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Journal articles on the topic 'Machine learning in education'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Kodelja, Zdenko. "Is Machine Learning Real Learning?" Center for Educational Policy Studies Journal 9, no. 3 (September 24, 2019): 11. http://dx.doi.org/10.26529/cepsj.709.

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The question of whether machine learning is real learning is ambiguous, because the term “real learning” can be understood in two different ways. Firstly, it can be understood as learning that actually exists and is, as such, opposed to something that only appears to be learning, or is misleadingly called learning despite being something else, something that is different from learning. Secondly, it can be understood as the highest form of human learning, which presupposes that an agent understands what is learned and acquires new knowledge as a justiﬁed true belief. As a result, there are also two opposite answers to the question of whether machine learning is real learning. Some experts in the field of machine learning, which is a subset of artificial intelligence, claim that machine learning is in fact learning and not something else, while some others – including philosophers – reject the claim that machine learning is real learning. For them, real learning means the highest form of human learning. The main purpose of this paper is to present and discuss, very briefly and in a simplifying manner, certain interpretations of human and machine learning, on the one hand, and the problem of real learning, on the other, in order to make it clearer that the answer to the question of whether machine learning is real learning depends on the definition of learning.

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2

Kim, Jihyun. "New Era of Education: Incorporating Machine Teachers into Education." Journal of Communication Pedagogy 4 (2021): 121–22. http://dx.doi.org/10.31446/jcp.2021.1.11.

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This editorial briefly discusses the potential of machine agents in education that can assist in creating more positive and meaningful teaching and learning environments. Then, it introduces three articles, two empirical research studies and one research-based instructional activity, compromising a special section on “Machine Teachers in Education” of Journal of Communication Pedagogy. Collectively, these articles help us better understand the role of machines in education and facilitate intellectual dialogues

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Lim, Daniel. "Philosophy through Machine Learning." Teaching Philosophy 43, no. 1 (2020): 29–46. http://dx.doi.org/10.5840/teachphil202018116.

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In a previous article (2019), I motivated and defended the idea of teaching philosophy through computer science. In this article, I will further develop this idea and discuss how machine learning can be used for pedagogical purposes because of its tight affinity with philosophical issues surrounding induction. To this end, I will discuss three areas of significant overlap: (i) good / bad data and David Hume’s so-called Problem of Induction, (ii) validation and accommodation vs. prediction in scientific theory selection and (iii) feature engineering and Nelson Goodman’s so-called New Riddle of Induction.

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Cui, Zhongmin. "Machine Learning and Small Data." Educational Measurement: Issues and Practice 40, no. 4 (November 25, 2021): 8–12. http://dx.doi.org/10.1111/emip.12472.

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Biswas, Rupayan, Richa Rashmi, and Upakarasamy Lourderaj. "Machine Learning in Chemical Dynamics." Resonance 25, no. 1 (January 2020): 59–75. http://dx.doi.org/10.1007/s12045-019-0922-1.

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Gómez-Pulido, Juan A., Young Park, Ricardo Soto, and José M. Lanza-Gutiérrez. "Data Analytics and Machine Learning in Education." Applied Sciences 13, no. 3 (January 20, 2023): 1418. http://dx.doi.org/10.3390/app13031418.

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The widespread application of information and communication technologies in education, especially in the context of learning management platforms, is generating a large amount of data related to the academic activities in which students and teachers participate. These data stand out not only for their quantity and heterogeneity, but also for their relationship with the behavior and performance of the educational actors. For this reason, these data must be properly stored, processed and analyzed, with the aim of extracting knowledge that can be highly useful for improving educational processes. For this purpose, this Special Issue aims to present cutting-edge research on the application of advanced data analysis and machine learning techniques in education [...]

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Hazzan, Orit, and Koby Mike. "Teaching core principles of machine learning with a simple machine learning algorithm." ACM Inroads 13, no. 1 (March 2022): 18–25. http://dx.doi.org/10.1145/3514217.

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Roy, Sayan, and Debanjan Rana. "Machine Learning in Nonlinear Dynamical Systems." Resonance 26, no. 7 (July 2021): 953–70. http://dx.doi.org/10.1007/s12045-021-1194-0.

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N. B. Sultangazina, M. A. Ermaganbetova, Zh. B. Akhayeva, and A. B. Zakirova. "Artificial intelligence and machine learning." Bulletin of Toraighyrov University. Physics & Mathematics series, no. 3.2021 (September 27, 2021): 24–33. http://dx.doi.org/10.48081/wcct7602.

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Artificial Intelligence (AI) is an area of research driven by innovation and development that culminates in computers, machines with human-like intelligence characterized by cognitive ability, learnability, adaptability and decision-making ability. The study found that AI is widely adopted and used in education, especially by educational institutions, in various forms. This article reviewed articles by various scientists from different countries. The paper discusses the prospects for the application of artificial intelligence and machine learning technologies in education and in everyday life. The history of the development of artificial intelligence is described, technologies of machine learning and neural networks are analyzed. An overview of already implemented projects for the use of artificial intelligence is given, a forecast of the most promising, according to the authors, directions for the development of artificial intelligence technologies for the next period is given. This article provides an analysis of how educational research is being transformed into an experimental science. AI is combined with the study of science into new ‘digital laboratories’, in which ownership of data, as well as power and authority in the production of educational knowledge, are redistributed between research complexes of computers and scientific knowledge.

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10

An, Chang. "Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning." E3S Web of Conferences 275 (2021): 03028. http://dx.doi.org/10.1051/e3sconf/202127503028.

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Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system. In order to specifically understand the current situation of the construction and application of machine teaching based on supervised teaching of ideological and political theory courses in local colleges and universities, this experiment first conducted a statistical analysis of the learning results of the surveyed classes in two semesters from March 2020 to December 2020. The experimental data show that there is a positive interaction between teachers and students. Most students use the interactive communication mode of machines, while a small number of students use real-time interactive discussions with teachers. The experimental results show that the excellent rate of ABC classes in the first semester is 80%, 82% and 90%, respectively, through the machine-supervised teaching mode. Therefore, supervised machine learning can greatly help students improve their academic performance. In the future, we should further explore the application of other personalized and extensible machine learning methods in quality education.

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Munir, Hussan, Bahtijar Vogel, and Andreas Jacobsson. "Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision." Information 13, no. 4 (April 17, 2022): 203. http://dx.doi.org/10.3390/info13040203.

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The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature’s themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

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Cole, David R., and Paul Hager. "Learning-Practice: The Ghosts in the Education Machine." Education Inquiry 1, no. 1 (March 2010): 21–40. http://dx.doi.org/10.3402/edui.v1i1.21930.

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Li, Deming. "Machine Learning Based Preschool Education Quality Assessment System." Mobile Information Systems 2022 (October 6, 2022): 1–8. http://dx.doi.org/10.1155/2022/2862518.

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Preschool education (PE) is the initial stage of life education, and early childhood is an unrepeatable process. PE has the same importance as other education stages because of the significant impact it can have on later childhood development. Furthermore, from the perspective of educational equity theory, every child has the right to receive PE, the right to obtain the same high-quality educational resources, and the right to fair final results. Therefore, the research on the quality of PE has theoretical value and practical significance. In order to strengthen the quality of PE, this paper designs a PE quality assessment system to evaluate teachers’ teaching achievements. In this regard, the performance of each functional module in the system is tested, and the test results show that the module access is successful at more than 97%, indicating that the system meets the operating requirements. This paper uses the characteristics of the KNN algorithm classification in the machine learning algorithm to classify the teaching quality (TQ) of 7 pre-school teachers, and obtains the membership degrees of teachers in the four categories of grades, indicating that the KNN algorithm is more suitable for the classification of TQ assessment results than the general classification algorithm.

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Yildirim, Yetkin, and Akif Celepcikay. "Artificial Intelligence and Machine Learning Applications in Education." Eurasian Journal of Higher Education 2, no. 4 (September 24, 2021): 1–11. http://dx.doi.org/10.31039/ejohe.2021.4.49.

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Artificial Intelligence (AI), Data Analytics, and Machine Learning technologies are poised to transform the field of education as we know it. They have already upended industries from retail to manufacturing and now that the coronavirus pandemic has accelerated the shift to online classrooms, with remote teacher-student interaction and remote curriculum test, AI-powered tools are more critical for teachers and students than ever before. AI-powered intelligent tutoring systems, AI chatbots can interact with students to increase engagement of students in studies, and Machine Learning algorithms can analyze student data. Together these provide great opportunities for improving student learning, will help teachers also and will also help in many other aspects of education. This chapter will highlight some of the most interesting real-world applications of AI and Machine Learning and explain the methodology of their implementation, then describe how they can improve student learning and the effectiveness of education systems. This chapter will also discuss the critical challenges that educators and researchers face when applying these technologies in the field of education. Finally, the chapter is concluded with a discussion of the roles AI and Machine Learning can play in post-pandemic education world and of promising technologies that could be significant driving forces for even more AI and Machine Learning applications in education in the future.

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Hadijah, Idah, Agus Hery Supadmi Irianti, Nurul Aini, Rizky Yulianingrum Pradani, Nurlaila Indra Sapitra Wahyu Ilyasari, and Wimma Septiana Dwi Anggraini. "Development of innovative learning media based on video applications in the introduction to the usage of sewing tools course for fashion students in the state university of malang." International Journal of Scientific Research and Management 10, no. 04 (April 27, 2022): 840–45. http://dx.doi.org/10.18535/ijsrm/v10i4.ec06.

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The media make it easier for students and lecturers in the student-centered online teaching and learning process, in sewing equipment courses. it is necessary to develop learning media based on video tutorials for operating high speed sewing machines to facilitate online learning. The purpose of this research is to develop three applicable video tutorial products (VTA) namely (overlock sewing machine operation, overdeck sewing machine operation, and buttonhole machine operation). The method used is the ADDIE learning media development model. The results from expert's validation related to media 73.8%, related to material category 96. 8%, and feasible category 89.01%.. The discussion of applicative video tutorials for the operation of overlock sewing machines, overdeck sewing machines, and buttonhole machines begins with the initial display/opening, presentation of sewing machine operation material, ends with assignments/practice. Video displays are made to facilitate and improve students' understanding and motivation in learning. This is because video is one of the efforts to increase understanding and interest in learning for students, which is a technology for capturing, recording, processing, transmitting and rearranging moving images, which are related to vision. and hearing, so that the video is suitable to be used for learning the introduction/use of sewing machines in sewing equipment courses.

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Hadj Kacem, Yessine, Safa Alshehri, and Tala Qaid. "Categorizing Well-Written Course Learning Outcomes Using Machine Learning." Journal of Information Technology Education: Innovations in Practice 21 (2022): 061–75. http://dx.doi.org/10.28945/4997.

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Aim/Purpose: This paper presents a machine learning approach for analyzing Course Learning Outcomes (CLOs). The aim of this study is to find a model that can check whether a CLO is well written or not. Background: The use of machine learning algorithms has been, since many years, a prominent solution to predict learner performance in Outcome Based Education. However, the CLOs definition is still presenting a big handicap for faculties. There is a lack of supported tools and models that permit to predict whether a CLO is well written or not. Consequently, educators need an expert in quality and education to validate the outcomes of their courses. Methodology: A novel method named CLOCML (Course Learning Outcome Classification using Machine Learning) is proposed in this paper to develop predictive models for CLOs paraphrasing. A new dataset entitled CLOC (Course Learning Outcomes Classes) for that purpose has been collected and then undergone a pre-processing phase. We compared the performance of 4 models for predicting a CLO classification. Those models are Support Vector Machine (SVM), Random Forest, Naive Bayes and XGBoost. Contribution: The application of CLOCML may help faculties to make well-defined CLOs and then correct CLOs' measures in order to improve the quality of education addressed to their students. Findings: The best classification model was SVM. It was able to detect the CLO class with an accuracy of 83%. Recommendations for Practitioners: We would recommend both faculties’ members and quality reviewers to make an informed decision about the nature of a given course outcome. Recommendation for Researchers: We would highly endorse that the researchers apply more machine learning models for CLOs of various disciplines and compare between them. We would also recommend that future studies investigate on the importance of the definition of CLOs and its impact on the credibility of Key Performance Indicators (KPIs) values during accreditation process. Impact on Society: The findings of this study confirm the results of several other researchers who use machine learning in outcome-based education. The definition of right CLOs will help the student to get an idea about the performances that will be measured at the end of a course. Moreover, each faculty can take appropriate actions and suggest suitable recommendations after right performance measures in order to improve the quality of his course. Future Research: Future research can be improved by using a larger dataset. It could also be improved with deep learning models to reach more accurate results. Indeed, a strategy for checking CLOs overlaps could be integrated.

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Israel, Steven A., Philip Sallee, Franklin Tanner, Jonathan Goldstein, and Shane Zabel. "Applied Machine Learning Strategies." IEEE Potentials 39, no. 3 (May 2020): 38–42. http://dx.doi.org/10.1109/mpot.2019.2927899.

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Naicker, Nalindren, Timothy Adeliyi, and Jeanette Wing. "Linear Support Vector Machines for Prediction of Student Performance in School-Based Education." Mathematical Problems in Engineering 2020 (October 1, 2020): 1–7. http://dx.doi.org/10.1155/2020/4761468.

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Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

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Siddique, Sarkar, and James C. L. Chow. "Machine Learning in Healthcare Communication." Encyclopedia 1, no. 1 (February 14, 2021): 220–39. http://dx.doi.org/10.3390/encyclopedia1010021.

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Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

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Wu, Bo, and Changlong Zheng. "An Analysis of the Effectiveness of Machine Learning Theory in the Evaluation of Education and Teaching." Wireless Communications and Mobile Computing 2021 (October 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/4456222.

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Artificial intelligence was first proposed in the 1950s, when it was only a forward-looking concept. If machines can have the same learning ability as human beings and the computing power of computers themselves, this concept has been placed high hopes. Until about 2010, with the explosion of data volume and the improvement of computer performance, machine learning has become a leader in breaking through the bottleneck of artificial intelligence. Research on machine learning in education and teaching has attracted much attention. From the above research status, we can see that in the current period of the vigorous development of machine learning, many applications are still not perfect and ordinary education and teaching evaluation is difficult to meet people’s requirements, so how to gradually improve its effectiveness is a significant goal with research significance and practical interests. However, in the environment of colleges and universities, prediction information and evaluation methods have important application value and development space in education and teaching. In this context, according to the theory of machine science, the effectiveness of several conventional prediction and evaluation methods is analyzed. In this paper, machine learning theory is used to study college students’ performance prediction and credit evaluation, as well as teaching quality evaluation and comprehensive ability evaluation in colleges and universities. Questionnaire survey is used to investigate and analyze the results. The effectiveness of machine theory in teaching is analyzed. It is found that machine learning has great advantages in education and teaching evaluation. It builds models in complex computing environment and is not affected by human factors; the effectiveness of prediction and evaluation is significant.

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Li, Yuping. "Similar Classification Algorithm for Educational and Teaching Knowledge Based on Machine Learning." Wireless Communications and Mobile Computing 2022 (May 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/7222236.

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From ancient times, machines did adhere to the commands that a human or a user prepared. According to the program, the machines are controlled by implementing machine learning (ML). It plays a significant part in the development of information technology (IT) companies and the rise of the education system. Using stored memories, people learn new things, making them feel better than before. Machines are pretty different from human knowledge. Instead of using memory power, they use statistical comparison to analyze the data. Here, the amount of data is stored in a database, and according to the reaction received from the user, it gets additional data to create new data. For example, once a person hears music using the application, they will hear repeated music before further entry. In this case, the application is working based on the machine learning algorithm. First, it collects the information from the user, and then, it uses the same information (data) to make the user’s work more efficient when they return. The existing system like Support Vector Machine (SVM) and learning management system approaches the necessity and development of the higher education system using machine learning algorithms. This proposed system focuses on classifying education and teaching knowledge by implementing the machine learning-based similar classification algorithm (ML-SCA). ML-SCA focuses on classifying similar teaching videos and the recommendations to improve the teaching and academic knowledge for the teachers and the students. ML-SCA is compared with the existing neural network and K -means algorithms. Based on the efficiency results, it is observed that the proposed ML-SCA has achieved 92% higher than the existing algorithms.

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Hodges, Jaret, and Soumya Mohan. "Machine Learning in Gifted Education: A Demonstration Using Neural Networks." Gifted Child Quarterly 63, no. 4 (September 9, 2019): 243–52. http://dx.doi.org/10.1177/0016986219867483.

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Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

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MAHAJAN, SHWETA. "News Classification Using Machine Learning." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 5 (May 31, 2021): 23–27. http://dx.doi.org/10.17762/ijritcc.v9i5.5464.

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There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement.

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Li, Yanjie, and He Mao. "Study on Machine Learning Applications in Ideological and Political Education under the Background of Big Data." Scientific Programming 2022 (March 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/3317876.

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With the development of big data and data mining technology, machine learning has been applied in many fields. However, there are a large number of difficulties for students who majored in ideological and political education. It is very necessary for those students to integrate machine learning technology into ideological and political education courses. In this paper, we introduced how to integrate machine learning into ideological and political education courses in class. Firstly, we explained what teachers should do before/in/after class for teaching machine learning courses and what students should prepare. Secondly, we took the introduction section of machine learning courses as an example to connect each content with ideological and political education and illustrate them in the way of ideological and political education. Thirdly, we took the decision tree algorithm that belongs to machine learning as an example to explore the ideological and political education philosophy in the decision tree algorithm. Finally, we make a questionnaire from the perspective of learning attitude, learning influence, and learning effect to investigate the outcomes of students with our teaching way. Our results presented valuable meaningful information for students who majored in not only computer science but also ideological and political education, thus promoting the progress of interdisciplinary and making machine learning courses understood more easily in the class of ideological and political education.

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James, Cornelius A., Kevin M. Wheelock, and James O. Woolliscroft. "Machine Learning: The Next Paradigm Shift in Medical Education." Academic Medicine 96, no. 7 (January 25, 2021): 954–57. http://dx.doi.org/10.1097/acm.0000000000003943.

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Li, Yanjing. "Machine Learning-Based Aesthetic Music Education Informatics Assessment Method." Wireless Communications and Mobile Computing 2022 (August 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/4301761.

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Purpose. An instrumental mechanism for the identification, theoretical substantiation, and introduction of didactic conditions is a dynamic functional-structural model that performs orientation, managerial, formative, and analytical functions in the optimal organization of students’ independent educational activity with the use of information and communication technologies. Method. During the research, a comprehensive target program of research and experimental work was developed, which was based on the step-by-step implementation of the didactic model, and its effectiveness was proved. Findings. The integration of traditional and electronic learning technologies within the framework of research and experimental work ensured systematic, planned, optimized organization, and enhanced control and diagnostic procedures of students’ autonomous skills due to the requirements of manufacturability, adaptability, and control laid down in the tried-and-tested models of blended learning. Following the completion of the experimental work, quantitative, qualitative, and statistical analyses revealed positive dynamics in the levels of organization of independent educational activity of students of technological and pedagogical specialties with the use of information and communication technologies in motivational, content, operational, and productive criteria in the experimental group. Implications for Research and Practice. According to the results of experimental work in the experimental groups, statistically significant positive dynamics were recorded: 10% more students became with a high level of organization of independent educational activity, 13.3% ones were with a sufficient level, thus a number of students with indicators of critical and insufficient levels decreased on 23.3%.

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Hagihara, Kazu. "Attempt to introduce machine learning software assuming landscape education." Reports of the City Planning Institute of Japan 21, no. 2 (September 9, 2022): 235–42. http://dx.doi.org/10.11361/reportscpij.21.2_235.

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Zhou, Ya, and Zhuoqing Song. "Effectiveness analysis of machine learning in education big data." Journal of Physics: Conference Series 1651 (November 2020): 012105. http://dx.doi.org/10.1088/1742-6596/1651/1/012105.

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Alenezi, Hadeel S., and Maha H. Faisal. "Utilizing crowdsourcing and machine learning in education: Literature review." Education and Information Technologies 25, no. 4 (January 14, 2020): 2971–86. http://dx.doi.org/10.1007/s10639-020-10102-w.

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Hua, YongMing, Fang Li, and Shuwen Yang. "Application of Support Vector Machine Model Based on Machine Learning in Art Teaching." Wireless Communications and Mobile Computing 2022 (June 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/7954589.

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The purpose of the evaluation is to reflect on whether education provides a good environment and conditions for the development of students and to reflect on the effect of teaching and the practicability of the talents cultivated by teaching to the society. When art education is evaluated, a number of positive outcomes have been achieved in terms of the development of art education, including the improvement of art education as a whole, the development of art talent, and a stronger role for the educational and social communities concerned about the quality of art education. Machine learning-based support vector machine (SVM) can better tackle issues like nonlinearity, high dimensionality, and local minima, which have been effectively implemented in the area of teaching quality evaluation (TQE) with the fast growth of information technology and the Internet. The main work of this paper is as follows: (1) it briefly expounds the research progress of TQE and multiclassification algorithm based on SVM at home and abroad and introduces the relevant basic theories of these two aspects. (2) One-to-one combination method is used in this research, reducing training time to a certain degree. Tests prove the procedure to be objective and equitable. (3) This research claims that an art TQE approach based on SVM is suited for limited professional assessment sample data and provides a method for this purpose.

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Issaro, Sasitorn, and Pallop Piriyasurawong. "Machine Learning Ecosystem to Enhance Grade Point Average." Universal Journal of Educational Research 10, no. 7 (July 2022): 448–58. http://dx.doi.org/10.13189/ujer.2022.100703.

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FLEISCHER, YANNIK, ROLF BIEHLER, and CARSTEN SCHULTE. "TEACHING AND LEARNING DATA-DRIVEN MACHINE LEARNING WITH EDUCATIONALLY DESIGNED JUPYTER NOTEBOOKS." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 2 (July 4, 2022): 7. http://dx.doi.org/10.52041/serj.v21i2.61.

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This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.

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Self, John. "The application of machine learning to student modelling." Instructional Science 14, no. 3-4 (May 1986): 327–38. http://dx.doi.org/10.1007/bf00051826.

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Casanova, Ramon, Santiago Saldana, Michael W. Lutz, Brenda L. Plassman, Maragatha Kuchibhatla, and Kathleen M. Hayden. "Investigating Predictors of Cognitive Decline Using Machine Learning." Journals of Gerontology: Series B 75, no. 4 (April 27, 2018): 733–42. http://dx.doi.org/10.1093/geronb/gby054.

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Abstract Objectives Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate modifiable and genetic risk factors for Alzheimer’s disease (AD), to predict cognitive decline. Methods Health and Retirement Study participants, aged 65–90 years, with DNA and >2 cognitive evaluations, were included (n = 7,142). Predictors included age, body mass index, gender, education, APOE ε4, cardiovascular, hypertension, diabetes, stroke, neighborhood socioeconomic status (NSES), and AD risk genes. Latent class trajectory analyses of cognitive scores determined the form and number of classes. Random Forests (RF) classification investigated predictors of cognitive trajectories. Performance metrics (accuracy, sensitivity, and specificity) were reported. Results Three classes were identified. Discriminating highest from lowest classes produced the best RF performance: accuracy = 78% (1.0%), sensitivity = 75% (1.0%), and specificity = 81% (1.0%). Top ranked predictors were education, age, gender, stroke, NSES, and diabetes, APOE ε4 carrier status, and body mass index (BMI). When discriminating high from medium classes, top predictors were education, age, gender, stroke, diabetes, NSES, and BMI. When discriminating medium from the low classes, education, NSES, age, diabetes, and stroke were top predictors. Discussion The combination of latent trajectories and RF classification techniques suggested that nongenetic factors contribute more to cognitive decline than genetic factors. Education was the most relevant predictor for discrimination.

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Järvelä, Sanna, Dragan Gašević, Tapio Seppänen, Mykola Pechenizkiy, and Paul A. Kirschner. "Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning." British Journal of Educational Technology 51, no. 6 (March 6, 2020): 2391–406. http://dx.doi.org/10.1111/bjet.12917.

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Vartiainen, Henriikka, Tapani Toivonen, Ilkka Jormanainen, Juho Kahila, Matti Tedre, and Teemu Valtonen. "Machine learning for middle schoolers: Learning through data-driven design." International Journal of Child-Computer Interaction 29 (September 2021): 100281. http://dx.doi.org/10.1016/j.ijcci.2021.100281.

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Rodriguez, Walter, Patricia Angle, and Michele Snyder Browning. "Can Machine Learning Enhance Human Learning in Times of Disruption?" Ubiquitous Learning: An International Journal 14, no. 1 (2021): 33–46. http://dx.doi.org/10.18848/1835-9795/cgp/v14i01/33-46.

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Felicia and Ferren. "Exploring Secondary School Performance by Using Machine Learning Algorithms." Journal of Educational Analytics 1, no. 1 (May 25, 2022): 41–60. http://dx.doi.org/10.55927/jeda.v1i1.429.

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Education is an important factor to achieve a better life and to help the economy. There are lots of levels of education and the education level that we will analyze is secondary education. Secondary education provides lots of benefits, starting from knowledge and skills, training in attitudes, instincts, and ensuring students will get a job after graduating. Not only that, but Portuguese secondary education also guides the development of the students so they will be well prepared for work and real-life situations. The educational level of Portuguese has also improved from last decades because in the past, lots of students failed and this causing failure rates is increasing. The failures are caused by Mathematics which are the core subjects. Because of this, Portuguese schools are still monitoring students who didn’t pass yet by using the data. We will analyze using 3 operators (i.e. Generalized Linear Model , Random Forest, Naive Bayes) and found out that past grades, demographic, and several attributes play a role in education (Cortez & Silva, 2008). We also found that Naive Bayes method has a high accuracy. The goal of these projects is to identify what makes education successful and fail and to aim for any new prediction.

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Dias, Roger D., Avni Gupta, and Steven J. Yule. "Using Machine Learning to Assess Physician Competence." Academic Medicine 94, no. 3 (March 2019): 427–39. http://dx.doi.org/10.1097/acm.0000000000002414.

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Rowe, Michael. "An Introduction to Machine Learning for Clinicians." Academic Medicine 94, no. 10 (October 2019): 1433–36. http://dx.doi.org/10.1097/acm.0000000000002792.

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Talavera, Alvaro, and Ana Luna. "Machine Learning: A Contribution to Operational Research." IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 15, no. 2 (May 2020): 70–75. http://dx.doi.org/10.1109/rita.2020.2987700.

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Et. al., Mathew Chacko,. "Cyber-Physical Quality Systems in Manufacturing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 2006–18. http://dx.doi.org/10.17762/turcomat.v12i2.1805.

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Digital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysing sensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical/mathematical models (Smart PLS) and using machine learning algorithms. This allows the operator to take corrective actions before the resultant part ends in a quality failure and reduces the inspection time. The proposed approach forms the basis in expanding this concept to a large machine shop wherein by monitoring various parameters of the machines and state variables of the tools we can detect quality issues and develop an automated quality system using machine learning techniques.

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Muttakin, Fitriani, Jui-Tang Wang, Mulyanto Mulyanto, and Jenq-Shiou Leu. "Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment." Electronics 11, no. 1 (December 30, 2021): 114. http://dx.doi.org/10.3390/electronics11010114.

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Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset.

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Saunders, Catherine H., Curtis L. Petersen, Marie-Anne Durand, Pamela J. Bagley, and Glyn Elwyn. "Bring on the Machines: Could Machine Learning Improve the Quality of Patient Education Materials? A Systematic Search and Rapid Review." JCO Clinical Cancer Informatics, no. 2 (December 2018): 1–16. http://dx.doi.org/10.1200/cci.18.00010.

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PurposeClear and trustworthy information is essential for people who are ill. People with cancer, in particular, are targeted with vast quantities of patient education material, but of variable quality. Machine learning technologies are popular across industries for automated tasks, like analyzing language and spotting readability issues. With the experience of patients with cancer in mind, we reviewed whether anyone has proposed, modeled, or applied machine learning technologies for the assessment of patient education materials and explored the utility of this application.MethodsWe systematically searched the literature to identify English-language articles published in peer-reviewed journals or as conference abstracts that proposed, used, or modeled the use of machine learning technology to assess patient education materials. Specifically, we searched MEDLINE, Web of Science, CINAHL, and Compendex. Two reviewers assessed study eligibility and performed study screening.ResultsWe identified 1,570 publications in our search after duplicate removal. After screening, we included five projects (detailed in nine articles) that proposed, modeled, or used machine learning technology to assess the quality of patient education materials. We evaluated the utility of each application across four domains: multidimensionality (2 of 5 applications), patient centeredness (1 of 5 applications), customizability (0 of 5 applications), and development stage (theoretical, 1 of 5 applications; in development, 3 of 5 applications; complete and available, 1 of 5 applications). Combining points across each domain, the mean utlity score across included projects was 1.8 of 5 possible points.ConclusionGiven its potential, machine learning has not yet been leveraged substantially in the assessment of patient education materials. We propose machine learning systems that can dynamically identify problematic language and content by assessing the quality of patient education materials across a range of flexible, customizable criteria. Assessment may help patients and families decide which materials to use and encourage developers to improve materials overall.

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Et. al., Zakoldaev D. A. ,. "Machine Learning Methods Performance Evaluation*." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2664–66. http://dx.doi.org/10.17762/turcomat.v12i2.2284.

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In this paper, we describe an approach for air pollution modeling in the data incompleteness scenarios, when the sensors cover the monitoring area only partially. The fundamental calculus and metrics of using machine learning modeling algorithms are presented. Moreover, the assessing indicators and metrics for machine learning methods performance evaluation are described. Based on the conducted analysis, conclusions on the most appropriate evaluation approaches are made.

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Sinha, Sakshi Rajesh, and Prof Sumedh Pundkar. "Geolocation Analysis Using Machine Learning." International Journal of Engineering Research in Computer Science and Engineering 9, no. 6 (June 20, 2022): 40–44. http://dx.doi.org/10.36647/ijercse/09.06.art007.

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A new journey commences every time a student leaves his/her home for education or work thereby leading themselves to self-discovery and self-reliance. But along with new adventures comes various challenges such as unfamiliar health care systems, personal safety issues, financial problems, etc, but the major problem of them all is accommodation issues. Students and young adults often face difficulties when it comes to immigrating to new cities or states for pursuing higher studies from colleges or for work purposes. As different people have different priorities and interests, finding a suitable place that fits their budget and have easy access to their daily requirements for sustainable living becomes a challenge. The objective of this project is to create a system to find the best accommodation for the user in a particular city by classifying the user based on the preferences given by them such as budget, proximity to a certain location, daily necessities, etc. This system can be expanded and further be used for various purposes such as finding a suitable location for any business (for eg. restaurants/cafes or stationery shops are best suited near an educational institution) or for the best area of land for crop cultivation for maximum yield etc.

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Embarak, Ossama. "A New Paradigm Through Machine Learning: A Learning Maximization Approach for Sustainable Education." Procedia Computer Science 191 (2021): 445–50. http://dx.doi.org/10.1016/j.procs.2021.07.055.

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Lee, Hyunguk, and Inhwan Yoo. "Development and application of supervised learning-centered machine learning education program using micro:bit." Journal of The Korean Association of Information Education 25, no. 6 (December 31, 2021): 995–1003. http://dx.doi.org/10.14352/jkaie.2021.25.6.995.

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Kishan Das Menon, H., and V. Janardhan. "Machine learning approaches in education." Materials Today: Proceedings, October 2020. http://dx.doi.org/10.1016/j.matpr.2020.09.566.

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Kolachalama, Vijaya B., and Priya S. Garg. "Machine learning and medical education." npj Digital Medicine 1, no. 1 (September 27, 2018). http://dx.doi.org/10.1038/s41746-018-0061-1.

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