Dynamic Assessment of L2 Listening Comprehension in Transcendence Tasks

AbstractL2 assessment researcher have long been preoccupied with the question of whether the learners can reapply their newly gained abilities in the non-assessment contexts, a concept traditionally known as generalizability (Bachman, 2004; Cronbach, 1990; Messick, 1995; Poehner, 2008) The present study was designed to flesh out the concept of generalizability from a qualitatively different perspective, namely, Vygotsky's (1978) Socio-cultural Theory (SCT) of Mind under a new framework known as transcendence (TR) (Poehner, 2010). This study, inspired by Poehner's (2009) interactionist group dynamic assessment G-DA, and Feuerstein et al.’s (1976) Mediated Learning Experience (MLE) concept, has set out to track the developmental trajectories of L2 learners’ listening comprehension ability within a microgenetic framework in hopes to bring into perspective learners’ qualitative changes during the interaction and mediation collaboratively negotiated in their ZPD across a set of different innovative tasks. The data for this G-DA study were collected from the classroom context (Poehner, 2009). The results, including both the quantitative and qualitative, indicated that non-dynamic assessment (NDA) procedure stops short of fully capturing the learners’ underlying potential and leaves aside the abilities which are in the state of ripening. It was shown that the learners’ ability to recognize an unrecognized word of the pretest transcended beyond the posttest task to the TR session, an improvement signaling their progressive trajectories towards higher levels of ZPD. On implication side, the paper recommends the use of DA as a development-oriented procedure to assess the learners’ abilities, a procedure which focuses on the learners’ emerging abilities in constantly innovative tasks

Iranian EFL Teachers' Emotional Intelligence and their Use of Speaking Strategies

The study was designed to investigate the differences among Iranian EFL teachers in terms of emotional intelligence (EI) and their use of speaking strategies. To this end, 90 EFL male and female teachers teaching English at 9 institutes in Behshahr, Sari, and Amol cities in Mazandaran Province (north of Iran) were randomly selected. The research data were collected through the Bar-On EQ-I scale and teachers' use of speaking strategies questionnaire. Results on Independent Sample t-test reported significant differences in teachers' EI across gender. One-way ANOVA revealed differences in teachers' EI across years of teaching experience. Furthermore, results on Kruskal Wallis Test indicated differences in teachers' use of each speaking strategy regarding their level of EI.  Based on the findings, teachers with a higher level of EI preferred to focus on both accuracy and fluency and apply story-telling activities to create more successful interaction. While teachers with a lower level of EI preferred to focus on accuracy, they liked to apply information-gap activities. They preferred to offer implicit feedback through reformulation and tended to design groups and pairs to make silent students interact in the classroom. Moreover, both groups preferred to correct their learners later

Pre-emption with or without Pre-task Planning: A Probe into L2 Lexical Diversity

The current study, setting a two-fold goal, attempted to see whether the preemptive focus on form (FonF) under either planned or unplanned conditions could contribute to increasing lexical diversity in written narratives and, second, to find whether there was a trade-off between the lexical diversity and accuracy. To this end, 32 beginner learners were selected following a Quick Oxford Placement Test and assigned into two groups to receive preemptive FonF under no-planning and pre-task planning conditions. The analysis of the results through a set ofrepeated measure ANOVAs and independent-samples t-tests revealed that the first group with unplanned condition outperformed the one with pre-task planning in lexical diversity. The results also revealed the trade-off between the lexical diversity and accuracy. That is, both lexical diversity and accuracy were significantly taken care of under unplanned preemptive condition whereas pre-task planning hindered attendingto lexical diversity and, thus, both aspects simultaneously. It was concluded that providing learners with appropriate conditions through form-focused instruction can set the ground for activating their linguistic knowledge and letting them attend to different linguistic aspects during writing

AN INTEGRATED APPROACH FOR SIMULATION AND PREDICTION OF LAND USE AND LAND COVER CHANGES AND URBAN GROWTH (CASE STUDY: SANANDAJ CITY IN IRAN)

One of the growing areas in the west of Iran is Sanandaj city, the center of Kordestan province, which requires the investigation of the city's growth and the estimation of land degradation. Today, the combination of remote sensing data and spatial models is a useful tool for monitoring and modeling land use and land cover (LULC) changes. In this study, LULC changes and the impact of Sanandaj city growth on land degradation in geographical directions during the period 1989 to 2019 were investigated. Also, the accuracy of three models, artificial neural network-cellular automata (ANN-CA), logistic regression-cellular automata (LR-CA), and the weight of evidence-cellular automata (WOE-CA) for modeling LULC changes was evaluated, and the results of these models were compared with the CA-Markov model. According to the results of the study, ANN-CA, LR-CA, and WOE-CA models, with an accuracy of more than 80%, are efficient and effective for modeling LULC changes and growth of urban areas

EN-BIRTH Data Collector Training - Handbook and Manual

The EN-BIRTH study aims to validate selected newborn and maternal indicators for routine facility-based tracking of coverage and quality of care for use at district, national and global levels. The item contains the EN-BIRTH_Trainer's Manual (14 June 2017) and EN-BIRTH_Training Handbook (23 May 2017)

EN-BIRTH Data Collector Training - Supporting Annexes

The EN-BIRTH study aims to validate selected newborn and maternal indicators for routine facility-based tracking of coverage and quality of care for use at district, national and global levels. The item contains consent forms and participant information, in addition to standard operating procedures (SOP) for adverse clinical events, and managing distress in interviews. The full complement of annex files used during the training can be requested via this site if required

EN-BIRTH Data Collection Tools

The EN-BIRTH study aims to validate selected newborn and maternal indicators for routine facility-based tracking of coverage and quality of care for use at district, national and global levels. The item contains the following data collection tools: Register data extraction, Observation checklist (labour and delivery ward), Observation checklist (kangaroo mother care), Patient record verification tools for antenatal corticosteroid administration, Patient record verification tools for antibiotic administration, and the Maternal recall survey

EN-BIRTH Data Collector Training – Training Module material

The EN-BIRTH study aims to validate selected newborn and maternal indicators for routine facility-based tracking of coverage and quality of care for use at district, national and global levels. The item contains PowerPoint slides used for the nine modules of the Data Collector's Training Programme delivered during May and June 2017. Module 1 (introduction) provides an overview of the training syllabus; Module 2 (Registration) helps tracking officers to understand their roles and responsibilities in the project and how to best execute them; Module 3 (Observation: Labour & Delivery) is intended to help Labour & Delivery observers to conduct themselves, and their work, in accordance with project guidelines and training handbook; Module 4 (Observation: Resuscitation - Nepal) covers the function of CCTV cameras and the value of collecting extra observation data from filmed clinical events; Module 5 (Observation: KMC) outlines expectations and practices to be applied by KMC (kangaroo mother care) observers; Module 6 (Data Extraction & Verification) outlines how data collectors should extract and verify register data and record information in the app extraction form in the L&D ward and KMC ward; Module 7 (Maternal Pre-discharge Recall Survey) outlines how to conduct high-quality interviews and administer the maternal pre-discharge recall survey; Module 8 (Supervision) equips supervisors with the skills to be good team managers, ensure team effectiveness and happiness, respond to incidents in the health facility, and monitor data quality; and finally Module 9 (Training Summary) provides a recap of key information taught over the week

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI