Search results for: learning outcomes assessment

Authors: Suparna Bagchi

Abstract:

Education as a discipline will probably be shaped by the importance it places on a conceptual, curricular, and pedagogical need to shift the emphasis toward transformative classrooms working for positive change through cultural diversity. Awareness of cultural diversity and race equality has heightened following George Floyd’s killing in the USA in 2020. This increasing awareness is particularly relevant in areas of historically low ethnic diversity which have lately experienced a rise in ethnic minority populations and where inclusive growth is a challenge. This research study aims to explore the perspectives of practitioners, students, and parents towards multiculturalism in four South West England primary schools. A qualitative case study methodology has been adopted framed by sociocultural theory. Data were collected through virtually conducted semi-structured interviews with school practitioners and parents, observation of students’ classroom activities, and documentary analysis of classroom displays. Although one-third of the school population includes ethnically diverse children, BAME (Black, Asian, and Minority Ethnic) characters featured in children's books published in Britain in 2019 were almost invisible, let alone a BAME main character. The Office for Standards in Education, Children's Services and Skills (Ofsted) are vocal about extending the Curriculum beyond the academic and technical arenas for pupils’ broader development and creation of an understanding and appreciation of cultural diversity. However, race equality and community cohesion which could help in the students’ broader development are not Ofsted’s school inspection criteria. The absence of culturally diverse content in the school curriculum highlighted by the 1985 Swann Report and 2007 Ajegbo Report makes England’s National Curriculum look like a Brexit policy three decades before Brexit. A revised National Curriculum may be the starting point with the teachers as curriculum framers playing a significant part. The task design is crucial where teachers can place equal importance on the interwoven elements of “how”, “what” and “why” the task is taught. Teachers need to build confidence in encouraging difficult conversations around racism, fear, indifference, and ignorance breaking the stereotypical barriers, thus helping to create students’ conception of a multicultural Britain. Research showed that trainee teachers in predominantly White areas often exhibit confined perspectives while educating children. Irrespective of the geographical location, school teachers can be equipped with culturally responsive initial and continuous professional development necessary to impart multicultural education. This may aid in the reduction of employees’ unconscious bias. This becomes distinctly pertinent to avoid horrific cases in the future like the recent one in Hackney where a Black teenager was strip-searched during period wrongly suspected of cannabis possession. Early research findings show participants’ eagerness for more ethnic diversity content incorporated in teaching and learning. However, schools are considerably dependent on the knowledge-focused Primary National Curriculum in England. Moreover, they handle issues around the intersectionality of disability, poverty, and gender. Teachers were trained in times when foregrounding ethnicity matters was not happening. Therefore, preoccupied with Curriculum requirements, intersectionality issues, and teacher preparations, schools exhibit an incapacity due to which keeping momentum on ethnic diversity is somewhat endangered.

Keywords: case study, curriculum decolonisation, inclusive education, multiculturalism, qualitative research in Covid19 times

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

Abstract:

Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

Procedia PDF Downloads 36