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Effect of Personality Styles of Teacher on Academic Achievement of Elementary School School Students in Mathematics

Thesis Info

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External Link

Author

Devi, Shiela

Program

PhD

Institute

Iqra University

City

Karachi

Province

Sindh

Country

Pakistan

Thesis Completing Year

2017

Thesis Completion Status

Completed

Subject

Education

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/12209/1/Shiela%20Devi_Edu_2017_Iqra_PRR.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676724659206

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The interaction between teachers and students is one of the aspect that helps the students in their career grooming because teachers’ personality and their behavior is responsible for his/her effectiveness in the teaching, and plays an important role in learning process in terms of achievement and success in the classroom as well as in their entire life. From various studies, it has been proven that the personality factors such as extraversion, conscientiousness, openness to experience, neuroticism and agreeableness are the key characteristics of teachers’ personality and behavior. Based on this, the purpose of this study is to investigate the effect of teachers’ personality on students’ academic achievement particularly in mathematics subject. In order to pursue the objectives of the study, researcher used sequential explanatory mixed method approach; both quantitative and qualitative paradigms. Researcher adopted the survey research design for collection of quantitative data. In this way, Big Five Inventory (BFI) is used to identify the teacher’s personality type. This inventory comprised of forty statements about big five personality traits (eight statements about each personality trait). Moreover, the quantitative data is also collected from students’ midterm examination results from the chosen schools. In contrast, qualitative data were collected through in-depth interviews with the students of selected schools. Concerning to the sample, stratified random sampling method was adopted, where, fifty seven (57) schools were randomly selected from public sector and forty five (45) schools were selected from the private sector the participants of the study comprised of 486 public and private sector elementary 6th -8thgrade school teachers in Karachi, Pakistan. Out of them, 278 schools were selected from public sector elementary schools and the rest 208 were selected from private sector elementary schools. In addition, a mathematics score of Effect of Personality Styles of Teacher on Academic Achievement of Students in Mathematics 3224 students from both chosen private and public schools was collected for the research. For the data analysis, the quantitative part is analyzed through statistical analysis in which independent sample t-test, ANOVAs, stepwise multiple regression analysis and Corelation are computed through Factor Analysis to test the hypothesis and make predictions. Besides, qualitative data are analyzed, coded and themes are generated to get the clear picture about the research problem. In result, it is obtained that that the study variables, Extraversion, Conscientiousness, Openness to experience, Neuroticism and Agreeableness, are the significant predictor of student achievement. Since, there is strong and positive correlations are found among the variables, it implies that the teacher personality has a positive and strong association with students’ achievement in Mathematics in Karachi''s public and private schools. Other factors like teachers’ qualification and years of experience in teaching mathematics showed low associated with students’ academic achievement in Mathematics.
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۲-عبادات کے بیان میں ا ستفہامی اسلوب

-عبادات کے بیان میں ا ستفہامی اسلوب

قرآن مجید میں اللہ تعالیٰ اپنی عبادت کا حکم دینے اور انسانی نفس کو عبادت کیلئے آمادہ کرنے کیلئے بھی استفہامی اسلوب کو ہی استعمال کیا ہے،چنانچہ اللہ تعالیٰ نے انسانوں سے انکی تخلیق سے پہلے ایک وعدہ لیا تھا جس کا ذکر قرآن مجید میں اس انداز میں فرمایا:

"أَلَسْتُ بِرَبِّكُمْ قَالُواْ بَلَىٰ شَهِدْنَآ"۔ [[1]]

"کیا میں تمہارا رب نہیں ہوں؟ اس وقت سب نے یہ کہا کیوں نہیں اے ہمارے رب! "۔

اس وقت سب نے ربوبیت کا اقرار کیا تھا گویا اللہ تعالیٰ کی ربوبیت کا اعتراف و اقرار انسانوں کی فطرت میں داخل اور انکے وجدان میں شامل ہے۔

سورة الانعام میں اللہ تعالیٰ توحیدِ خالص کے بیان میں بھی استفہامی اسلوب کو بیان کرتے ہیں:

"قُلْ أَغَيْرَ ٱللَّهِ أَبْغِى رَبّاً وَهُوَ رَبُّ كُلِّ شَيْءٍ وَلاَ تَكْسِبُ كُلُّ نَفْسٍ إِلاَّ عَلَيْهَا وَلاَ تَزِرُ وَازِرَةٌ وِزْرَ أُخْرَىٰ ثُمَّ إِلَىٰ رَبِّكُمْ مَّرْجِعُكُمْ فَيُنَبِّئُكُمْ بِمَا كُنْتُمْ فِيهِ تَخْتَلِفُونَ"۔ [[2]]

"کہہ دیجئے کیا میں اللہ کے سوا کوئی اور رب تلاش کروں اور وہی ہر چیز کا رب ہے اور جو کوئی گناہ کرتا ہے وہ اسی کے ذمہ پر ہےایک شخص دوسرے کا بوجھ نہ اٹھائے گا تم سب کو اسی کی طرف لوٹ کر جانا ہےپس وہ تمہیں خبر دے دے گا جس بات میں تم جھگڑتے ہو"۔

"یہ آیت مشرکین مکہ ولید بن مغیرہ کی اس بات کا جواب ہے جو وہ رسول اللہ صلی اللہ علیہ وسلم اور مسلمانوں سے کہا کرتے تھے کہ ہمارے دین میں...

فقہی قاعدہ

The experts from various fields have maintained some rules and regulations in understanding and acquiring skills in this field of knowledge. This is due to their efforts which make very simple to understand it similarly the experts of ʽilm-ul-Fiqah i.e. Mujtahedyn and respected Fuqahāʼ have formulated some rules and regulations in the light of Qurʼan, Sunnah and decision act of companions of Muhammad (SAW) which also made it not only simple but it furnish skills and strategies in solving problems in field of Fiqah.

Generic Urdu Nlp Framework for Urdu Text Analysis: Hybridization of Heuristics and Machine Learning Techniques

The internet was initially designed to present information to users in English. However, with the passage of time and the development of standard web technologies such as browsers, programming languages, libraries, frameworks, databases, front and back-ends, protocols, APIs, and data formats, the internet became a multilingual source of information. In the last few years, the natural language processing (NLP) research community has observed a rapid growth in online multilingual contents. Thus, the NLP community maims to explore monolingual and cross-lingual information retrieval (IR) tasks. Digital online content in Urdu is also currently increasing at a rapid pace. Urdu, the national language of Pakistan and the most widely spoken and understandable language of Indian sub-continent, is considered a low-resources language (Mukund, Srihari, & Peterson, 2010). Part of speech (POS) tagging and named entity recognition (NER) are considered the most basic NLP tasks. Investigation of these two tasks in Urdu is very hard. POS tagging, the assignment of syntactic categories for words in running text is significant to natural language processing as a preliminary task in applications such as speech processing, information extraction, and others. Named entity recognition (NER) corresponds to the identification and classification of all proper nouns in texts, and predefined categories, such as persons, locations, organizations, expressions of times, quantities and monetary values, etc. it is considered as a sub-task and/or sub-problem in information extraction (IE) and machine translation. NER is one of the hardest task in Urdu language processing. Previously majority Urdu NER systems are based on machine learning (ML) models. However, the ML model needs sufficiently large annotated corpora for better performance(Das, Ganguly, & Garain, 2017). Urdu is termed as a scared resource language in which sufficiently large annotated corpus for ML models’ evaluation is not available. Therefore, the adoption of semi-supervised approach which is largely dependent on usage of the huge amount of unlabeled data is a feasible solution. In this thesis, we propose a generic Urdu NLP framework for Urdu text analysis based on machine learning (ML) and deep learning approaches. Initially, we addressed POS challenges by developing a novel tagging approach using the linear-chain conditional random fields (CRF). We employed a strong, stable, balanced language-independent and language dependent feature set for Urdu POS task and used the method of context words window. Our approach was evaluated against a support vector machine (SVM) technique for Urdu POS - considered Abstract WAHAB KHAN Reg: No. 72-FBAS/PHDCS/S12 vi as the state of the art - on two benchmark datasets. The results show our CRF approach to improving upon the F-measure of prior attempts by 8.3 to 8.5%. Secondly, we adopted deep recurrent neural network (DRNN) learning algorithms with various model structures and word embedding as a feature for the task of Urdu named entity recognition and classification. These DRNN models include long short-term memory (LSTM) forward recurrent neural network (RNN), LSTM bi-directional RNN, backpropagation through time (BPTT) forward RNN and BPTT bi-directional RNN. We consider language-dependent features such as part of speech (POS) tags as well as language independent features such as N-grams. Our results show that the proposed DRNN-based approach outperforms existing work that employ CRF based approaches. Our work is the first to use DRNN architecture and word embedding as a feature for Urdu NER task and improves upon prior attempts by 9.5% in the case of maximum margin.