The study sought to determine the effectiveness of a training program based on the theory of cognitive flexibility in developing some habits of productive mind and preferred learning methods among female student teachers, by identifying the level of
habits of mind necessary for female student teachers in kindergartens and their preferred learning methods, and determining the procedures of the training program based on the theory of cognitive flexibility. To study its effectiveness in developing some habits of the productive mind and to know the percentage of the contribution of the habits of the productive mind to their preferred learning methods, so the study followed the quasi-experimental approach by designing two equal groups (control and experimental), by preparing a scale of the sixteen habits of the productive mind according to Costa & Kallick’s list. (2009) and a measure of productive mind habits necessary for female kindergarten students, and applying Felder and Silverman’s preferred learning styles scale (Index of learning style, 1999), on a purposive sample consisting of (46) female kindergarten students from the third year because they are in the intermediate learning stage according to the theory. Cognitive flexibility, as the sample represents 20% of the research population, and the results of the study revealed a low level of six habits of the productive mind in the sample: perseverance, control of recklessness, flexibility of thinking, creativity, continuous learning, and striving for accuracy. The sample’s learning preferences also varied between... Methods of processing, perception, input and thinking. The results showed the effectiveness of the training program based on the theory of cognitive flexibility in developing the necessary productive mind habits for kindergarten students. The results also revealed the contribution of productive mind habits to preferences for learning methods, as the habits of the productive mind individually predict preferred learning methods in proportion. It ranges from 31% to 64% in the post-measurement, and the six habits of the productive mind contribute together over time, as they predict preferred learning methods by rates ranging from 18% to 63.8%, with the exception of the processing style, of which the creativity habit predicted 34%. Some Recommendations in light of these results.
An ideal integration of autonomous agents in a human world implies that they are able to collaborate on human terms. In particular, theory of mind plays an important role in maintaining common ground during human collaboration and communication. To e
nable theory of mind modeling in situated interactions, we introduce a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. It provides information that captures partners' beliefs of the world and of each other as an interaction unfolds, bringing abundant opportunities to study human collaborative behaviors in situated language communication. As a first step towards our goal of developing embodied AI agents able to infer belief states of collaborative partners in situ, we build and present results on computational models for several theory of mind tasks.
Sememes are defined as the atomic units to describe the semantic meaning of concepts. Due to the difficulty of manually annotating sememes and the inconsistency of annotations between experts, the lexical sememe prediction task has been proposed. How
ever, previous methods heavily rely on word or character embeddings, and ignore the fine-grained information. In this paper, we propose a novel pre-training method which is designed to better incorporate the internal information of Chinese character. The Glyph enhanced Chinese Character representation (GCC) is used to assist sememe prediction. We experiment and evaluate our model on HowNet, which is a famous sememe knowledge base. The experimental results show that our method outperforms existing non-external information models.
Starting from an existing account of semantic classification and learning from interaction formulated in a Probabilistic Type Theory with Records, encompassing Bayesian inference and learning with a frequentist flavour, we observe some problems with
this account and provide an alternative account of classification learning that addresses the observed problems. The proposed account is also broadly Bayesian in nature but instead uses a linear transformation model for classification and learning.
Statements that are intentionally misstated (or manipulated) are of considerable interest to researchers, government, security, and financial systems. According to deception literature, there are reliable cues for detecting deception and the belief t
hat liars give off cues that may indicate their deception is near-universal. Therefore, given that deceiving actions require advanced cognitive development that honesty simply does not require, as well as people's cognitive mechanisms have promising guidance for deception detection, in this Ph.D. ongoing research, we propose to examine discourse structure patterns in multilingual deceptive news corpora using the Rhetorical Structure Theory framework. Considering that our work is the first to exploit multilingual discourse-aware strategies for fake news detection, the research community currently lacks multilingual deceptive annotated corpora. Accordingly, this paper describes the current progress in this thesis, including (i) the construction of the first multilingual deceptive corpus, which was annotated by specialists according to the Rhetorical Structure Theory framework, and (ii) the introduction of two new proposed rhetorical relations: INTERJECTION and IMPERATIVE, which we assume to be relevant for the fake news detection task.
The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue. It is widely used to identify sentence-level sentiment. In this paper, we study the Moral Foundation Theory in tweets by US politic
ians on two politically divisive issues - Gun Control and Immigration. We define the nuanced stance of politicians on these two topics by the grades given by related organizations to the politicians. First, we identify moral foundations in tweets from a huge corpus using deep relational learning. Then, qualitative and quantitative evaluations using the corpus show that there is a strong correlation between the moral foundation usage and the politicians' nuanced stance on a particular topic. We also found substantial differences in moral foundation usage by different political parties when they address different entities. All of these results indicate the need for more intense research in this area.
This study aims to investigate the impact of e-marketing and its seven elements
(7Ps: e-Product, e-Price, e-Place, e-Promotion, e-Process, e-People, e-Physical
evidence) in Social Media Marketing at the internet service provider companies
(CallU,
Hadara, Mada) on gaining the six competitive advantage elements
(Quality, Market Domination, Improvement, Cost, Time and Flexibility).
Toward that end, the researcher adopted the Exploratory Research approach appropriate to the nature of this study, whereas a questionnaire was designed as a tool for gathering data and information beside observation and monitoring, and hence the questionnaire was offered onto a number of competent arbitrators, and carried the amendments proposed by those arbitrators, and hence then distribute it onto the study sample, represented in the internet service provider companies in Palestine, whereas around 185 questionnaires were distributed, while total number of valid questionnaires for analysis reached around 166 ones, and the researcher used the very suitable statistical methods and techniques through SPSS program.
Neural language models, including transformer-based models, that are pre-trained on very large corpora became a common way to represent text in various tasks, including recognition of textual semantic relations, e.g. Cross-document Structure Theory.
Pre-trained models are usually fine tuned to downstream tasks and the obtained vectors are used as an input for deep neural classifiers. No linguistic knowledge obtained from resources and tools is utilised. In this paper we compare such universal approaches with a combination of rich graph-based linguistically motivated sentence representation and a typical neural network classifier applied to a task of recognition of CST relation in Polish. The representation describes selected levels of the sentence structure including description of lexical meanings on the basis of the wordnet (plWordNet) synsets and connected SUMO concepts. The obtained results show that in the case of difficult relations and medium size training corpus semantically enriched text representation leads to significantly better results.
Natural Language Processing tools and resources have been so far mainly created and trained for standard varieties of language. Nowadays, with the use of large amounts of data gathered from social media, other varieties and registers need to be proce
ssed, which may present other challenges and difficulties. In this work, we focus on English and we present a preliminary analysis by comparing the TwitterAAE corpus, which is annotated for ethnicity, and WordNet by quantifying and explaining the online language that WordNet misses.
اهتم علماء النفس ، منذ أن وجدت حركة القياس النفسى ، بتحقيق صدق وثبات الاختبارات والمقاييس النفسية ، سعياً منهم لتحقيق أعلى درجة من الموضوعية فى هذه الأدوات ، عند استخدامها فى عملية القياس 0 ووفق نظرية القياس التقليدية Classical Theory يمكن التعبير ع
ن قدرة الفرد من خلال الدرجة الحقيقية والتى تتضح من خلال أدائه على الاختبار ، وبناءً عليه فإنه سيتغير وضع قدرة الفرد حسب تغير مستوى الاختبار 0 إن الاختبار والبنود تتغير خصائصها بتغير خصائص الأفراد ، كما أن خصائص الأفراد تتغير بتغير خصائص الاختبار من حيث السهولة والصعوبة 0
وقد أسفرت جهود العلماء عن ظهور بعض الاتجاهات الحديثة فى مجال القياس والتقويم ، ومن بين هذه الاتجاهات نظرية الاستجابة للمفردة Item Response Theory(IRT) أو نظرية السمات الكامنة Latent Traits Theory (LTT) وحظى هذا المدخل الجديد باهتمام الباحثين حيث يتغلب على كثير من مشكلات القياس التقليدية 0
فالاختبارات النفسية والتربوية بعامة تفترض أن هناك سمات أو خصائص معينة يشترك فيها جميع الأفراد ، ولكنهم يختلفون فى مقدارها وبالرغم من أن هذه السمات غير منظورة 0 إلا أنه يمكن الاستدلال على مقدارها من السلوك الملاحظ للفرد المتمثل فى استجاباته على مفردات الاختبار وهذا ما يبرر تسميتها بالسمات الكامنة 0 فالسـمة التى تكمن وراء استجابة الفـرد على مفردات اختبار لفظى ، تختلف عن السـمة التى تكمن
* أستاذ مساعد - جامعة الزقازيق - كلية التربية - قسم علم النفس التربوى
وراء استجاباته على مفردات اختبار عددى أو مكانى 0 ولكن يمكن أن تكمن سمة واحدة وراء استجاباته على مفردات اختبارين مختلفين متعلقين بنفس المحتوى ( صلاح الدين علام ، 1987 : 22)0
كما تشير "أمينة كاظم" (1996) إلى أن نماذج السمات الكامنة تحدد العلاقة المتوقعة بين الاستجابات الملاحظة على الاختبار والسمات غير الملاحظة التى يفترض أنها تحدد هذه الاستجابات ، كما أن السمة بعد كمى يمكن أن يحدد عليه مواضع الأفراد ، ولا يصح نظرياً أن يتوقف موضع الفرد على بعد السمة على صفات أى من العينات التى ينتمى إليها الفرد ، فالقياس هنا متحرر من العينة 0
ويلخص "صلاح الدين علام" (2000) الفكرة الأساسية لنماذج الاستجابة للمفردة فى أنها تحاول اشتقاق قيم تقديرية للسمات التى تنطوى عليها مجموعة من الاستجابات لمجموعة من المفردات ، وعادة يفترض أن السمة المقاسة هى قدرة معينة أو خاصية من خصائص الفرد الذى يختبر بها ، بحيث لا توجد علاقة منتظمة بين مستويات السمة المقاسة لدى أفراد مختلفين واحتمالات الاستجابة الصحيحة لمفردات مختلفة بمعنى آخر 0
