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Impact of artificial intelligence adoption on students' academic performance in open and distance learning: A systematic literature review

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Heliyon 2024年第22期10卷 e40025页

作者： Adewale, Muyideen Dele Azeta, Ambrose Abayomi-Alli, Adebayo Sambo-Magaji, Amina Africa Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Abuja Nigeria Department of Software Engineering Namibia University of Science and Technology Namibia Department of Computer Science Federal University of Agriculture Abeokuta Nigeria Digital Literacy & Capacity Development Department National Information Technology Development Agency Abuja Nigeria

The role of artificial intelligence (AI) in education has been extensively studied, focusing on its ability to enhance learning and teaching processes. However, the precise impact of AI adoption on academic performance in open and distance learning (ODL) remains largely unexplored. This systematic literature review critically evaluates AI's impact on academic performance within ODL environments. Drawing from a curated selection of 64 papers from an initial pool of 700, spanning from 2017 to 2023 and sourced from Scopus, Google Scholar, and Web of science, this study delves into the multifaceted role of AI in enhancing learning outcomes. The meta-analysis reveals a diverse methodological landscape: machine learning methods, employed in 29.69 % of the studies, stand out for their ability to predict academic achievement, which is matched in prevalence by classical statistical methods. Although less common at 3.13 %, hybrid methods are a burgeoning area of research, while a significant 40.63 % of works prioritise nonempirical methods, focusing on theoretical analysis and literature reviews. This investigation highlights the critical factors driving AI adoption in education and its tangible benefits for student performance. It identifies a crucial literature gap: the absence of a process-based framework designed to forecast AI's educational impacts with greater precision, especially across gender and regional lines. By proposing this framework, this study contributes to the academic discourse on AI in education. It underscores the urgent need for structured methodologies to navigate the challenges and opportunities of AI integration. This framework, aligned with UNESCO's 2030 educational objectives, promises to bridge educational divides, ensuring equitable access to quality education across diverse demographics. The findings advocate for future research to design, refine, and test such a framework, paving the way for more inclusive and effective educational technologies

关键词： Academic performance AI in education Artificial intelligence Gender and geographical differences Open and distance learning

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Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

Attention-Weighted Federated Deep Reinforcement Learning for...

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作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. College of Intelligence and Computing Tianjin University Tianjin China Ministry of Education China Department of Communications and Networking School of Electrical Engineering Aalto University Espoo Finland College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Information Technology and Electrical Engineering Oulu University Oulu90570 Finland Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic. © 1983-2012 IEEE.

关键词： Reinforcement learning

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Scaling up ridge regression for brain encoding in a massive individual fMRI dataset

arXiv

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arXiv 2024年

作者： Ahmadi, Sana Bellec, Pierre Glatard, Tristan Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Université de Montréal MontréalQC Canada Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal Montréal Canada

Brain encoding with neuroimaging data is an established analysis aimed at predicting human brain activity directly from complex stimuli features such as movie frames. Typically, these features are the latent space representation from an artificial neural network, and the stimuli are image, audio or text inputs. Ridge regression is a popular prediction model for brain encoding due to its good out-ofsample generalization performance. However, training a ridge regression model can be highly time-consuming when dealing with large-scale deep functional magnetic resonance imaging (fMRI) datasets that include many space-time samples of brain activity. This paper evaluates different parallelization techniques to reduce the training time of brain encoding with ridge regression on the CNeuroMod Friends dataset, one of the largest deep fMRI resource currently available. With multi-threading, our results show that the Intel Math Kernel Library (MKL) significantly outperforms the OpenBLAS library, being 1.9 times faster using 32 threads on a single machine. Yet, the performance benefits of multi-threading are limited, and reached a plateau after 8 threads in our main experiment. We then evaluated the Dask multi-CPU implementation of ridge regression readily available in scikit-learn (MultiOutput), and we proposed a new "batch" version of Dask parallelization, motivated by a time complexity analysis. With this Batch-MultiOutput approach, batches of brain targets are processed in parallel across multiple machines, and multi-threading is applied concurrently to further accelerate computation within a batch. In line with our theoretical analysis, MultiOutput parallelization was found to be impractical, i.e., slower than multithreading on a single machine. In contrast, the Batch-MultiOutput regression scaled well across compute nodes and threads, providing speed-ups of up to 33× with 8 compute nodes and 32 threads compared to a single-threaded scikit-learn execution. Batch paralleliz

关键词： Brain

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software Development in Startup Companies: The Greenfield Startup Model

arXiv

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arXiv 2023年

作者： Giardino, Carmine Paternoster, Nicolò Unterkalmsteiner, Michael Gorschek, Tony Abrahamsson, Pekka The Faculty of Computer Science Free University of Bolzano/Bozen Dominikanerplatz 3 Bozen Bolzano39100 Italy The Software Engineering Research Lab Sweden Blekinge Institute of Technology Campus Gräsvik Karlskrona371 79 Sweden The Department of Computer and Information Science Norwegian University of Science and Technology Ntnu Sem Sælandsvei 7-9 Trondheim7491 Norway

software startups are newly created companies with no operating history and oriented towards producing cutting-edge products. However, despite the increasing importance of startups in the economy, few scientific studies attempt to address software engineering issues, especially for early-stage startups. If anything, startups need engineering practices of the same level or better than those of larger companies, as their time and resources are more scarce, and one failed project can put them out of business. In this study we aim to improve understanding of the software development strategies employed by startups. We performed this state-of-practice investigation using a grounded theory approach. We packaged the results in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible. This strategy allows startups to verify product and market fit, and to adjust the product trajectory according to early collected user feedback. The need to shorten time-to-market, by speeding up the development through low-precision engineering activities, is counterbalanced by the need to restructure the product before targeting further growth. The resulting implications of the GSM outline challenges and gaps, pointing out opportunities for future research to develop and validate engineering practices in the startup context. © 2023, CC BY-NC-SA.

关键词： Precision engineering

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Experimentation of human activity recognition by using accelerometer data based on lstm 15th

Experimentation of human activity recognition by using accel...

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15th International Conference on Future Information Technology, Future Tech 2020

作者： Sa-nguannarm, Phataratah Elbasani, Ermal Kim, Bongjae Kim, Eung-Hee Kim, Jeong-Dong Division of Computer Science and Engineering Sun Moon University Asan Korea Republic of Department of Global Software Engineering Sun Moon University Asan Korea Republic of Genome-based BioIT Convergence Institute Sun Moon University Asan Korea Republic of

ISBN: (纸本)9789811593086

The majority of studies on Human Activity Recognition (HAR) were conducted utilizing video and/or image data which caused critical problems such as privacy exposure and storage capacity issues. To tackle these issues, we propose a Long Short-Term Memory (LSTM) architecture for HAR taking accelerometer data acquired from smartphones as its input values. The experimental results showed the outstanding performance of the proposed model against a convolutional neural network (CNN) based model which is one of the state-of-art methods for HAR. More precisely, it was observed that the accuracy of the LSTM based model is about 12% superior to that of the CNN-based model. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Signature Identification and User Activity Analysis on Whatsapp Web through Network Data

Research Square

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Research Square 2021年

作者： Ramraj, S. Usha, G. Department of Computer Science and Engineering SRM IST Kattankullathur India Department of Software Engineering SRM IST Kattakullathur India

WhatsApp messenger is a popular instant messaging application that employs end-to-end encryption for communication. WhatsApp Web is the browser-based implementation of WhatsApp messenger. Users of WhatsApp communicate securely using SSL protocol. Encryption and use of common port for communication by multiple applications poses challenge in traffic classification for application identification. It is highly needed to analyze the network traffic for the purpose of QoS, Intrusion Detection and application specific traffic classification. In this paper, we have done traffic analysis on the network packets captured through data transfer in whatsapp web. In the result, we have explored the user activities such as message texting, contact sharing, voice message, location sharing, media transfer and status viewing. Packet level traffic analysis of user activities reveal patterns in the encrypted SSL communication. This pattern is identified across SSL packet lengths for WhatsApp media transfer and voice message communication. Other important features WhatsApp is the ability to view the status of the message being sent. We have identified the read and unread message status in these data packets by exposing signatures in the network layer. These signatures are identified with the help of the SSL lengths in the TLS header information of WhatsApp Web network traffic traces. Various other information on WhatsApp traffic presented in our study is relevant to the version of WhatsApp Web v0.3.2386. © 2021, CC BY.

关键词： Cryptography

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Matrix-Based Ant Colony System for Traveling Salesman Problem

Matrix-Based Ant Colony System for Traveling Salesman Proble...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xu Li Jian-Yu Li Chun-Hua Chen Zhi-Hui Zhan Sam Kwong Jun Zhang School of Computer Science and Engineering South China University of Technology Guangzhou P. R. China College of Artificial Intelligence Nankai University Tianjin P. R. China School of Software Engineering South China University of Technology Guangzhou China Department of Computing and Decision Science Lingnan University Hong Kong Hanyang University Ansan South Korea

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Ant colony system algorithm (ACS), as an important evolutionary computation (EC) algorithm, has demonstrated significant advantages in solving complex optimization problems. However, traditional EC algorithms and traditional ACS algorithm often face the challenge of slow computational speed when dealing with large-scale problems. In recent years, matrix-based EC approaches have been proposed to accelerate the computational speed, which has obtained promising results in dealing with large-scale problems. However, most existing matrix-based EC algorithms are designed for continuous optimization problems, while the matrix-based approach integrated with ACS has not attracted enough attention, which will be efficient for solving large-scale discrete optimization problems. Therefore, in this paper, we propose a matrix-based ACS (MACS) algorithm and apply it to solve the traveling salesman problem (TSP). MACS is an innovative improvement over the traditional ACS algorithm, utilizing matrix operations to parallelly let ants select city and update pheromone. Experimental results show that the MACS algorithm has significantly better efficiency in accelerating computational speed while maintaining the remarkable problem-solving ability in solving large-scale TSP.

关键词： Proteins Job shop scheduling Supply chain management Processor scheduling Urban areas Transportation Traveling salesman problems Problem-solving Resource management Optimization

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Fast Symbolic Algorithms for Omega-Regular Games under Strong Transition Fairness

arXiv

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arXiv 2022年

作者： Banerjee, Tamajit Majumdar, Rupak Mallik, Kaushik Schmuck, Anne-Kathrin Soudjani, Sadegh Department of Computer Science and Engineering Indian Institute of Technology Delhi India Max Planck Institute for Software Systems Germany Institute of Science and Technology Austria Austria Newcastle University United Kingdom

We consider fixpoint algorithms for two-player games on graphs with ω-regular winning conditions, where the environment is constrained by a strong transition fairness assumption. Strong transition fairness is a widely occurring special case of strong fairness. It requires that any execution is strongly fair with respect to a specified set of live edges: whenever the source vertex of a live edge is visited infinitely often along a play, the edge itself is traversed infinitely often along the play as well. We show that, surprisingly, strong transition fairness retains the algorithmic characteristics of the fixpoint algorithms for ω-regular games-the new algorithms have the same alternation depth as the classical algorithms but invoke a new type of predecessor operator. For example, for Rabin games with k pairs under strong transition fairness, the complexity of the new algorithm is O(nk+2k!) symbolic steps, which is independent of the number of live edges in the strong transition fairness assumption. In contrast, strong fairness necessarily requires increasing the alternation depth depending on the number of fairness assumptions. We get symbolic algorithms for (generalized) Rabin, parity, and GR(1) objectives under strong transition fairness assumptions as well as a direct symbolic algorithm for qualitative winning in stochastic ω-regular games that runs in O(nk+2k!) symbolic steps, improving the state of the art. Previous approaches for handling fairness assumptions would either increase the alternation depth of the fixpoint algorithm or require an up-front automata-theoretic construction that would increase the state space, or both. We have implemented a BDD-based synthesis engine based on our algorithm. We show on a set of synthetic and real benchmarks that our algorithm is scalable, parallelizable, and outperforms previous algorithms by orders of magnitude. © 2022, CC BY.

关键词： Stochastic systems

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A comparative analysis of machine learning algorithms to build a predictive model for detecting diabetes complications

Informatica (Slovenia)

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Informatica (Slovenia) 2021年第1期45卷 117页

作者： Abaker, Ali A. Saeed, Fakhreldeen A. Department of Accounting Information Systems Faculty of Computer Science Al-Neelain University Khartoum Sudan Department of Software Engineering Faculty of Computer Science Al-Neelain University Khartoum Sudan

Diabetes complications have a significant impact on patients’ quality of life. The objective of this study was to predict which patients were more likely to be in a complicated health condition at the time of admission to allow for the early introduction of medical interventions. The data were 644 electronic health records from Alsukari Hospital collected from January 2018 to April 2019. We used the following machine learning methods: logistic regression, random forest, and k-nearest neighbor (KNN). The logistic regression algorithm performed better than the other algorithms achieving an accuracy of 81%, recall of 81%, and F1 score of 75%. Also, attributes such as infection years, swelling, diabetic ketoacidosis, and diabetic septic foot were significant in predicting diabetes complications. This model can be useful for the identification of patients requiring additional care to limit the complications and help practitioners in making decisions on whether the patient should be hospitalized or sent home. Furthermore, we used the sequential feature selection (SFS) algorithm which reduced the features to six, which is fewer than any model built before to predict diabetes complications. The primary goal of this study was achieved. The model had fewer attributes which means we have a simple and understandable model in addition to, it has a better performance. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Decision trees

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The Low-Cost IoT Mornitoring the Quality of River: A Case Study of Wang River in Lampang Municipality 27

The Low-Cost IoT Mornitoring the Quality of River: A Case St...

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27th International computer science and engineering Conference, ICSEC 2023

作者： Krueganta, Narong Chieochan, Oran Saokaew, Anukit Yana, Ekkachai Singkaew, Chaowoot Sawangtook, Weerachai Boonchieng, Ekkarat Wongchai, Warakhom Srimakorn, Sakchai School of Industrial Technology Lampang Rajabhat University Faculty of Industrial Technology Lampang52000 Thailand School of Information Technology Lampang Rajabhat University Faculty of Sciences Lampang52100 Thailand School of Environmental Innovation Lampang Rajabhat University Faculty of Science Lampang52100 Thailand School of Computer Engineering Lampang Rajabhat University Faculty of Industrial Technology Lampang52100 Thailand Chiang Mai University Faculty of Sciences Department of Computer Sciences Chiang Mai50200 Thailand School of Industrial Electrical Engineering Lampang Rajabhat University Faculty of Industrial Technology Lampang52000 Thailand School of Software Engineering Lampang Rajabhat University Faculty of Industrial Technology Lampang52000 Thailand

ISBN: (纸本)9798350342109

The research project focuses on prototyping an IoT (Internet of Things) system for measuring and monitoring the quality of the Wang River in Lampang Municipality. The system utilizes EC (Electrical Conductivity), pH, and temperature sensors connected to an ESP32 microcontroller with a custom-developed board. Additionally, the IoT system is supported by a solar cell system to provide power. The software programming is implemented using Arduino C++ on the PlatformIO platform, which is integrated with Visual Studio code. The IoT platform chosen for this project is ThingsBoard, specifically the community version. The research findings indicate that the utilization of IoT technology combined with the sensors has improved the prototype of a low-cost IoT monitoring system for assessing the quality of the Wang River in Lampang Municipality. © 2023 IEEE.

关键词： Rivers

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