As the demand for higher-order thinking skills continues to rise in the 21st century, the integration of Generative Artificial Intelligence (GAI) into educational practices has emerged as a promising tool. However, its full potential in enhancing collaborative problem-solving and team creativity within educational contexts, particularly in Digital Storytelling (DST), remains insufficiently explored. This study investigated the effects of GAI tools, including ChatGPT, Midjourney, and Runway, on university students’ Collaborative Problem-Solving (CPS) skills, Team Creativity Performance (TCP), and their perceptions of GAI’s role in DST creation. Employing a mixed-methods approach, the study utilized pre- and post-surveys along with semi-structured interviews to assess these outcomes. Over a 20-week DST training program, sixty university students were randomly assigned to either an experimental group creating their DST with the aid of GAI tools or a conventional group designed DST via traditional methods. Results indicated that the integration of GAI tools in DST creation significantly enhanced students’ CPS skills. Furthermore, the experimental group demonstrated better performance in team creativity, particularly in the user experience and novelty dimensions. It also revealed that while students generally held a positive attitude toward the use of GAI in DST creation, they also expressed concerns about potential cognitive offloading, over-reliance on GAI technology, and the lack of emotional support. This study offers valuable insights for educators and researchers seeking to incorporate GAI technologies into DST creation as a way to foster collaborative learning and creativity in educational settings.

To tackle the exponentially increasing throughput of Next-Generation Sequencing (NGS), most of the existing short-read aligners can be configured to favor speed in trade of accuracy and sensitivity. SOAP3-dp, through leveraging the computational power of both CPU and GPU with optimized algorithms, delivers high speed and sensitivity simultaneously. Compared with widely adopted aligners including BWA, Bowtie2, SeqAlto, CUSHAW2, GEM and GPU-based aligners BarraCUDA and CUSHAW, SOAP3-dp was found to be two to tens of times faster, while maintaining the highest sensitivity and lowest false discovery rate (FDR) on Illumina reads with different lengths. Transcending its predecessor SOAP3, which does not allow gapped alignment, SOAP3-dp by default tolerates alignment similarity as low as 60%. Real data evaluation using human genome demonstrates SOAP3-dp's power to enable more authentic variants and longer Indels to be discovered. Fosmid sequencing shows a 9.1% FDR on newly discovered deletions. SOAP3-dp natively supports BAM file format and provides the same scoring scheme as BWA, which enables it to be integrated into existing analysis pipelines. SOAP3-dp has been deployed on Amazon-EC2, NIH-Biowulf and Tianhe-1A.

In this paper, we give a simple scheme for identifying ε-approximate frequent items over a sliding window of size n. Our scheme is deterministic and does not make any assumption on the distribution of the item frequencies. It supports O(1/ε) update and query time, and uses O(1/ε) space. It is very simple; its main data structures are just a few short queues whose entries store the position of some items in the sliding window. We also extend our scheme for variable-size window. This extended scheme uses O(1/ε log(εn)) space. Copyright 2006 ACM.link_to_subscribed_fulltex

Learning analytics In the past decade, "learning analytics" has become one of the fields of educational technology that receives growing attention from educational researchers and practitioners (Hui and Kwok 2019). There are no generally accepted definitions of learning analytics but the most widely referenced one was defined by the 1st International Conference on Learning Analytics and Knowledge: "Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs" (Long and Siemens 2011). It is expected that, from the traces left by students and teachers in teaching and learning, learning analytics can enhance understanding of learning behaviors (Wong and Chong 2018); provide useful suggestions for policymakers, instructors, and learners (Hwang et al. 2014); and help educational practitioners to improve teaching and learning effectiveness (Bienkowski et al. 2012). Learning analytics involves the use of a broad range of data and techniques for analysis, which includes the development of metrics (such as predicators and indicators for various factors) to understand the current situation and measure teaching and learning effectiveness, the use of different educational technologies to visualize and interpret data and to prompt remedial actions, and the refinement of the metrics and derivation of interventions to shape the learning environment (Avella et al.

The recent integration of educational technologies and emerging learning approaches into education systems has been driven largely by the pandemic. This paper conducts a systematic review and delves into the new wave of research on serious games designed for innovative learning using augmented reality (AR), virtual reality (VR), and mixed reality (MR). The review was referenced to the review protocol, PRISMA 2020. Using the Scopus Database with a time filter from 2007 to 2023 (27 July), we searched 329 articles and shortlisted 273 relevant studies. Notably, European countries contributed the most (62.9%) to this research area. Among the most frequent keywords, VR (90.9%) was commonly used in AR/VR/MR, while e-learning (95.3%) was among the popular innovative learning approaches. Further research studies are needed to employ AR and MR technologies, as well as other innovative learning approaches, to enable performance evaluation and comparison of various educational technologies and learning approaches. We conducted an in-depth analysis of the relevant studies and their basic characteristics. Additionally, we introduced 15 essential and recently published AR/VR/MR standards to ensure better reliability, quality, and safety of architectures, systems, products, services, and processes. To facilitate performance evaluation and analysis, we surveyed 15 recently published benchmark education datasets. This review suggested four future research directions, including multisensory experiences, generative artificial intelligence, personalization and customization, and real-time interaction.