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Objective: This study assessed the features of the 100 most‐cited papers on oral health‐related quality of life (OHRQoL). Methods: The 100 most‐cited OHRQoL papers were collected from Web of Science, adopting a combined keyword search strategy. Google Scholar and Scopus databases were searched to compare citations. The following data were extracted from papers: title of the paper, number of citations, authorship, country, year of publication, title of the journal, study design, sample size, topic and OHRQoL instruments used. Graphical bibliometric networks were created using VOSviewer software. Results: The number of citations of the top 100 most‐cited OHRQoL papers ranged from 73 to 949. Fifty‐six papers received at least 100 citations and two received more than 400 citations. Most papers were from Canada (23%) and had been published in Community Dentistry and Oral Epidemiology (37%). David Locker was the most‐cited author (25 papers; 3,521 citations). The cross‐sectional study design was the most common (68%). The impact of oral health conditions on OHRQoL (43%) was the most frequent topic, and the Oral Health Impact Profile (OHIP) was the most commonly used OHRQoL instrument (48%). Conclusions: This bibliometric analysis highlighted the characteristics of the 100 most‐cited OHRQoL papers, demonstrating that this field is far from saturated. This list of the most‐cited articles can provide a reference point to guide oral health research, education and services. [ABSTRACT FROM AUTHOR]

In 2014 leading publishers withdrew more than 120 nonsensical publications automatically generated with the SCIgen program. Casual observations suggested that similar problematic papers are still published and sold, without follow‐up retractions. No systematic screening has been performed and the prevalence of such nonsensical publications in the scientific literature is unknown. Our contribution is 2‐fold. First, we designed a detector that combs the scientific literature for grammar‐based computer‐generated papers. Applied to SCIgen, it has a 83.6% precision. Second, we performed a scientometric study of the 243 detected SCIgen‐papers from 19 publishers. We estimate the prevalence of SCIgen‐papers to be 75 per million papers in Information and Computing Sciences. Only 19% of the 243 problematic papers were dealt with: formal retraction (12) or silent removal (34). Publishers still serve and sometimes sell the remaining 197 papers without any caveat. We found evidence of citation manipulation via edited SCIgen bibliographies. This work reveals metric gaming up to the point of absurdity: fraudsters publish nonsensical algorithmically generated papers featuring genuine references. It stresses the need to screen papers for nonsense before peer‐review and chase citation manipulation in published papers. Overall, this is yet another illustration of the harmful effects of the pressure to publish or perish. [ABSTRACT FROM AUTHOR]

We are developing a deterministic fluidic assembly process which uses software to sense, sort and assemble micron‐scale devices into desired patterns. This process can be adapted to assemble large numbers of microLEDs (uLEDs) over large areas with high throughput and nearly perfect yield. We present data demonstrating the precision and reliability of the process to illustrate its potential as a scalable process for manufacturing uLED displays. [ABSTRACT FROM AUTHOR]

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The modern world greatly depends on information systems and the software that governs them. The software architecture defines and designs the holistic structure that the software will have, its components, the interaction between them and all the development is done around it. The purpose of this systematic literature review (SLR) is to analyse the software architectures used in educational websites, methodologies, technological components and empirical results. The search of the SLR yielded 23 studies from the most significant academic sources in education and software engineering. The results of the SLR show that the analysed educational websites were proposing a software architecture, developing a system, proposing a model, assessing of a platform, proposing a Folksonomy‐Based Ontology Maintenance, reviewing smart home design, and proposing a Web‐based platform to aid parallel, distributed and high‐performance computing education. Of the 23 selected studies, 13 carried out an evaluation of their research with either students, teachers, professionals or a combination of these. In conclusion, the selected studies present narrated experiments of projects or individuals that seek to improve collaborative learning in the educational area. Finally, an important finding is that the proposed software architectures do not contemplate laws or quality standards for universal access. [ABSTRACT FROM AUTHOR]

Summary: Given the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software defect prediction (SDP). Feature selection (FS) has a vital impact on the initial data preparation phase of SDP. Nonetheless, existing FS methods suffer from issues such as insignificant dimensionality reduction, low accuracy in classifying chosen optimal feature sets, and neglect of complex interactions and dependencies between defect data and features as well as between features and classes. To tackle the aforementioned problems, this paper proposes a many‐objective SDPFS (MOSDPFS) model and the binary many‐objective PSO algorithm with adaptive enhanced selection strategy (BMaOPSO‐AR2) is proposed within this paper. MOSDPFS selects F1 score, the number of features within subsets, and correlation and redundancy measures based on mutual information (MI) as optimization objectives. BMaOPSO‐AR2 constructs a binary version of MaOPSO using transfer functions specifically for binary classification. Adaptive update formulas and the introduction of the R2 indicator are employed to augment the variety and convergence of algorithm. Additionally, performance of MOSDPFS and BMaOPSO‐AR2 are tested on the NASA‐MDP and PROMISE datasets. Numerical results prove that a proposed model and algorithm effectively reduces feature count while enhancing predictive accuracy and minimizing model complexity. [ABSTRACT FROM AUTHOR]

Organizations creating software commonly utilize software development lifecycles (SDLCs) to structure development activities. Secure development lifecycles (SDLs) integrate into SDLCs, adding security or compliance activities. They are widely used and have been published by industry leaders and in literature. These SDLs, however, were mostly designed before or while cloud services and other hosted solutions became popular. Such offerings widen the provider's responsibilities, as they not only deliver software but operate and decommission it as well. SDLs, however, do not always account for this change. Security maturity models (SMMs) help to assess SDLs and identify improvements by introducing a baseline to compare against. Multiple of these models were created after the advent of hosted solutions and are more recent than commonly referenced SDLs. Recent SMMs and SDLs may therefore support hosted solutions better than older proposals do. This paper compares a set of current and historic SDLs and SMMs in order to review their support for hosted solutions, including how support has changed over time. Security, privacy, and support for small or agile organizations are considered, as all are relevant to hosted solutions. The SDLs analyzed include Microsoft's SDL, McGraw's Touchpoints, the Cisco's SDL, and Stackpole and Oksendahl's SDL2. The SMMs reviewed are OWASP's Software Assurance Maturity Model 2 and DevSecOps Maturity Model. To assess the support for hosted solutions, the security and privacy activities foreseen in each SDLC phase are compared, before organizational compatibility, activity relevance, and efficiency are assessed. The paper further demonstrates how organizations may select and adjust a suitable proposal. The analyzed proposals are found to not sufficiently support hosted solutions: Important SDLC phases, such as solution retirement, are not always sufficiently supported. Agile practices, such as working in sprints, and small organizations are often not sufficiently considered as well. Efficiency is found to vary based on the application context. A clear improvement trend from before the proliferation of hosted solutions cannot be identified. Future work is therefore found to be required. [ABSTRACT FROM AUTHOR]

Background: Those supporting children and young people who use augmentative and alternative communication (AAC) contribute to ongoing complex decision‐making about communication aid selection and support. Little is known about how these decisions are made in practice and how attributes of the communication aid are described or considered. Aims: To understand how communication aid attributes were described by those involved in AAC recommendations and support for children and young people, and how these attributes were described as impacting on AAC use. Methods & Procedures: A secondary qualitative analysis was completed of interview and focus group data from 91 participants involved in the support of 22 children and young people. Attributes of communication aids described by participants were extracted as themes and this paper reports a descriptive summary of the identified software (non‐hardware) attributes. Main Contribution: Decisions were described in terms of comparisons between commercially available pre‐existing vocabulary packages. Attributes related to vocabulary, graphic representation, consistency and intuitiveness of design, and ease of editing were identified. Developmental staging of vocabularies, core and fringe vocabulary, and vocabulary personalization were attributes that were described as being explicitly considered in decisions. The potential impact of graphic symbol choice did not seem to be considered strongly. The physical and social environment was described as the predominant factor driving the choice of a number of attributes. Conclusions & Implications: Specific attributes that appear to be established in decision‐making in these data have limited empirical research literature. Terms used in the literature to describe communication aid attributes were not observed in these data. Practice‐based evidence does not appear to be supported by the available research literature and these findings highlight several areas where empirical research is needed in order to provide a robust basis for practice. What This Paper Adds: What is already known on the subject: Communication aid attributes are viewed as a key consideration by practitioners and family members in AAC decision‐making; however, there are few empirical studies investigating language and communication attributes of communication aids. It is important to understand how those involved in AAC recommendations and support view communication aid attributes and the impact different attributes have. What this paper adds to existing knowledge: This study provides a picture of how communication aids are described by practitioners and family members involved in AAC support of children and young people. A range of attributes is identified from the analysis of these qualitative data as well as information about how participants perceive these attributes as informing decisions. What are the potential or actual clinical implications of this work?: This study provides a basis on which practitioners and others involved in AAC support for children and young people can review and reflect on their own practice and so improve the outcomes of AAC decisions. The study provides a list of attributes that appear to be considered in practice and so also provides a resource for researchers looking to ensure there is a strong empirical basis for AAC decisions. [ABSTRACT FROM AUTHOR]

Crater mapping using neural networks and other automated methods has increased recently with automated Crater Detection Algorithms (CDAs) applied to planetary bodies throughout the solar system. A recent publication by Benedix et al. (2020, https://doi.org/10.1029/2019ea001005) showed high performance at small scales compared to similar automated CDAs but with a net positive diameter bias in many crater candidates. I compare the publicly available catalogs from Benedix et al. (2020, https://doi.org/10.1029/2019ea001005) and Lee and Hogan (2021, https://doi.org/10.1016/j.cageo.2020.104645) and show that the reported performance is sensitive to the metrics used to test the catalogs. I show how the more permissive comparison methods indicate a higher CDA performance by allowing worse candidate craters to match ground‐truth craters. I show that the Benedix et al. (2020, https://doi.org/10.1029/2019ea001005) catalog has a substantial performance loss with increasing latitude and identify an image projection issue that might cause this loss. Finally, I suggest future applications of neural networks in generating large scientific datasets be validated using secondary networks with independent data sources or training methods. Plain Language Summary: I have compared two computer programs that use neural networks to identify craters on solar system bodies. The crater catalogs created by these programs have been previously compared against an existing human‐made catalog to measure their performance, but each comparison used a different method making it challenging to understand the difference between the catalogs. In this paper, I use the comparison methods from these two independent papers, discuss where the catalogs agree and disagree, and suggest why they disagree. I emphasize the need for more accurate and consistent methods to validate automatic crater‐mapping programs, especially when it is impractical for humans to check the craters found by automated methods. Key Points: The apparent performance of automatic crater detection algorithms is sensitive to the choice of metric used to gauge the performanceFeature Detection Algorithms trained to find spatial features should be used with appropriately projected images where possibleAs neural networks extend crater detection beyond practical human limits, validating their results with a independent networks is critical [ABSTRACT FROM AUTHOR]

The state space explosion restricts the error detection of concurrent software. The abstraction can provide a solution to avoid state space explosion, but it is easy to ignore important details, resulting in inaccurate detection results. This paper proposes a methodology of fine‐coarse‐grained automatic modelling for Java source programs. By the principle that the execution details of property‐unchecked, non‐interactive, and unrelated statements do not affect the model checking results, we model coarse‐grained model fragments for such statements, while fine‐grained model fragments for property‐checked, interactive, and related statements. Our method reduces the model and state space and ensures the error detection of the source program based on model checking. Moreover, we prove the equivalence of the fine‐grained model, the coarse‐grained model, and the program. Finally, this paper gives an experiment to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Software effort estimation accuracy is a key factor in effective planning, controlling, and delivering a successful software project within budget and schedule. The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation. The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in software development. In this article, the performance of the machine learning ensemble and solo techniques are investigated on publicly and non‐publicly domain datasets based on the two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment (QA) criteria, extracting data, and drawing results. We have evaluated a state‐of‐the‐art accuracy performance of 35 selected studies (17 ensemble, 18 solo) using mean magnitude of relative error and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques. [ABSTRACT FROM AUTHOR]

Background: Humans often use co‐speech gestures to promote effective communication. Attention has been paid to the cortical areas engaged in the processing of co‐speech gestures. Aims: To investigate the neural network underpinned in the processing of co‐speech gestures and to observe whether there is a relationship between areas involved in language and gesture processing. Methods & Procedures: We planned to include studies with neurotypical and/or stroke participants who underwent a bimodal task (i.e., processing of co‐speech gestures with relative speech) and a unimodal task (i.e., speech or gesture alone) during a functional magnetic resonance imaging (fMRI) session. After a database search, abstract and full‐text screening were conducted. Qualitative and quantitative data were extracted, and a meta‐analysis was performed with the software GingerALE 3.0.2, performing contrast analyses of uni‐ and bimodal tasks. Main Contribution: The database search produced 1024 records. After the screening process, 27 studies were included in the review. Data from 15 studies were quantitatively analysed through meta‐analysis. Meta‐analysis found three clusters with a significant activation of the left middle frontal gyrus and inferior frontal gyrus, and bilateral middle occipital gyrus and inferior temporal gyrus. Conclusions: There is a close link at the neural level for the semantic processing of auditory and visual information during communication. These findings encourage the integration of the use of co‐speech gestures during aphasia treatment as a strategy to foster the possibility to communicate effectively for people with aphasia. WHAT THIS PAPER ADDS: What is already known on this subject: Gestures are an integral part of human communication, and they may have a relationship at neural level with speech processing. What this paper adds to the existing knowledge: During processing of bi‐ and unimodal communication, areas related to semantic processing and multimodal processing are activated, suggesting that there is a close link between co‐speech gestures and spoken language at a neural level. What are the potential or actual clinical implications of this work?: Knowledge of the functions related to gesture and speech processing neural networks will allow for the adoption of model‐based neurorehabilitation programs to foster recovery from aphasia by strengthening the specific functions of these brain networks. [ABSTRACT FROM AUTHOR]

An important cause of stunting is limited consumption of complementary foods, in terms of both quantities and nutrients. Although existing studies show a positive association between fathers' engagement and children's diet, programmes designed to improve complementary feeding practices often only target mothers. In response to this, maternal behaviour change communication (BCC), paternal BCC and food voucher programmes were designed and implemented in Ethiopia using a clustered randomized controlled trial design. The paternal BCC programme included gender‐equal messages to increase fathers' participation in childcare, household labour and decision making. The research reported in this paper is an examination of the BCC programmes, characterizing the behavioural, normative and control beliefs of both mothers and fathers in BCC households compared to those in control households. In this study, a total of 40 participants were included, with 13 mother–father pairs in the BCC + food voucher group, and seven pairs in the control group. Each participant was interviewed separately. We found that BCC mothers showed more gender‐equal tendencies than the control mothers despite being more rural in location. By contrast, the beliefs of BCC and control fathers were similar overall, suggesting men are more resistant to gender‐equal BCC. More work is needed to develop and test effective methods for changing fathers' beliefs and practices. Key messages: Mothers in behaviour change communication (BCC) group mothers held more gender‐equal beliefs than control mothers, while BCC and control fathers shared similar views, suggesting a male resistance to gender equality.Mothers found fathers' involvement in childcare socially acceptable but often perceived them as inexperienced, which limited their participation.Control mothers' traditional view on household chores maintained the conventional labour division, influencing fathers' involvement.Fathers typically resisted maternal control of resources; however, they agreed that the more knowledgeable should lead decision‐making. BCC mothers showed greater confidence in making household decisions as effectively as fathers. [ABSTRACT FROM AUTHOR]

From employees' varied interpretations of software efficacy to consumers' diverse beliefs about data privacy, technology frames refer to cognitive interpretations, assumptions and expectations that people use to comprehend the essence of information technology within a particular context. These frames differ across groups with different values, interests, experiences and expertise, having critical implications for researchers, managers and organizations. Despite theoretical enthusiasm to understand technology frames, limited methodological insights exist on how to systematically explore and compare technology frames. This gap impedes researchers from exploring novel questions related to technology frames, their variations and how they can be managed effectively. This paper proposes a cognitive method for comparing and elaborating on technology frames. Building on causal mapping and empirical studies, the method formulates steps to plan, elicit, compare and elaborate on the relationships that underlie framing differences. The method offers detailed recommendations and templates for effectively organizing and communicating diverse manifestations of framing differences and their implications. The paper concludes by highlighting the method's practical implications and encouraging research to advance extant knowledge of technology frames in the rapidly changing digital world. [ABSTRACT FROM AUTHOR]

The paper reviews the evolution of Interactive Management, later referred to as Structured Democratic Dialogue, starting from the early 1970s up to this date. The authors propose a generational classification scheme consisting of five periods based primarily on whether some or all stages of the process were implemented synchronously or asynchronously and whether the participants' presence was physical, virtual or hybrid. Other aspects such as modifications in the steps of the process; the evolution of the software; domains of applications; file management; methods of collecting or recording contributions, votes, clarifications and preparation of reports; and key players are also considered and reported within the context of the primary scheme. The paper considers key advances achieved at each generational stage in terms of process or software, discusses associated challenges and concludes with a view towards the future of the emerging fifth generation. [ABSTRACT FROM AUTHOR]

Background: Children with Down syndrome have speech and language difficulties that are disproportionate to their overall intellectual ability and relative strengths in the use of gesture. Shared book reading between parents and their children provides an effective context in which language development can be facilitated. However, children with Down syndrome often take a passive role in shared book reading and the use of key word signing (KWS) as a shared book reading technique has never been investigated. Aims: This study aimed to compare children with Down syndrome's participation and use of KWS across two methods of shared book reading – one in which a book had key‐word sign prompts embedded (signed condition) and the other in which a book was read as normal (unsigned condition). Measures of child and parent communicative behaviour were taken in each condition to establish if differences emerged. Methods & Procedures: A total of 36 children with Down syndrome (aged between 18 and 61 months) and their mothers took part in the study. Parent–child dyads were videoed at home reading two books, one in a signed and one in an unsigned condition. Child measures included total number of signs produced in each condition and levels of attention and initiation as measured by the Pivotal Behaviour Rating Scale. Parent measures included total number of utterances, mean length of utterance (MLU) in morphemes and vocabulary diversity (VOCD). Parental measures were transcribed using the Codes for Human Analysis Transcripts (CHAT) software and analysed by the Computerised Language Analysis software (CLAN). Contrasts in outcomes between the signed and unsigned conditions were estimated using Poisson and linear mixed‐effects models, determined by the type of data. Outcomes & Results: Results showed that children attempted to sign significantly more in the signed than unsigned condition, as well as showing significant increases in their levels of attention and initiation. There was also a significant increase in the total number of utterances used by parents in the signed versus unsigned condition and a decrease in MLU. VOCD was similar in both conditions. Conclusions & Implications: This study shows that the simple act of embedding key word signs into commercially available books, during shared book reading between parents and young children with Down syndrome, positively affects children's participation (initiation and attention) and use of KWS. The use of KWS as a core shared book reading technique may therefore be a fruitful avenue to facilitate growth in the language abilities of young children with Down syndrome. What this paper adds: What is already known on this subject: Most children with Down syndrome have significant speech and language difficulties, with relative strengths in the use of gesture. Shared book reading is an activity reported to positively affect language. However, children with Down syndrome are reported to take a passive role in shared book reading and are therefore more dependent on their parents to use techniques that facilitate their levels of participation, in order to maximise potential benefits. To the best of our knowledge, the communicative effects of embedding key word signing (KWS) in shared book reading have never been examined with children with Down syndrome. What this paper adds to existing knowledge: This is the first study to investigate the communicative impact of parents embedding KWS in a shared book reading activity with their young children with Down syndrome. Our findings show that this relatively simple manipulation resulted in Increase in children's sign attempts.Increase in children's overall participation in shared book reading (indicated by levels of attention and initiation).Increase in the number of utterances produced by parents (primarily as a result of repetitions).Decrease in parental mean length of utterance. These findings suggest that embedding KWS in shared book reading is likely to facilitate increased language abilities in this cohort. What are the potential or actual clinical implications of this work?: Shared book reading is part of the daily routine for many parents and their children with Down syndrome. Integrating KWS is a relatively simple adaptation to this activity which is likely to enhance children's language skills. Therapists can encourage parents to do this at home to support work carried out at school and in a clinical setting. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this umbrella review was to determine the most effective nursing interventions for the prevention and management of suicidal behavior. In order to do so, the review identifies interventions from the Nursing Interventions Classifications taxonomy with evidence to this end. Methods: A systematic search was conducted for systematic reviews included in the PubMed, CINAHL, Cochrane, Scopus, Web of Knowledge, and Joanna Briggs Institute databases between January 1, 2011 and May 1, 2020. Findings: The review is made up of 21 systematic reviews. In order to carefully analyze the interventions described, these were coded into categories, grouping the interventions into those based on psychological therapy, interventions related to pharmacotherapy, interventions related to the professional relationship between health professional and patient, and interventions aimed at the general public. Conclusions: Nursing interventions for suicide risk management are prevalent in the scientific literature and are shown to be effective for patients with suicidal ideation or suicide attempts. Prevention and treatment of suicidal behavior require the provision of coping tools, behavioral interventions that directly address suicidal thoughts and behaviors, and support through therapeutic partnerships, among others. Implications for nursing practice: This paper synthesizes the most current evidence on the most effective Nursing Interventions Classifications interventions for the treatment and prevention of suicidal behavior. It provides nursing practitioners with a comprehensive review of the therapeutic interventions with the best evidence and is useful for the development of clinical guidelines and protocols, as well as for the development of health policies and plans. [ABSTRACT FROM AUTHOR]

This paper presents an inventive software interface for the integration of some extensively used information systems with the R programming environment. The contribution of this paper is two‐fold. First, a more comprehensive and specialized data analysis tool is provided. Second, a notable software code analysis model has been proposed to perform statistical tests and execute machine learning algorithms using the capabilities of the R programming language. The recommended application programming interface is successfully tested and evaluated based on a real‐life data set. The obtained results verify the high computing performance of the model in terms of the execution proceedings and achieved objectives. [ABSTRACT FROM AUTHOR]

Software resource allocation is an significant factor of system configuration which plays a critical role in guaranteeing the performance of multitier web service systems. Computing the optimal allocation of different software resources in order to meet performance requirements under dynamic workloads conditions is in highly challenging. Existing approaches mostly rely on translating domain knowledge from experts into computational solutions through heuristics‐based optimization techniques. While such techniques are useful, they cannot leverage actual usage data generated by system users which may contain allocation strategies that are not captured by domain experts' knowledge. In this paper, we propose an iterative feedback mechanism which solves the problem to some extent by optimizing software resource allocation of multitier web systems through imitating system users who have achieved excellent performance. Specifically, we propose a deep Q‐learning network‐based approach for performance prediction to deal with the dynamic changes of complex workloads. The performance prediction method involves the reinforcement learning method for capturing the dynamics of online software resource allocation, and then computing the current optimal policy. We implement the approach in the multitier web benchmark system, and the experimental results demonstrated significant improvement compared to models built based on domain knowledge. [ABSTRACT FROM AUTHOR]

Background: Digital‐first assessments leverage the affordances of technology in all elements of the assessment process: from design and development to score reporting and evaluation to create test taker‐centric assessments. Objectives: The goal of this paper is to describe the engineering, machine learning, and psychometric processes and technologies of a test security framework (part of a larger ecosystem; Burstein et al., 2021) that can be used to create systems that protect the integrity of test scores. Methods: We use the Duolingo English Test to exemplify the processes and technologies that are presented. This includes methods for actively detecting and deterring malicious behaviour (e.g., a custom desktop app). It also includes methods for passively detecting and deterring malicious behaviour (e.g., a large item bank created through automatic generation methods). We describe the certification process that each test administration undergoes, which includes both automated and human review. Additionally, we describe our quality assurance dashboard which leverages psychometric data mining techniques to monitor test quality and inform decisions about item pool maintenance. Results and Conclusions: As assessment developers transition to online delivery and to a design approach that places the test taker at the centre, it becomes increasingly important to take advantage of the tools and methodological advances in different fields (e.g., engineering, machine learning, psychometrics). These tools and methods are essential to maintaining the security of assessments so that the score reliability is sustained and the interpretations and uses of test scores remain valid. Lay Description: What is known about this topic?: As more and more testing programmes transition to test taker‐centric administrations, effective measures to prevent cheating and protect content are critical to ensure the validity and integrity of scores.Two of the most common forms of cheating in online testing are (a) having someone other than the person who has registered take the test, and (b) stealing content and providing it to others to assist them in achieving a higher score. What does this paper add?: In designing a test taker‐centric digital‐first assessment, a test security framework must inform decisions from end‐to‐end (i.e., registration, onboarding, communications regarding test taker behaviours, test preparation and practice, test administration, and post‐administrative activities including scoring).Security is safeguarded through active and passive design methods; active methods include having test takers attest that they will follow the rules governing testing and informing them that they will be videoed during testing; passive methods include a computer adaptive design that limits item exposure and test overlap rates, development of a large item pool using automated item generation, and applying artificial intelligence to review test administration videos to flag unauthorized behaviours for human review. Implications: With more educational and assessment programmes transitioning to online digital models, the paper presents a comprehensive review of security issues and identifies an integrated approach for preventing cheating and other unauthorized behaviours. [ABSTRACT FROM AUTHOR]

Background Study: The research discusses the need for a paradigm shift in engineering education current practices to accommodate the digital native students. The paper emphasizes the importance of integrating disruptive technologies, namely Virtual Reality (VR) through Head Mounted Displays VR (HMD VR) and Desktop Based VR (DB VR) and comparing it with the traditional teaching methods. The research focuses on second‐year undergraduate students enrolled in a concrete structures course. Objectives: The objective of the study is to analyse the impact of DB VR and HMD VR on students' motivation, performance and workload in comparison with traditional 2D drawings. The study also aims to investigate how simulation sickness and system usability could impact students' motivation and performance when interpreting concrete structures. Methods: The study divided second‐year civil engineering students into three groups, 2D, DB VR and HMD VR. The performance and motivation levels of students were assessed based on the number of mistakes identified and an intrinsic motivation survey. Furthermore, data on system usability and simulation sickness were collected. Spearman rank correlation was conducted to analyse the relationships between the variables tested. Results: The results have shown that students' motivation and performance were much higher when using VR in general and HMD VR specifically, students' efficiency and accuracy were also observed. On the other hand, usability was found to be moderately correlated with motivation, with students rating HMD VR as the most usable approach. A moderate inverse correlation was observed between simulation sickness and motivation, indicating that higher simulation sickness led to lower motivation levels. Conclusion: The study concludes that integrating VR in general, DB VR, or HMD VR into a concrete structures course for undergraduate students can significantly improve students' performance and motivation. Furthermore, enhancing usability of any of the methods used can also improve the performance and motivation. On the other hand, simulation sickness inversely affects motivation and performance, therefore, VR developers and educators should not neglect it. Lay Description: What is already known about the topic: Traditional teaching methods in engineering education require improvement.Integrating digital technologies into engineering education enhances performance.Digital technologies can potentially enhance motivation. What this paper adds: Compares traditional teaching methods for concrete structures interpretation with virtual reality.Establishes correlations between motivation, performance and usabilityIntroduces how desktop‐based VR and head‐mounted display VR can produce varying results. Implications for practice and/or policy: Decision‐makers can use the information to integrate updated pedagogy into engineering curricula.Allows for further exploration of different digital technologies that can be used for concrete structure interpretation.Allows instructors to introduce virtual reality in their courses. [ABSTRACT FROM AUTHOR]

The significant interest in using of the blockchain technology in higher education for keeping secure student's records storage and retrieval is noted. This paper describes a new system based on blockchain approach to store students' records accumulated in the educational process at the higher institution in extended format. Every block of the records contains four types of information about student: academic activities, research participation, cultural activities and achievements. The system suggests participation of educational institution, student and potential employers in usage of integrated machine learning and blockchain technologies. The novelty of our paper is that the system allows university and student to keep complete and accurate data about the entire student learning process, avoid forgeries of educational records, e‐portfolio inaccuracies and fabricated degrees. Additionally, it could be used by employers to find qualifying potential employees, using large amount of verified and systematically accumulated data. [ABSTRACT FROM AUTHOR]

Companies often claim to follow specific software development methodologies (SDM) when performing their software development process. These methodologies are often supported by dedicated tools that keep track of work activities carried out by developers. The purpose of this paper is to provide a novel approach that integrates analytical insights from both the perceptions of SDM stakeholders and software development tools logs to provide SDM improvement recommendations. This paper develops a new process improvement approach that combines two significantly different sources of data on the same phenomenon. First, it uses a questionnaire to gather software development stakeholder SDM perceptions (managers and developers). Second, it leverages process mining to analyze software development tools logs to obtain additional information on software development activities. Finally, it develops recommendations based on concurrent analysis of both sources. Our novel process improvement approach is evaluated in three directions: Does the presented approach (RQ1) enable managers to gain additional insights into employees' performance, (RQ2) deliver additional insights into project performance, and (RQ3) enable development of additional SDM improvement recommendations? We find that integrated analysis of software development perception data and software development tools logs opens new possibilities to more precisely identify and improve specific SDM elements. The evaluation of our novel process improvement approach follows a single case study design. Our approach can only be used in enterprises in which software development tools logs are available. The study should be repeated in different cultural settings. We practically show how concurrently analyzing data about developer SDM perceptions and event log data from software development tools enables management to gain additional insights in the software development process regarding the performance of individual developers. The main theoretical contribution of our paper is a novel process improvement approach that effectively integrates data from management and developer perspectives and software development tools logs. [ABSTRACT FROM AUTHOR]

A companion paper in this issue of Surface and Interface Analysis (Surf Interface Anal. 2021;1–6. doi: 10.1002/sia.7029) discusses corrections for the effects of analyzer modulation on peak intensity in derivative Auger spectra. Today, most Auger spectra are acquired in the direct mode, E·N(E), and derivatives are taken using a computer program to measure peak intensities (peak‐to‐peak heights) with the background suppressed, for ultimate quantification. In taking a derivative, there is sometimes a choice of the algorithm used for taking the derivative, but the most common method is to use the Savitzky–Golay algorithm. In using the Savitzky–Golay algorithm, there is also a choice in the number of data points used in the differentiation. To improve quantification, Martin Seah suggested using relatively large Gaussian broadening in the differentiation process to reduced effects of line shape differences in measuring signal intensities, as often occur due to chemical effects on spectra. This paper shows the effects of the number of points used with the Savitzky–Golay algorithm in taking the derivative on peak‐to‐peak heights in derivative Auger spectra and how dynamic background subtraction can be used to obtain Auger peak area values independent of the broadening of spectra caused by such differentiation. [ABSTRACT FROM AUTHOR]

The readers of this special issue can have a closer exploration to these papers and find more details from these papers. Due to the massive progress in hardware/software and clouding computation for information and communication technologies recently, artificial intelligent (AI) technologies become more powerful and feasible than before. [Extracted from the article]

Background: In 2018, it was found that only a quarter of Grade 3 children in India were reading at grade level. A growing demand for English education has further limited children's literacy achievement. Despite a strong evidence base in favour of using systematic phonics for building English literacy skills, many teachers in India continue to use rote‐methods of literacy instruction. Objectives: We aimed to examine the efficacy of GraphoLearn (GL) English Rime, a computer‐assisted reading intervention, in improving the foundational literacy skills of 1st and 2nd grade students who were attending an English medium school in India. Methods: A total of 136 students across 6 classrooms were randomly allocated to play either GL or a control math game over a 5‐week intervention period. Students were pre‐ and post‐tested on various English literacy skills using tasks built into the GL software as well as through oral and paper‐based tasks. Results and Conclusions: Students who played GL showed significantly greater and faster development on in‐game measures of letter‐sound knowledge, rime unit recognition, and word recognition as compared to students who did not play GL. In addition, GL resulted in greater effects on these measures for students with stronger English literacy skills prior to the start of the intervention. No differences were found between groups on the oral and paper‐based tasks. Implications: GL was able to quickly and effectively teach critical sub‐skills for reading. However, a lack of effects on the out‐of‐game measures opens the door for further discussion on the successful implementation of such interventions. Lay Description: What is already known about this topic:India has an ongoing literacy crisis with many children failing to acquire functional literacy skills.Under‐resourced schools,outdated pedagogical methods, and a demand for English‐medium education are partially to blame.Globally, technology is being increasingly looked at to help improve student learning levels across all subject areas.There are a lack of studies examining the efficacy of various ed‐tech tools to improve literacy in the context of India.What this paper adds:The study aimed to test GraphoLearn English (GL), a mobile game designed to teach and practice early reading skills through phonics with first and second graders.Results showed that children who played GL outperformed those who did not play on critical literacy skills such as letter‐sound knowledge and word recognition.In addition, GL was more effective for those who had higher pre‐existing English literacy skills.However, improvements were only seen on measures assessed in the game and not on measures assessed orally or on paper.Implications for practice and/or policy:GL and other educational technologies can be extremely effective tools which allow for an individualized learning experience.However, it is important that for any educational game, learning moves beyond the game itself.Teachers are critical players in this process and must ensure that tech‐based instruction is integrated with teacher‐led instruction.Policy makers and school administration must ensure that teachers are adequately trained on when, with whom, and how to use such technologies before they are introduced. [ABSTRACT FROM AUTHOR]

Background: Phonetic transcription of disordered speech samples is especially crucial for the assessment and treatment of functional or organic speech‐sound disorders. Previous studies show that students who struggle with the identification and segmentation of speech sounds are more likely to encounter difficulties with clinical phonetic transcription. Aims: To clarify the connection between phonemic awareness and phonetic transcription in Mandarin‐speaking undergraduate students, as well as the improvement of these skills after phonetic transcription training. Methods & Procedures: A pretest–post‐test design was adopted for this study. The experimental group (n = 38) consisted of students majoring in audiology and speech–language pathology who were given 12 weeks of transcription training. The control group (n = 33) consisted of audiology and speech–language pathology majors who completed the same module a year earlier without undergoing the transcription training. Two instruments were used to assess their skills: (1) the Phonemic Awareness Skill Test (PA test) and (2) the Phonetic Transcription Skill Test (PT test) designed for the purposes of the present study. Outcomes & Results: Performance on most subtests of the PT and PA tests improved significantly at post‐test for the experiment group, although only PT training was provided. After phonetic transcription training, the relationship between phonemic addition abilities and phonetic transcription strengthened. Phonemic identification was found to be a predictor of undergraduate students' PT skills of disordered speech samples. Conclusions & Implications: Results from this study provide insights into the role of phonetic transcription training in improving phonemic awareness and phonetic transcription skills. What This Paper Adds: What is already known on the subject: Phonetic transcription of disordered speech samples is especially crucial for the assessment and treatment of functional or organic speech‐sound disorders. Phonemic awareness is not only an essential ability for reading alphabetic language but also associated with acquiring phonetic transcription skills. What this paper adds to existing knowledge: A dynamically changing association between phonemic transcription and phonetic transcription during transcription training was reported. Phonetic transcription training of disordered speech samples acted as a driving force for deeper relationships between the two skills. What are the potential or actual clinical implications of this work?: Training phonetic recognition and transcription equips future and practising speech–language pathologists with better skills in managing speech‐sound disorders. Implications for phonemic awareness training also have the potential to extend to the literacy skills of alphabetic language speakers. [ABSTRACT FROM AUTHOR]

Context: GitHub is the world's most prominent host of source code, with more than 327M repositories. However, most of these repositories are not labelled or inadequately, making it harder for users to find relevant projects. Various proposals for software application domain classification over the past years have been proposed. However, these several of those approaches suffer from multiple issues, called antipatterns of software classification, that reduce their usability. Objective: In this paper, we propose a new taxonomy in the GitHub ecosystem, called GitRanking, starting from a well‐structured data set, composed of curated repositories annotated with topics. The main objective is to create a baseline methodology for software classification that is expandable, hierarchical, grounded in a knowledge base, and free of antipatterns. Method: We collected 121K topics from GitHub and used GitRanking to create a taxonomy of 301 ranked application domains. GitRanking (1) uses active sampling to ensure a minimal number of annotations to create the ranking; and (2) links each topic to Wikidata, reducing ambiguities and improving the reusability of the taxonomy. Furthermore, we adopt the conceived taxonomy in a classification task by considering a state‐of‐the‐art classifier. Results: Our results show that GitRanking can effectively rank terms in a hierarchy according to how general or specific their meaning is. Furthermore, we show that GitRanking is a dynamically extensible method: it can currently accept further terms to be ranked, and with a minimum number of annotations (≈15$$ \approx 15 $$). Concerning the classification task, we show that the model achieves an F1‐score of 34%, with a precision of 54%. Conclusion: This paper is the first collective attempt at building a ground‐up taxonomy of software domains. Our vision is that our taxonomy, and its extensibility, can be used to better and more precisely label software projects. [ABSTRACT FROM AUTHOR]

Software refactoring focuses on improving software quality by applying changes to the internal structure that do not alter the observable behavior. Determining which refactorings should be applied and presented to developers the most relevant and optimal refactorings is often challenging. Existing literature suggests that one of the potential sources to identify and recommend required refactorings is the past software development and evolution histories which are often archived in software repositories. In this article, we review a selection of existing literature that has attempted to propose approaches that facilitate refactoring by exploiting information mined from software repositories. Based on the reviewed papers, existing works leverage software history mining to support analysis of code smells, refactoring, and guiding software changes. First, past history information is used to detect design flaws in source code commonly referred to as code smells. Moreover, other studies analyze the evolution of code smells to establish how and when they are introduced into the code base and get resolved. Second, software repositories mining provides useful insights that can be used in predicting the need for refactoring and what specific refactoring operations are required. In addition, past history can be used in detecting and analyzing previously applied refactorings to establish software change facts, for instance, how developers refactor code and the motivation behind it. Finally, change patterns are used to predict further changes that might be required and recommend a set of files for change during a given modification task. The paper further suggests other exciting possibilities that can be pursued in the future in this research direction. This article is categorized under:Algorithmic Development > Text MiningApplication Areas > Data Mining Software Tools [ABSTRACT FROM AUTHOR]

This paper presents software for calculating the optimal mass of samples with complex compositions (e.g. supported metal catalysts) for X-ray absorption spectroscopy (XAS) and scattering measurements. The ability to calculate the sample mass and other relevant parameters needed for an XAS measurement allows experimentalists to be better prepared in terms of detector selection, energy range of scan and overall time needed to complete the measurement, thus increasing efficiency. CatMass builds on existing sample mass calculators allowing users to determine the optimum sample preparation, collection geometry, usable energy range for a scan and approximate edge step of the absorption event. Visualization tools present the absorption calculation results in a format familiar to XAS experimentalists, with the added ability to save calculations and plots for future reference or recalculation. CatMass is a program broadly applicable in catalysis and is helpful for users with complex samples due to composition/stoichiometry or multiple competing elements. [ABSTRACT FROM AUTHOR]

Artificial neural networks are an integral part of the curriculum in many undergraduate computer science programs. This paper introduces NNeduca, a simulation software system designed to teach undergraduate students the fundamental concepts of artificial neural networks. The system is written in Java and aims to provide an intuitive user interface, support for research and learning activities, and the ability to expand. This tool allows students to design and train a neural network with any architecture they desire and visualize some artificial neural network theoretical concepts such as definitions, topologies, training methods, and structure. An overview of educational software systems to aid in learning the fundamental concepts of neural networks was created based on a systematic literature review. A comparison of existing simulation systems and the developed NNeduca tool was performed in this paper. The evaluation was carried out using a newly developed model for assessing the quality of selected systems using established criteria formed as a result of a thorough examination of relevant methods of evaluating educational software. The usability evaluation results show that the NNeduca tool received the highest ratings, indicating that it can significantly improve the teaching process in artificial neural network courses. [ABSTRACT FROM AUTHOR]

Background: For pregnant and birthing women, maintaining a relationship with the same health care practitioner is critical to establishing a sense of safety. In postsocialist Serbia, where care is fragmented and depersonalized, this continuity is often missing. Patients are frequently handed over to new practitioners, sometimes without even knowing the name of the person overseeing their care. In response, many women turn to the private sector to keep the same practitioner across their prenatal period and their birth by establishing a personal "connection" within the hospital that they believe will shield them from mistreatment during childbirth. Methods: Data presented in this paper were collected through participant observation in one public maternity hospital and one public primary care center in Serbia; semistructured interviews with 14 physicians; and semistructured interviews with 80 women who had given birth in one public maternity hospital in Serbia. Results: Public maternity hospital physicians who supplement their income working in the private sector have the power to blur the distinctions between favors and services. They offer continuity of personalized care to their private clients/patients once they enter the public system, and they themselves obtain social and economic security unavailable to those working in only one sector. At the same time, there is evidence that personalized continuity of care does not actually shield women from mistreatment in hospitals. Conclusions: The private sector is reshaping existing informal strategies and blurring the lines between formal and informal payments. In the postsocialist context, consumer practices are not separate from, but entangled with, informality. Neoliberalism has not brought about empowerment and eliminated informality, but has instead further exacerbated existing inequalities in maternity care in Eastern Europe. [ABSTRACT FROM AUTHOR]

As software projects evolve and grow in size and complexity, so do their test suites. Test suite reduction (TSR) aims at reducing the size of a test suite by removing redundant and obsolete test cases based on a coverage metric while preserving its fault detection capabilities. The contributions of this paper are twofold: (1) We examine a lesser‐known coverage criterion, that is, checked coverage. Checked coverage not only investigates if a part of the code was executed but also if it was checked by a test oracle. In an empirical evaluation, we performed TSR based on different reduction algorithms, coverage metrics, and open‐source Java projects with our own TSR tool to determine the most effective and efficient combination of metric and method. (2) Given the results of the first evaluation, we further investigate the potential of parameter optimization in regard to a genetic reduction algorithm. In particular, we focus on finding a general setting for the parameters crossover rate and mutation rate such that test suites can be reduced in a reasonable time while maintaining a high fault detection power. [ABSTRACT FROM AUTHOR]

Summary: In software development, developer turnover is among the primary reasons for project failures, leading to a great void of knowledge and strain for newcomers. Unfortunately, no established methods exist to measure how the problem domain knowledge is distributed among developers. Awareness of how this knowledge evolves and is owned by key developers in a project helps stakeholders reduce risks caused by turnover. To this end, this paper introduces a novel, realistic representation of problem domain knowledge distribution: the ConceptRealm. To construct the ConceptRealm, we employ a latent Dirichlet allocation model to represent textual features obtained from 300 K issues and 1.3 M comments from 518 open‐source projects. We analyze whether the newly emerged issues and developers share similar concepts or how aligned the individual developers' concepts are with the team over time. We also investigate the impact of leaving developers on the frequency of concepts. Finally, we also evaluate the soundness of our approach on a closed‐source software project, thus allowing the validation of the results from a practical standpoint. We find out that the ConceptRealm can represent the problem domain knowledge within a project and can be utilized to predict the alignment of developers with issues. We also observe that projects exhibit many keepers independent of project maturity and that abruptly leaving keepers correlates with a decline of their core concepts as the remaining developers cannot quickly familiarize themselves with those concepts. [ABSTRACT FROM AUTHOR]

Regression test prioritization (RTP) is an active research field, aiming at re‐ordering the tests in a test suite to maximize the rate at which faults are detected. A number of RTP strategies have been proposed, leveraging different factors to reorder tests. Some techniques include an analysis of changed source code, to assign higher priority to tests stressing modified parts of the codebase. Still, most of these change‐based solutions focus on simple text‐level comparisons among versions. We believe that measuring source code changes in a more refined way, capable of discriminating between mere textual changes (e.g., renaming of a local variable) and more structural changes (e.g., changes in the control flow), could lead to significant benefits in RTP, under the assumption that major structural changes are also more likely to introduce faults. To this end, we propose two novel RTP techniques that leverage tree kernels (TK), a class of similarity functions largely used in Natural Language Processing on tree‐structured data. In particular, we apply TKs to abstract syntax trees of source code, to more precisely quantify the extent of structural changes in the source code, and prioritize tests accordingly. We assessed the effectiveness of the proposals by conducting an empirical study on five real‐world Java projects, also used in a number of RTP‐related papers. We automatically generated, for each considered pair of software versions (i.e., old version, new version) in the evolution of the involved projects, 100 variations with artificially injected faults, leading to over 5k different software evolution scenarios overall. We compared the proposed prioritization approaches against well‐known prioritization techniques, evaluating both their effectiveness and their execution times. Our findings show that leveraging more refined code change analysis techniques to quantify the extent of changes in source code can lead to relevant improvements in prioritization effectiveness, while typically introducing negligible overheads due to their execution. [ABSTRACT FROM AUTHOR]

Background: Effective learning in computer programming courses has been a constant challenge for university teachers and has become a relevant competence for current professionals. The literature on gamification in learning presents mixed results, mainly due to problems in instructional design and inconsistency in gamification. Studies with positive empirical results on computer programming formative activities and showing under what conditions gamification is effective still need to be included. Objectives: This study aimed to quantify the effects of gamification techniques in software tools that support computer programming learning applied to data‐structures university students. The study focused on determining whether there are differences in learning when using gamification techniques in online exercising formative activities. The effects of gamification on learning objectives with diverse complexity are studied. Methods: The study was conducted on computer engineering university students following a pretest‐intervention‐posttest design. They were divided into control and experimental groups, where both used a bank of exercises in Moodle to support learning. The experimental group considered gamification techniques, including points, leaderboards, and badges. Results and Conclusions: Gamification techniques significantly outperformed in learning gains in exercising activities without gamification. Regarding learning complexity, increasing taxonomic complexity did not negatively affect gamification learning effects. Lay Description: What is currently known about the subject matter?: Gamification has been explored as a strategy to enhance learning motivation in various educational contexts, including computer programmingLiterature shows mixed quantitative results on learning improvements under gamification.Few studies cover formative exercises, deal with computer programming tasks, or use a LMS like Moodle.Lack of literature exploring the conditions for effective gamification use, and involving learning objective complexity. What does this paper add to the existing body of research?: Quantifies the impact of gamification techniques on learning outcomes, specifically focusing on computer‐programming in university.Provides empirical evidence on gamification significantly improving learning gains compared to non‐gamified data‐structures exercisesGamification is effective in tasks of diverse levels of taxonomic complexity, with consistent effectiveness regardless of learning difficulty. Implications of study findings for practitioners: Gamification techniques, can be used in higher education to enhance student engagement and learning outcomes in computer programmingThe gamification information (e.g. points and ranking) do not require an automatic update, can be processed manually and published daily.It is effective to use gamification for learning tasks of medium‐high difficulty in Moodle on computer programming topics.It is key a thoughtful instructional design when applying gamification, ensuring alignment with learning objectives and task complexity. [ABSTRACT FROM AUTHOR]

In a drone system with dual data links, this article presents a redundant data processing algorithm that can minimize flight control instability without increasing the weight and price of the aircraft by software processing of duplicate received messages. [ABSTRACT FROM AUTHOR]

Despite progress in computer automated solutions, constitutional isomer verification by NMR using one‐ and two‐dimensional data sets is still, in the main, a manual, user‐intensive activity that is challenging for a number of reasons. These include the problem of simultaneously keeping track of the information from a number of separate NMR experiments and the difficulty of another researcher subsequently verifying the assignments made without having to independently repeat the whole analysis. This paper describes a graphical interactive approach that overcomes some of these problems. By using concepts used to visualise graph networks, we have been able to represent the NMR data in a manner that highlights directly the link between the different NMR experiments and the molecule of interest. Furthermore, by making the graph networks interactive, a user can easily validate and correct the assignment and understand the decisions made in arriving at the solution. We have developed a usable proof‐of‐concept computer program, 'simpleNMR', written in Python to illustrate the ideas and approach. [ABSTRACT FROM AUTHOR]

Summary: Concurrent software needs to be maintained over time, and the differences between continuous versions tend to be localized. The expense that simply reapplying standard model checking techniques to the new version as they evolve may be infeasible. The existing methods reuse partial state‐space to reduce the scope. However, it is obviously costly to analyze on the explosive interleaving space of the evolving concurrent programs. The conservative change‐impact analysis methods without considering the specific property and leveraging the verified result from the prior version often results in exploring redundant state‐space irrelevant to this property. Moreover, the impact of the deleted elements needs to be analyzed on old version, and their impact needs to be mapped to new version, bringing some dispensable costs. In this paper, we propose a change‐aware model checking method based on program dependence net (PDNet) for linear temporal logic (LTL). We first propose an incremental modeling method to construct a PDNet of new version by modification rules. Then, we propose a reuse checking algorithm to judge whether the verified result can be reused based on the PDNet slice. Finally, we implement change‐aware model checking tool (DAMER) and validate the advantages of our methods. [ABSTRACT FROM AUTHOR]

Requirements prioritization is giving priority to software requirements on the basis of its importance. The current research paper addresses the importance and limitations of existing research on requirements prioritization. A lot of requirements prioritization techniques are presented by authors, but these techniques have certain limitations. Not all techniques are either applied or suitable to prioritize all types of requirements. Similarly, in these techniques, only few are scalable for large size requirements. The objective of the current research is to identify all possible software requirements prioritization techniques and to show the suitability of these techniques to prioritize different types and sizes of requirements. To achieve research objectives, a systematic literature review (SLR) is carried out. In conducting SLR, planned protocol is designed, which include all the necessary steps necessary for SLR process. As a result of SLR, 60 primary studies were selected for data extraction as a result of SLR. Outcome of SLR shows that not all techniques are scalable to prioritize any size requirements. Also, there are few techniques found that are either applied on all types of requirements or not suitable to prioritize all types of requirements as these techniques are specifically designed for particular types of requirements. [ABSTRACT FROM AUTHOR]

Background Study: Several meta‐analysis studies have investigated the effects of mobile learning on learning performance. However, limited attention has been paid to pedagogy in mobile learning, making quantitative evidence of the effects of pedagogical approaches on learning performance in mobile learning scarce. Filling this gap can therefore help stakeholders understand which mobile pedagogical approaches might work or not under which learning conditions, hence achieving better learning experience and performance. Objectives: To address this gap, this study conducted a meta‐analysis and research synthesis of the effects of integrated pedagogical approaches on students' learning performance in mobile learning. Additionally, this study analysed the field of education, level of education, learning setting, sample size, and mobile device as moderating variables of the effect of pedagogical approaches. Methods: The software Comprehensive Meta‐Analysis V.3 was used for this meta‐analysis, where Hedges' g was calculated for the effect sizes. Specifically, 70 quantitative studies (N = 5575 participants) were coded and analysed. Results: The results indicate that pedagogical approaches in mobile learning have a large effect on students' learning performance (g = 0.93, p < 0.001). The most effective pedagogical approach was project‐based learning (huge effect), while collaborative learning, situated learning and game‐based learning had large effects. Finally, cognitive theory of multimedia learning and inquiry‐based learning had medium effects. The results also indicate that the effect is moderated by the field of education, the level of education, the learning setting, the sample size, and the mobile device. Finally, it is found that study quality might influence the overall effect size. Conclusions: The findings of this study can be beneficial to both researchers and practitioners as they highlight and discuss which pedagogical approaches could be more effective in mobile learning under specific conditions. Further research, on the other hand, should focus on covering additional moderators of learning performance like mobile device type and screen size which is one of the limitations of this study. Lay Description: What is already known about this topic: Mobile learning is widely adopted in education.Mobile learning can enhance learning outcomes. What this paper adds: Pedagogical approaches in mobile learning have different effects on students' learning performance.The most effective pedagogical approach in mobile learning is project‐based learning (huge effect).Which pedagogical approaches could be more effective in mobile learning under specific conditions. Implications for practice and/or policy: This study enriches the ongoing debate on how to design effective mobile learning interventions.This study provides substantial evidence about which pedagogical approaches in mobile learning would be more effective under which circumstances. [ABSTRACT FROM AUTHOR]

Background: Immersive virtual reality (IVR) is a new technology that could motivate learners, but also could contain distracting elements that increase cognitive demands on learners. In contrast, learning with conventional media, such as a narrated slideshow could be less motivating, but also less distracting. Objectives: This experiment investigates: (1) in what situations executive function and working memory capacity play a role in learning in IVR and a slideshow lesson and (2) whether people learn science content better with IVR or with a narrated slideshow lesson. Method: Participants watched a lesson on ocean acidification either using a head‐mounted display IVR device or a pre‐recorded narrated slideshow lesson. A week later, they took a test on this material and completed tasks to assess executive function and working memory capacity. Results and Conclusion: On a post‐test, there was no difference between IVR and slideshow lessons, but there were differences in the role of executive function in learning with these two media. For students learning in IVR, executive function correlated significantly with post‐test scores, such that learners with stronger executive function did better on the posttest than those with weaker executive function, while for students learning with a slideshow lesson, executive function and working memory capacity did not correlate significantly with posttest scores. This research indicates that it is important to understand how a learner's attentional controls impact their understanding when using IVR for learning. Lay Description: What is already know about this topic: Immersive virtual reality increases demands on cognition for learners.Immersive virtual reality for learning has inconsistent effects in prior research.Executive function and working memory capacity are important components in processing material while learning. What this paper adds: Research focused on the role individual differences play in learning with technology.Using common cognitive assessments to understand the process of learning better. Implications for practice and/or policy: Executive function predicted test score for an immersive virtual reality lesson.Use of immersive technology needs to take into account executive function to reduce potential inequity in learning. [ABSTRACT FROM AUTHOR]

Background: With the development of computational literacy, there has been a surge in both research and practice application of text‐based and block‐based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education settings. Objectives: To further compare programming effects between and within text‐based and block‐based modalities, this research conducted a quasi‐experimental research in China's secondary school. Methods: An online programming platform, Code4all, was developed to allow learners to program in text‐based and block‐based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag‐sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two modalities. Results and Conclusions: The results indicated that learners in text‐based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block‐based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural patterns. Implications: Three major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process‐oriented and summative analysis to reveal learners' programming learning quality. Lay Description: What is currently known about the subject matter: Programming has the potential to improve learners' higher‐order thinking skills.Block‐based and text‐based modalities are two major instructional methods.There has been a growing interest to understand how learning occurs in two modes.Most previous work has evaluated two modalities based on learners' knowledge, skills, and attitudes. What this paper adds: Code4all allows learners to programming in text‐based and block‐based modalities.Quasi‐experimental research was conducted to examine block‐based and text‐based programming modalities.Multimodal learning analytics were used to compare programming under two modalities.Learners' programming features, behaviouralbehavioral patterns, and knowledge gains were identified under two modalities. The implications of study findings for practitioners: Instructors should integrate text‐based and block‐based modalities into programming courses.Process‐oriented assessment should be integrated with summative assessment. Adaptive, timely scaffoldings should be provided with the external support (this should be marked like above two points). [ABSTRACT FROM AUTHOR]

Background: Online collaboration tools have been identified as potentially effective means for enhancing student learning, motivation, and engagement in science education. However, their effectiveness in improving science education outcomes among middle school students remains uncertain. Objectives: The study aimed to investigate the impact of online collaboration tools on science education outcomes among middle school students, focusing on learning achievement, engagement, and motivation. Methods: A quasi‐experimental design with a pretest‐posttest control group was used in this study. A total of 60 eighth‐grade middle school students were involved in the study, with both the experimental and control groups comprising 30 students each. The experimental group incorporated the use of digital collaboration platforms, including Asana, Slack, and Team Viewer, as instrumental components of group project execution and discussion in their science lessons. Conversely, the control group adhered to the current educational approach in Turkey, characterized by the employment of an inquiry‐based learning strategy in their science instruction. Data was collected over eight weeks using a science achievement test, engagement scale, and science motivation scale. Results: The experimental group demonstrated significantly higher post‐test scores compared to the control group, suggesting that online collaboration tools positively impacted science education outcomes. Additionally, survey data indicated high levels of engagement and motivation among the experimental group students when using the online collaboration tools. Conclusions: The findings support the notion that online collaboration tools can effectively enhance learning, motivation, and engagement in science education among middle school students. These results have significant implications for educators, educational institutions, policymakers, and curriculum developers. Further research is needed to examine the potential of these tools in various educational contexts and with different student populations. This will help broaden the understanding of how online collaboration tools can be integrated into diverse learning environments, potentially benefiting a larger number of students and further improving science education outcomes. Lay Description: What is already known about this topic: Technology's role, particularly online collaboration tools, in enriching science education is undebatable (Donkin & Rasmussen, 2021; Rahmawati et al., 2022). Such tools have been theorized to bolster student learning outcomes, motivation, and engagement (Donnelly et al., 2013; Ekici, 2017; Khazanchi & Khazanchi, 2019). Yet, the empirical evidence validating their efficacy in science educational settings remains sparse. What this paper adds: This research delves into the influence of online collaboration tools on science learning outcomes among middle school learners. Data indicates that employing these digital tools substantially elevates student performance, as gauged by a science achievement test. Furthermore, students navigating through these online collaborative platforms recorded elevated levels of both engagement and motivation. The primary effectiveness of online collaboration tools stems from their ability to foster specific behaviours, such as increased communication frequency and structured task management, which can amplify learning and group collaboration through their facilitated structured interactions. Implications for practice and/or policy: This study's conclusions offer pivotal insights for educators, academic institutions, policy framers, and curriculum architects. Online collaborative platforms exhibit promise in amplifying student outcomes and engagement within the realm of science education. It is imperative to broaden this research scope to discern the tools' potential across varied educational environments and diverse student demographics. There is a compelling case for stakeholders, especially educators and policymakers, to contemplate incorporating these online tools into science pedagogy, aiming to magnify student learning experiences and engagement levels. [ABSTRACT FROM AUTHOR]

Background: Total laryngectomy (TL) results in permanent functional changes requiring rapid development of complex new skills. A significant portion of this learning happens in the acute post‐surgical stage. There is increasing interest in enhanced recovery after surgery (ERAS) protocols in TL; however, implementation has been difficult. COVID‐19 has placed significant pressures on acute services, requiring rapid service changes for TL patients. Aims: To understand the acute patient experience of having a TL both before and during COVID‐19. Methods & Procedures: Semi‐structured interviews using a pre‐designed topic guide were conducted with 10 people who had undergone a TL within the last 2 years. Participants were recruited by their speech and language therapists using purposive sampling. Braun and Clarke's iterative approach to data collection and thematic analysis was used to generate key themes from the data. Outcomes & Results: Thematic analysis identified four main themes: (1) pre‐operative information‐giving: 'it was just words'; (2) decision‐making influences: 'I just wanted them to get it all out and get it over with'; (3) coping with adjustment to the new normal: 'this is part of me now'; and (4) the importance of relationship‐building: 'when you've had something like this, you need some care and understanding'. Conclusions & Implications: The need for an individualized approach to TL intervention which incorporates medical and psycho‐social approaches from pre‐treatment to acute discharge is vital. ERAS models should be reviewed to shift beyond the medical model alone. Rapid service changes due to COVID‐19 did not contribute any major changes to the acute patient‐reported experience. What this paper adds: What is already known on the subject: We know that ERAS protocols have the potential to improve patient outcomes following TL. However, the research does not consider anything other than the early oral feeding debate and it has therefore been difficult to implement ERAS protocols in current service models. COVID19 required head and neck cancer services to make quick changes to surgical pathways, with the potential that some ERAS protocols had been adopted inadvertently. In order to understand the impact of this, we need to understand the patient experience following TL both before and during COVID19. What this paper adds to existing knowledge: This paper used qualitative interviews to understand the acute patient experience following TL both during and before COVID19. Findings from these interviews highlighted that people were on the most part, well prepared for the functional changes they would experience after surgery. However, people felt there were gaps in service delivery at the pre‐treatment and early discharge home period. Overall, the gaps identified were from a more psycho‐social need suggesting that future ERAS models of care should consider both medical and psycho‐social principles to enhance patient experience and outcome. What are the potential or actual clinical implications of this work?: Pre‐treatment services provided to people who have a TL could be reviewed to help maximize long‐term adjustment to life. Areas which could be reviewed include the method and mode of information delivery. Further work needs to be done in partnership with community services to improve the immediate discharge home experience. [ABSTRACT FROM AUTHOR]

This paper explores the dynamic long‐term relationship between software protection and software piracy rates across 108 countries over the period of 20 years (1994–2015), with the help of the ARDL model. The analysis employs a new way of utilising the membership duration of Trade related Intellectual Property Rights agreement and World Intellectual Property Organisation Copyright Treaty agreements of different countries in a panel framework, apart from constructing an index of copyright software protection by focusing on enforcement provisions. The paper overcomes various shortcomings of earlier literatures by thoughtful inclusion of independent variables and methodological techniques. Results show that both copyright software protection and income share a negative long‐term relationship with software piracy. Even the enforcement provisions are important in mitigating software piracy. In addition, the model finds a negative relationship between demand for intellectual property protection and software piracy rates. [ABSTRACT FROM AUTHOR]

In this paper, the simulation model of TCO Luminance angular uniformity is established using TechWiz and LightTools software. The simulation result is highly consistent with the measured value, which can realize the early evaluation of the design scheme, avoid the repeated modification of design due to noncompliance with the specifications after MDL production, and save R & D cost and time. Through this simulation, the influencing factors related to the panel and BLU of TCO Luminance angular uniformity are deeply studied. Based on the BLU with DPP film structure, a high brightness and low‐cost MNT TFT‐LCD design scheme meeting TCO certification and CR 2200 under ES8.0 is given. [ABSTRACT FROM AUTHOR]

Background: Screen‐sharing technologies enable applications such as screen mirroring, video streaming and instant messaging across multiple device screens. Despite their increasing use in many contemporary classrooms, there is a paucity of research directly examining pedagogical benefits and issues of these technologies. Objectives: This study investigated the influence of screen‐sharing technologies on teachers' practices, highlighting pedagogical benefits and issues encountered. Methods: The paper drew on a sample of 321 K‐12 teachers and utilised principal components analysis, descriptive statistics and inductive coding. Results and Conclusions: Teachers reported greater mobility, increased ease of content sharing and deeper learner cognition as attributes of improved learning and teaching with the technologies. However, a minority highlighted technical issues such as network and hardware problems affecting their confidence. Despite increased mobility as an affordance, teachers' perceptions about mobility largely appeared to be predicated on assumptions that the technologies were intended for didactic instruction. Takeaways: The study contributes to a more nuanced understanding of screen‐sharing affordances while underscoring learner‐led screensharing as a focus for future research and practice. Lay Description: What is already known about this topic: Screen‐sharing technologies are becoming commonplace in many contemporary educational settings.The technologies represent integrated systems that enable functions such as mirroring, video streaming, online polls, real‐time editing, screen‐capturing and learner analytics.Screen‐sharing is indirectly examined in several studies with findings indicating improvements in group awareness, emotional engagement, learner confidence to share ideas and teachers' visualization of concepts.Existing findings are largely anecdotal and there are very few studies that have focused directly on screen‐sharing affordances. What this paper adds: An empirical study directly examining teachers' first‐hand perspectives of using screen‐sharing technologies.Benefits relate to greater task focus, deeper thinking, improvements to teaching practice and ease of sharing content.A minority of teachers experienced issues such as technical problems, inappropriate sharing and distraction.Mobility was recognized as a key affordance though remained underutilized by many teachers. Implications for practice and/or policy: Professional learning accompanying the use of screen‐sharing technologies can support teachers to take advantage of all features of screen sharing such as enhanced teacher mobility.Institutions need to provide requisite technical and professional support for teachers to effectively and reliably leverage screen‐sharing in the classroom.There is a need to explore teacher's and learner's screen‐sharing uses in situ to more fully understand issues and possibilities. [ABSTRACT FROM AUTHOR]

Background and Context: Most studies about programming misconceptions are conducted at the undergraduate and graduate levels. Since the age level for starting learning programming is getting lower, there is a need for determining programming misconceptions for younger learners. Objective: Our goal is to determine programming misconceptions and their time‐resistance for programming novices at the K‐12 level using Python as a text‐based programming language. Method: We conducted research in the school settings during informatics classes among 98 fifth‐grade students in three elementary schools. We analysed the data collected from two Python tests students took immediately after programming lectures and 5 months later. Results and Conclusions: We detected seven misconceptions: (a) Assigning the expression instead of the calculated value, (b) When reassigning the variable value, assigning the sum value to the variable, (c) Using the symbolic name of the variable instead of its value, (d) Using the first (or previously) assigned variable value, (e) Datatype misconception, (f) Sequencing misconception, and (g) Swapping variable values. By shifting focus on variables and spending more time introducing variables and sequencing during teaching, the appearance of known misconceptions is minimized compared to previous findings. Lay Description: What is already known about this topic: Programming misconceptions are known issues since the beginning of learning programming in the early 1980sMost programming misconceptions are time resistant.Most studies about misconceptions are conducted at the undergraduate and graduate levels. What this paper adds: The goal of the study is to determine programming misconceptions for programming novices at the K‐12 level.Three more misconceptions about basic programming concepts are revealed compared to the previous findings.By shifting focus on variables and spending more time introducing variables and sequencing during teaching, the appearance of known misconceptions is minimized. Implications for practice and/or policy: Being aware of misconceptions is an important aspect of a teacher's pedagogical knowledge to foster students learning.Aiming to prevent the known misconceptions, teachers can minimize it by interventions during teaching.The educators should emphasize the underlying conceptual programming concepts rather than syntactic ones related to the programming language. [ABSTRACT FROM AUTHOR]

Background: In learning mechanics, students often believe that "force is exerted on moving objects." As this misconception called "motion implies a force" (MIF) is difficult to correct, various teaching methods have been proposed, such as showing refutational/explanatory text (Palmer & Flanagan, 1997; Takagaki, 2004), promoting students' conceptual development (Dekkers & Thijs, 1998) and epistemology‐focused instruction (Elby, 2001). Objectives: The purpose of this research is to empirically examine the effectiveness of an alternative method we have proposed for helping students overcome MIF misconceptions. The method is called the "Error‐based simulation (EBS)" that creates a counterexample to students' misconceptions by simulating erroneous phenomena based on students' incorrect ideas. Methods: We conducted a two‐year experimental implementation of EBS in mechanics classes at a technical college, and investigated the results. Students worked on the pre‐test, the exercise with EBS, the post‐test, and the delayed post‐test. Results and Conclusions: In both years, MIF‐answers (wrong answers due to MIF) in the learning task (problems learned with EBS) significantly decreased from the pre‐test to the post‐test and the delayed post‐test. The decrease in other erroneous answers was not significant. EBS was effective mainly in correcting MIF‐answers, and the effect was maintained over the long term. In the transfer task (new problems), the ratio of MIF‐answers to all erroneous answers in both the post‐test and delayed post‐test was much lower than that on the learning task in the pre‐test. These results suggest that learning with EBS not only corrected MIF‐answers but also promoted the correction of MIF misconceptions. A comparison between our results and those of other methods for correcting MIF misconceptions revealed that the effect of EBS was equal to or greater than that of other methods mentioned above. Lay Description: What is currently known about the subject matter: In learning mechanics, students often believe "continuing force is exerted on moving objects."This misconception called "motion implies a force" (MIF) is difficult to correct.Various teaching methods have been proposed and practiced.However, MIF misconceptions do not completely disappear yet. What our paper adds to this: We propose an alternative method for correcting MIF misconceptions called EBS.EBS provides a counterexample to students' misconceptions by simulation.EBS helps students discover the correct concept by themselves.We report the results of a two‐year experimental use of EBS at a technical college. The implications of study findings for practitioners: Our experience shows EBS is useful in correcting MIF misconceptions.The effectiveness of EBS is at least equal to or greater than other methods.Learning with EBS probably promotes the correction of errors at a conceptual levelWe believe our results have a certain degree of generality and usefulness. [ABSTRACT FROM AUTHOR]