapping and abduction, accurate census, and effective drug delivery. This paper explores the possibility of using face recognition toward an affordable and friendly biometric modality for newborns. The paper proposes an autoencoder-based feature representation followed by problem specific distance metric learning via one-shot similarity with one class-online support vector machine. The largest publicly available database of newborns collected from various sources to study face recognition is introduced. Several existing face recognition approaches and commercial systems are also evaluated on a common benchmark protocol. The efficacy of the proposed algorithm is evaluated under both verification and identification settings. With multiple galleries, rank-1 identification accuracy of 78.5% and verification accuracy of 63.4% at 0.1% false accept rate are achieved.

L'A. examine de maniere critique l'hypothese de Rhode et Plaut selon laquelle l'apprentissage de la structure d'une langue naturelle est possible en depit du manque d'une retroaction negative explicite en l'absence de mecanismes innes precis de l'acquisition du langage

In this work, we propose a 3D fully convolutional architecture for video saliency prediction that employs hierarchical supervision on intermediate maps (referred to as conspicuity maps) generated using features extracted at different abstraction levels. We provide the base hierarchical learning mechanism with two techniques for domain adaptation and domain-specific learning. For the former, we encourage the model to unsupervisedly learn hierarchical general features using gradient reversal at multiple scales, to enhance generalization capabilities on datasets for which no annotations are provided during training. As for domain specialization, we employ domain-specific operations (namely, priors, smoothing and batch normalization) by specializing the learned features on individual datasets in order to maximize performance. The results of our experiments show that the proposed model yields state-of-the-art accuracy on supervised saliency prediction. When the base hierarchical model is empowered with domain-specific modules, performance improves, outperforming state-of-the-art models on three out of five metrics on the DHF1K benchmark and reaching the second-best results on the other two. When, instead, we test it in an unsupervised domain adaptation setting, by enabling hierarchical gradient reversal layers, we obtain performance comparable to supervised state-of-the-art. Source code, trained models and example outputs are publicly available at https://github.com/perceivelab/hd2s.

This study compared the effects of support for peer feedback, peer feedforward and their combination on students’ peer learning processes, argumentative essay quality and domain-specific learning. Participants were 86 BSc students who were randomly divided over 43 dyads. These dyads, in a two-factorial experimental design, were assigned to four conditions including: peer feedback (n = 22), peer feedforward (n = 22), mixed (n = 20) and control group (n = 22) conditions. An online peer feedback environment named EduTech was designed which allowed us to implement various types of support in the form of question prompts. In this online environment, students were asked to write an argumentative essay on a controversial topic, to engage in peer learning processes and to revise their essay. Overall, the results showed that students in the three experimental conditions (peer feedback, peer feedforward and their combination) benefited more than students in the control group condition (without any support) in term of peer learning processes, argumentative essay quality and domain-specific learning. However, there was no significant difference among the three experimental conditions. This implies that peer feedforward can be as important as peer feedback in collaborative learning environments which is often neglected both in theory and practice. Practitioner Notes What is already known about this topic? Writing argumentative essays is a common practice for higher education students in various disciplines which deal with controversial issues. Writing argumentative essay requires solid argumentation strategies which makes it a challenging task for higher education students. Additional instructional support is needed to help students write high-quality argumentative essays. What this paper adds? Peer learning is a promising instructional strategy for improving students’ argumentative essay writing and learning. Online support in the form of question prompts to guide students during peer learning can improve their argumentative essay writing and learning. Next to the peer feedback, peer feedforward is also a promising instructional approach to support students’ argumentative essay writing and learning. Implications for practice and/or policy Given the positive effects of peer learning processes, the use of peer feedback and peer feedforward should be given more attention by teachers to support students write high-quality argumentative essays for controversial issues. Teachers and educational designers should not only provide opportunities for students to engage in peer feedback processes (how I am doing?) but also in peer feedforward processes (where to next?).