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Deep learning with TensorFlow : take your machine learning knowledge to the next level with the power of TensorFlow 1.x
Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. -- Publisher description.
Book
Students' satisfaction and continuance intention of the cloud-based e-learning system: roles of interactivity and course quality factors
PurposeThe purpose of this study is to propose a research model based on expectation-confirmation model (ECM) to examine whether interactivity and course quality factors (i.e. course content quality, course design quality) as antecedents to student beliefs can influence students' satisfaction and continuance intention of the cloud-based electronic learning (e-learning) system within the educational institution.Design/methodology/approachSample data were collected from students enrolled in a comprehensive university in Taiwan. A total of 600 questionnaires were distributed in the campus, and 515 (85.8%) useable questionnaires were analyzed using structural equation modeling.FindingsFindings showed that students' perceptions of interactivity, course content quality and course design quality positively significantly contributed to their perceived usefulness, confirmation and satisfaction with the cloud-based e-learning system, which in turn directly or indirectly led to their continuance intention of the system. Thus, the results strongly supported the research model based on ECM via positioning key constructs as the drivers with all hypothesized links being significant.Originality/valueThis study identifies three factors (i.e. interactivity, course content quality, course design quality) as drivers from the learner perspective within the cloud-based e-learning environment, and links these factors to students' satisfaction and continuance intention of the cloud-based e-learning system based on ECM. It is particularly worth mentioning that the three drivers can serve as precursors for recognizing the determinants that are crucial to understand students' satisfaction and continuance intention of the cloud-based e-learning system. Hence, this study may provide new insights in nourishing the cloud-based e-learning continuance literature in the future.
Journal Article
How does task-technology fit influence cloud-based e-learning continuance and impact?
Purpose
According to expectation–confirmation model (ECM) and task-technology fit (TTF) model, the purpose of this paper is to examine the role of TTF in students’ cloud-based e-learning continuance and evaluate whether TTF affects students’ perceived impact on learning of the cloud-based e-learning system within the educational institution.
Design/methodology/approach
Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 500 questionnaires were distributed in the campus, and 391 (78.2 percent) usable questionnaires were analyzed using structural equation modeling in this study.
Findings
This study’s results verified that both task characteristics and technology characteristics affected students’ perceived TTF, which significantly contributed to their perceived usefulness, confirmation and satisfaction with the cloud-based e-learning system, and these in turn directly or indirectly led to their continuance intention of the system and perceived impact on learning; essentially, the results strongly supported the research model integrating ECM and TTF model via positioning key constructs as the drivers with all hypothesized links being significant.
Originality/value
This study contributes to an understanding of the TTF in explaining students’ cloud-based e-learning continuance that is difficult to explain with only their utilitarian perception of the cloud-based e-learning system, and further places considerably more emphasis upon students’ perceived impact on learning greatly driven by their TTF in the system. Thus, this study’s empirical evidence on incorporating ECM and TTF model can shed light on the outcome for cloud-based e-learning continuance and enhance better understanding of a richer post-adoption model.
Journal Article
Artificially Intelligent Tactile Ferroelectric Skin
Lightweight and flexible tactile learning machines can simultaneously detect, synaptically memorize, and subsequently learn from external stimuli acquired from the skin. This type of technology holds great interest due to its potential applications in emerging wearable and human‐interactive artificially intelligent neuromorphic electronics. In this study, an integrated artificially intelligent tactile learning electronic skin (e‐skin) based on arrays of ferroelectric‐gate field‐effect transistors with dome‐shape tactile top‐gates, which can simultaneously sense and learn from a variety of tactile information, is introduced. To test the e‐skin, tactile pressure is applied to a dome‐shaped top‐gate that measures ferroelectric remnant polarization in a gate insulator. This results in analog conductance modulation that is dependent upon both the number and magnitude of input pressure‐spikes, thus mimicking diverse tactile and essential synaptic functions. Specifically, the device exhibits excellent cycling stability between long‐term potentiation and depression over the course of 10 000 continuous input pulses. Additionally, it has a low variability of only 3.18%, resulting in high‐performance and robust tactile perception learning. The 4 × 4 device array is also able to recognize different handwritten patterns using 2‐dimensional spatial learning and recognition, and this is successfully demonstrated with a high degree accuracy of 99.66%, even after considering 10% noise.
An artificially intelligent electronic skin that is capable of sensing and learning tactile stimuli is presented. This ferroelectric field effect transistor platform can implement spatial sensory synaptic functions through electrical and/or tactile spikes without complicated integration. These unique characteristics enable the demonstrated device array to recognize different handwriting patterns with a high degree of accuracy, similar to biological neural networks.
Journal Article
A digital recipe for enhancing clinical reasoning: the role of e-learning by concordance (E-LbC): a quasi-experimental study
Background
Clinical reasoning (CR) is a critical competency in medical education, essential for effective decision-making in clinical practice. This study aimed to enhance CR skills among undergraduate medical students by comparing two instructional strategies: the E-learning by Concordance (e-LbC) approach and an interactive lecture-based method.
Methods
A quasi-experimental comparative study was conducted at the Faculty of Medicine, Suez Canal University, Egypt, during the 2021–2022 academic year. The study involved 60 fifth-year medical students through comprehensive sampling and was implemented over one academic term. It consisted of three phases. In the first phase, an online Script Concordance Test (SCT) was used via the Wooclap platform to assess students’ baseline CR skills. The second phase included the educational intervention, in which the e-LbC method was used to teach the topic of painless vision loss, while the interactive lecture method was used for painful vision loss. In the final phase, a researcher-developed questionnaire assessed students’ perceptions regarding the impact of each instructional method on CR development, difficulty level, and satisfaction. The questionnaire’s validity was established by medical education experts, and reliability was confirmed using Cronbach’s alpha.
Results
Statistical analysis using paired t-tests revealed no significant difference in the pre-SCT scores between groups. However, post-SCT scores showed a statistically significant improvement in both groups, with the e-LbC, painless vision loss theme, demonstrating a greater effect size (Cohen’s d) and overall higher performance (
p
< 0.001). Additionally, 62% of students expressed satisfaction with the e-LbC method.
Conclusion
the e-LbC approach positively influenced students’ clinical reasoning skills and engagement. Its integration with real-time assessment tools like Wooclap, combined with its cost-effectiveness, flexibility, and user-friendliness, positions it as a valuable tool for enhancing medical education in diverse learning environments.
Journal Article
An Empirical Study to Explore the Adoption of E-Learning Social Media Platform in Taiwan: An Integrated Conceptual Adoption Framework Based on Technology Acceptance Model and Technology Threat Avoidance Theory
Currently, social media is ubiquitous and essential for social networking and content sharing. It is an effective platform for teaching and learning in higher education and provides a novel way to communicate between instructors and pupils. Thus, the purpose of this study was to present a research framework to examine students’ motivation to adopt an e-learning system with social medial platforms. This cross-sectional study used the questionnaire to collect data from the students in Taiwan. A framework has examined students’ motivation to adopt an e-learning system with social medial platforms based on the modified technology acceptance model (TAM) and technology threat avoidance theory (TTAT). The research framework was evaluated by structural equation modeling (SEM) and represented by Smart-PLS. A total of 262 valid responses were used for statistical analysis. The results recommended modified research model explains 77.0% of the variance of motivation to adopt (R2 = 0.77). The findings also supported perceived usefulness, perceived ease of use, perceived cost, perceived effectiveness, and self-efficacy significantly influenced students’ motivation to use. Results also indicated threat appraisal, perceived susceptibility, and perceived severity are not significant factors for predicting students’ motivation to adopt e-learning in higher education.
Journal Article