A Multi-Layered Framework for Analyzing Primary Students’ Multimodal Reasoning in Science

Classroom communication is increasingly accepted as multimodal, through the orchestrated use of different semiotic modes, resources, and systems. There is growing interest in examining the meaning-making potential of other modes (e.g., gestural, visual, kinesthetic) beyond the semiotic mode of language, in classroom communication and in student reasoning in science. In this paper, we explore the use of a multi-layered analytical framework in an investigation of student reasoning during an open inquiry into the physical phenomenon of dissolving in a primary classroom. The 24 students, who worked in pairs, were video recorded in a facility purposefully designed to capture their verbal and non-verbal interactions during the science session. By employing a multi-layered analytical framework, we were able to identify the interplays between the different semiotic modes and the level of reasoning undertaken by the students as they worked through the tasks. This analytical process uncovered a variety of ways in which the students negotiated ideas and coordinated semiotic resources in their exploration of dissolving. This paper highlights the affordances and challenges of this multi-layered analytical framework for identifying the dynamic inter-relationships between different modes that the students drew on to grapple with the complexity of the physical phenomenon of dissolving.

Utilizing Machine LearningTechniques for Detection of Intrusion in a Network

Growing the volume and influence of the association's assaults, compelling corporate constructions to fix the association's security arrangements to keep away from tremendous money related mishaps. Blackout identification frameworks are presumably the most basic security gadgets to guarantee the security of any association. When pondering tremendous volumes of data about the association and complex nature of blackouts, improving on the introduction of the organization interruption location framework has become an open inquiry that is acquiring and more thought by researchers in nowadays. The objective of this report is to recognize an AI estimation that gives high exactness and a nonstop casing application. This article evaluates the openness of 15 distinctive AI computations utilizing the NSL-KDD dataset dependent on the bogus exposure rate, ordinary exactness, root mean square mistake, and model form time. Initial, 5 of the 15 AI computations are chosen dependent on the most limit accuracy and minimal mistake in WEKA. Entertainment of these AI estimations is done through a ten-time cross-endorsement. From that point, the best AI estimation is picked dependent on the most extreme exactness and least edge season of the model, so it tends to be performed rapidly and logically in interruption recognition frameworks

Using a Self-Determination Theory Approach to Understand Student Perceptions of Inquiry-Based Learning

Inquiry-based laboratory activities, as a part of science curricula, have been advocated to increase students’ learning outcomes and improve students’ learning experiences, but students sometimes struggle with open-inquiry activities. This study aims to investigate students’ perceptions of inquiry-based learning in a set of laboratory activities, specifically from a psychological (i.e., Self-Determination Theory) perspective. Students’ ratings of the level of inquiry in these activities indicate that students’ perceptions of inquiry align with the instructor-intended amount of inquiry in each exercise. Students’ written responses, explaining their ratings, indicate that students’ perceptions of the amount of inquiry in a given lab exercise relate to their feeling of freedom (or autonomy), competence, and relatedness (or support), during the inquiry-based learning activities. The results imply that instructors implementing inquiry-based learning activities should consider student motivation, and Self-Determination Theory can be a useful diagnostic tool during teaching development.

Enhanced conceptual understanding, 21st century skills and learning attitudes through an open inquiry learning model in Physics

The study is grounded in the fact that in Southeast Asia, there are few studies on the effects of authentic, inquiry-based learning, or instruction in the field of Science education. This study investigated the effects of the open inquiry learning model in Physics on the concept and 21st century skill attainment, and learning attitudes of grade 12 students of a state university in the Philippines. The study involved a pre-test-posttest experimental design using quantitative approaches. Normalized Hake gain was used to determine the effectiveness of the open inquiry learning model in enhancing the concept attainment of students. Non-parametric test, particularly, Wilcoxon signed ranks test, determined the significant difference between the pre and post-test scores. Net shift in pre-test and post-test scores in Colorado Learning Attitude about Science Survey (CLASS) identified a shift in the learning attitudes of students. The difference between the pre-test and post-test scores of students was found to be significant (Z=-3.927, p=0.000<0.05; Z=-3.387, p=0.001<0.05). Students achieved high Hake gain (0.82). There was also a positive shift in the learning attitudes of students. Thus, the open inquiry learning model is effective in improving the conceptual understanding, 21st century skills, and learning attitudes of students. Because of its positive effects on students’ holistic learning, further promotion of this learning pedagogy is needed, especially in the Philippines setting. A series of professional development programs anchored on this learning pedagogy may be launched to train teachers and pre-service teachers.