The purpose of the present study was to investigate the effect of Generative Learning teaching based to pattern cognitive Load on the memorization math of 7th grade students of in the academic year of 2021- 2022. The research method is quasi-experimental with pre-test and post-test with four groups: two control group and two experimental groups. The statistical population of the study consisted of all 7th grade male and female students of District 2 of Mashhad city that 112 person sample was selected by single stage cluster sampling method. The subjects in the experimental group involved in Generative Learning based to cognitive Load in 16 sessions and the control group did not receive any interventions. The research instrument consisted of a 20-questions researcher-made test. To analyze the obtained data, Multivariate analysis of covariance and SPSS version 24 software were used. The results showed that Generative Learning teaching based to approach cognitive Load have a positive and significant effect on 7th grade students' memorization math. Based on the findings, it can be concluded that one of the effective training on math memorization students is the use of Generative Learning on the based load cognitive pattern.

This article reviews and criticizes The Generative Lexicon by James Pustejovsky. Generative Lexicon (GL) is a linguistic semantic theory that proposes a framework for representing lexical knowledge. The lexical semantic representations as introduced in GL are, in fact, part of the information stored in the lexicon. GL is concerned with explaining the creative use of language. By taking this into account the structure and multiple representational levels for different types of lexical information, GL provides a new way for creating multiple meanings through a process of combining. This article firstly provides a short introduction about the book and then in the second part, Generative Lexicon theory is introduced, and then the structure and content of the book is mentioned in the third part. In the rest of the paper, the criticisms and evaluation of the book are presented in two sub-sections (a) appearance, form, and structure, and (b) evaluation of content. In the final section of the paper, the conclusion and concluding remarks on the book are presented.

Construction of knowledge based on the individual’ s experience of the world is one of the applications of Generative Learning. Generative Learning model is a constructivist Learning model where knowledge is created through student experiences and interactions. This Learning model is centered on integrating new knowledge with the initial knowledge students have in active Learning. The application of Generative Learning in physics Learning can improve student Learning outcomes, understanding of physics concepts, generic science skills, and able to reduce student misconceptions. This research aims to study the recent researches and the advantages/disadvantages of Generative Learning in the physics Learning process. The obtained results indicate that the Generative Learning model in physics Learning include students being more active creatively and critically in developing the knowledge obtained. Students are able to discover phenomena and solve problems. Using a virtual lab and other related software in these corona conditions eliminates problems such as lack of time to perform a real test and inaccurate testing due to technical or environmental conditions. The method of research is library research in which the recent results of research were collected.

Voiced-based age detection and gender recognition are important problems in the telephone speech processing to investigate the identity of an individual. In this paper, a new gender and age recognition system is introduced based on the Generative incoherent models learned using sparse non-negative matrix factorization and the atom correction step as a post-processing method. The proposed classification algorithm includes training step to provide the appropriate trained atoms for each data class and also the test phase to assess the classification performance. Since the classification accuracy depends highly on the selected features, the Mel-frequency cepstral coefficients are employed to train basis for the better representation of the voice structure. These bases are learned over the data of male and female speakers using non-negative matrix factorization with the sparsity constraint. Then, atom correction is carried out using an energy-based algorithm to decrease the coherence between different categories of the trained dictionaries. In the sparse representation of each data class, the atoms related to other sets with the highest energy are replaced with the lowest energy bases if the reconstruction error does not exceed from a specified limit. The experimental results showed that the proposed algorithm performs better than the earlier methods in this context especially in the presence of background noise.

The purpose of this research is to development of Generative instruction plan based on the components of Generative Learning theory to enhance self-regulation. The qualitative content analysis with deductive exploratory method used to identify Generative Learning theory components and development of Generative plan, And to provide strategies used of Thematic Matrix. The statistical population of this study include all experimental studies, review articles, dissertations and all resources related to Generative Learning from 1974 to 2017. With purposeful sampling selected 47 articles and one dissertation as units of study. For validation selected 20 experts in educational psychology and educational technology. Finally, extracted 14 main categories includes: attention, motivation, teacher, prior knowledge, Learning opportunities, Student perception, active learner, generation, evaluation, content, analysis, instruction, instructional environment and cooperative Learning. Based on these components, instructional plan with the following sections developed: identifying and analyzing the learner, providing instruction, learners engagement, activating prior knowledge, providing content, creating Learning opportunities, Facilitation and scaffolding, cooperative Learning, generation and evaluation.

Based on Wittrock’s Generative Learning theory (1974), Generative model of Learning emphasizes learners' active participation in the Learning process by generating relationships and meanings. The present research addressed the question as to whether learners trained under Generative model of Learning had better performance in experimental sciences progress tests than the learners trained under conventional educational models. The hypothesis stated that learners trained under Generative model of Learning had better performance in experimental sciences progress tests than the learners trained under conventional educational models. The statistical population included all female guidance-school second-graders of Kelardasht. A statistical sample of two classrooms of 50 students was randomly selected through the process of cluster sampling, and 26 students were placed in the experimental group and 24 students in the control group. The results confirmed the research hypothesis.