An Interdisciplinary Educational Proposal in Junior High School: The Fractal Geometry in Science, Computer Science and Art Lessons

The purpose of this study is to provide an interdisciplinary approach to fractals within the traditional school curriculum. The proposed activities are expected to help teachers to provide a comprehensive and engaging learning experience for students that fosters deeper understanding, creativity, and connections within the sciences. Fractals are complex geometric shapes that are self-similar, and therefore exhibit similar patterns at every scale. They are created by repeating a simple process over and over. Fractals differ from traditional geometric shapes because they are non-regular, but are very common in nature, such as clouds, mountains, trees and snowflakes. Also fractals are impressive mathematical creations and can contribute a lot to the understanding of Junior High School mathematics because they could be fun and at the same time an exciting way to introduce many areas of mathematics and physics. By connecting fractals to different mathematical concepts and applications, Junior High School students can develop their problem-solving skills and gain a deeper appreciation for the beauty and complexity of mathematics

Implementing the Flipped Classroom Model in Science Lessons for Junior High School Students

The present study refers to the idea of the flipped classroom. The flipped or otherwise called inverted classroom is a modern teaching concept, which essentially reverses the terms of the traditional learning method. The students study the lesson beforehand, outside the classroom using new technologies, while the time inside the classroom is mainly devoted to discussions and solving questions by the educator, hands-on activities that stimulate the students’ interest and connect the acquired knowledge with everyday problems. Reversing the traditional teaching process appears to be paying off for students, as since utilizing classical teaching techniques, their performance seemed to decline over time. For this purpose, the flipped classroom method was applied in a junior high school class in Athens, Greece, for Physics lessons during the 2022–2023 school year. At the end of the school year, the students that participated in the flipped classroom completed a survey that was developed jointly with the cooperation of the respective teachers who taught the course in order to establish the degree of the students’ acceptance of the specific method. The results of the survey and a correlation of the students’ performance who participated in the flipped classroom with the performance achieved by the rest of the students that followed traditional teaching methods, as well as with the previous year’s students’ performance who also followed the classical teaching model, are analyzed and discussed

Real Time Sentiment Change Detection of Twitter Data Streams

In the past few years, there has been a huge growth in Twitter sentiment analysis having already provided a fair amount of research on sentiment detection of public opinion among Twitter users. Given the fact that Twitter messages are generated constantly with dizzying rates, a huge volume of streaming data is created, thus there is an imperative need for accurate methods for knowledge discovery and mining of this information. Although there exists a plethora of twitter sentiment analysis methods in the recent literature, the researchers have shifted to real-time sentiment identification on twitter streaming data, as expected. A major challenge is to deal with the Big Data challenges arising in Twitter streaming applications concerning both Volume and Velocity. Under this perspective, in this paper, a methodological approach based on open source tools is provided for real-time detection of changes in sentiment that is ultra efficient with respect to both memory consumption and computational cost. This is achieved by iteratively collecting tweets in real time and discarding them immediately after their process. For this purpose, we employ the Lexicon approach for sentiment characterizations, while change detection is achieved through appropriate control charts that do not require historical information. We believe that the proposed methodology provides the trigger for a potential large-scale monitoring of threads in an attempt to discover fake news spread or propaganda efforts in their early stages. Our experimental real-time analysis based on a recent hashtag provides evidence that the proposed approach can detect meaningful sentiment changes across a hashtags lifetime

Detection of Fake Generated Scientific Abstracts

The widespread adoption of Large Language Models and publicly available ChatGPT has marked a significant turning point in the integration of Artificial Intelligence into people's everyday lives. The academic community has taken notice of these technological advancements and has expressed concerns regarding the difficulty of discriminating between what is real and what is artificially generated. Thus, researchers have been working on developing effective systems to identify machine-generated text. In this study, we utilize the GPT-3 model to generate scientific paper abstracts through Artificial Intelligence and explore various text representation methods when combined with Machine Learning models with the aim of identifying machine-written text. We analyze the models' performance and address several research questions that rise during the analysis of the results. By conducting this research, we shed light on the capabilities and limitations of Artificial Intelligence generated text

Neural network-based colonoscopic diagnosis using on-line learning and differential evolution

In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line back-propagation (BP) is proposed and used to seed an on-line evolution process that applies a differential evolution (DE) strategy to (re-) adapt the neural network to modified environmental conditions. Our approach looks at on-line training from the perspective of tracking the changing location of an approximate solution of a pattern-based, and thus, dynamically changing, error function. The proposed hybrid strategy is compared with other standard training methods that have traditionally been used for training neural networks off-line. Results in interpreting colonoscopy images and frames of video sequences are promising and suggest that networks trained with this strategy detect malignant regions of interest with accuracy