Search

Textbooks play significant role in teaching and learning continuum. Due to this reason, Curriculum wings and Directorate of staff development (Punjab) have had continuous check and monitoring in devising goals and standards in textbooks from grade 1 to grade 10. But at intermediate level specially in12th grade, the same text book i.e. "Good-bye Mr. Chips" is being taught more than 20 years. It is assumed that there lies a huge literacy and learning gap between the secondary level English textbooks and intermediate level English textbooks. The present study keeping in view the issues and gaps in mind was an attempt to investigate the importance of English textbooks being taught at intermediate level (Grade 12) in different schools and colleges in Punjab. Prospective study occupies descriptive type of research framework. The textbook of English being taught at intermediate level is evaluated according to specified models and strategies mentioned in previous research studies based upon textbook evaluation. Sample was determined through applying cluster random sampling technique. Questionnaire was designed, validated and administered after conducting pilot phase. The findings of the study reveal that the textbook being taught at intermediate level is not be up to snuff to convene the common objectives of the target language and is irreconcilable with the requirements of promoting confidence and ornamental language skills. Moreover, it is recommended that textbooks must be revised from time to time to obtain definite language learning objectives. The book being evaluated lacks innovation, students' interest, relevant content and appropriate skills.

The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting. [ABSTRACT FROM AUTHOR]