Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
The development of students’ independent learning skills has become a strategic priority in contemporary higher education because graduates are expected to manage information, set learning goals, monitor progress, solve problems, and update their competencies beyond formal instruction. This article analyzes modern approaches to strengthening independent learning through the lens of self-regulated learning theory. Particular attention is paid to flipped learning, project- and problem-based learning, digital learning environments, artificial-intelligence-assisted feedback, learning analytics, e-portfolios, peer assessment, and reflective practice. The discussion synthesizes international research and proposes an integrated model in which autonomy is developed through purposeful task design, transparent assessment, timely feedback, digital support, and systematic reflection. The article argues that technology alone does not create learner independence; its effect depends on pedagogical design, teacher guidance, ethical use, and students’ metacognitive competence. Practical recommendations are offered for universities seeking to modernize independent study and make it more measurable, personalized, and connected to real-life professional tasks.
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
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.