Digital literacy in educationEducation and Communication Studies
Abstract
Digital transformation in the world of education requires teachers to be able to adapt to technological developments while developing learning that is creative, innovative, and in accordance with the needs of students. This community service activity aims to improve teachers' competence in utilizing digital technology as a medium and learning resource and encourage the creation of more interactive and student-centered learning. Activities are carried out through socialization, training, direct practice, discussion, and mentoring. Activity materials include the use of digital platforms and applications for learning, the development of interactive learning media, the wise use of artificial intelligence (AI), and strategies for designing digital-based creative learning activities. The results of the activity show an increase in teachers' understanding and skills in selecting, using, and developing digital technology to support the learning process. Teachers are also able to produce more interesting, interactive, and contextual media designs and learning activities. This activity shows that digital transformation is not only related to mastery of technology, but also to teachers' ability to integrate technology, pedagogy, and creativity to create meaningful learning experiences. Thus, strengthening teachers' digital competencies needs to be carried out in a sustainable manner so that teachers are able to become creative, adaptive, and innovative educators in facing educational changes in the digital era.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9