Reviewerly/exHarmony huggingface dataset:
The Causal Impact of Task Difficulty and Instructional Style on Cognitive, Motivational, and Affective Outcomes Among Elementary School Children's Mathematics Education

This study investigated the effects of teachers' instruction style and the difficulty of math exercises on cognitive, motivational and affective outcomes.A quasi-experimental vignette study was conducted on 479 children in grade 4. A math teacher depicted in a comic strip gave either autonomy-supportive or controlling instructions to children.After reading the instructions in the comic strip, participating children solved either easy-medium or difficult math exercises for 7.5 minutes.Results indicated significant effects of difficulty and style, with the largest effects for difficulty.Solving difficult math exercises resulted in less self-confidence, more negative self-talk, less autonomy and competence satisfaction, less interest/enjoyment and more irritation.However, when the difficult exercises were introduced in an autonomy-supportive manner, the negative impact was less strong.These results point towards the undermining effect of teachers providing children with choice and rationale.Mediation through psychological need experiences was examined and revealed that the effects of difficulty were mediated through lower levels of self-confidence and more negative self-talk, which influenced the autonomy and competence satisfaction, and subsequently the outcome variables.Moderation analyses showed that the negative impact of difficulty on competence satisfaction was stronger for children with better math skills.Limitations and implications for practice are discussed.
Middle and High School Students' Learning Trajectories in Online Mathematics Courses

Parallel to K-12 online education's growth (Watson, Pape, Murin, Gemin, & Vashaw, 2015), online mathematics learning has continued to grow.For instance, annual reports on K-12 online learning in the state of Michigan (Freidhoff, 2015-2018;Freidhoff, DeBruler, & Kennedy, 2014) have indicated that while the total number of virtual course enrollments grew continually between the 2010-11 and 2016-17 academic years, mathematics courses consistently accounted for around one-fifth of all such enrollments.Despite this quantitative expansion, student learning outcomes appear to be disappointing given that for mathematics courses, the lowest pass rates among high-enrollment courses or the lowest among all course types by the same reports were documented.This issue is not limited to a particular state: mathematics exhibited the lowest passing rates among courses in an online credit recovery (CR) program in Montana (Stevens & Frazelle, 2016).Nevertheless, online mathematics learning is still underexplored.According to an effectiveness comparison research, online algebra courses were effective enough to become an alternative to the face-to-face format (Cavanaugh, Gillan, Bosnick, & Hess, 2008;Hughes, McLeod, Brown, Maeda, & Choi, 2007).Yet, another study (Heppen et al., 2016) revealed more negative attitudes toward learning mathematics and less likelihood of success when the summer algebra CR program was delivered in an online format rather than face-to-face.
Test Administration Mode Effects of Hands-On-Based Test Versus Paper-and-Pencil-Based Test

Multiple modes of assessments can be utilized to assess student applying scientific and engineering practices, in the context of disciplinary core ideas and crosscutting concepts.However, the unique features of different assessments may lead to test mode effects.Based on a measurement instrument involving two test administration modes (i.e., a paper-andpencil test and a hands-on based test), for measuring student proficiency in science, as expected in the NGSS, using Many-Facet partial credit Rasch model, this study aims to investigate the test administration mode effects, as well as the interaction effects between test mode and NGSS trait, gender and trait/mode.The results indicated that there are significant test administration mode ef fects and interaction effects between mode and trait, gender and trait.
Democratizing Reading Evidence in a Computer-Adaptive Learning Environment: The Quest for Improving Achievement Inequalities

An investigation of the effects of a computer-adaptive reading program on students' reading achievement was conducted among N=22,962 third grade students.Results indicated achievement scores (over the course of a year) improved significantly for students of all academic levels.Students in the lowest academic level (63% economically disadvantaged) and in the greatest need of intensive reading instruction evidenced the greatest gains and exceeded normed expectations.However, on average, the achievement gap remains evident among this population.The study provided democratized evidence for teacher professional development and preparation programs regarding the benefits of computer-adaptive environments to narrow achievement inequalities and increasing opportunities for all but in particular for students at the greatest risk of reading failure.
The Effects of a Positive College Classroom Motivational Environment on Procrastination and Achievement

This study examined the degree to which the classroom motivational environment predicts college students' academic procrastination after controlling for students' level of conscientiousness, a strong predictor of procrastination.We also investigated whether academic procrastination mediates the effect of the classroom motivational environment on course grades.Participants were 166 students enrolled in two four-year institutions in the southern U.S.Hierarchical regression analyses indicated that students who reported the presentation of course material in their classes as more interesting (course situational interest) also tended to report procrastinating less.Furthermore, mediation analyses indicated that the effects on course grades of instructor support, classroom mutual respect, and course situational interest were better explained as mediated by academic procrastination.
Fast Fourier Orthogonalization over NTRU Lattices

FALCON is an efficient and compact lattice-based signature scheme. It is also one of the round 3 finalists in the NIST PQC standardization process. The core of FALCON is a trapdoor sampling algorithm, which has found numerous applications in lattice-based cryptography. It needs the fast Fourier orthogonalization algorithm to build an LDL tree. But the LDL tree needs much RAM to store, which may limit the application of FALCON on memory-constrained devices. On the other hand, if building the LDL tree dynamically, the signature cost will almost double. In this work, we discover the LDL tree of FALCON has some symmetric structure, and prove why this phenomenon occurs. With this property, we can reduce the generation time and storage of the LDL tree by almost half without affecting the efficiency of FALCON. We verify the correctness and validity of our way in the implementations of FALCON. In addition, the result applies to the cyclotomic field $$\mathbb {Q}[x]/(x^n-x^{n/2}+1)$$ with $$n = 3 \cdot 2 ^\kappa $$ . But we can not apply it to NTRU module lattices so far.
Human Rights and COVID-19 Pandemic Challenge: What is the ECHR Approach?

The European Court of Human Rights has already addressed certain issues caused by or connected to COVID-19 pandemic situation and numerous restrictions introduced by states to counteract virus propagation. It is necessary to mention that there are many applications pending judgments or declared inadmissible. Herewith we are going to comment on recently decided cases on the topic of COVID-19 health crisis and human rights protection. Meanwhile, there are more cases expected to be decided as many applications are pending examination by the European Court in Strasbourg.
Solving the problems of the preliminary and main stages of the first All-Russian Olympiad in artificial intelligence for students of 8th-11th grades

The article discusses approaches to developing a system of Olympiad tasks, features of the event, as well as solving tasks of two stages of the first All-Russian Olympiad in artificial intelligence for 8th—11th grades, held in October-November 2021 by the Ministry of Education of the Russian Federation as part of the Federal Project "Artificial Intelligence" of the National Program "Digital Economy of the Russian Federation". The topics of the Olympiad tasks were Natural Language Processing and Computer Vision. At all stages of the Olympiad, the Python programming language with specialized libraries for solving artificial intelligence problems was used. The material with the analysis of tasks will be useful when teaching informatics at an advanced level at school, including when preparing schoolchildren to participate in the next Olympiad in artificial intelligence in an individual format.
CHALLENGES OF ESP CLASSES IN WARTIME CONDITIONS

The realities that Ukraine faces today, namely, the full-scale war with the imposition of martial law on the territory of the whole country, led to a number of discussions arising around the effective but safe conducting of the education process at universities and other educational establishments and concerned the organization and proper management of the students` learning process. Despite the previous experience of distance and blended learning modes implemented during the Covid 19 period, it should be noted that some aspects of the organization of the educational process require reconsidering, taking into account the new experience of studying in wartime and the need to regard the emotional tension of students, especially those caught up in the temporarily occupied territories. A survey was conducted among the students of the National Technical University of Ukraine "I. Sikorsky Kyiv Polytechnic Institute" with the aim to reveal and analyze the level of technical students` adaptation to learning a foreign language in extreme conditions and the relevance of tools and methods the teachers have been applying on their ESL lessons. Among the outcomes, it`s worth mentioning the non-fading motivation of students to learn foreign language, increased cross-cultural communication, and mostly high-level accommodation to distance learning mode. It was interesting to find out that students expressed unwillingness to discuss war and relevant news in the ESL lessons and are having issues with managing their academic study time. All the results obtained provide the ground for further reconsidering of the curricula to better meet the students' educational needs. Keywords: education in wartime; ESL classes; distance learning; open online courses; synchronous learning; asynchronous learning; teacher’s empathy; cross-cultural communication.
Formation of communication and cooperation skills in junior schoolchildren while creating animation at math lessons

The article discusses formation of communication and cooperation skills in a junior schoolchild as components of his functional literacy. The ways of working with animation at math lessons when students are combined into small groups are revealed. Forms of organization of educational activity of junior schoolchildren are considered; a description of the tasks most suitable for animation is proposed; the methodology for organizing a lesson is given with examples of tasks, the content of which allows organizing communication between students; examples of tasks solved with the help of animation created by children are given; methods for diagnosing the level of communication of students are described. An analysis of the results of several years of work on the formation of communication and cooperation skills through the creation of animation is presented. The material can be useful to school teachers, students of pedagogical universities and methodologists.
The possibilities of online programming environments for teaching Python at school

As part of teaching programming in a school informatics course, an important issue is the possibility of using various online programming environments that can be used when teaching the Python programming language. Online programming environments are specialized web services that provide the ability to write and run programs in the appropriate programming language.The article provides an analysis of the content of textbooks and manuals on the subject of "Informatics" in the context of the authors' description of the possibility of using online environments when teaching the Python language. The possibilities of online programming environments for working with the interactive and program mode of Python are considered. The functionality of four services is described: Python Shell, Online Python, JDoodle Online Python 3 IDE, Trinket Python. A demonstration of their work is given on examples of programs in the Python language. The advantages and disadvantages of these online environments are analyzed as part of the study of the "Programming" section in the school informatics course. A comparative description of the considered online programming environments is given. The possibilities of their use as a means of electronic support during lessons in the framework of studying the Python programming language are described.
Virtual robotics based on VEXcode VR and Open Roberta Lab robot simulators

The article substantiates the choice of VEXcode VR and Open Roberta Lab robot simulators for teaching robotics at school. A description of some problems solved by virtual robots in these environments is given, and possible algorithms for solving problems are presented. Methods of teaching presented in the form of mini tasks will be useful for teachers teaching robotics at school to conduct classes using several virtual robots in VEXcode VR and Open Roberta Lab robot simulators from 2nd to 7th grade. Acquaintance with the elementary concepts of the theory of automatic control on the basis of the methodology presented in the article will contribute to the formation of students' skills in the practical application of programming, the ability to apply information technologies to solve problems of control of robotic systems.
The elective course "Application of methods of the modeling of systems while working with text information"

The article discusses the concepts of linear and spatial text, describes the advantages and disadvantages of the latter. The elective course aimed at developing students' skills in processing text information using various approaches to modeling of systems by using modeling methods, including the use of graphic languages GNOM, MOLNIYA, GRAF, DRAKON, the use of the American standard for modeling production processes IDEF-0 and DFD Data Flow Diagrams is described in the article. The structure and content line of the course are described. Examples of students' work performed as part of the study of each of the sections of the course "Application of methods of the modeling of systems while working with text information" are given (the target audience of the course is students of the 10th grade of a comprehensive school who study informatics at a basic level). Based on the experience of implementing the described course, a number of conclusions have been formulated that characterize the results achieved.
Board games for the study of flow charts

The article presents the description of method of the study of flow charts using board games.The playing field in such a game is a flow chart. During the game, students roll the dice and move the chip around the playing field, depending on the number that has fallen. But at the same time, they must comply with the rules for the execution of the algorithmic structures included in the flow chart.The purpose of the proposed teaching method is to intensify the learning of educational material. This is especially important for informatics with its extremely fast rates of development.For the game, the class is divided into teams of 2–4 people. All teams play at the same time. During the game, each participant repeatedly goes through all the studied algorithmic constructions. Thus, one hundred percent coverage of students and repeated repetition of the material is achieved.Due to this, it becomes possible to advance far enough in the study of algorithmic constructions. In addition to traditional forks and two types of loops (with a precondition and with a given number of repetitions), input commands, subprograms and parameters are studied.The methodic were prepared as part of the Perm version of the propaedeutic course of informatics (team of authors: M. A. Plaksin, N. G. Ivanova, O. L. Rusakova; the working title of the course is "TRIZformatika"). But the technique can be applied in any other course. The article provides a description and playing fields of four board games for mastering the mechanism of flow charts, and also gives a number of methodological comments.
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment

Grammatical Error Correction (GEC) systems perform a sequence-to-sequence task, where an input word sequence containing grammatical errors, is corrected for these errors by the GEC system to output a grammatically correct word sequence. With the advent of deep learning methods, automated GEC systems have become increasingly popular. For example, GEC systems are often used on speech transcriptions of English learners as a form of assessment and feedback - these powerful GEC systems can be used to automatically measure an aspect of a candidate's fluency. The count of \textit{edits} from a candidate's input sentence (or essay) to a GEC system's grammatically corrected output sentence is indicative of a candidate's language ability, where fewer edits suggest better fluency. The count of edits can thus be viewed as a \textit{fluency score} with zero implying perfect fluency. However, although deep learning based GEC systems are extremely powerful and accurate, they are susceptible to adversarial attacks: an adversary can introduce a small, specific change at the input of a system that causes a large, undesired change at the output. When considering the application of GEC systems to automated language assessment, the aim of an adversary could be to cheat by making a small change to a grammatically incorrect input sentence that conceals the errors from a GEC system, such that no edits are found and the candidate is unjustly awarded a perfect fluency score. This work examines a simple universal substitution adversarial attack that non-native speakers of English could realistically employ to deceive GEC systems used for assessment.
Engineering random spin models with atoms in a high-finesse cavity

All-to-all interacting, disordered quantum many-body models have a wide range of applications across disciplines, from spin glasses in condensed-matter physics, over holographic duality in high-energy physics, to annealing algorithms in quantum computing. Typically, these models are abstractions that do not find unambiguous physical realisations in nature. Here, we realise an all-to-all interacting, disordered spin system by subjecting an atomic cloud in a cavity to a controllable light shift. Adjusting the detuning between atom resonance and cavity mode, we can tune between disordered versions of a central-mode model and a Lipkin-Meshkov-Glick model. By spectroscopically probing the low-energy excitations of the system, we explore the competition of interactions with disorder across a broad parameter range. We show how disorder in the central-mode model breaks the strong collective coupling, making the dark state manifold cross over to a random distribution of weakly-mixed light-matter, "grey", states. In the Lipkin-Meshkov-Glick model the ferromagnetic finite-size ground state evolves towards a paramagnet as disorder is increased. In that regime, semi-localised eigenstates emerge, as we observe by extracting bounds on the participation ratio. These results present significant steps towards freely programmable cavity-mediated interactions for the design of arbitrary spin Hamiltonians.
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability

Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistinguishable input perturbations may lead to different XAI results. Thus, it is vital to assess how robust DL interpretability is, given an XAI method. In this paper, we identify several challenges that the state-of-the-art is unable to cope with collectively: i) existing metrics are not comprehensive; ii) XAI techniques are highly heterogeneous; iii) misinterpretations are normally rare events. To tackle these challenges, we introduce two black-box evaluation methods, concerning the worst-case interpretation discrepancy and a probabilistic notion of how robust in general, respectively. Genetic Algorithm (GA) with bespoke fitness function is used to solve constrained optimisation for efficient worst-case evaluation. Subset Simulation (SS), dedicated to estimate rare event probabilities, is used for evaluating overall robustness. Experiments show that the accuracy, sensitivity, and efficiency of our methods outperform the state-of-the-arts. Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness.
Kernel Memory Networks: A Unifying Framework for Memory Modeling

We consider the problem of training a neural network to store a set of patterns with maximal noise robustness. A solution, in terms of optimal weights and state update rules, is derived by training each individual neuron to perform either kernel classification or interpolation with a minimum weight norm. By applying this method to feed-forward and recurrent networks, we derive optimal models, termed kernel memory networks, that include, as special cases, many of the hetero- and auto-associative memory models that have been proposed over the past years, such as modern Hopfield networks and Kanerva's sparse distributed memory. We modify Kanerva's model and demonstrate a simple way to design a kernel memory network that can store an exponential number of continuous-valued patterns with a finite basin of attraction. The framework of kernel memory networks offers a simple and intuitive way to understand the storage capacity of previous memory models, and allows for new biological interpretations in terms of dendritic non-linearities and synaptic cross-talk.
Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing

Multi-task learning of dense prediction tasks, by sharing both the encoder and decoder, as opposed to sharing only the encoder, provides an attractive front to increase both accuracy and computational efficiency. When the tasks are similar, sharing the decoder serves as an additional inductive bias providing more room for tasks to share complementary information among themselves. However, increased sharing exposes more parameters to task interference which likely hinders both generalization and robustness. Effective ways to curb this interference while exploiting the inductive bias of sharing the decoder remains an open challenge. To address this challenge, we propose Progressive Decoder Fusion (PDF) to progressively combine task decoders based on inter-task representation similarity. We show that this procedure leads to a multi-task network with better generalization to in-distribution and out-of-distribution data and improved robustness to adversarial attacks. Additionally, we observe that the predictions of different tasks of this multi-task network are more consistent with each other.