Teachers' Perspectives of the Sudden Shift Towards Online Learning: Challenges and Future Lessons

—Traditionally, teaching math using English as a foreign language creates many challenges because learners may not have yet acquired the skills they need to understand the lesson. The study aimed to identify the challenges faced by teachers of math in English when course instruction shifted to online because of the COVID-19 pandemic. Also, the study presents three essential lessons from this experience from the teachers' perspectives. During the COVID-19 outbreak, all schools abruptly switched to Online Education. Most participants in the study had never done online teaching before the pandemic and were unfamiliar with using technology. The researchers used a mixed methods approach, incorporating the following two ways of gathering data: a questionnaire and face-to-face semi-structured interviews. The questionnaire consisted of two parts: (1) English language challenges and (2) access to technology. The questionnaire was administered to a sample of 50 female teachers, followed by structured interviews. The interview consisted of one question about the most important lessons learned, and the data were analysed using SPSS Statistics. The findings indicated that the English language and a lack of technology constituted obstacles for teachers. According to the participants, they identified three future lessons. The researchers provide recommendations to teachers, students, and governments. © 2023 ACADEMY PUBLICATION.

Students home alone—profiles of internal and external conditions associated with mathematics learning from home

This study aims at describing differences in internal and external resources of students to handle mathematics learning from home. Based on data from N = 223 7th-grade secondary school students gathered via an online survey at the end of the first school year during the COVID-19 pandemic, we used latent profile analysis to identify student profiles defined by the internal factors perceived value and success of students' math learning from home and the external factors family support and teacher support—all specifically related to home learning. A number of general learning conditions, comprising internal (e.g., sustained attention) and external factors (e.g., socioeconomic status), are included as outcome variables. The best-fitting four-profile solution suggests one profile with comparably unfavorable internal and external resources. About 35% of the students are assigned to that profile. The other three profiles show combinations of, relative to the sample, more and less promising specific home learning and general learning conditions suggesting that these students have different resources available in the face of learning mathematics from home. © 2022, The Author(s).

Empowering mathematics teachers to meet evolving educational goals: the role of "epistemic objects” in developing actionable practice knowledge in tumultuous times

In recent decades, the aims and objectives of education–and therefore public discourse on the appropriate skills and attributes of mathematics teachers–have been rapidly shifting due to forces from outside the teaching profession. The forces driving change in mathematics are as diverse as the emergence of "Industry 4.0” and "STEM,” new directions in transnational education policy making, and the COVID-19 pandemic. This paper contributes to a growing literature seeking to empower teachers to respond to the complexity of such multifaceted change expansively rather than defensively. It does so through the refinement and application of practical theories of educational change and approaches to building actionable practice knowledge. Specifically, this paper will argue for the use of the epistemic object as a practical focus for changes to practice chosen by the profession. This argument will be made within the framework of practice architectures offered by Kemmis and others. The paper first considers the impact of some recent disruptions on teaching and then provides a "worked example” of using mathematical proficiencies as an epistemic object able to practically support teachers to develop actionable knowledge grounded in the specifics of their own professional context. © 2022 Australian Teacher Education Association.

The Impact of COVID-19 Distance Learning on Students' Math Anxiety: An Exploratory Study

In spring 2020, the COVID-19 pandemic forced a rapid shift to distance learning worldwide. Although recent research has focused on the impact that this transition had on students' education and well-being, little has been done in particular on math education and on math anxiety (MA). Since MA is believed to be linked to the teaching methods, it could be hypothesized that the new learning environment affected MA levels. Thus, this study investigated whether students' levels of MA evaluated before and during the first wave of the pandemic changed as a consequence of the distance learning implementation. Moreover, we were interested in investigating whether students' satisfaction with the teaching methods, their effort in math, and their academic achievement were correlated to MA before and during the COVID-19 distance learning. Participants were 117 Italian middle and high school students. No significant differences between pre- and mid-pandemic MA were found when considering the entire sample. Analyzing separately, results indicated that high-MA students reported significantly lower MA levels during distance learning, however no difference was observed for moderate- and low-MA individuals. Furthermore, satisfaction with the teaching methods, effort in math, and math achievement were negatively correlated with MA, both before and during distance education. © 2023 International Journal of Education in Mathematics, Science and Technology. All rights reserved.

At School or Home? Eight Graders' First Practices with Online Geometry Lessons

Undoubtedly, one of the areas most affected by the Covid-19 pandemic process was educational activities. In this study, the 33 eighth graders of a public elementary school in Turkiye were observed for a six-week online learning period. The aim was to obtain whether any changes occur in their geometry attitudes during the process and to reveal their preferences between online distance learning (ODL) and regular face-to-face education. In this context, structured as a mixed study, a Geometry Attitude Scale (GAS) and a questionnaire about online distance learning was administered at the beginning;further GAS and learners' opinions in response to open-ended questions were administered at the end of the process. Quantitative results indicated that gender and mathematics achievement levels have no relationship with GAS and ODL. Still, the qualitative analysis provided that ODL does not cause any change in students' attitudes towards geometry lessons;moreover, students commonly prefer face-to-face education over ODL.

Theoretical analysis of a measles model with nonlinear incidence functions

Measles is a highly contagious respiratory disease of global public health concern. A deterministic mathematical model for the transmission dynamics of measles in a population with Crowley–Martin incidence function to account for the inhibitory effect due to susceptible and infected individuals and vaccination is formulated and analyzed using standard dynamical systems methods. The basic reproduction number is computed. By constructing a suitable Lyapunov function, the disease-free equilibrium is shown to be globally asymptotically stable. Using the Center Manifold theory, the model exhibits a forward bifurcation, which implies that the endemic equilibrium is also globally asymptotically stable. To determine the optimal choice of intervention measures to mitigate the spread of the disease, an optimal control problem is formulated (by introducing a set of three time-dependent control variables representing the first and second vaccine doses, and the palliative treatment) and analyzed using Pontryagin's Maximum Principle. To account for the scarcity of measles vaccines during a major outbreak or other causes such as the COVID-19 pandemic, a Holling type-II incidence function is introduced at the model simulation stage. The control strategies have a positive population level impact on the evolution of the disease dynamics. Graphical results reveal that when the mass-action incidence function is used, the number of individuals who received first and second vaccine dose is smaller compared to the numbers when the Crowley–Martin incidence-type function is used. Inhibitory effect of susceptibles tends to have the same effect on the population level as the Crowley–Martin incidence function, while the control profiles when inhibitory effect of the infectives is considered have similar effect as when the mass-action incidence is used, or when there is limitation in the availability of measles vaccines. Missing out the second measles vaccine dose has a negative impact on the initial disease prevalence. © 2022 Elsevier B.V.

Assessing the effects of a collaborative problem-based learning and peer assessment method on junior secondary students' learning approaches in mathematics using interactive online whiteboards during the COVID-19 pandemic

Given the demands on instructors created by the COVID-19 pandemic, teachers have been compelled to integrate active learning pedagogies supported by mobile technologies to sustain students' interactive engagement. This study describes the implementation of a novel active pedagogy - the collaborative problem-based learning and peer assessment (Co-PBLa-PA) method, implemented through interactive online whiteboards (IOWBs) in junior secondary mathematics classes in Hong Kong. Data were collected from 87 Form 1 students and analysed to test three hypotheses postulating the main effects of the Co-PBLa-PA method on students' learning approaches using IOWBs. A pre-survey (SPQ) on students' learning approaches and a post-survey (SPQ) on students' learning approaches and their perceptions of technology-enabled active learning (TEAL) were administrated. Results showed the Co-PBLa-PA method, using IOWBs, increased students' learning performance and promoted significant deep learning. A significant positive correlation also emerged between deep learning approaches and students' perceptions of TEAL using IOWBs. Finally, limitations and directions for future research are discussed.

An efficient lightweight encryption model with re-encryption scheme to create robust blockchain architecture for COVID-19 data

Handling electronic health records from the Internet of Medical Things is one of the most challenging research areas as it consists of sensitive information, which targets attackers. Also, dealing with modern healthcare systems is highly complex and expensive, requiring much secured storage space. However, blockchain technology can mitigate these problems through improved health record management. The proposed work develops a scalable, lightweight framework based on blockchain technology to improve COVID-19 data security, scalability and patient privacy. Initially, the COVID-19 related data records are hashed using the enhanced Merkle tree data structure. The hashed values are encrypted by lattice based cryptography with a Homomorphic proxy re-encryption scheme in which the input data are secured. After completing the encryption process, the blockchain uses inter planetary file system to store secured information. Finally, the Proof of Work concept is utilized to validate the security of the input COVID based data records. The proposed work's experimental setup is performed using the Python tool. The performance metrics like encryption time, re-encryption time, decryption time, overall processing time, and latency prove the efficacy of the proposed schemes. © 2022 John Wiley & Sons Ltd.

Bifurcation analysis of the Microscopic Markov Chain Approach to contact-based epidemic spreading in networks

The dynamics of many epidemic compartmental models for infectious diseases that spread in a single host population present a second-order phase transition. This transition occurs as a function of the infectivity parameter, from the absence of infected individuals to an endemic state. Here, we study this transition, from the perspective of dynamical systems, for a discrete-time compartmental epidemic model known as Microscopic Markov Chain Approach, whose applicability for forecasting future scenarios of epidemic spreading has been proved very useful during the COVID-19 pandemic. We show that there is an endemic state which is stable and a global attractor and that its existence is a consequence of a transcritical bifurcation. This mathematical analysis grounds the results of the model in practical applications. © 2022 Elsevier Ltd

AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform.

Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This paper proposes an adaptive personalized E-learning platform with a novel combination of Visual/Aural/Read, Write/Kinesthetic (VARK) presentation or gamification and exercises difficulty scaffolding through skipping/hiding/ reattempting. Cognitive, behavior and affective adaptation means are included in developing a dynamic learner model, which detects and corrects each student's learning style and cognitive level. As adaptation targets, the platform provides adaptive content presentation in two groups (VARK and gamification), adaptive exercises navigation and adaptive feedback. To achieve its goal, the platform utilizes a Deep Q-Network Reinforcement Learning (DQN-RL) and an online rule-based decision making implementation. The platform interfaces front-end dedicated website and back-end adaptation algorithms. An improvement in learning effectiveness is achieved comparing the post-test to the pre-test in a pilot experiment for grade 3 mathematics curriculum. Both groups witnessed academic performance and satisfaction level improvements, most importantly, for the students who started the experiment with a relatively low performance. VARK group witnessed a slightly more improvement and higher satisfaction level, since interactive activities and games in the kinesthetic presentation can provide engagement, while keeping other presentation styles available, when needed.

Reading and math skills development among Finnish primary school children before and after COVID-19 school closure.

This study quantified the possible learning losses in reading and math skills among a sample of Finnish Grade 3 children (n = 198) who spent 8 weeks in distance learning during the first wave of the COVID-19 pandemic in spring 2020. We compared their reading and math skill development trajectories across Grades 1, 2, and 4 to a pre-COVID sample (N = 378). We also examined if gender, parental education, maternal homework involvement, and child's task-avoidant behavior predict children's academic skills at Grade 4 differently in the pre-COVID sample compared with the COVID sample. Children's reading and math skills were tested, mothers reported their education and homework involvement, and teachers rated children's task-avoidant behavior. The results showed, on average, lower reading skills in the COVID sample than in the pre-COVID sample but there were no differences in math skills. Although the COVID sample had lower levels in reading, their developmental trajectories in reading and math skills were not different from the pre-COVID sample before the pandemic in Grades 1 and 2. From Grade 2 to 4, however, the development was slower in reading fluency and comprehension in the COVID sample, but not in math. The predictors of change from Grade 2 to 4 in reading and math skills were not different in the samples. The results showed that the development of reading skills in particular may have been affected by the COVID-19 pandemic.

Futures for Post-Pandemic Mathematics Teacher Education: responsiveness and responsibility in the Face of a Crisis.

COVID-19 has caused unprecedented disruption to mathematics teacher education worldwide. This paper is anchored in our learnings from the experiences of teacher educators at four major universities from the Association of Southeast Asian Nations as they dealt with changes in their programs' delivery triggered by the pandemic, and raises challenges that remain for the futures of post-pandemic mathematics teacher education. Here, we use the two ethical constructs of responsiveness and responsibility to guide actions in response to a crisis, in order to discuss a range of decisions the participants made to respond to the crisis. Behind their initial response to the emergent conditions, the participants were concerned about maintaining the continuity of their students' education. Further, we identify remaining challenges for mathematics teacher educators to re-imagine their curriculum, teaching, assessment, and equitable access towards a more relevant, productive, and equitable mathematics teacher education. This study adds to the rapidly increasing literature on the effect of the pandemic on mathematics education in the following three ways: (1) here we take a comprehensive view of the disruptions instigated by the pandemic;, (2) we pay special attention to issues of equity; and (3) we address concerns about possible and desirable post-pandemic futures.

Differential impact of COVID-19 school closures on immigrant students: A transnational comparison

IntroductionThe COVID-19 pandemic had a major impact on many aspects of life, perhaps most notably education. Efforts to mitigate the negative effects of the pandemic, particularly lockdowns, led to major disruptions in schools and resulted in both learning loss and increased mental health challenges for students. These deleterious impacts appear to have been felt most keenly by students from marginalized communities, including first and second generation students. Materials and methodsThis study sought to investigate the mechanisms underlying these negative effects of pandemic mitigation efforts, particularly in terms of school efforts to support teachers and parents of students not speaking the language of instruction for three nations (Denmark, Russia, and Slovenia) included in the Responses to Educational Disruption Survey (REDS) survey. ResultsResults of the study revelated that in Denmark greater school-level support to teachers of non-native language students moderated the relationship between home language status and student perceptions of their relative academic performance before and during the pandemic, but that such was not the case in Russia or Slovenia. Likewise, school-level support to teachers moderated the relationship between home language status and perceived teacher support in Denmark but not in Russia or Slovenia. Implications of these results are discussed.

Secondary Teachers' Remote Instructional Practices in Mathematics for Students With Disabilities

Increasing numbers of students with disabilities (SWD) receive remote or online instruction in secondary mathematics. Unprecedented shifts in teaching modalities during the COVID-19 pandemic highlighted the need for effective remote instruction. The present study surveyed 31 general and special education teachers to identify features of remote instruction in secondary mathematics for SWD and understand the changes between Spring and Fall of 2020. Teachers reported increases in the variety of presentation and practice methods and the use of synchronous methods of feedback. Assessment and methods of providing feedback on assessments remained stable over time. Shifts represented alignment with existing frameworks for best practices in online learning and provided opportunities for the incorporation of evidence-based practices (EBPs) into secondary mathematics instruction. Barriers, such as progress monitoring and providing intensive instruction, remain prevalent and critical areas for continued investigation.

How Can Flipped Learning Continue in a Fully Online Environment? Lessons Learned During the COVID-19 Pandemic

Teachers and students had an unusual experience in 2020. Due to the outbreak of the COVID-19 pandemic, regular teaching and learning in schools were suspended in many countries. The prolonged school closure presented unprecedented challenges because all teaching and learning activities had to be converted to a fully online format. This article reports on an attempt to sustain flipped learning using real-time online instruction. A flipped lesson on the cross-disciplinary applications of linear systems (i.e., balancing chemical equations) is presented in detail. Student feedback and the lessons learned from the experience are discussed.

Longest k-tuple Common Sub-Strings

We focus on a new problem that is formulated to find a longest k-tuple of common sub-strings (abbr. k-CSSs) of two or more strings. We present a suffix tree based algorithm for this problem, which can find a longest k-CSS of m strings in O(kmn-{k}) time and O(kmn) space where n is the length sum of the m strings. This algorithm can be used to approximate the longest k-CSS problem to a performance ratio frac{1}{epsilon} in O(kmn-{lceilepsilon krceil}) time for epsilonin(0,1]. Since the algorithm has the space complexity in linear order of n, it will show advantage in comparing particularly long strings. This algorithm proves that the problem that asks to find a longest gapped pattern of non-constant number of strings is polynomial time solvable if the gap number is restricted constant, although the problem without any restriction on the gap number was proved NP-Hard. Using a C++ tool that is reliant on the algorithm, we performed experiments of finding longest 2-CSSs, 3-CSSs and 5-CSSs of 2 14 COVID-19 S-proteins. Under the help of longest 2-CSSs and 3-CSSs of COVID-19 S-proteins, we identified the mutation sites in the S-proteins of two COVID-19 variants Delta and Omicron. The algorithm based tool is available for downloading at https://github.com/lytt0/k-CSS. © 2022 IEEE.

The solo pantomime in the pandemic: Distance postgraduate education in the department of mathematics education during COVID-19

Turkey experiences distance education at the master's and doctorate degrees for the first time. This study aims to reveal the essence of the distance education experiences of mathematics teachers who continue their postgraduate education with distance education due to the COVID-19 pandemic. This study was carried out using the phenomenological research design with six mathematics teachers who continue their postgraduate education at a state university in the Central Anatolia Region in the 2019-2020 academic year. Of the participants selected by the criterion sampling, three were master's degree students and three were doctoral degree students. Research data were collected using semi-structured interview forms designed in line with expert opinions. The interviews were conducted online via video call on the WhatsApp application due to the COVID-19 pandemic. The experiences of the participants were identified with the phenomenon of "solo pantomime”. Participants had positive experiences such as easy access, possibility of review, improvement in technological pedagogical content knowledge, and negative experiences such as communication and connection problems, the irregularity in the schedule, inadequacy of the lesson hours, and focusing problems regarding synchronized distance education. Distance graduate education is also considered quite suitable for mathematics education courses, but insufficient for mathematics field courses. It is also understood that some participants had plans to make radical changes in their thesis topics. Participants avoid long-term experimental studies or studies that can be conducted with a large sample, and they tend towards studies that can be carried out with document analysis or small groups and had problems with their supervisors.

Home Learning Experiences Through the Covid-19 Pandemic

The COVID-19 pandemic has drastically affected the education process almost all over the world. Some countries closed their schools, Slovakia was among them (schools in Slovakia were closed intermittently for almost two years). Teachers faced the challenge of developing alternative educational practices through digital technologies. Students also faced personal, technological, and social challenges. Distance education, as a replacement of imparting and receiving knowledge, was in many aspects also very demanding for parents. It was necessary to overcome several technical problems (availability of appropriate and reliable Internet connection, provision of appropriate computer equipment and sufficient personal educational space for each member of family). An important role was also played by the student's ability to mobilize his own motivation for asynchronous and autonomous learning. The discussion with the professional public and the review of the relevant literature indicated that the teaching of mathematics is more sensitive to the interruption of attendance education. As the students themselves expressed: for the understanding of mathematical concepts, the personal presence of the teacher necessary and fundamentally affects the student's ability to obtain new knowledge and understand it. The testing of knowledge of students in Slovakia in 2022 at all levels of schools (after almost two years of distance learning) indicates that in the field of mathematics education there has been the biggest drop in knowledge compared to other subjects. Our study focused on the analysis and uncovering of negative but also positive factors operating in the online teaching of mathematics, which significantly affect the results and level of knowledge of students at the university. Mapping and identification of problematic moments in this process helped us reveal the results of a survey (study) conducted among students of the 1st year of bachelor's studies at the University of Žilina. © 2022 by Cherkas Global University All rights reserved. Published in the USA

Fractional type multivariate neural network operators

In this paper, we introduce a novel family of multivariate neural network operators involving Riemann‐Liouville fractional integral operator of order α. Their pointwise and uniform approximation results are presented, and new results concerning the rate of convergence in terms of the modulus of continuity are estimated. Moreover, several graphical and numerical results are presented to demonstrate the accuracy, applicability, and efficiency of the operators through special activation functions. Finally, an illustrative real‐world example on the recent trend of novel corona virus Covid‐19 has been investigated in order to demonstrate the modeling capabilities of the proposed neural network operators.