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How Competition in a Game-based Science Learning Environment Influences Students' Learning Achievement, Flow Experience, and Learning Behavioral Patterns
Although educational games have become prevalent in recent research, only a limited number of studies have considered learners' learning behaviors while playing a science problem-solving game. Introducing a competitive element to game-based learning is promising; however, research has produced ambiguous results, indicating that more studies should investigate its pros and cons of competition. A total of 57 seventh-grade students participated in the study and were assigned to two conditions: competition or non-competition. Results revealed that students in the non-competition condition performed significantly better on the learning achievement test than those in the competition condition. With regard to the flow experience, no significant differences were found between the two conditions. The results of learning behavioral analyses revealed that, while both conditions resulted in students acquiring through means-ends strategies, students in the non-competition condition tended to read the instructions carefully and repeatedly sought additional supports to help themselves advance their conceptual understanding. These findings, when examined in light of previous research, call into question other types of competition in promoting engagement and supporting learning.
Journal Article
Relationship among High School Students’ Science Academic Hardiness, Conceptions of Learning Science and Science Learning Self-Efficacy in Singapore
This study used three previously validated instruments, namely Science Academic Hardiness (SAH), Students’ Conceptions of Learning Science (COLS) and Science Learning Self-Efficacy (SLSE) on 431 Singaporean students. Using structural equation modeling, results showed that the SAH commitment dimension a positive predictor explaining both the reproductive (e.g. science learning as memorizing or testing) and constructivist (e.g. science learning as understanding or seeing in a new way) conceptions of science learning as well as all dimensions of students’ self-efficacy among high school students. It was also found that the SAH control dimension is a positive predictor for explaining the SLSE science communication dimension but is a negative predictor for explaining reproductive COLS. Finally, only students with constructivist COLS had significant associations with all SLSE dimensions. These findings suggest that students’ personal commitment to learning science is an important aspect to cultivate since it has the ability to predict conceptions of science learning and self-efficacy. Further, creating opportunities for students to be engaged in learning through constructivist ways—such as designing tasks to help students understand and see phenomena in new ways and occasions for students to apply their science knowledge to solve science problems—is likely to lead to positive self-efficacy in practical science work, science communication, and everyday applications of scientific knowledge. Additionally, students’ engagement in reproductive ways of learning science—such as memorization, testing, and calculating and practicing—could be reduced since these do not contribute to building students’ science learning self-efficacy.
Journal Article
Deep learning approach for natural language processing, speech, and computer vision : techniques and use cases
\"Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision provides an overview of general deep learning methodology and its applications of natural language processing (NLP), Speech and Computer Vision tasks. It simplifies and presents the concepts of deep learning in a comprehensive manner, with suitable, full-fledged examples of deep learning models, with aim to bridge the gap between the theoretical and the applications using case studies with code, experiments, and supporting analysis. Features: Covers latest developments in deep learning techniques as applied to audio analysis, computer vision, and Natural Language Processing Introduces contemporary applications of deep learning techniques as applied to audio, textual, and visual processing Discovers deep learning frameworks and libraries for NLP, Speech and Computer vision in Python Gives insights into using the tools and libraries in python for real-world applications. Provides easily accessible tutorials, and real-world case studies with code to provide hands-on experience. This book is aimed at researchers and graduate students in computer engineering, image, speech, and text processing\"-- Provided by publisher.
BOOK
The Influence of the Implementation of Android-Assisted Experiential Learning Model-Based Science Learning on Students' Scientific Literacy
This study aims to determine the effect of implementing an android application which was based on Experiential Learning Model (ELM) on students' scientific literacy in terms of knowledge, competencies, and attitude. This research was a quasi-experiment with pre-post research of non-equivalent control group design. The population of the study were students of grade VII Junior High Schools in Kotawaringin Timur Regency. The sample was selected by stratified random sampling technique based on school stratification, which included high, medium, and low category schools. Research data of scientific literacy knowledge and competencies were measured using test instrument, while scientific research literacy attitude data was measured using non-test instrument (self-assessment questionnaire). The data analysis technique used was one-way manova with significance level of .05. The result of the data analysis showed that science learning which was based on Experiential Learning Model (ELM) with android support has significant influence on the scientific literacy, covering some aspects: knowledge, competencies, and attitude of students of grade VII Junior High Schools in Kotawaringin Timur Regency.
Journal Article
Learning through school science investigation : teachers putting research into practice
This book explores teaching and learning through science investigation and practical work. It draws upon two representative case studies from New Zealand and examines what students are learning from science investigation; in addition, it identifies and describes ways in which teachers can make changes that benefit student learning when given time to reflect and respond to research literature and findings. The book illustrates how teaching through science investigations in ways that are informed by research can lead to positive learning outcomes for students. As such, it offers valuable insights for practitioners, researchers, and educators with an interest in learning through science investigation.
Book
Sex and frequency of practical work as determinants of middle-school science students’ learning environment perceptions and attitudes
In this study of 431 Grade 9 and 10 students, we investigated gender and frequency of practical work as determinants of science students’ perceptions of their learning environment and attitudes. We assessed classroom environment with the Science Laboratory Environment Inventory (SLEI) and attitudes with the Students’ Adaptive Learning Engagement in Science (SALES) questionnaire and a scale involving students’ future intentions to study science. The surveys exhibited sound factorial validity and reliability. Interesting differences were found in the learning environment and student attitudes according to student gender and three different frequencies of practical work (namely, at least once a week, once every 2 weeks, or once every 3 weeks or more). More-frequent practical work was more effective than less-frequent practical work in terms of perceived open-endedness, integration and material environment in the laboratory environment and more-positive task value and self-regulation attitudes (with modest effect sizes exceeding one-third of a standard deviation). Although small gender differences existed for some scales, increasing the frequency of practical work was not differentially effective for male and female students.
Journal Article
Learn Keras for deep neural networks : a fast-track approach to modern deep learning with Python
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras. You will: Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks.
BOOK
Animated video as a Media for Learning Science in Elementary School
This study aims to develop a product in the form of an animated video on material characteristics and changes in the form of objects in MI Walisongo Gempol Pasuruan with research subjects of class III A. This research uses Research and Development, with a research design adapted from the ADDIE model. The results of this study indicate that the animated video is valid to be used in science learning in class III A MI Walisongo Gempol Pasuruan. This is evidenced by the results of validation from media experts, design experts, content experts, and students' responses by using questionnaire. The results of data analysis from media experts is 87.5%, which means it is very valid, content experts is 90%, and design experts is 82.5%. For the media test, the students obtained 93.76%. After using animated video as a learning media, the number of students who are able to reach the standard of minimum completeness was 85%, while the number of students who did not reach the standard of minimum completeness was 15%. The trial of using animated video as a learning media has been effective. Based on the results of the study, it can be concluded that the animated video is valid to be used as a learning media on material characteristics and changes in the form of objects. Animated video media can improve student learning outcomes and be able to encourage students to think critically in learning science.
Journal Article