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Use of gamification and game-based learning in educating Generation Alpha: A systematic literature review

Educational Technology and Society 2024年第2期27卷 114-132页

作者： Fernando, Pumudu A. Premadasa, H. K. Salinda Department of Computing Informatics Institute of Technology Sri Lanka Centre for Computer Studies Sabaragamuwa University Sri Lanka

Children born after 2010 are labelled as members of Generation Alpha, who currently pursue their primary education. Gamification and game-based learning methodologies have gained popularity in the global education sector in the recent past. The goal of this study is to investigate the present state of adopting gamification and game-based learning for primary education student cohorts, with the aid of recent peer-reviewed research publications. By employing a systematic mapping design, reviewed paper results are categorized and further evaluated in relation to the attributes such as type of gamification, game mechanics and elements used, evaluation context, type of experimental outcome, gamified academic subject area, and type of application. With the aid of review results, this study proposes a mapping of gamification learning mechanics, suited to address Generation Alpha traits. It also recommends future research directions, which include investigating the realistic learning preferences of Generation Alpha, the need for designing novel educational game elements and adaptive gamification learning strategies, and the importance of long-term studies with diverse learner samples. The findings of this study will support prospective stakeholders, such as researchers, educators, parents, and educational policymakers, to utilize gamification approaches effectively in educating Generation Alpha. © (2023), (International Forum of Educational Technology and Society). All Rights Reserved.

关键词： Adaptive learning Game-based learning Gamification Generation Alpha Primary education

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LSTM-QGAN: Scalable NISQ Generative Adversarial Network

LSTM-QGAN: Scalable NISQ Generative Adversarial Network

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chu, Cheng Hastak, Aishwarya Chen, Fan Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

ISBN: (纸本)9798350368741

Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can diminish the QGAN's effectiveness. Second, methods that segment inputs into smaller patches processed by multiple generators face scalability issues. In this work, we propose LSTM-QGAN, a QGAN architecture that eliminates PCA preprocessing and integrates quantum long short-term memory (QLSTM) to ensure scalable performance. Our experiments show that LSTM-QGAN significantly enhances both performance and scalability over state-of-the-art QGAN models, with visual data improvements, reduced Fréchet Inception Distance scores, and reductions of 5× in qubit counts, 5× in single-qubit gates, and 12× in two-qubit gates. © 2025 IEEE.

关键词： Long Short-Term Memory NISQ Quantum Generative Adversarial Network

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Key Determinants of Value-Based Management for ICT Project Delivery: Insights from Kansei Engineering and Emotion Research 10th

Key Determinants of Value-Based Management for ICT Project D...

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10th International Conference on Kansei Engineering and Emotion Research, KEER 2024

作者： Hussein, Surya Sumarni Rosli, Nur Hanis Solehah Mohd Ahmad, Azran College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia College of Computing and Informatic Universiti Tenaga Nasional Putrajaya Malaysia

ISBN: (纸本)9789819798896

This study explores the key determinants of Value-Based Management (VBM) in Information and Communication Technology (ICT) project delivery, incorporating insights from Kansei Engineering and emotion research. VBM emphasizes optimizing project outcomes to align with organizational strategic goals and stakeholder value. However, traditional approaches often overlook the emotional and psychological aspects influencing user satisfaction and project success. By integrating Kansei Engineering, which focuses on translating users’ emotional responses into design elements, the study employed a qualitative method with interview sessions to collect information. Five questions were formulated based on a few relevant works of literature to obtain feedback from the targeted population and a thematic analysis was conducted to analyse the interview data. At the end of this study, the objectives of this research were achieved as the eleven key determinants of value-based management and its contribution toward the successful delivery of ICT projects respectively were identified and practical guidance to improve value-based management in ICT projects was provided. The results suggest that a holistic understanding of user emotions and their translation into project deliverables can lead to more successful and value-driven ICT projects. This study contributes to the field by bridging the gap between emotion research and value-based management, proposing practical strategies for enhancing project delivery in the ICT sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Project management

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Automation of interaction—interaction design at the crossroads of user experience (UX) and artificial intelligence (AI)

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Personal and Ubiquitous computing 2023年第6期27卷 2281-2290页

作者： Wiberg, Mikael Stolterman Bergqvist, Erik Interaction Design Division Chalmers University of Technology Gothenburg Sweden Department of Informatics Umeå University Umeå Sweden Computing and Engineering Luddy School of Informatics Indiana University Bloomington United States

Interaction design/HCI seems to be at a crossroads. On the one hand, it is still about designing for engaging user experiences (UX). Still, on the other hand, it seems to be increasingly about reducing interaction and automating human–machine interaction through the use of AI and other new technologies. In this paper, we explore this seemingly unavoidable gap. First, we discuss the fundamental design rationality underpinning interaction and automation of interaction from the viewpoints of classic theoretical standpoints. We then illustrate how these two come together in interaction design practice. Here we examine four examples from already published research on automation of interaction, including how different levels of automation of interaction affect or enable new practices, including coffee making, self-tracking, automated driving, and conversations with AI-based chatbots. Through an interaction analysis of these four examples, we show (1) how interaction and automation are combined in the design, (2) how interaction is dependent on a certain level of automation, and vice versa, and (3) how each example illustrates a different balance between, and integration of interaction and automation. Based on this analysis, we propose a two-dimensional design space as a conceptual construct that takes these aspects into account to understand and analyze ways of combining interaction and automation in interaction design. We illustrate the use of the proposed two-dimensional design space, discuss its theoretical implications, and suggest it as a useful tool—when designing for engaging user experiences (UX), with interaction and automation as two design materials. © 2023, The Author(s).

关键词： Automation

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Gemina: A Coordinated and High-Performance Memory Deduplication Engine 31

Gemina: A Coordinated and High-Performance Memory Deduplicat...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Zhang, Zhehua Wu, Suzhen You, Wenyan Du, Chunfeng Mao, Bo Xiamen University Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Fujian Xiamen China

ISBN: (纸本)9798331506476

Memory deduplication is widely used to effectively reduce the memory footprint in operating systems, while huge pages are employed to enhance access performance by increasing TLB hit rates. Unfortunately, duplicate huge pages are very rare in memory, leading to most huge pages being split into base pages during deduplication, which degrades access performance by up to {4 3%}. Although asynchronous huge page promotion attempts to elevate multiple base pages to huge pages to maximize performance, deduplication disrupts the uniformity of page attributes, limiting the effectiveness of such promotions. To address this problem, we designed Gemina, a new memory deduplication scheme that coordinates huge page management. Gemina employs a fine-grained detector to distinguish pages, redundancy and hotness, a distributor to process pages conservatively and avoid frequent switches, and an adapter to organize memory for fast conversion. This approach balances the trade-off between performance and space efficiency in memory management. Experimental evaluations show that, compared to the naive KSM-based deduplication scheme, Gemina can achieve 96.1% of the memory savings of KSM while also improving memory access performance by {4 3. 1%} in random access. © 2025 IEEE.

关键词： Memory management

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Learning Human Anatomy Using Augmented Reality Mobile Application

Learning Human Anatomy Using Augmented Reality Mobile Applic...

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Abdullah, Nur Atiqah Sia Rokmain, Nur Shakila Sahira Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Media Selangor Shah Alam Malaysia

ISBN: (纸本)9798350310689

In this current era of technological advancement, the development of Augmented Reality (AR) technology already has a major influence on the education domain. Therefore, more efforts to create educational applications for learners to understand diverse topics using AR. This paper aims to share the experience of developing a mobile application for learning human anatomy using AR. We selected the marker-based technique as the AR technique and Android platform for mobile application development. We follow the Mobile Application Development Life Cycle (MADLC) for the development process. During the design phase, we define the system architecture, use case diagram, algorithm design, and user interface design. Then, we utilize Unity3D, Vuforia, Visual Studio Code, Blender, and AR Core during the development. The Unity's features in this AR application include importing the Vuforia database, image targets, camera conversion, 3D modeling of human anatomy and organs, canvas, and panel, and the lean touch tool kit for scaling and rotating 3D models. As a result, we successfully developed a mobile application for learning human anatomy using AR. This application helps the learner understand as well as enhances the learning experience in human anatomy. © 2023 IEEE.

关键词： Augmented reality

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Sales Promotion Mobile Application using Augmented Reality 8

Sales Promotion Mobile Application using Augmented Reality

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Sia Abdullah, Nur Atiqah Anak Ulan, Amelia Natasha Dora Rosli, Marshima Mohd Universiti Teknologi MARA School of Computing Sciences College of Computing Informatics and Mathematics Selangor Shah Alam Malaysia

ISBN: (纸本)9798350315516

Nowadays, most people utilize innovative technologies to fulfill their needs, which drives the retail industry's global evolution. To stay relevant and survive in an industry, retailers acquire a competitive advantage over their competitors. Thus, this paper aims to share an enhanced shopping experience in retail stores with augmented reality. It covers the process of identifying a suitable augmented reality (AR) approach in retail stores, constructing a mobile application, and evaluating the functionality of the application. After extensive research, marker-based AR techniques on Android were best suited to create the mobile application. By using the Mobile Development Life Cycle, we cover requirement gathering, then planning out the system architecture, use case diagram, algorithm design, and user interface design. Unity, Figma, and Vuforia are utilized to develop the application. After testing, the mobile application is ready for its final launch. We believe that this mobile application provides the competitive advantage that retailers need. © 2023 IEEE.

关键词： Augmented reality

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Multi-Space Tree with Incremental Construction for GPU-Accelerated Range Queries 31

Multi-Space Tree with Incremental Construction for GPU-Accel...

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31st Annual IEEE International Conference on High Performance computing, Data, and Analytics, HiPC 2024

作者： Donnelly, Brian Gowanlock, Michael School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States

ISBN: (纸本)9798331509095

Performing range queries is prohibitively expensive as the dimensionality of the data increases. Indexing data structures reduce the time complexity of these searches by eliminating superfluous distance calculations. The state-of-the-art utilizes the GPU due to its high distance calculation throughput as compared to multi-core CPUs. Previous state-of-the-art indexes fall into two categories: metric- and coordinate-based indexes, both of which partition the space using different approaches. The indexes partition the space to generate a set of candidate points for a given query which are later refined by distance calculations. Popular metric-based indexes partition the data based on distances to reference points, where the placement of the reference points determines the partitioning of the data space but the effectiveness depends on the distribution of the data. In high-dimensions, coordinate-based indexes typically partition the data based on a subset of the coordinate dimensions. Regardless of the index type there is a tradeoff between index search overhead and the number of distance calculations, where increasing the number of partitions will increase the search overhead but will decrease the number of distance calculations computed. In this paper, we propose Multi-Space Tree with Incremental Construction (MISTIC), a blended approach which uses both metric-based and coordinate-based partitioning strategies coupled with incremental index construction. We evaluate MISTIC on 5 real-world datasets and compare performance to both a state-of-the-art metric-based index, COSS, and a state-of-the-art coordinate-based index, GDS-JOIN. We find that MISTIC outperforms the state-of-the-art methods with an average speedup of 2.53× over COSS and 2.73× over GDS-JOIN. © 2024 IEEE.

关键词： Graphics processing unit

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Integrating Deep Learning and Counterfactual Methods for Causal Inference in Genomics

Integrating Deep Learning and Counterfactual Methods for Cau...

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2024 International Conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Gobonamang, Tshepo Kitso Mpoeleng, Dimane School of Computing and Information System Botswana Accountancy College Gaborone Botswana School of Pure and Applied Sciences BIUST Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331539733

In the dynamic field of genomics, understanding the causal connections between biological variables is pivotal for advancing personalized healthcare. This research presents an innovative method for causal inference in human genomics, combining deep learning, counterfactual reasoning, and a hybrid suffix tree algorithm. Utilizing the principles of causal calculus, particularly do-calculus, the study tackles the challenges associated with identifying causal relationships from observational datasets. The methodology focuses on disease prediction by analyzing genomic information, such as DNA sequences, mRNA transcription profiles, and mutation patterns, sourced from the Critical Assessment of Functional Annotation (CAFA) dataset. The study introduces a robust AI-based framework for causal inference, structured around Pearl's three-tier hierarchy: association, intervention, and counterfactual reasoning. This frame-work addresses misconceptions around correlation and causation, illustrates the effects of variable manipulation, and constructs hypothetical scenarios to assess the impact of interventions. Using this approach on the CAFA dataset uncovered causal links between genomic features and disease outcomes, offering deeper insights into disease mechanisms and the efficacy of potential interventions. This work highlights the necessity of embedding causal inference into machine learning models for genomic research, ensuring more precise and fair AI-driven solutions. By merging deep learning, causal inference, and genomics, this study lays the groundwork for future innovations in personalized medicine and beyond. © 2024 IEEE.

关键词： Deep learning

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Homestay Recommender System Using Content-Based Filtering and K-Means Algorithm

Homestay Recommender System Using Content-Based Filtering an...

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2024 International Visualization, informatics and Technology Conference, IVIT 2024

作者： Azman, Nur Atierah Binti Nor Daud, Nik Marsyahariani Nik Sabri, Norlina Mohd Abu Bakar, Nor Azila Awang Universiti Teknologi Mara Cawangan Terengganu College of Computing Informatics and Mathematics Kuala Terengganu Malaysia

ISBN: (纸本)9798350354447

The challenge of finding an ideal homestay that aligns with user preferences is significant due to the volume of available choices and the limitations of existing search filters on accommodation websites. Users face several issues when searching for homestays online, such as limited search filters, lack of location or landmark information, irrelevant recommendations, outdated or inaccurate homestay details, and time-consuming search processes. Additionally, websites often mix homestay listings with other types of accommodations, leading to user confusion and inefficient search experiences. The objective of this study is to investigate the requirement needed to develop a homestay recommender system using Content-based filtering and to evaluate the accuracy and performance of the homestay recommender system. The data used in this research, which contain the criteria of the homestays, were collected from the online accommodation website. Recommendation list is generated using Content-based filtering approach, which includes few techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) to analyze the importance of terms in the dataset, Cosine Similarity to measure the similarity between the user's preferences and homestays characteristic and K-Means algorithm for the evaluation part. The system's performance is evaluated using precision, recall, F1-score, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The prototype achieved an accuracy of 79.3%, a recall of 93%, and an F1 score of 86%. The RMSE and MAE were recorded at 9.95 and 9.42, respectively. As for future works, expanding the dataset and implementing a proper database to store the user profile could be done to enrich the recommendation process. © 2024 IEEE.

关键词： Wiener filtering

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