title: An Approach to Model Children’s Inhibition During Early Literacy and Numeracy Acquisition

text: Early literacy and numeracy skills are developed during childhood at kindergarten level. Among the many factors that influence the development of such skills, the literature shows that the executive function of inhibition – i.e. the blocking out or tuning out of information or action that is irrelevant to the learning task – is one of the most important. There are many tests to assess children’s inhibition skills; however, such tests are generally time-consuming and have a short lifespan. In this context, we propose a computational approach to model children’s inhibition skills by using only student traces from a learning app as input. We propose a mathematical formalization of three related inhibition features, which could be used as input to classification algorithms.


title: The Potential for the Use of Deep Neural Networks in e-Learning Student Evaluation with New Data Augmentation Method

text: This study attempts to use a deep neural network to assess the acquisition of knowledge and skills by students. This module is intended to shape a personalized learning path through the e-learning system. Assessing student progress at each stage of learning in an individualized process is extremely tedious and arduous. The only solution is to automate assessment using Deep Learning methods. The obstacle is the relatively small amount of data, in the form of available assessments, which is needed to train the neural network. The specifity of each subject/course taught requires the preparation of a separate neural network. The paper proposes a new method of data augmentation, Asynchronous Data Augmentation through Pre-Categorization (ADAPC), which solves this problem. It has been shown that it is possible to train a very effective deep neural network with the proposed method even for a small amount of data.


title: Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing

text: Personalized recommendation as a practical approach to overcoming information overloading has been widely used in e-learning. Based on learners individual knowledge level, we propose a new model that can predict learners needs for recommendation using dynamic graph-based knowledge tracing. By applying the Gated Recurrent Unit (GRU) and the Attention model, this approach designs a dynamic graph over different time steps. Through learning feature information and topology representation of nodes/learners, this model can predict with high accuracy of 80,63% learners with low knowledge acquisition and prepare them for further recommendation.


title: Scanpath Analysis of Student Attention During Problem Solving with Worked Examples

text: We report on the analysis of scanpath data captured by an eye tracker as students solved problems with access to worked examples. Our work makes two contributions: (1) it reports on scanpath analysis using the MultiMatch tool, (2) it investigates how type of problem-example similarity and assistance influenced attention patterns captured by scanpaths. We show that both problem-example similarity and type of assistance impact scanpaths.


title: Investigating Transformers for Automatic Short Answer Grading

text: Recent advancements in the field of deep learning for natural language processing made it possible to use novel deep learning architectures, such as the Transformer, for increasingly complex natural language processing tasks. Combined with novel unsupervised pre-training tasks such as masked language modeling, sentence ordering or next sentence prediction, those natural language processing models became even more accurate. In this work, we experiment with fine-tuning different pre-trained Transformer based architectures. We train the newest and most powerful, according to the glue benchmark, transformers on the SemEval-2013 dataset. We also explore the impact of transfer learning a model fine-tuned on the MNLI dataset to the SemEval-2013 dataset on generalization and performance. We report up to 13% absolute improvement in macro-average-F1 over state-of-the-art results. We show that models trained with knowledge distillation are feasible for use in short answer grading. Furthermore, we compare multilingual models on a machine-translated version of the SemEval-2013 dataset.


title: Learning from Interpretable Analysis: Attention-Based Knowledge Tracing

text: Knowledge tracing is a well-established problem and non-trivial task in personalized education. In recent years, many existing works have been proposed to handle the knowledge tracing task, particularly recurrent neural networks based methods, e.g., Deep Knowledge Tracing (DKT). However, DKT has the problem of vibration in prediction outputs. In this paper, to better understand the problem of DKT, we utilize a mathematical computation model named Finite State Automaton(FSA), which can change from one state to another in response to the external input, to interpret the hidden state transition of the DKT when receiving inputs. And we discover the root cause of the two problems is that the DKT can not handle the long sequence input with the help of FSA. Accordingly, we propose an effective attention-based model, which can solve the above problem by directly capturing the relationships among each item of the input regardless of the length of the input sequence. The experimental results show that our proposed model can significantly outperform state-of-the-art approaches on several well-known corpora.


title: Distinguishing Anxiety Subtypes of English Language Learners Towards Augmented Emotional Clarity

text: Public Speaking Anxiety (PSA) and Foreign Language Anxiety (FLA) afflict most English Language Learners (ELLs) during a presentation. However, few tools are available to help multicultural learners clearly identify which type of anxiety they are feeling. In this paper, we present a field study conducted in real language classrooms. We developed machine learning models based on features of electrodermal activity (EDA) to predict non-verbal behaviors manifested as PSA and FLA. The students were labeled with the anxiety categories both PSA and FLA, PSA more, FLA more, or no anxiety. To classify the ELLs into their respective anxiety categories, prominent EDA features were employed that supported the predictions of anxiety sources. These results may encourage both ELLs and instructors to be aware of the origins of anxiety subtypes and develop a customized practice for public speaking in a foreign language.


title: Raising Academic Performance in Socio-cognitive Conflict Learning Through Gamification

text: Drawing on the social interdependence theory, we experimentally compared the between-group effects of social learning gamification, competition gamification, and non-gamification on the academic performance in the within-group socio-cognitive conflict learning. Findings show that the positive learning effects of socio-cognitive conflict in within-group are strengthened when the between-group gamification is designed by social learning or competitive strategy.


title: Examining Students’ Intrinsic Cognitive Load During Program Comprehension – An Eye Tracking Approach

text: Programming as a cognitive activity requires the utilization of various kinds of mental models that involve different cognitive loads while students learn to program. The article discusses the results of an experiment aimed at answering the following question: are eye tracking based measures related to the intrinsic cognitive load (ICL) connected with program comprehension? Thirty one students of computer science took part in the experiment. They analyzed two program codes written in the C++ language to search for (1) logical errors (LER) and (2) syntax errors (SER). ICL was measured by subjective rating of the difficulty of each task. There were significant differences found for the subjective measures of intrinsic load, the effectiveness and the time of tasks performance, and the values of eye tracking parameters: fixation duration average (FDA) and saccade amplitude average (SAA) in two experiment conditions. Longer fixation and shorter saccades were associated with higher ICL. The results obtained suggest that FDA and SAA are eye tracking measures sensitive of intrinsic cognitive load.


title: Understanding Collaborative Question Posing During Computational Modeling in Science

text: Curricular standards in STEM [9] and computer science have emphasized the role of asking questions to support inquiry learning in K-12 education. In this paper, we examine the role of questioning during collaborative computational modeling of scientific processes through discourse analysis to understand how students grapple with the synergistic application of STEM and CT to build, test, and evaluate their models. To our knowledge, limited research has targeted a systematic understanding of question posing during computational modeling in science. We aim to develop a better understanding of question posing in support of inquiry and problem-solving during model building.


title: The Double-Edged Sword of Automating Personalized Interventions in Makerspaces: An Exploratory Study of Potential Benefits and Drawbacks

text: While the affordance of a project-based and instructor-facilitated curriculum is a strength of makerspaces, they can be challenging learning environments for many students. This paper recognizes the need for instructors to personalize their approach in supporting students’ needs. While there are opportunities to create automated systems to help instructors personalize their interventions, much care must be taken to prevent the introduction of unintended outcomes. In this study, we designed a weekly personalized intervention cycle based on students’ self-reports. The effect of such personalized intervention was then evaluated using a repeated measure ANOVA. Findings suggest that students receiving personalized interventions were more time efficient in makerspaces and on assignments. Additionally, they reported a lower level of frustration. Students with personalized intervention, however, expressed a lower sense of community. This suggests that while additional data provided to instructors can support personalized assistance, a more nuanced approach may be needed to avoid unintended consequences.


title: Explaining Errors in Predictions of At-Risk Students in Distance Learning Education

text: Despite recognising the importance of transparency and understanding of predictive models, little effort has been made to investigate the errors made by these models. In this paper, we address this gap by interviewing 12 students whose results and predictions of submitting their assignment differed. Following our previous quantitative analysis of 25,000+ students, we conducted online interviews with two groups of students: those predicted to submit their assignment, yet they did not (False Negative) and those predicted not to submit, yet they did (False Positive). Interviews revealed that, in False Negatives, the non-submission of assignments was explained by personal, financial and practical reasons. Overall, the factors explaining the different outcomes were not related to any of the student data currently captured by the predictive model.


docid: mllkrppi

title: Extended Multi-document Cohesion Network Analysis Centered on Comprehension Prediction

text: Theories of discourse argue that comprehension depends on the coherence of the learner’s mental representation. Our aim is to create a reliable automated representation to estimate readers’ level of comprehension based on different productions, namely self-explanations and answers to open-ended questions. Previous work relied on Cohesion Network Analysis to model a cohesion graph composed of semantic links between multiple reference texts and student productions. From this graph, a set of features was derived and used to build machine learning models to predict student comprehension scores. In this paper, we build on top of the previous study by: a) extending the CNA graph by adding new semantic links targeting specific sentences that should have been captured within the learner’s productions, and b) cleaning the self-explanations by eliminating frozen expression, as well as entries which seemed nearly identical to the source text. The results are in line with the conclusions of the previous study regarding the importance of both self-explanations and question answers in predicting the students’ reading comprehension level. They also outline the limitations of our feature generation approach, in which no substantial improvements were detected, despite adding more fine-grained features.


title: Learner-Context Modelling: A Bayesian Approach

text: The following paper is a proof-of-concept demonstration of a novel Bayesian model for making inferences about individual learners and the context in which they are learning. This model has implications for both efforts to create rich open leaner models, develop automated personalization and increase the breadth of adaptive responses that machines are capable of. The purpose of the following work is to demonstrate, using both simulated data and a benchmark dataset, that the model can perform comparably to commonly used models. Since the model has fewer parameters and a flexible interpretation, comparable performance opens the possibility of utilizing it to extend automation greater variety of learning environments and use cases.


title: Scientific Modeling Using Large Scale Knowledge

text: The intelligent research assistant, VERA, supports inquiry-based modeling by supplying contextualized large-scale domain knowledge in the Encyclopedia of Life. Learners can use VERA to construct conceptual models of ecological phenomena, run them as simulations, and review their predictions. A study on the use of VERA by college-level students indicates that providing access to large scale but contextualized knowledge helped students build more complex models and generate more hypotheses in problem-solving.


title: Exercise Hierarchical Feature Enhanced Knowledge Tracing

text: Knowledge tracing is a fundamental task in the computer-aid educational system. In this paper, we propose a hierarchical exercise feature enhanced knowledge tracing framework, which could enhance the ability of knowledge tracing by incorporating knowledge distribution, semantic features, and difficulty features from exercise text. Extensive experiments show the high performance of our framework.


title: Towards Practical Detection of Unproductive Struggle

text: Extensive literature in artificial intelligence in education focuses on developing automated methods for detecting cases in which students struggle to master content while working with educational software. Such cases have often been called “wheel-spinning,” “unproductive persistence,” or “unproductive struggle.” We argue that most existing efforts rely on operationalizations and prediction targets that are misaligned to the approaches of real-world instructional systems. We illustrate facets of misalignment using Carnegie Learning’s MATHia as a case study, raising important questions being addressed by on-going efforts and for future work.


title: Supporting Empathy Training Through Virtual Patients

text: For the training of interpersonal skills, such as those required in the medical field, virtual agents can provide a safe environment for practice. However, many agent systems are not developed with the ability to understand non-verbal input. Being able to automatically parse such input is essential for the practice of interpersonal skills such as empathy. Currently, it is still an open question which prosodic or visual features would aid automatic classification of empathy and how this knowledge can be used to support the practice of these skills. As a first step towards this goal, we report on 42 second-year nursing students practicing their empathy skills with a virtual patient or through collaborative role playing. We found that across both the role playing and simulation, students assessed their empathy as increasing over time but as higher during the role playing. This work contributes to the continued development of virtual agents for the training of interpersonal skills.


title: Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach

text: For effective learning, students must set learning objectives and adopt the ad hoc cognitive behavior to achieve them. Our research work aims to ensure good scaffolding by offering tutors the opportunity to observe learners’ cognitive behaviors, especially their cognitive engagement. In this respect, we propose in the present work an automatic system for classifying learners according to their levels of cognitive engagement. To this end, we focus on the analysis of social interactions within online discussion forums. Hence, the proposed system has two main steps: 1/Learners’ vector construction and 2/SVM-based classifier. The results show the efficiency of the proposed system with an accuracy = 0.9 and a cohen’s K = 0.89.


title: Promoting Learning and Satisfaction of Children When Interacting with an Emotional Companion to Program

text: Teaching how to program in Primary Education has attracted a great deal of attention in the last years. However, it is still unclear the approach to achieve higher learning and satisfaction levels. In this paper, the proposal is focused on the use of an emotional learning companion called Alcody. To compare whether to insert emotional elements have an equal or more significant effect on students’ satisfaction and learning than personalization and execution, 137 children between 10–12 years were randomly split into four groups for three months. The higher learning and satisfaction levels are registered for the students in the group with execution, personalization and emotion management.


title: Confrustion and Gaming While Learning with Erroneous Examples in a Decimals Game

text: Prior studies have explored the potential of erroneous examples in helping students learn more effectively by correcting errors in solutions to decimal problems. One recent study found that while students experience more confusion and frustration (confrustion) when working with erroneous examples, they demonstrate better retention of decimal concepts. In this study, we investigated whether this finding could be replicated in a digital learning game. In the erroneous examples (ErrEx) version of the game, students saw a character play the games and make mistakes, and then they corrected the characters’ errors. In the problem solving (PS) version, students played the games by themselves. We found that confrustion was significantly, negatively correlated with performance in both pretest (r = −.62, p < .001) and posttest (r = −.68, p < .001) and so was gaming the system (pretest r = −.58, p < .001, posttest r = −.66, p < .001). Posthoc (Tukey) tests indicated that students who did not see any erroneous examples (PS-only) experienced significantly lower levels of confrustion (p < .001) and gaming (p < .001). While we did not find significant differences in post-test performance across conditions, our findings show that students working with erroneous examples experience consistently higher levels of confrustion in both game and non-game contexts.


title: Contingent Scaffolding for System Safety Analysis

text: System safety analysis is a creative process that can often be undertaken by people who are not experts in the system under analysis whilst also learning the analysis methodology. With the increase of system complexity, the high demand for analyses conducted at a scale and the potentially catastrophic consequences of inadequate analysis, there is an urgent need for supporting the development of system analysis skills. Technological solutions can effectively scaffold this ill-defined domain. We propose a generic framework for Contingent Scaffolding capable of providing flexible learning support while conducting system safety analysis. This has been implemented into an intelligent agent, Oswin, which offers Ontology-driven scaffolding with interactive nudges.


title: Using Eye-Tracking and Click-Stream Data to Design Adaptive Training of Children’s Inhibitory Control in a Maths and Science Game

text: Computerised educational neuroscience interventions that train within-domain inhibitory control (IC) can improve children’s counterintuitive reasoning. However, the HCI or adaptive design of such environments often receive less attention. Eye-tracking and click data were used to compare four versions of an IC-training game in terms of their HCI design and potential for supporting adaptive feedback. Our results provide insights for developing an adaptive system to scaffold pupils’ transition towards using IC in un-cued, self-regulated scenarios.


title: Relationships Between Math Performance and Human Judgments of Motivational Constructs in an Online Math Tutoring System

text: This paper explores how early grade school students’ math performance relates to human ratings of students’ affect, identity, and social awareness based on the content of messages to an online tutoring system avatar. There is an expanding body of research which investigates connections between these features and success in mathematics. This study used principle component analysis to identify four components related to motivational constructs. These components were examined using correlations with mathematics performance at three difficulty levels. Data from 572 students were examined, with results indicating little to no links between human judgments of motivational constructs and math performance. These findings have implications for how motivational constructs in math are evaluated and how they can predict mathematics performance.


title: Learning Outcomes and Their Relatedness Under Curriculum Drift

text: A typical medical curriculum is organized as a hierarchy of learning outcomes (LOs), each LO is a short text that describes a medical concept. Machine learning models have been applied to predict relatedness between LOs. These models are trained on examples of LO-relationships annotated by experts. However, medical curricula are periodically reviewed and revised, resulting in changes to the structure and content of LOs. This work addresses the problem of model adaptation under curriculum drift. First, we propose heuristics to generate reliable annotations for the revised curriculum, thus eliminating dependence on expert annotations. Second, starting with a model pre-trained on the old curriculum, we inject a task-specific transformation layer to capture nuances of the revised curriculum. Our approach makes significant progress towards reaching human-level performance.


title: Prediction of Group Learning Results from an Aggregation of Individual Understanding with Kit-Build Concept Map

text: With the development of information and communication technology, we can collect and analyze a variety of data for optimization. It is expected that the prediction of learning with the data enables a deep reflection for enhancing the learning experience. This paper describes a method to predict the group learning results from aggregation of an individual’s understanding with the Kit-build concept map (KBmap). KBmap is a reconstruction-type concept map with automated diagnosis of the content. To test this method, we examined the prediction results from the data collected from a classroom lesson. The results show that most of the actual results are in good agreement with the prediction, and the comparison between the actual results and the predictions could be useful for the teacher.


title: Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data

text: Understanding the effect of learning behavior is fundamental to improving learning outcomes. In this paper, we perform a behavioral analysis based on data from a large high-stakes exam preparation platform. By measuring the importance of a set of candidate learning behaviors in predicting final exam outcomes, we identify a suite of beneficial behaviors. In particular, we find that breadth (wide coverage of content per week) and intensity together with consistency (frequent and equal-length practice for a limited period) are most predictive of final exam success rate, among eleven studied behaviors.


title: Generating Game Levels to Develop Computer Science Competencies in Game-Based Learning Environments

text: Game-based learning environments hold significant potential for supporting K-12 computer science (CS) education by providing CS learning experiences embedded within engaging virtual worlds. However, many game-based learning environments do not adaptively support individual students based on their specific knowledge and skills. Often, this is because creating game levels is highly labor-intensive, which limits the number of levels created to support student learning. Procedural content generation (PCG) is a promising direction for addressing this challenge by dynamically creating game levels that address specific student needs without requiring extensive development effort. In this paper, we investigate a PCG framework driven by answer set programming (ASP), a variant of logic programming that utilizes well-formed logical rules to express constraints for valid game levels. We demonstrate how variations in CS learning objectives and game-playing skills can be incorporated into ASP-based rules to generate learner-adaptive levels in a middle-grades CS game-based learning environment. Evaluations of the generated levels suggest that the ASP-based level generator not only reliably generates desired CS educational game levels but also synthesizes a large set of diverse game levels. The findings suggest that the ASP-based PCG approach has considerable promise for creating highly engaging and adaptive game-based learning experiences for K-12 CS education.


title: Towards Interpretable Deep Learning Models for Knowledge Tracing

text: Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design knowledge tracing (KT) models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as their outputs and working mechanisms suffer from the intransparent decision process and complex inner structures. We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model by backpropagating the relevance from the model’s output layer to its input layer. The experiment results show the feasibility using the LRP method for interpreting the DLKT model’s predictions, and partially validate the computed relevance scores. We believe it can be a solid step towards fully interpreting the DLKT models and promote their practical applications.


title: Toward an Automatic Speech Classifier for the Teacher

text: Our system classifies audio from microphones worn by the teacher in order to determine (1) whether the teacher is addressing the whole class or talking to individuals or groups of students. In the latter case, it determines (2) whether the teacher is giving formative feedback, giving corrective feedback, chatting socially, or addressing administrative or workflow concerns. This paper reports the initial accuracy of this system against human coding of middle school math classroom behavior. We also compared audio collected through professional hardware versus more accessible alternatives.


title: Evaluating Student Learning in a Synchronous, Collaborative Programming Environment Through Log-Based Analysis of Projects

text: In this paper we present an initial analysis of synchronous, collaborative programming in a robotics platform. Students worked in dyads and triads to complete a week-long curriculum targeting the learning of cybersecurity and computational thinking concepts, and their application using realistic robotics scenarios. We demonstrate how an analysis of individual student activity data within a group can be extrapolated to understand the group’s collaborative problem-solving. We compare our findings to past literature and discuss future implications of collaborative programming research.


title: Early Prediction of Success in MOOC from Video Interaction Features

text: The popularity of online learning, such as MOOCs (Massive Open Online Courses), continues to increase among students. However, MOOCs dropout remains high. Prediction of student performance that could feed instructors’ dashboards and help them adapt their course structure and material, or trigger help and tailor interventions to specific groups of students, is a valuable research objective. Towards that end, this paper focuses on three predictive metrics (student attendance rate: [Formula: see text], utilization rate: [Formula: see text], and watching index: [Formula: see text]) of how students interact with MOOC videos in order to predict which group of students will pass or fail the course. Results show that these metrics, taken after the first week and the midpoint, can be highly effective for predicting the students that will pass or fail the course.


title: Siamese Neural Networks for Class Activity Detection

text: Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms. A CAD solution helps teachers get instant feedback on their pedagogical instructions. However, CAD is very challenging because (1) classroom conversations contain many conversational turn-taking overlaps between teachers and students; (2) the CAD model needs to be generalized well enough for different teachers and students; and (3) classroom recordings may be very noisy and low-quality. In this work, we address the above challenges by building a Siamese neural framework to automatically identify teacher and student utterances from classroom recordings. The proposed model is evaluated on real-world educational datasets. The results demonstrate that (1) our approach is superior on the prediction tasks for both online and offline classroom environments; and (2) our framework exhibits robustness and generalization ability on new teachers (i.e., teachers never appear in training data).


title: Identification of Students’ Need Deficiency Through a Dialogue System

text: In the domain of moral education, students’ need deficiency refers to the unsatisfied need that would result in problem behaviors. Timely and accurate identification of students’ need deficiency is crucial to moral education and the students themselves. Previous psychology research focusing on distinct factors only provides scattered guidelines to identify such need deficiencies and meanwhile few teachers and parents have the related expertise, which makes the identification task difficult to accomplish. To address these issues, we develop a task-oriented dialogue system to help teachers and parents identify students’ need deficiency through multi-turn dialogues. Specifically, relevant factors of need deficiency are summarized based on psychology theories, which provides a theoretical foundation for the newly proposed system. In addition, reinforcement learning methodology is adopted to learn dialogue policy to serve the designed dialogue system. Experimental results demonstrate that the developed dialogue system achieves its design objectives.


title: Agent-in-the-Loop: Conversational Agent Support in Service of Reflection for Learning During Collaborative Programming

text: Dynamic conversational agent-based support for collaborative learning has shown significant positive effects on learning over no-support or static-support control conditions in prior studies. In order to understand the boundary between human-led and AI-led support for collaboration, we compare in this study an approach where the agent’s primary role is to help students regulate their own collaboration with two more typical prompting strategies that are used only during a reflection phase: one designed to provide a specific informational focus for the reflection, and the other designed to draw out evaluation, elaboration, and exploration of alternative perspectives. Significant positive effects on learning over and above just the human-led form of support are observed when either of the prompting strategies are used.


title: Predicting Reading Comprehension from Constructed Responses: Explanatory Retrievals as Stealth Assessment

text: Open-ended constructed responses promote deeper processing of course materials. Further, evaluation of these explanations can yield important information about students’ cognition. This study examined how students’ constructed responses, generated at different points during learning, relate to their later comprehension outcomes. College students (N = 75) produced self-explanations during reading and explanatory retrievals after reading. The Constructed Response Assessment Tool (CRAT) was used to analyze these responses across multiple dimensions of language and relate these textual features to comprehension performance. Results indicate that the linguistic features of post-reading explanatory retrievals were more predictive of comprehension outcomes than self-explanations. Further, these models relied on different indices to predict performance.


title: Effect of Immediate Feedback on Math Achievement at the High School Level

text: We examine the use of computer-based learning in the classroom and the effect of immediate feedback on student performance. Since it is well known in educational research that it is possible to observe a “Matthew Effect” in which the rich get richer, we wanted to see if feedback was useful for low prior knowledge students, as defined by students whose pretest score was at or below the median. In this counterbalanced randomized controlled trial, 243 tenth and eleventh grade mathematics students were exposed to one of two conditions, as we measured their learning from: 1) immediate feedback (where the computer told them correctness and they could also ask for hints) or 2) practice only (where they received feedback only after taking a posttest). Results suggest that immediate feedback from computer-based learning tasks benefit both high and low prior knowledge students, with low prior knowledge students exhibiting greater gains. The implications of these findings support further investigation into the use of computer-based learning tasks that provide immediate feedback.


docid: itzuehdq

title: Sense of Agency in Times of Automation: A Teachers’ Professional Development Proposal on the Ethical Challenges of AI Applied to Education

text: The possibilities given by artificial intelligence are becoming enactments of what once were just distant fictional displays. Even if we restrict the context to Artificial Intelligence in Education (AIEd) the horizon is still wide. But which society layers and ethical frameworks are being considered in the process of conceiving AIEd scope? Committed with this debate, this research focuses the ethical challenges of AIEd in terms of sense of agency development across formal education.


title: Mastery Learning Heuristics and Their Hidden Models

text: Mastery learning algorithms are used in many adaptive learning technologies to assess when a student has learned a particular concept or skill. To assess mastery, some technologies utilize data-driven models while others use simple heuristics. Prior work has suggested that heuristics may often perform comparably to model-based algorithms. But is there any reason we should expect these heuristics to be reasonable? In this paper, we show that two prominent mastery learning heuristics can be reinterpreted as model-based algorithms. In particular, we show that the N-Consecutive Correct in a Row heuristic and a simplified version of ALEKS’ mastery learning heuristic are both optimal policies for variants of the Bayesian knowledge tracing model. By putting mastery learning heuristics on the same playing field as model-based algorithms, we can gain insights on their hidden assumptions about learning and why they might perform well in practice.


title: Constructing Automated Revision Graphs: A Novel Visualization Technique to Study Student Writing

text: This paper introduces a novel technique of constructing Automated Revision Graphs (ARG) to facilitate the study of revisions in writing. ARG plots sentences of a written text as nodes, and their similarities to sentences from its previous draft as edges to visualize text as graph. Implemented in two forms: simple and multi-stage, the graphs demonstrate how sentence-level differences can be visualized in short texts to study revision products, processes, and student interaction with feedback in student writing.


docid: ofpdiklv

title: A Gamified Solution to the Cold-Start Problem of Intelligent Tutoring System

text: Today’s intelligent tutoring systems provide more intelligent but complex services to learners. These systems encounter two critical issues: 1) the initial lack of new learner’s information for running complex services, and 2) new learners failing to actively interact with these complex services due to the initial unfamiliarity. We define such issues as the “cold-start” problem in today’s intelligent tutoring systems, and propose a gamified solution to tackle it. By leveraging on the established MDA model, a three-layer framework with narrative elements and task-oriented elements is designed and implemented in an ITS for mathematics. The preliminary evaluations show its effectiveness, and the proposed solution is generally applicable to other ITS systems.


title: Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service

text: Aims to provide flexible, effective and personalized online learning service, micro learning has gained wide attention in recent years as more people turn to use fragment time to grasp fragmented knowledge. Widely available online knowledge sharing is one of the most representative approaches to micro learning, and it is well accepted by online learners. However, information overload challenges such personalized online learning services. In this paper, we propose a deep cross attention recommendation model to provide online users with personalized resources based on users’ profile and historical online behaviours. This model benefits from the deep neural network, feature crossing, and attention mechanism mutually. The experiment result showed that the proposed model outperformed the state-of-the-art baselines.


title: The Personal Social Services Today and Tomorrow

text: The final chapter gives recognition to the contribution and achievements of the personal social services and social work during the past 50 years. It also reflects on what has been lost compared to the ambitions and intentions when local authority personal social services were first integrated in England and when social work became a unified profession across the United Kingdom. This chapter looks forward to the threats and opportunities ahead and notes the important contribution that social work and the personal social services can and still need to make.


title: Investigating the Role of Politeness in Human-Human Online Tutoring

text: This study aims to investigate the role of politeness in online-tutoring practices by analyzing a large-scale human-human tutorial dialogue dataset. To this end, we employed linguistic theories of politeness to identify the politeness strategies contained in utterances made by tutors and students, and these strategies were further combined to quantify the politeness levels of tutors and students in a tutorial session. The results revealed that tutors had a similar level of politeness at the beginning of all dialogues, while students were more polite at the end if they successfully solved problems.


title: What Happens When Gamification Ends?

text: Nowadays, the application of typical game elements in non-ludic environments has been extended. Gamification has become a very interesting resource to promote engagement and participation in a wide variety of areas including education. For this reason, researchers are increasingly interested in the study of gamification. There are many papers related to the impact that this methodology has on the student’s motivation, engagement or satisfaction. The aim of this paper is to analyze four aspects: “Pressure/tension”, “Perceived choice”, “Perceived competence” and “Effort/importance”. The first part of the study analyzes the aspects at two weeks between a test group and a control group. The second part is even more novel, because it analyzes the aspects when the test group is no longer gamified. Most studies focus on what happens when gamifying, but not when a group of students stops gamifying. The results obtained will serve to advance a part of the knowledge about gamification.


title: Adaptive Forgetting Curves for Spaced Repetition Language Learning

text: The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revision strategies that counteract memory decay and ensure long-term retention. In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language. We evaluate the impact of the models and their features using data from an online vocabulary teaching platform and find that word complexity is a highly informative feature which may be successfully learned by a neural network model.


title: Towards Improving Sample Representativeness of Teachers on Online Social Media: A Case Study on Pinterest

text: Many teachers utilize online social media to supplement their students’ needs and enhance their professional activities, curating millions of educational resources. In fact, during the Coronovirus pandemic, online curation of resources provides teachers a repository of materials to provide students in online space. Teachers’ engagement online then provides the ability to learn more about how teachers are addressing students’ learning needs and potentially improve the quality of the resources they share. Historically, to perform such a study, we often survey some teachers and then leverage their shared resources to investigate education-related research questions. However, this can lead to problems including sample representativeness where surveyed teachers may not be representative of the population of teachers in social media. In this paper, we attempt to improve the sample representativeness of teachers on Pinterest. We first survey 541 teachers in the United States as seed samples and then collect their online data and social connections on Pinterest. Then, we devise a heuristic that automatically identifies other Pinterest accounts that are likely to be teachers thus improving the sample representativeness. Finally, we evaluate our heuristic with advanced machine learning techniques.


title: BERT and Prerequisite Based Ontology for Predicting Learner’s Confusion in MOOCs Discussion Forums

text: The use of Massive Open Online Courses (MOOCs) is rapidly increasing due to the convenience and ease that provide to learners. However, MOOCs suffer from high drop out rate owing mostly to the confusion and frustration going with the learning process. Based on MOOCs discussion forums, this paper aims to explore different levels of confusion in specific concept using prerequisite based ontology for extracting relevant posts, and Bidirectional Encoder Representations from Transformers (BERT) classification algorithm to describe the degree of confusion for each post. The analysis of discussion posts from Stanford University dataset affirms the effectiveness of our model. BERT achieve good classification accuracy; this will help in early drop out detection and also facilitate future support for learners in confusion state.


title: Improving Students’ Problem-Solving Flexibility in Non-routine Mathematics

text: A key issue in mathematics education is supporting students in developing general problem-solving skills that can be applied to novel, non-routine situations. However, typical mathematics instruction in the U.S. too often is dominated by rote learning, without exposing students to the underlying reasoning or alternate ways to solve problems. As a first step in addressing this problem, we present a cognitive task analysis study that investigates how students without a mathematics-related background solve novel non-routine problems. We found that most students were able to identify the underlying pattern that yields the final solution in each problem. Furthermore, they tended to use various forms of visualization in their draft work, but occasionally made computational mistakes. Based on these results, we propose our plan for developing an instructional platform that leverages learning science principles to train students in problem-solving abilities.


title: Automated Short-Answer Grading Using Deep Neural Networks and Item Response Theory

text: Automated short-answer grading (ASAG) methods using deep neural networks (DNN) have achieved state-of-the-art accuracy. However, further improvement is required for high-stakes and large-scale examinations because even a small scoring error will affect many test-takers. To improve scoring accuracy, we propose a new ASAG method that combines a conventional DNN-ASAG model and an item response theory (IRT) model. Our method uses an IRT model to estimate the test-taker’s ability from his/her true-false responses to objective questions that are offered with a target short-answer question in the same test. Then, the target short-answer score is predicted by jointly using the ability value and a distributed short-answer representation, which is obtained from an intermediate layer of a DNN-ASAG model.


title: Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System

text: We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints. Our model is used in Korbit (https://www.korbit.ai), a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.


title: Relationships Between Body Postures and Collaborative Learning States in an Augmented Reality Study

text: In this paper we explore how Kinect body posture sensors can be used to detect group collaboration and learning, in the context of dyad pairs using augmented reality system. We leverage data collected during a study (N = 60 dyads) where participant pairs learned about electromagnetism. Using unsupervised machine learning methods on Kinect body posture sensor data, we contribute a set of dyad states associated with collaboration quality, attitudes toward physics and learning gains.


title: A Socratic Tutor for Source Code Comprehension

text: Reported here are the findings of a comparative study on the effects of using a Socratic Intelligent Tutoring System for source code comprehension and learning computer programming. The result shows there are significant differences between the two groups where students who used Socratic Tutor ITS improved their knowledge by 45% in term of learning gain, developed a better understanding of concepts such as nested if-else and for loop, and improved their confidence level by 13%. Furthermore, the result of the Pearson product-moment correlation coefficient shows a positive correlation (r = 0.68) between feedback from the ITS and learning gain.


title: Exploring Automatic Short Answer Grading as a Tool to Assist in Human Rating

text: This project proposes using BERT (Bidirectional Encoder Representations from Transformers) as a tool to assist educators with automated short answer grading (ASAG) as opposed to replacing human judgement in high-stakes scenarios. Many educators are hesitant to give authority to an automated system, especially in assessment tasks such as grading constructed response items. However, evaluating free-response text can be time and labor costly for one rater, let alone multiple raters. In addition, some degree of inconsistency exists within and between raters for assessing a given task. Recent advances in Natural Language Processing have resulted in subsequent improvements for technologies that rely on artificial intelligence and human language. New, state-of-the-art models such as BERT, an open source, pre-trained language model, have decreased the amount of training data needed for specific tasks and in turn, have reduced the amount of human annotation necessary for producing a high-quality classification model. After training BERT on expert ratings of constructed responses, we use subsequent automated grading to calculate Cohen’s Kappa as a measure of inter-rater reliability between the automated system and the human rater. For practical application, when the inter-rater reliability metric is unsatisfactory, we suggest that the human rater(s) use the automated model to call attention to ratings where a second opinion might be needed to confirm the rater’s correctness and consistency of judgement.


title: Automatic Grading System Using Sentence-BERT Network

text: The integration of digital learning technologies into higher education enhances students’ learning by providing opportunities such as online examinations. However, many online examinations tend to have multiple-choice questions, as the marking of text-based questions can be a tedious task for academic staff, especially in large classes. In this study, we utilised SBERT, a pre-trained neural network language model to perform automatic grading of three variations of short answer questions on an Introduction to Networking Computer Science subject. A sample of 228 near-graduation Information Science students from one research-intensive tertiary institution in West African participated in this study. The course instructor manually rated short answers provided by the participants, using a scoring rubric and awarded scores ranging from 0 to 5. Some of the manually graded students’ answers were randomly selected and used as a training set to fine-tune the neural network language model. Then quadratic-weighted kappa (QWKappa) was used to test the agreement level between the ratings generated by the human rater compared with that of the language model, on three variations of questions, including description, comparison and listing. Further, the accuracy of this model was tested on the same questions. Overall results showed that the level of the inter-rater agreement was good on the three variety of questions. Also, the accuracy measures showed that the model performed very well on the comparison and description questions compared to the listing question.


title: Automatic Dialogic Instruction Detection for K-12 Online One-on-One Classes

text: Online one-on-one class is created for highly interactive and immersive learning experience. It demands a large number of qualified online instructors. In this work, we develop six dialogic instructions and help teachers achieve the benefits of one-on-one learning paradigm. Moreover, we utilize neural language models, i.e., long short-term memory (LSTM), to detect above six instructions automatically. Experiments demonstrate that the LSTM approach achieves AUC scores from 0.840 to 0.979 among all six types of instructions on our real-world educational dataset.


title: Allowing Revisions While Providing Error-Flagging Support: Is More Better?

text: In this study, we studied whether the number of revisions allowed per problem when error-flagging feedback is provided has a significant effect on learning. We used a partial cross-over study and analyzed the data collected by two adaptive tutors on while loops and for loops over six semesters. We found that when students were unfamiliar with the concepts, they solved fewer problems and therefore, learned significantly less when they were provided more opportunities for revision with error-flagging feedback. But, once they became more familiar with the concepts, allowing for more revisions had no deleterious effect on learning.


National Approach in Response to the COVID-19 Pandemic in Iran


Considerations for Mitigation of the Psychological Impacts of COVID-19 in Older Adults


Necessity of Attention to Mental Health of the Front Line Nurses against COVID-19: A Forgotten Requirement


title: Meteorological impacts on the incidence of COVID-19 in the U.S.

text: Since the World Health Organization has declared the current outbreak of the novel coronavirus (COVID-19) a global pandemic, some have been anticipating that the mitigation could happen in the summer like seasonal influenza, while medical solutions are still in a slow progress. Experimental studies have revealed a few evidences that coronavirus decayed quickly under the exposure of heat and humidity. This study aims to carry out an epidemiological investigation to establish the association between meteorological factors and COVID-19 in high risk areas of the United States (U.S.). We analyzed daily new confirmed cases of COVID-19 and seven meteorological measures in top 50 U.S. counties with the most accumulative confirmed cases from March 22, 2020 to April 22, 2020. Our analyses indicate that each meteorological factor and COVID-19 more likely have a nonlinear association rather than a linear association over the wide ranges of temperature, relative humidity, and precipitation observed. Average temperature, minimum relative humidity, and precipitation were better predictors to address the meteorological impact on COVID-19. By including all the three meteorological factors in the same model with their lagged effects up to 3 days, the overall impact of the average temperature on COVID-19 was found to peak at 68.45 °F and decrease at higher degrees, though the overall relative risk percentage (RR %) reduction did not become significantly negative up to 85 °F. There was a generally downward trend of RR % with the increase of minimum relative humidity; nonetheless, the trend reversed when the minimum relative humidity exceeded 91.42%. The overall RR % of COVID-19 climbed to the highest level of 232.07% (95% confidence interval = 199.77, 267.85) with 1.60 inches of precipitation, and then started to decrease. When precipitation exceeded 1.85 inches, its impact on COVID-19 became significantly negative. Our findings alert people to better have self-protection during the pandemic rather than expecting that the natural environment can curb coronavirus for human beings.


title: Guidelines of the Indian Society for Sleep Research (ISSR) for Practice of Sleep Medicine during COVID-19

text: BACKGROUND: Sleep services are assigned a non-essential status during COVID-19. The American Academy of Sleep Medicine strongly urges sleep clinicians to continue postponing non-urgent care until a later date, if such a recommendation is made by state officials due to local conditions. At the same time, one cannot ignore the fact that sleep is important for people’s health and wellbeing. Therefore, to protect the health of the population, it is essential to find ways and means to continue the practice of sleep medicine even during the COVID-19 pandemic. METHOD: Social environment and work ethics in sleep clinics and sleep laboratories in Asia, Africa, and Latin America are different from those in the US. Under these circumstances, the Indian Society for Sleep Research (ISSR) created a task force to develop guidelines for the practice of sleep medicine, not only for the Indian environment but also for other countries that are affected by the COVID-19 pandemic. The task force examined documents regarding practice of sleep medicine and associated specialities during COVID-19 by various professional organizations and governmental authorities. The recommendations were examined for their applicability. Wherever gaps were identified, consensus was reached keeping in view the available evidences. OUTCOME AND RECOMMENDATIONS: The emphasis of the guidelines is on avoiding doctor to patient contact during the pandemic. Teleconsultation and other modes of audio-visuals can be used as modes for medical practice during the COVID-19 pandemic. However, in addition to the patient, the presence of a family member, or a reliable informant is recommended. Patients of most sleep disorders can be provided tele-aftercare service. ISSR guidelines also give a list of medications allowed to be prescribed during the first and the follow-up teleconsultation. Hospitals and clinics are slowly opening in India and many other countries. As sleep services resume operations, there is a need to find innovative ways to reduce contact with COVID-19 patients, follow personal protection guidelines, as well as social distancing. This article does discuss strategies for the safe conduct of Level 1 sleep studies. Home sleep testing, which had greater acceptance during the last few years, should be given more attention during the COVID-19 period. Once the decision to reopen the sleep laboratory and resume operations is made, the safety of the patients and office staff should become the major priority. The ISSR recommendation is to postpone and reschedule in-laboratory positive pressure therapy, but it mentions the considerations to be followed in emergency situations. At the same time, high clinical risk patients may be diagnosed on the basis of clinical findings, and without performing polysomnography or home sleep testing. However, at some point, there is a need to reinitiate the in-lab testing. In addition, daily assessment of the COVID-19 situation in the community, along with a review of the situation with local public health and the state health department is advised.


Reply to Amit Bansal and Anant Kumar’s Letter to the Editor re: Lan Zhu, Nianqiao Gong, Bin Liu, et al. Coronavirus Disease 2019 Pneumonia in Immunosuppressed Renal Transplant Recipients: A Summary of 10 Confirmed Cases in Wuhan, China. Eur Urol 2020;77:748–54


FOREWORD


Suboptimal management of dyslipidemia in everyday clinical practice: Alarming signals from real-world data


docid: giktwgbb

title: Volume 45 Editorial


Is this the end of the Oral and Maxillofacial Toolbox? A retrospective analysis of funding for acute OMFS soft tissue trauma provision within the UK


Modification of the microscope drape to provide a closed surgical field in transoral robotic surgery


Covid-19 pandemic and digital transformation in critical care units()


Oxygenation device with reservoir and PEEP (ODRPEEP) in patients with acute respiratory distress due to COVID-19()


docid: hpgrbhkl

title: Hydroxychloroquine in the treatment of COVID-19: how to use it waiting for conclusive scientific evidence()


title: When Similarities Among Devices are Taken for Granted: Another Look at Portability

text: The original idea of profiling implies attacking one device with a leakage model generated from an “identical copy”, but this concept cannot be always enforced. The leakage model is commonly generated with traces from an “open device”, assuming that a model which works for one device should work for another copy as well. In practice, applying a leakage model to a different copy of the same device (commonly called portability) is a hard problem to deal with, as intrinsic differences in the devices or the experimental setups used to obtain the traces cause behavioural variations which lead to an unsuccessful attack. In this paper we propose a novel similarity assessment technique that allows evaluators to quantify the differences among various copies of the same device. Moreover, we support this technique with actual experiments to show that this metric is directly related to the portability issue. Finally, we derive a method that improves the performance of template attacks.


title: A SAT-Based Approach for Index Calculus on Binary Elliptic Curves

text: Logical cryptanalysis, first introduced by Massacci in 2000, is a viable alternative to common algebraic cryptanalysis techniques over boolean fields. With xor operations being at the core of many cryptographic problems, recent research in this area has focused on handling xor clauses efficiently. In this paper, we investigate solving the point decomposition step of the index calculus method for prime-degree extension fields [Formula: see text], using sat solving methods. We experimented with different sat solvers and decided on using WDSat, a solver dedicated to this specific problem. We extend this solver by adding a novel symmetry breaking technique and optimizing the time complexity of the point decomposition step by a factor of m! for the [Formula: see text](th) summation polynomial. While asymptotically solving the point decomposition problem with this method has exponential worst time complexity in the dimension l of the vector space defining the factor base, experimental running times show that the presented sat solving technique is significantly faster than current algebraic methods based on Gröbner basis computation. For the values l and n considered in the experiments, the WDSat solver coupled with our symmetry breaking technique is up to 300 times faster than Magma’s F4 implementation, and this factor grows with l and n.


title: Online Template Attack on ECDSA:: Extracting Keys via the Other Side

text: We retrieve the ephemeral private key from the power trace of a single scalar multiplication in an ECDSA signature generation and from that the signing private key using an online template attack. The innovation is that we generate the profiling traces using ECDSA signature verification on the same device. The attack can be prevented by randomization of the (projective) coordinates of the base point.


title: LMS vs XMSS: Comparison of Stateful Hash-Based Signature Schemes on ARM Cortex-M4

text: Stateful hash-based signature schemes are among the most efficient approaches for post-quantum signature schemes. Although not suitable for general use, they may be suitable for some use cases on constrained devices. LMS and XMSS are hash-based signature schemes that are conjectured to be quantum secure. In this work, we compared multiple instantiations of both schemes on an ARM Cortex-M4. More precisely, we compared performance, stack consumption, and other figures for key generation, signing and verifying. To achieve this, we evaluated LMS and XMSS using optimised implementations of SHA-256, SHAKE256, Gimli-Hash, and different variants of Keccak. Furthermore, we present slightly optimised implementations of XMSS achieving speedups of up to [Formula: see text] for key generation, [Formula: see text] for signing, and [Formula: see text] for verifying.


title: Attacking RSA Using an Arbitrary Parameter

text: In this paper, we introduce a parameter u that is related to N via an arbitrary relation. By knowing the parameter along with RSA public key pairs, (N, e), we conduct two new attacks on the RSA cryptosystem. The first attack works on the equation [Formula: see text] where [Formula: see text] is the best known lower bound of [Formula: see text]. It combines the continued fraction method and Coppersmith’s method to factor N in polynomial time. The second attack shows that given [Formula: see text] for [Formula: see text] and a fixed X, we can simultaneously factor the k RSA moduli. It manipulates the result from diophantine approximation to enable the conditions of Coppersmith’s method. These attacks show that there are more possible weak RSA key pairs.


title: Hash-Based Signatures Revisited: A Dynamic FORS with Adaptive Chosen Message Security

text: FORS is the underlying hash-based few-time signing scheme in SPHINCS[Formula: see text], one of the nine signature schemes which advanced to round 2 of the NIST Post-Quantum Cryptography standardization competition. In this paper, we analyze the security of FORS with respect to adaptive chosen message attacks. We show that in such a setting, the security of FORS decreases significantly with each signed message when compared to its security against non-adaptive chosen message attacks. We propose a chaining mechanism that with slightly more computation, dynamically binds the Obtain Random Subset (ORS) generation with signing, hence, eliminating the offline advantage of adaptive chosen message adversaries. We apply our chaining mechanism to FORS and present DFORS whose security against adaptive chosen message attacks is equal to the non-adaptive security of FORS. In a nutshell, using SPHINCS[Formula: see text]-128s parameters, FORS provides 75-bit security and DFORS achieves 150-bit security with respect to adaptive chosen message attacks after signing one message. We note that our analysis does not affect the claimed security of SPHINCS[Formula: see text]. Nevertheless, this work provides a better understanding of FORS and other HORS variants, and furnishes a solution if new adaptive cryptanalytic techniques on SPHINCS[Formula: see text] emerge.


title: LESS is More: Code-Based Signatures Without Syndromes

text: Devising efficient and secure signature schemes based on coding theory is still considered a challenge by the cryptographic community. In this paper, we construct a signature scheme by exploring a new approach to the area. To do this, we design a zero-knowledge identification scheme, which we then render static via standard means (e.g. Fiat-Shamir). We show that practical instances of our protocol have the potential to outperform the state of the art on code-based signatures, achieving small data sizes with a low computational complexity.


title: A Tale of Three Signatures: Practical Attack of ECDSA with wNAF

text: Attacking ECDSA with wNAF implementation for the scalar multiplication first requires some side channel analysis to collect information, then lattice based methods to recover the secret key. In this paper, we reinvestigate the construction of the lattice used in one of these methods, the Extended Hidden Number Problem (EHNP). We find the secret key with only 3 signatures, thus reaching a known theoretical bound, whereas best previous methods required at least 4 signatures in practice. Given a specific leakage model, our attack is more efficient than previous attacks, and for most cases, has better probability of success. To obtain such results, we perform a detailed analysis of the parameters used in the attack and introduce a preprocessing method which reduces by a factor up to 7 the total time to recover the secret key for some parameters. We perform an error resilience analysis which has never been done before in the setup of EHNP. Our construction find the secret key with a small amount of erroneous traces, up to [Formula: see text] of false digits, and [Formula: see text] with a specific type of error.


title: MixColumns Coefficient Property and Security of the AES with A Secret S-Box

text: The MixColumns operation is an important component providing diffusion for the AES. The branch number of it ensures that any continuous four rounds of the AES have at least 25 active S-Boxes, which makes the AES secure against the differential and linear cryptanalysis. However, the choices of the coefficients of the MixColumns matrix may undermine the AES security against some novel-type attacks. A particular property of the AES MixColumns matrix coefficient has been noticed in recent papers that each row or column of the matrix has elements that sum to zero. Several attacks have been developed taking advantage of the coefficient property. In this paper we investigate further the influence of the specific coefficient property on the AES security. Our target, which is also one of the targets of the previous works, is a 5-round AES variant with a secret S-Box. We will show how we take advantage of the coefficient property to extract the secret key directly without any assistance of the S-Box information. Compared with the previous similar attacks, the present attacks here are the best in terms of the complexity under the chosen-plaintext scenario.


title: Cryptanalysis of FlexAEAD

text: This paper analyzes the internal keyed permutation of FlexAEAD which is a round-1 candidate of the NIST LightWeight Cryptography Competition. In our analysis, we report an iterated truncated differential leveraging on a particular property of the AES S-box that becomes useful due to the particular nature of the diffusion layer of the round function. The differential holds with a low probability of [Formula: see text] for one round which allows it to penetrate the same number of rounds as claimed by the designers, but with a much lower complexity. Moreover, it can be easily extended to a key-recovery attack at a little extra cost. We further report a Super-Sbox construction in the internal permutation, which is exploited using the Yoyo game to devise a 6-round deterministic distinguisher and a 7-round key recovery attack for the 128-bit internal permutation. Similar attacks can be mounted for the 64-bit and 256-bit variants. All these attacks outperform the existing results of the designers as well as other third-party results. The iterated truncated differentials can be tweaked to mount forgery attacks similar to the ones given by Eichlseder [Formula: see text] [Formula: see text] Success probabilities of all the reported distinguishing attacks are shown to be high. All practical attacks have been experimentally verified. To the best of our knowledge, this work reports the first key-recovery attack on the internal keyed permutation of FlexAEAD.


title: On Adaptive Attacks Against Jao-Urbanik’s Isogeny-Based Protocol

text: The k-SIDH protocol is a static-static isogeny-based key agreement protocol. At Mathcrypt 2018, Jao and Urbanik introduced a variant of this protocol which uses non-scalar automorphisms of special elliptic curves to improve its efficiency. In this paper, we provide a new adaptive attack on Jao-Urbanik’s protocol. The attack is a non-trivial adaptation of Galbraith-Petit-Shani-Ti’s attack on SIDH (Asiacrypt 2016) and its extension to k-SIDH by Dobson-Galbraith-LeGrow-Ti-Zobernig (IACR eprint 2019). Our attack provides a speedup compared to a naïve application of Dobson et al.’s attack to Jao-Urbanik’s scheme, exploiting its inherent structure. Estimating the security of k-SIDH and Jao-Urbanik’s variant with respect to these attacks, k-SIDH provides better efficiency.


title: Impossible Differential Cryptanalysis of Reduced-Round Tweakable TWINE

text: Tweakable TWINE (T-TWINE) is a new lightweight tweakable block cipher family proposed by Sakamoto et al. at IWSEC 2019. T-TWINE is the first Tweakable Block Cipher (TBC) that is built on Generalized Feistel Structure (GFS). It is based on the TWINE block cipher in addition to a simple tweak scheduling based on SKINNY’s tweakey schedule. Similar to TWINE, it has two versions, namely, T-TWINE-80 and T-TWINE-128, both have a block length of 64 bits and employ keys of length 80 and 128 bits, respectively. In this paper, we present impossible differential attacks against reduced-round versions of T-TWINE-80 and T-TWINE-128. First, we present an 18-round impossible differential distinguisher against T-TWINE. Then, using this distinguisher, we attack 25 and 27 rounds of T-TWINE-80 and T-TWINE-128, respectively.


title: A New Encoding Algorithm for a Multidimensional Version of the Montgomery Ladder

text: We propose a new encoding algorithm for the simultaneous differential multidimensional scalar point multiplication algorithm d-MUL. Previous encoding algorithms are known to have major drawbacks in their efficient and secure implementation. Some of these drawbacks have been avoided in a recent paper in 2018 at a cost of losing the general functionality of the point multiplication algorithm. In this paper, we address these issues. Our new encoding algorithm takes the binary representations of scalars as input, and constructs a compact binary sequence and a permutation, which explicitly determines a regular sequence of group operations to be performed in d-MUL. Our algorithm simply slides windows of size two over the scalars and it is very efficient. As a result, while preserving the full generality of d-MUL, we successfully eliminate the recursive integer matrix computations in the originally proposed encoding algorithms. We also expect that our new encoding algorithm will make it easier to implement d-MUL in constant time. Our results can be seen as the efficient and full generalization of the one dimensional Montgomery ladder to arbitrary dimension.


title: QA-NIZK Arguments of Same Opening for Bilateral Commitments

text: Zero-knowledge proofs of satisfiability of linear equations over a group are often used as a building block of more complex protocols. In particular, in an asymmetric bilinear group we often have two commitments in different sides of the pairing, and we want to prove that they open to the same value. This problem was tackled by González, Hevia and Ràfols (ASIACRYPT 2015), who presented an aggregated proof, in the QA-NIZK setting, consisting of only four group elements. In this work, we present a more efficient proof, which is based on the same assumptions and consists of three group elements. We argue that our construction is optimal in terms of proof size.


title: New Ideas to Build Noise-Free Homomorphic Cryptosystems

text: We design a very simple private-key encryption scheme whose decryption function is a rational function. This scheme is not born naturally homomorphic. To get homomorphic properties, a nonlinear additive homomorphic operator is specifically developed. The security analysis is based on symmetry considerations and we prove some formal results under the factoring assumption. In particular, we prove IND-CPA security in the generic ring model. Even if our security proof is not complete, we think that it is convincing and that the technical tools considered in this paper are interesting by themselves. Moreover, the factoring assumption is just needed to ensure that solving nonlinear equations or finding non-null polynomials with many roots is difficult. Consequently, the ideas behind our construction could be re-used in rings satisfying these properties. As motivating perspectives, we then propose to develop a simple multiplicative operator. To achieve this, randomness is added in our construction giving hope to remove the factoring assumption in order to get a pure multivariate encryption scheme.


title: UC Updatable Databases and Applications

text: We define an ideal functionality [Formula: see text] and a construction [Formula: see text] for an updatable database ([Formula: see text]). [Formula: see text] is a two-party protocol between an updater and a reader. The updater sets the database and updates it at any time throughout the protocol execution. The reader computes zero-knowledge (ZK) proofs of knowledge of database entries. These proofs prove that a value is stored at a certain position in the database, without revealing the position or the value. (Non-)updatable databases are implicitly used as building block in priced oblivious transfer, privacy-preserving billing and other privacy-preserving protocols. Typically, in those protocols the updater signs each database entry, and the reader proves knowledge of a signature on a database entry. Updating the database requires a revocation mechanism to revoke signatures on outdated database entries. Our construction [Formula: see text] uses a non-hiding vector commitment (NHVC) scheme. The updater maps the database to a vector and commits to the database. This commitment can be updated efficiently at any time without needing a revocation mechanism. ZK proofs for reading a database entry have communication and amortized computation cost independent of the database size. Therefore, [Formula: see text] is suitable for large databases. We implement [Formula: see text] and our timings show that it is practical. In existing privacy-preserving protocols, a ZK proof of a database entry is intertwined with other tasks, e.g., proving further statements about the value read from the database or the position where it is stored. [Formula: see text] allows us to improve modularity in protocol design by separating those tasks. We show how to use [Formula: see text] as building block of a hybrid protocol along with other functionalities.


title: Signatures of Knowledge for Boolean Circuits Under Standard Assumptions

text: This paper constructs unbounded simulation sound proofs for boolean circuit satisfiability under standard assumptions with proof size [Formula: see text] bilinear group elements, where d is the depth and n is the input size of the circuit. Our technical contribution is to add unbounded simulation soundness to a recent NIZK of González and Ràfols (ASIACRYPT’19) with very small overhead. Our new scheme can be used to construct the most efficient Signature-of-Knowledge based on standard assumptions that also can be composed universally with other cryptographic protocols/primitives.


title: Round Optimal Secure Multisignature Schemes from Lattice with Public Key Aggregation and Signature Compression

text: This paper presents the first construction for an efficient multisignature (MS) in the lattice setting, achieving signature compression and public key aggregation simultaneously with single round signature generation. The multisignature size in our construction is the same as that of a single signature. The verification of a multisignature can be performed with the aggregated public key and the verifier gets convinced that the message has been signed by all the signers. More positively, our aggregated public key size is also the same as that of a single signer. Additionally, we extend our multisignature to an accountable subgroup multisignature (ASM) that permits any subset of potential signers to sign a common message with the property that the signature reveals the identities of the signers to any verifier. Our ASM scheme enjoys the same efficiency as that of our MS scheme without incurring any loss in the security reduction. We design our schemes in the plain public key model where there is no need to verify individual public keys. Our constructions are built in the standard lattice and are proven to be secure under the hardness of the short integer solution (SIS) problem in the random oracle model.


title: Sieve, Enumerate, Slice, and Lift:: Hybrid Lattice Algorithms for SVP via CVPP

text: Motivated by recent results on solving large batches of closest vector problem (CVP) instances, we study how these techniques can be combined with lattice enumeration to obtain faster methods for solving the shortest vector problem (SVP) on high-dimensional lattices. Theoretically, under common heuristic assumptions we show how to solve SVP in dimension d with a cost proportional to running a sieve in dimension [Formula: see text], resulting in a [Formula: see text] speedup and memory reduction compared to running a full sieve. Combined with techniques from [Ducas, Eurocrypt 2018] we can asymptotically get a total of [Formula: see text] dimensions for free for solving SVP. Practically, the main obstacles for observing a speedup in moderate dimensions appear to be that the leading constant in the [Formula: see text] term is rather small; that the overhead of the (batched) slicer may be large; and that competitive enumeration algorithms heavily rely on aggressive pruning techniques, which appear to be incompatible with our algorithms. These obstacles prevented this asymptotic speedup (compared to full sieving) from being observed in our experiments. However, it could be expected to become visible once optimized CVPP techniques are used in higher dimensional experiments.


title: BBB Secure Nonce Based MAC Using Public Permutations

text: In the recent trend of CAESAR competition and NIST light-weight competition, cryptographic community have witnessed the submissions of several cryptographic schemes that are build on public random permutations. Recently, in CRYPTO 2019, Chen et al. have initiated an interesting research direction in designing beyond birthday bound PRFs from public random permutations and they proposed two instances of such PRFs. In this work, we extend this research direction by proposing a nonce-based MAC build from public random permutations. We show that our proposed MAC achieves 2n/3 bit security (with respect to the state size of the permutation) and the bound is essentially tight. Moreover, the security of the MAC degrades gracefully with the repetition of the nonce.


title: New Results on the SymSum Distinguisher on Round-Reduced SHA3

text: In ToSC 2017 Saha et al. demonstrated an interesting property of SHA3 based on higher-order vectorial derivatives which led to self-symmetry based distinguishers referred to as SymSum and bettered the complexity w.r.t the well-studied ZeroSum distinguisher by a factor of 4. This work attempts to take a fresh look at this distinguisher in the light of the linearization technique developed by Guo et al. in Asiacrypt 2016. It is observed that the efficiency of SymSum against ZeroSum drops from 4 to 2 for any number of rounds linearized. This is supported by theoretical proofs. SymSum augmented with linearization can penetrate up to two more rounds as against the classical version. In addition to that, one more round is extended by inversion technique on the final hash values. The combined approach leads to distinguishers up to 9 rounds of SHA3 variants with a complexity of only [Formula: see text] which is better than the equivalent ZeroSum distinguisher by the factor of 2. To the best of our knowledge this is the best distinguisher available on this many rounds of SHA3.


title: Reframing Time Spent Alone: Reappraisal Buffers the Emotional Effects of Isolation

text: BACKGROUND: Loneliness, a transdiagnostic feature of psychopathology, is an experience of perceived isolation only weakly linked to the amount of time spent alone. Although traditional loneliness interventions aim to increase social contact, targeting maladaptive cognition about time alone may be an effective way to reduce loneliness. We investigated whether a brief reappraisal manipulation enables individuals to experience their time alone more positively. We also tested the impact of trait loneliness, compulsive social media use, and trait reappraisal on experiences of time alone. METHODS: College students and community members (N = 220) were randomly assigned to read a passage about the benefits of solitude (n = 74), the true prevalence of loneliness (n = 72), or a control topic (n = 74). Participants then sat alone for 10 min. RESULTS: Across conditions, positive and negative mood significantly decreased after sitting alone. Participants who read about the benefits of solitude experienced a smaller reduction in positive mood than those in the control condition. Participants who less frequently used reappraisal in their everyday lives benefited most from the manipulation. CONCLUSIONS: Our results provide preliminary evidence that reappraising time alone as solitude may boost resilience to the decrements in positive mood associated with time alone. Limitations, clinical implications, and directions for future research are discussed.


Carpe Diem: An opportunity for the ABR to support its trainees with family-friendly policies


title: Parental knowledge and practice on antibiotic use for upper respiratory tract infections in children, in Aksum town health institutions, Northern Ethiopia: a cross-sectional study

text: INTRODUCTION: worldwide, antibiotics are the most commonly prescribed and abused drugs for upper respiratory tract infections. Acute upper respiratory infections are common in children who attend childcare and preventing transmission of disease in health setting depends on actions by parents and staff. Therefore the objective of this study is to assess the parental knowledge and practice on antibiotic use for upper respiratory tract infections in children, in Aksum town health institutions, northern Ethiopia, 2018. METHODS: a facility-based cross-sectional study design was adopted involving 384 parents of children visited governmental health facilities in Aksum town from February to March, 2018. Respondents were selected based on the proportion of nurses in the health facilities. SPSS version 22 was applied for data entry and analysis. RESULTS: the total number of questionnaires was 384 resulting in a 100% response rate. Almost half of the parents had poor knowledge of the use of antibiotics in children for URTIs 183(47.7%), followed by 156(40.6%) moderate knowledge and 45(11.7%) good knowledge. Practices regarding antibiotic use in children with URTI varied. Only 12.8% of the parents did not always follow the doctors´ advice regarding antibiotic use. In this study has reported many areas in which parental awareness on antibiotic use for acute URTI is considered inadequate, consequently inappropriate knowledge and practices. CONCLUSION: nearly half of the parents attending the physicians for their children with URTI expected to get antibiotics.


title: Clinical characteristics and outcomes of patients with H1N1 influenza pneumonia admitted at a tertiary care hospital in Karachi, Pakistan

text: INTRODUCTION: Influenza viruses specifically, A and B mainly contribute to seasonal outbreaks that occur globally. However, due to limited diagnostics for influenza there is little data regarding clinical outcomes of patients with H1N1 pneumonia in our region. Our objective was to determine the clinical characteristics and outcomes of patients hospitalized with H1N1 pneumonia at a tertiary care facility in Karachi, Pakistan. METHODS: A retrospective study of adult patients admitted with influenza pneumonia from November 2017 to February 2018 at a tertiary care hospital in Karachi, Pakistan. Patient characteristics were compared between influenza A H1N1 and other types of influenza using multivariable logistic regression analysis and subgroup analysis for factors associated with mortality in H1N1 Pneumonia was performed. RESULTS: Out of 497 adult patients with community acquired pneumonia (CAP), 172 fulfilled the criteria for Influenza like illness (ILI). 88 patients had PCR confirmed Influenza pneumonia of whom n = 57 (65%) had Influenza A H1N1. The mean age of patients 53.5 years (SD: 17.3) and 60% were male. The overall mortality from Influenza in this study was 15.9% (n = 14); out of these 11 (78.5%) had Influenza A H1N1. Multivariable analysis showed that the increase in length of hospital admission was significantly associated with H1N1 Influenza A infection (OR: 1.47 CI: 1.2–1.8). Factors associated with mortality showed that presence of ARDS, Septic shock and multi-organ failure was highly significantly associated with death (p-value < 0.001) along with deranged liver function tests (p-value 0.01) and presence of nosocomial infection (p-value 0.027). CONCLUSION: Influenza A H1N1 is associated with greater length of stay compared with infection due to other types of Influenza and mortality in H1N1 Pneumonia was found to be associated with presence of nosocomial infection among several other factors which may have implications given higher rates in a low-middle income country.


title: Update on invasive fungal infections in the Middle Eastern and North African region

text: In the recent years, the epidemiology of invasive fungal infections (IFIs) has changed worldwide. This is remarkably noticed with the significant increase in high-risk populations. Although surveillance of such infections is essential, data in the Middle Eastern and North African (MENA) region remain scarce. In this paper, we reviewed the existing data on the epidemiology of different IFIs in the MENA region. Epidemiological surveillance is crucial to guide optimal healthcare practices. This study can help to guide appropriate interventions and to implement antimicrobial stewardship and infection prevention and control programs in countries.


Re: Equity in health care: is it achievable?


An Historic Event: The First Female Officer of the AATS


docid: dbvdxbaz

title: Arbeiten nach Corona: Ist Homeoffice das Modell der Zukunft?


‘Shining a light on the gaps for learning’


title: Elephants in the neighborhood: patterns of crop-raiding by Asian elephants within a fragmented landscape of Eastern India

text: Loss of forest cover, rise in human populations and fragmentation of habitats leads to decline in biodiversity and extinction of large mammals globally. Elephants, being the largest of terrestrial mammals, symbolize global conservation programs and co-occur with humans within multiple-use landscapes of Asia and Africa. Within such shared landscapes, poaching, habitat loss and extent of human–elephant conflicts (HEC) affect survival and conservation of elephants. HEC are severe in South Asia with increasing attacks on humans, crop depredation and property damage. Such incidents reduce societal tolerance towards elephants and increase the risk of retaliation by local communities. We analyzed a 2-year dataset on crop depredation by Asian elephants (N = 380) events in North Bengal (eastern India). We also explored the effect of landscape, anthropogenic factors (area of forest, agriculture, distance to protected area, area of human settlements, riverine patches and human density) on the spatial occurrence of such incidents.Crop depredation showed a distinct nocturnal pattern (22.00–06:00) and majority of the incidents were recorded in the monsoon and post-monsoon seasons. Results of our spatial analysis suggest that crop depredation increased with an increase in the area of forest patches, agriculture, presence of riverine patches and human density. Probability of crop depredation further increased with decreasing distance from protected areas. Villages within 1.5 km of a forest patch were most affected. Crop raiding incidents suggest a deviation from the “high-risk high-gain male biased” foraging behavior and involved proportionately more mixed groups (57%) than lone bulls (43%). Demographic data suggest that mixed groups comprised an average of 23 individuals with adult and sub adult females, bulls and calves. Crop depredation and fatal elephant attacks on humans were spatially clustered with eastern, central and western parts of North Bengal identified as hotspots of HEC. Our results will help to prioritize mitigation measures such as prohibition of alcohol production within villages, improving condition of riverine patches, changing crop composition, fencing agriculture fields, implement early warning systems around protected areas and training local people on how to prevent conflicts.


title: Organisational changes and challenges for inflammatory bowel disease services in the UK during the COVID-19 pandemic

text: OBJECTIVE: To determine the challenges in diagnosis, monitoring, support provision in the management of inflammatory bowel disease (IBD) patients and explore the adaptations of IBD services. METHODS: Internet-based survey by invitation of IBD services across the UK from 8 to 14 April 2020. RESULTS: Respondents from 125 IBD services completed the survey. The number of whole-time equivalent gastroenterologists and IBD nurses providing elective outpatient care decreased significantly between baseline (median 4, IQR 4–7.5 and median 3, IQR 2–4) to the point of survey (median 2, IQR 1–4.8 and median 2, IQR 1–3) in the 6-week period following the onset of the COVID-19 pandemic (p<0.001 for both comparisons). Almost all (94%; 112/119) services reported an increase in IBD helpline activity. Face-to-face clinics were substituted for telephone consultation by 86% and video consultation by 11% of services. A variation in the provision of laboratory faecal calprotectin testing was noted with 27% of services reporting no access to faecal calprotectin, and a further 32% reduced access. There was also significant curtailment of IBD-specific endoscopy and elective surgery. CONCLUSIONS: IBD services in the UK have implemented several adaptive strategies in order to continue to provide safe and high-quality care for patients. National Health Service organisations will need to consider the impact of these changes in current service delivery models and staffing levels when planning exit strategies for post-pandemic IBD care. Careful planning to manage the increased workload and to maintain IBD services is essential to ensure patient safety.


title: Major Neurologic Adverse Drug Reactions, Potential Drug–Drug Interactions and Pharmacokinetic Aspects of Drugs Used in COVID-19 Patients with Stroke: A Narrative Review

text: Stroke has been considered as one of the underlying diseases that increases the probability of severe infection and mortality. Meanwhile, there are ongoing reports of stroke subsequent to COVID-19 infection. In this narrative paper, we reviewed major neurologic adverse drug reactions (ADRs) and pharmacokinetics of drugs which are routinely used for COVID-19 infection and their potential drug–drug interactions (PDDIs) with common drugs used for the treatment of stroke. It is highly recommended to monitor patients on chloroquine (CQ), hydroxychloroquine (HCQ), antiviral drugs, and/or corticosteroids about initiation or progression of cardiac arrhythmias, delirium, seizure, myopathy, and/or neuropathy. In addition, PDDIs of anti-COVID-19 drugs with tissue plasminogen activator (tPA), anticoagulants, antiaggregants, statins, antihypertensive agents, and iodine-contrast agents should be considered. The most dangerous PDDIs were interaction of lopinavir/ritonavir or atazanavir with clopidogrel, prasugrel, and new oral anticoagulants (NOACs).


title: The Digital/Technological Connection with COVID-19: An Unprecedented Challenge in University Teaching

text: In March 2020, due to the COVID19 virus that is spreading throughout the world, Spain lives an anomalous situation concerning the normal course of basic, secondary, and higher education. On March 2nd, 2020, the state authorities announced the end of face-to-face teaching in schools and universities. Then nothing suggests that a week later all classes went canceled. However, this unusual fact does not end with the teaching–learning process, forcing all educational institutions, as well as the teaching community to reinvent themselves to continue with online teaching. This is the only possible way to do it. The authors must face a specific scenario to readapt the learning-teaching process. The way of teaching of the University of La Rioja is online but it will have to face some problems. The Polytechnic University of Madrid classroom education is compulsory, so there is a big gap between what teachers are used to and what they will have to face. This crisis makes us reflect on new technologies as necessary and essential tools in the new teaching–learning scenario presented. But it must also go beyond considering only the tools available to do it. Reflection must also consider the members of this process and those who eventually make possible education to take place.


docid: sltofaox

title: Obesity, Cardiovascular Disease, and Influenza: How Are They Connected?

text: PURPOSE OF REVIEW: To better understand the impact of obesity and cardiovascular diseases on influenza A infection. RECENT FINDINGS: This infection could have detrimental outcomes in obese patients with cardiovascular diseases, such as an increased risk, length of hospitalization, disease severity, morbidity, and mortality. Nevertheless, there also might be some cardioprotective benefits associated with influenza vaccination, such as a reduced mortality, hospitalization, and acute coronary syndromes, in patients with coronary heart disease and/or heart failure. SUMMARY: Obesity negatively impacts immune function and host defense. Recent studies report obesity to be an independent risk factor for increased morbidity and mortality following infection. Obese patients might need special considerations in the treatment; however, there is not enough evidence to fully comprehend the mechanisms behind the reduced immunocompetence when influenza A infection occurs. Future studies should focus on special consideration treatments when the patients have not been vaccinated and have cardiovascular diseases.


title: Evaluating peak inspiratory pressures and tidal volume in premature neonates on NAVA ventilation

text: Neurally adjusted ventilatory assist (NAVA) ventilation allows patients to determine their peak inspiratory pressure and tidal volume on a breath-by-breath basis. Apprehension exists about premature neonates’ ability to self-regulate breath size. This study describes peak pressure and tidal volume distribution of neonates on NAVA and non-invasive NAVA. This is a retrospective study of stored ventilator data with exploratory analysis. Summary statistics were calculated. Distributional assessment of peak pressure and tidal volume were evaluated, overall and per NAVA level. Over 1 million breaths were evaluated from 56 subjects. Mean peak pressure was 16.4 ± 6.4 in the NAVA group, and 15.8 ± 6.4 in the NIV-NAVA group (t test, p < 0.001). Mean tidal volume was 3.5 ± 2.7 ml/kg. Conclusion:In neonates on NAVA, most pressures and volumes were within or lower than recommended ranges with pressure-limited or volume-guarantee ventilation.