Competitive Learning Research Articles

Technology readiness and implications for higher education in Universities in North-Central Nigeria

The fourth industrial revolution emphasises classroom innovation with digital and smart technology. It examined students' preparation for technology-driven education in higher education and the fourth industrial revolution. A descriptive survey research design was adopted for this study. The population consisted of students from tertiary institutions, while the target population included university undergraduate and postgraduate students in universities in North-Central Nigeria. The sample comprised five hundred and seventy students across three types of institutions in Nigeria (i.e. private, state, and federal-owned institutions), drawn using proportionate and cluster sampling techniques. Data were collected using a researcher-designed and validated questionnaire with an overall reliability index of 0.86. The collected data were analysed descriptively, and non-parametric inferential statistics were tested at a 0.05 level of significance. Students were ready to use educational apps for studying, engage with immersive tech tools while learning, learn about IoT as part of their learning trajectories for 4IR compliance, embrace many technological innovations while learning, and engage in lifelong learning. Moderately technology-ready students had significant gender, age, institution, and degree of study demographics. Also, given the contemporary 4IR reality, pupils were somewhat ready for technology-enhanced education and positively inclined toward technology-driven abilities. Group dynamics are crucial to preparation. Therefore, it is recommended that students be given the opportunity to improve their technical abilities to prepare them for meaningful teaching and learning. This can be done by investing in technology, training students, and passing regulations that support worldwide competitive online learning.

EFFECTS OF COMPETITIVE AND JIGSAW COLLABORATION LEARNING STRATEGIES ON STUDENTS’ INTEREST IN GEOMETRY AMONG SENIOR SECONDARY SCHOOL STUDENTS IN FCT, ABUJA, NIGERIA

This study investigated the Effects of Competitive and Jigsaw collaborative Learning Strategies on Students’ Interest in Geometry among Senior Secondary School students in Abuja. The study employed a quasi – experimental design using pre-test posttest and non-equivalent control group. A sample of 245 SSII students, comprising of One Hundred and thirty -one (131) males and One Hundred fourteen (114) females were used for the study. A multi stage sampling techniques were used for the study. Instrument for data collection was Students’ Geometry Interest Inventory (SGII) for competitive and jigsaw collaborative learning strategies. The instruments were validated by five experts in mathematics education, three Senior Lectures from Department of Science Education Federal University of Technology, Minna, two Principal Lectures from FCT – COE, Zuba as well as two teachers of mathematics from FCT-SEB. The reliability coefficient of the instruments were obtained using Cronbach Alpha was 0.87 and 0.79 for SGII for competitive and jigsaw collaborative learning strategies. Three research questions were raised and three null hypotheses that were tested at 0.05 level of significant. Ranks Mean were used to answered research questions and Mann Whitney U-test was statistical tool used for testing null hypotheses. The results revealed that the use of competitive learning strategy enhance students’ achievement in geometry. Among other recommendations is that, mathematics teachers should be encouraged to adopt these strategies in the classroom.

Upaya Meningkatkan Motivasi Belajar Siswa dengan Metode Team Games Tournament (TGT) Terhadap Siswa Kelas XI SMA Negeri 1 Gowa

This study aims to enhance students’ learning motivation through the application of the team games tournament (TGT) method in the 11th grade students at SMA N 1 Gowa. The study employs a classroom action research (CAR) design consisting of two cycles, with each cycle including the stages of planning, implementation, observation, and reflection. The subjectof the study were 11th grade students at SMA N 1 Gowa. Data were collected through observation, interviews, and questionnaires, and then analyzed qualitatively and quantitatively. The results showed a significant increase in students’ learning motivation after the implementation of the TGT method. This method proved effective in creating a more interactive and competitive learning environment, therby boosting students’ enthusiasm and engagement in the learning process

A customer-driven quality function deployment approach for intelligent product design: A case of automatic-dishwasher

Quality Function Deployment (QFD) is one of the very effective customer-driven product development tools. With automatic dishwashers entering the Chinese market, manufacturers can use QFD to develop products that meet the unique Chinese customer requirements and explore the Chinese market. To help manufacturers maximize profits by developing automatic dishwashers to China’s market situation and competitive environment, this paper proposes an improved QFD model considering competitive learning and cost analysis based on market information analysis with Product Life Cycle (PLC) theory. On the one hand, the improved QFD model determines the life cycle stage of the product by analyzing the characteristics of the market. The improvement rate of customer requirements (CRs) determined by competitive learning in different stages has different influence on the importance weights of CRs, so that the new products developed based on design requirements (DRs) converted from CRs can better adapt to the current market environment. On the other hand, the cost strategy will also change according to different PLC stages. In cost analysis, different cost criteria weights will be determined in different life cycle stages to deal with product development risks in different market environments and help enterprises achieve better sustainable development. Finally, we take Chinese HE company’s automatic-dishwasher design as an example to show the specific application of the proposed model. The proposed model identifies the important DRs and their relative importance ranking for the product development. Comparison with existing QFD models shows the ranking obtained through the model is better integrated with market information, enabling the development of products that are better suited to the market, and helping companies to achieve sustainable development.

Students’ and faculty continuous intention to use gamification in higher education: A structural analysis

Over the past decade, the integration of technology, particularly gamification, has initiated a substantial transformation within the field of education. However, educators frequently confront the challenge of identifying suitable competitive game-based learning platforms amidst the growing emphasis on cultivating creativity within the classroom and effectively integrating technology into pedagogical practices. The current study examines students and faculty continuous intention to use gamification in higher education. The data was collected through an online survey with a sample size of 763 Pakistani respondents from various universities around Pakistan. The structural equation modeling was used to analyze the data and to investigate how continuous intention to use gamification is influenced by, extended TAM model with inclusion of variables such as task technology fit, social influence, social recognition and hedonic motivation. The results have shown that task technology has no significant influence on perceived usefulness (PU) where as it has a significant influence on perceived ease of use (PEOU). Social influence (SI) indicates no significant influence on perceived ease of use. Social recognition (SR) indicates positive influence on perceived usefulness, perceived ease of use, and continuous intention. The dimensional analysis indicated that perceived ease of use has insignificant influence on perceived usefulness. Both PEOU and PU exhibit positive influence on attitude. Hedonic motivation (HM) and attitude were observed to have a positive influence on continuous intention (CI). Moreover, gamification is found to efficiently and effectively achieve meaningful goals by tapping intrinsic motivation of the users through engaging them in playful experiences.

Developing Graduate Attributes Through Competitive Gameplay and Learning Theories

The term competitive gameplay or gaming is predominantly associated with esports. Esports, a synonym, or abbreviation for electronic sports is an industry that has a global outreach with a lucrative market. This is accentuated by the event driven, business model focus and organised competitive events, often involving sponsorship. These additional characteristics associated with esports is what distinguishes it from traditional or casual gaming. Though esports games can be played individually, they are predominantly viewed as a team activity. Beyond the debate as to whether esports can be classified as a sport, it is an area that has attracted a significant amount of academic research. Research areas have often included esports psychology, esports player health and nutrition and esports management. Some research studies have also reviewed how esports have the potential to e.0quip esports players with relevant 21st century skills such as critical and lateral thinking. One area where esports appears to be under researched is in the relationship between competitive gameplay and learning theories. It can be argued that this is an important consideration when delivering esports from an educational perspective. The aim of this paper is to provide a scoping review of the esports literature identifying empirical research associated with competitive gaming and graduate attributes. Esports game genres will be explored from a gameplay perspective demonstrating via learning theory examples how these games can facilitate graduate attributes in players. Exploring the domain of esports game genres, learning theories and meta-skills, the paper presents a conceptual model illustrating the interrelationship between these areas. The paper also has practical value to educators, who may be as yet unfamiliar with esports, informing them of the subject’s educational benefits from a graduate attribute and employability perspective.

Fostering Teamwork Satisfaction in Competitive Project-Based Learning Environments

This 10-week study investigates the relationships between diversity beliefs, teamwork satisfaction, perceived classroom competitiveness, and attitudes toward project-based activities among 105 marketing students. Findings indicate that diversity beliefs enhance teamwork satisfaction, whereas performance-approach goals reduce it. Classroom competitiveness negatively moderates the impact of teamwork satisfaction on project attitudes, whereas teammate competitiveness has a positive moderating effect. These insights can help educators balance competition and collaboration to optimize learning environments.

Suppressed possibilistic fuzzy c-means clustering based on shadow sets for noisy data with imbalanced sizes

Possibilistic fuzzy c-means clustering (PFCM) algorithm generates fewer overlapping clustering centers than the possibilistic c-means clustering (PCM) algorithm and possesses better noise immunity than fuzzy c-means clustering (FCM) algorithm. However, with the increasing noise intensity and number of clusters, PFCM still faces the problem of getting partially overlapping clustering centers or mislocated centers in noise regions. Moreover, the sample-size imbalance increasingly intensifies the difficulty of positioning centers of small clusters. To solve the above problems, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets (S-SPFCM) is proposed by introducing the shadow set theory and the "suppressed competitive learning" strategy. Firstly, KL divergence is introduced in the objective function of PFCM to increase the anti-noise robustness of fuzzy memberships against long-range noise and outliers. Secondly, to reduce the number of overlapping centers caused by possibilistic memberships, the shadow set theory is introduced to divide each class adaptively into three regions (core, shadow, and exclusion regions) by an uncertainty balance method. The suppressed competitive learning method is extended by modifying the memberships of points within the three regions, thus artificially guiding the iterative track of clustering centers. Meanwhile, to further reduce the influence of imbalanced sizes, a scheme to reset mislocated centers in the core and shadow regions is also designed. In addition, to improve the segmentation performance of S-SPFCM for noisy images, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets and local information (SL-SPFCM) is also proposed. The SL-SPFCM first improves the Euclidean distance by a distance filtering scheme. Then SL-SPFCM introduces the local median membership of each pixel into the KL divergence of the objective function. Finally, experiments on synthetic datasets and color images which are characteristic of imbalanced sizes and noise injection demonstrate the proposed S-SPFCM and SL-SPFCM algorithms achieve smaller center deviations and higher clustering accuracy compared with several state-of-the-art clustering algorithms.

Exploring the potential of 5G uplink communication: Synergistic integration of joint power control, user grouping, and multi-learning Grey Wolf Optimizer

Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.

Immune-related cell death index and its application for hepatocellular carcinoma

Regulated cell death (RCD) plays a crucial role in the immune microenvironment, development, and progression of hepatocellular carcinoma (HCC). However, reliable immune-related cell death signatures have not been explored. In this study, we collected 12 RCD modes (e.g., apoptosis, ferroptosis, and cuproptosis), including 1078 regulators, to identify immune-related cell death genes based on HCC immune subgroups. Using a developed competitive machine learning framework, nine genes were screened to construct the immune-related cell death index (IRCDI), which is available for online application. Multi-omics data, along with clinical features, were analyzed to explore the HCC malignant heterogeneity. To validate the efficacy of this model, more than 18 independent cohorts, including survival and diverse treatment cohorts and datasets, were utilized. These findings were further validated using in-house samples and molecular biological experiments. Overall, the IRCDI may have a wide application in individual therapeutic decision-making and improving outcomes for HCC patients.

Achievement goal orientation and academic social comparison emotions in competitive learning environments: Latent profile analysis approach

Achievement goal orientation and academic social comparison emotions in competitive learning environments: Latent profile analysis approach

Drone-DETR: Efficient Small Object Detection for Remote Sensing Image Using Enhanced RT-DETR Model.

Performing low-latency, high-precision object detection on unmanned aerial vehicles (UAVs) equipped with vision sensors holds significant importance. However, the current limitations of embedded UAV devices present challenges in balancing accuracy and speed, particularly in the analysis of high-precision remote sensing images. This challenge is particularly pronounced in scenarios involving numerous small objects, intricate backgrounds, and occluded overlaps. To address these issues, we introduce the Drone-DETR model, which is based on RT-DETR. To overcome the difficulties associated with detecting small objects and reducing redundant computations arising from complex backgrounds in ultra-wide-angle images, we propose the Effective Small Object Detection Network (ESDNet). This network preserves detailed information about small objects, reduces redundant computations, and adopts a lightweight architecture. Furthermore, we introduce the Enhanced Dual-Path Feature Fusion Attention Module (EDF-FAM) within the neck network. This module is specifically designed to enhance the network's ability to handle multi-scale objects. We employ a dynamic competitive learning strategy to enhance the model's capability to efficiently fuse multi-scale features. Additionally, we incorporate the P2 shallow feature layer from the ESDNet into the neck network to enhance the model's ability to fuse small-object features, thereby enhancing the accuracy of small object detection. Experimental results indicate that the Drone-DETR model achieves an mAP50 of 53.9% with only 28.7 million parameters on the VisDrone2019 dataset, representing an 8.1% enhancement over RT-DETR-R18.

(Invited) Enabling Bio-Realistic Artificial Intelligence Hardware with Neuromorphic Nanoelectronics

The exponentially improving performance of digital computers has recently slowed due to the speed and power consumption issues resulting from the von Neumann bottleneck. In contrast, neuromorphic computing aims to circumvent these limitations by spatially co-locating logic and memory in a manner analogous to biological neuronal networks [1]. Beyond reducing power consumption, neuromorphic devices provide efficient architectures for image recognition, machine learning, and artificial intelligence [2]. This talk will explore how low-dimensional nanoelectronic materials enable gate-tunable neuromorphic devices [3]. For example, by utilizing self-aligned, atomically thin heterojunctions, dual-gated Gaussian transistors have been realized, which show tunable anti-ambipolarity for artificial neurons, competitive learning, spiking circuits, and mixed-kernel support vector machines [4,5]. In addition, field-driven defect motion in polycrystalline monolayer MoS2 enables gate-tunable memristive phenomena that serve as the basis of hybrid memristor/transistor devices (i.e., ‘memtransistors’) that concurrently provide logic and data storage functions [6]. The planar geometry of memtransistors further allows multiple contacts and dual gating that mimic the behavior of biological systems such as heterosynaptic responses [7]. Moreover, control over polycrystalline grain structure enhances the tunability of potentiation and depression, which enables unsupervised continuous learning in spiking neural networks [8]. Finally, the moiré potential in asymmetric twisted bilayer graphene/hexagonal boron nitride heterostructures gives rise to robust electronic ratchet states. The resulting hysteretic, non-volatile injection of charge carriers enables room-temperature operation of moiré synaptic transistors with diverse bio-realistic neuromorphic functionalities and efficient compute-in-memory designs for low-power artificial intelligence and machine learning hardware [9].[1] V. K. Sangwan, et al., Matter, 5, 4133 (2022).[2] V. K. Sangwan, et al., Nature Nanotechnology, 15, 517 (2020).[3] M. E. Beck, et al., ACS Nano, 14, 6498 (2020).[4] M. E. Beck, et al., Nature Communications, 11, 1565 (2020).[5] X. Yan, et al., Nature Electronics, DOI: 10.1038/s41928-023-01042-7 (2023).[6] X. Yan, et al., Advanced Materials, 34, 2108025 (2022).[7] H.-S. Lee, et al., Advanced Functional Materials, 30, 2003683 (2020).[8] J. Yuan, et al., Nano Letters, 21, 6432 (2021).[9] X. Yan, et al., Nature, DOI: 10.1038/s41586-023-06791-1 (2023).

Competitive Team-Based Learning (CTBL): A Revolutionary Approach to Contemporary Language Teaching/Education

This article dissects part of the socio-educational/political background to my innovative approach to Language Teaching/Education namely Competitive Team-Based Learning (CTBL), which he formulated based on his seminal edu-political theories. (See, 2023) It then clarifies certain misgivings regarding integrating the element of competition within cooperative learning settings to pave the way to a to-the-point introduction to CTBL. The article also discusses – at length – distinguishing features and characteristics of CTBL with reference to the present methods and approaches like Communicative Language Teaching (CLT) and particularly Cooperative Learning (CL) methods. It also throws light on the mechanisms underlying the implementation of CTBL in real classroom situations and illustrates presentations and classroom techniques in classes where this approach may be applied by teachers. Moreover, the article highlights the kind of design, objectives, evaluation system, syllabi, materials, tasks, and activities emphasized by CTBL. Importantly, the significance of motivation in CTBL settings has been thrown into sharp relief. Finally, the article gives a glimpse of the importance of CTBL for today (language) classes in our complicated competitive world context, and elaborates the howabouts of its contribution to significant changes in our societies, for peace making and compassionate civilizations building. CTBL makes it possible via bringing that sort of changes in our students’ minds first.

CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval

The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently. Since various matching patterns can exist between queries and relevant documents, previous work tries to combine multiple retrieval models to find as many relevant results as possible. The constructed ensembles, whether learned independently or jointly, do not care which component model is more suitable to an instance during training. Thus, they cannot fully exploit the capabilities of different types of retrieval models in identifying diverse relevance patterns. Motivated by this observation, in this paper, we propose a Mixture-of-Experts (MoE) model consisting of representative matching experts and a novel competitive learning mechanism to let the experts develop and enhance their expertise during training. Specifically, our MoE model shares the bottom layers to learn common semantic representations and uses differently structured upper layers to represent various types of retrieval experts. Our competitive learning mechanism has two stages: (1) a standardized learning stage to train the experts equally to develop their capabilities to conduct relevance matching; (2) a specialized learning stage where the experts compete with each other on every training instance and get rewards and updates according to their performance to enhance their expertise on certain types of samples. Experimental results on retrieval benchmark datasets show that our method significantly outperforms the state-of-the-art baselines in the in-domain and out-of-domain settings.

Intelligent Recommendation Systems Powered by Consensus Neural Networks: The Ultimate Solution for Finding Suitable Chiral Chromatographic Systems?

The selection of suitable combinations of chiral stationary phases (CSPs) and mobile phases (MPs) for the enantioresolution of chiral compounds is a complex issue that often requires considerable experimental effort and can lead to significant waste. Linking the structure of a chiral compound to a CSP/MP system suitable for its enantioseparation can be an effective solution to this problem. In this study, we evaluate algorithmic tools for this purpose. Our proposed consensus model, which uses multiple optimized artificial neural networks (ANNs), shows potential as an intelligent recommendation system (IRS) for ranking chromatographic systems suitable for the enantioresolution of chiral compounds with different molecular structures. To evaluate the IRS potential in a proof-of-concept stage, 56 structural descriptors for 56 structurally unrelated chiral compounds across 14 different families are considered. Chromatographic systems under study comprise 7 cellulose and amylose derivative CSPs and acetonitrile or methanol aqueous MPs (14 chromatographic systems in all). The ANNs are optimized using a fit-for-purpose version of the chaotic neural network algorithm with competitive learning (CCLNNA), a novel approach not previously applied in the chemical domain. CCLNNA is adapted to define the inner ANN complexity and perform feature selection of the structural descriptors. A customized target function evaluates the correctness of recommending the appropriate CSP/MP system. The ANN-consensus model exhibits no advisory failures and requires only an experimental attempt to verify the IRS recommendation for complete enantioresolution. This outstanding performance highlights its potential to effectively resolve this problem.

Efektivitas Norm-Referenced Assessment (PAN) dalam Meningkatkan Pemahaman Mata Pelajaran dan Motivasi Siswa: Studi Kasus di SDN 03/III Sungai Deras

This study aims to evaluate the effectiveness of testing based on norms at SDN 03/III Sungai Deras involving 19 grade V students. The research sample was 19 students in grade V at SDN 03/III Sungai Deras. Data were collected through written tests and motivational questionnaires, then analyzed using descriptive statistics and paired t-tests. Results showed a significant increase in the average written test score from 65 to 78 post-intervention, with a corresponding decrease in standard deviation indicating improved consistency. Similarly, the motivation questionnaire revealed an increase in average scores from 3.2 to 4.1 post-intervention, with improved consistency in motivation levels. Statistical analysis confirmed significant differences between pre-test and post-test scores (t = 5.80, p < 0.05). Discussion underscores the positive impact of norm-referenced assessment on enhancing subject understanding and student motivation, emphasizing its relevance in fostering a competitive yet supportive learning.

Cooperative and competitive learning as transformative factors of educational processes for extracurricular skill enhancement

Cooperative and competitive learning as transformative factors of educational processes for extracurricular skill enhancement

Studi Komparatif Sistem Pendidikan di Korea Selatan Dengan Indonesia

Comparative studies have two syllables, consisting of "study" and "comparative". In the large Indonesian dictionary, the word "study" is a study, research or analysis. Meanwhile, the meaning of the word "comparative" is to compare. In this comparative research, it is usually used to compare the similarities or differences of a fact or the nature of the research object which is based on the research framework. The type of research used is a qualitative research method through a literature review (Kartiningrum, 2016). Qualitative research refers to the thoughts, feelings, opinions or beliefs of the person being studied and cannot be measured quantitatively (Ulfatin, 2022). The data used to prepare this work came from various articles related to the topics discussed. The social studies curriculum in South Korea began in 1946, when South Korea was liberated from Japanese colonial rule and came under the United States military. The main aim of this curriculum is to eradicate the remnants of Japanese imperialism. The curriculum in Indonesia has changed many times since independence. Since 2013/2014, Indonesia has begun implementing the 2013 Curriculum in schools in Indonesia for grades 1, 4, 7 and 10. The implementation of the 2013 curriculum will be carried out in stages until it is implemented in all classes in Indonesia. In 2020 in South Korea there will be an additional curriculum, namely the curriculum local content as well as in Indonesia. Comparative studies between the education systems in South Korea and Indonesia reveal striking differences in educational approaches, quality and outcomes. South Korea is known for its focus on quality education with high standards and a competitive learning culture, while Indonesia often places more emphasis on equal access to education throughout the region.

Progressive Feature Extraction with a Greedy Network-growing Algorithm

In this paper, a new information theoretic method called the greedy network-growing algorithm is proposed. The method is called "greedy," because a network with this algorithm grows while absorbing as much information as possible from outside. The method is based upon information theoretic competitive learning and can solve the fundamental problems inherent in competitive learning, such as the dead neurons and inappropriate number of neurons problems. The new model can grow networks by repeatedly maximizing information content and by gradually extracting salient features from input patterns. Because the new model can cope with inappropriate feature detection in the early stage of learning, extracted features should cover most of the input patterns. The new method is applied to political data analysis, medical data analysis, and information science education analysis. Experimental results confirm that the new method can acquire significant information and that more explicit features can be extracted.