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Journal articles on the topic 'Regression learner'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Ahrens, Achim, Christian B. Hansen, and Mark E. Schaffer. "pystacked: Stacking generalization and machine learning in Stata." Stata Journal: Promoting communications on statistics and Stata 23, no. 4 (2023): 909–31. http://dx.doi.org/10.1177/1536867x231212426.

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The pystacked command implements stacked generalization (Wolpert, 1992, Neural Networks 5: 241–259) for regression and binary classification via Python’s scikit-learn. Stacking combines multiple supervised machine learners—the “base” or “level-0” learners—into one learner. The currently supported base learners include regularized regression, random forest, gradient boosted trees, support vector machines, and feed-forward neural nets (multilayer perceptron). pystacked can also be used as a “regular” machine learning program to fit one base learner and thus provides an easy-to-use application pr

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Gries, Stefan Th, and Stefanie Wulff. "The genitive alternation in Chinese and German ESL learners." International Journal of Corpus Linguistics 18, no. 3 (2013): 327–56. http://dx.doi.org/10.1075/ijcl.18.3.04gri.

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This paper exemplifies an approach to learner corpus data that adopts a multifactorial definition of ‘context’. We apply a logistic regression to 2,986 attestations of genitive alternation (the squirrel’s nest vs. the nest of the squirrel) from the Chinese and German sub-sections of the International Corpus of Learner English and the British component of the International Corpus of English that were coded for 12 factors. Importantly, the speakers’ L1 was included as a predictor to be able to compare properly the native speakers with the learners as well as the two learner groups with each othe

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Gervet, Theophile, Ken Koedinger, Jeff Schneider, and Tom Mitchell. "When is Deep Learning the Best Approach to Knowledge Tracing?" Journal of Educational Data Mining 12, no. 3 (2020): 31–54. https://doi.org/10.5281/zenodo.4143614.

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Intelligent tutoring systems (ITSs) teach skills using learning-by-doing principles and provide learners with individualized feedback and materials adapted to their level of understanding. Given a learner's history of past interactions with an ITS, a learner performance model estimates the current state of a learner's knowledge and predicts her future performance. The advent of increasingly large scale datasets has turned deep learning models for learner performance prediction into competitive alternatives to classical Markov process and logistic regression models. In an extensive empirical co

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Hao, Xiaowei, and Shanshan Han. "An Algorithm for Generating a Recommended Rule Set Based on Learner's Browse Interest." International Journal of Emerging Technologies in Learning (iJET) 13, no. 11 (2018): 102. http://dx.doi.org/10.3991/ijet.v13i11.9604.

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To personalize the recommended learning information according to the interests of the learner, a recommendation rule set generation algorithm based on learner browsing interests was proposed. First, the learner's browsing behavior was captured. A multivariate regression method was used to calculate the quantitative relationship between the learner's browsing behavior and the degree of interest in the web page to generate a learner's current interest view (CIV). With this current interest view, a content-based collaborative filtering personalized information recommendation service was provided

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Mwanthi, Mary Mbithe, Charles Misiko Wafula, and John Mwaura Mbugua. "Smallholder Farmer Engagement in School Feeding Program and Learner Participation in Public Primary Schools in MAKUENI, County, KENYA." Australian Journal of Business and Management Research 09, no. 01 (2024): 01–12. https://doi.org/10.52283/nswrca.ajbmr.20240901a01.

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Smallholder farmers’ engagement in the school feeding program was aimed at the regular supply of foodstuffs to schools and enhancing learner participation. Learners from marginalized households are affected by food shortages, poor healthcare, and a lack of nutritious meals. The study aimed to assess the influence of smallholder farmer engagement in school feeding program and learner participation. A sample of 275 was employed from a population of 875. Information was gathered using a questionnaire, and interview guide. Quantitative and qualitative information were analyzed using descriptive, a

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Jayapermana, Rama, Aradea Aradea, and Neng Ika Kurniati. "Implementation of Stacking Ensemble Classifier for Multi-class Classification of COVID-19 Vaccines Topics on Twitter." Scientific Journal of Informatics 9, no. 1 (2022): 8–15. http://dx.doi.org/10.15294/sji.v9i1.31648.

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Purpose: However, from the variety of uses of these algorithms, in general, accuracy problems are still a concern today, even accuracy problems related to multi-class classification still require further research.Methods: This study proposes a stacking ensemble classifier method to produce better accuracy by combining Logistic Regression, Random Forest, and Support Vector Machine (SVM) algorithms as first-level learners and using Logistic Regression as a meta-learner for the multi-class classification of COVID-19 vaccine topics on Twitter.Result: Based on the evaluation, the proposed Stacking

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Dewi, Desi Surlitasari, and Eka Wilany. "THE RELATIONSHIP BETWEEN LEARNER AUTONOMY AND MOTIVATION IN EFL SPEAKING CLASS." ELP (Journal of English Language Pedagogy) 8, no. 2 (2023): 194–208. http://dx.doi.org/10.36665/elp.v8i2.758.

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Despite continuous effort to establish an engaging learning environment, there is a lack of motivation among EFL learners in speaking class. Understanding the relationship between autonomy and motivation might provide insights into effective methods of instruction in EFL speaking classes when learners need to strengthen their speaking skills. This study aimed to investigate the relationship between learner autonomy and motivation in higher education, particularly in EFL speaking classes. The research method employed in this correlational study was a questionnaire to assess learner autonomy and

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Smith, Bevan I., Charles Chimedza, and Jacoba H. Bührmann. "Treatment Effect Performance of the X-Learner in the Presence of Confounding and Non-Linearity." Mathematical and Computational Applications 28, no. 2 (2023): 32. http://dx.doi.org/10.3390/mca28020032.

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This study critically evaluates a recent machine learning method called the X-Learner, that aims to estimate treatment effects by predicting counterfactual quantities. It uses information from the treated group to predict counterfactuals for the control group and vice versa. The problem is that studies have either only been applied to real world data without knowing the ground truth treatment effects, or have not been compared with the traditional regression methods for estimating treatment effects. This study therefore critically evaluates this method by simulating various scenarios that incl

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CHEN, MENG YING, and ALI MOHAMMED LUBNA. "THE EFFECT OF INTERACTION ON LEARNER SATISFACTION WITH OPEN DISTANCE LEARNING." Quantum Journal of Social Sciences and Humanities 5, no. 5 (2024): 140–48. https://doi.org/10.55197/qjssh.v5i5.443.

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Understanding the rules and characteristics of online teaching is of great importance in ensuring the teaching effectiveness of open and distance education. Assessing students’ online learning satisfaction is crucial, as it not only reflects the “learner-centered” approach but also serves as a vital measure of the quality of online teaching in open universities, particularly in the context of the Internet. One significant characteristic of open distance learning is the “separation of teaching and learning”, which can lead to feelings of loneliness among learners, ultimately affecting their onl

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Dewaele, Jean-Marc. "The Effect of Classroom Emotions, Attitudes Toward English, and Teacher Behavior on Willingness to Communicate Among English Foreign Language Learners." Journal of Language and Social Psychology 38, no. 4 (2019): 523–35. http://dx.doi.org/10.1177/0261927x19864996.

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Willingness to communicate (WTC) in a foreign language is linked to a range of interacting learner-internal and learner-external variables. The present study identified the predictors of WTC of 210 foreign language learners of English from Spain. Multiple regression analyses revealed that the strongest (negative) predictor of WTC was foreign language classroom anxiety, while foreign language enjoyment and frequency of foreign language use by the teacher were positive predictors.

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Römer, Ute, and James R. Garner. "The development of verb constructions in spoken learner English." Corpus-based Approaches to Spoken L2 Production 5, no. 2 (2019): 207–30. http://dx.doi.org/10.1075/ijlcr.17015.rom.

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Abstract Based on datasets of L1 Italian and Spanish learner language culled from the Trinity Lancaster Corpus Sample, this paper investigates how verb-argument constructions (VACs) develop in the spoken English of L2 learners across proficiency levels. In addition to proficiency and L1 effects, we focus on the potential influence of native English usage on learner VAC production. Insights into learners’ productive knowledge of five target VACs and the verbs used in those VACs are gained through (1) comparisons of normalized entropy scores for verbs in VACs; (2) correlation analyses comparing

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Kim, Dong-Ryool. "The Effect of Adult Learners' Motivation to participate in Lifelong Learning on Learning Transfer: The Mediating Effect of Learner Satisfaction." Korean Association For Learner-Centered Curriculum And Instruction 23, no. 21 (2023): 831–43. http://dx.doi.org/10.22251/jlcci.2023.23.21.831.

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Objectives The purpose of this study is to empirically analyze the relationship between lifelong learning partic-ipation motivation, learner satisfaction, and learning transfer for adult learners. Methods An empirical analysis was conducted by conducting a survey on 342 adult learners participating in life-long learning at lifelong education institutions located in the Seoul metropolitan area by multi-regression analysis and bootstrapping with SPSS statistical program. Results The following major results were derived. First, both adult learners' learning-oriented motivation, goal-o

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Chen, Po Hsuan. "E-Learner Characteristics and E-Learner Satisfaction." International Journal of Computer-Assisted Language Learning and Teaching 2, no. 2 (2012): 1–15. http://dx.doi.org/10.4018/ijcallt.2012040101.

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The present study aimed at furthering the understanding of the potential effects of e-learner characteristics on e-learner satisfaction in an EFL context. Specifically, it examined the collective impact of computer anxiety, computer-mediated communication (CMC) apprehension, metacognitive self-regulation strategy use, mastery goal orientation, and performance goal orientation on EFL e-learner satisfaction. Multiple regression analyses revealed that not all university EFL students are satisfied with e-learning English courses; only students with high levels of metacognitive self-regulation stra

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Kim, EunJung. "AI-assisted English learning: A tool for all or only a select few?" Language Learning & Technology 29, no. 1 (2025): 1. https://doi.org/10.64152/10125/73633.

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This study investigated English learner profiles and challenges among 884 Korean 5th-grade students, with a focus on the role of AI-assisted language learning in shaping proficiency outcomes. While AI-based interventions have gained popularity, their effectiveness across diverse learner populations remains insufficiently explored. The study aimed to (a) empirically identify distinct learner profiles based on motivational, contextual, and socio-affective characteristics through latent class analysis (LCA), and (b) examine the predictive effects of AI participation and learner-specific factors o

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Zhang, Pengbo, and Zhixin Yang. "A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/260970.

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Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost.RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost.RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold f

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Shava, Herring. "Linear Predictors of Perceived Graduate Employability among South Africa’s Rural Universities’ Learners during the Covid-19 Pandemic." International Journal of Learning, Teaching and Educational Research 21, no. 3 (2022): 106–26. http://dx.doi.org/10.26803/ijlter.21.3.7.

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After the outbreak of Covid-19, universities in South Africa adopted blended learning and learning continued online. The outbreak of Covid-19 brought to light inherent problems that South African rural universities are facing in the utilisation of educational technologies. These problems include inadequate online support technologies, inadequate electricity supply and poor educator and learner technological skills. Resultantly, questions have been raised on rural university graduates’ employability prospects and how these can be enhanced. This study provides evidence revealing that learner car

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Tak, Hayeon. "The Effect of Learning Management System(LMS) Quality on Learner Satisfaction in University Distance Education." Korean Society of Culture and Convergence 45, no. 9 (2023): 1291–300. http://dx.doi.org/10.33645/cnc.2023.09.45.09.1291.

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The purpose of this study was to analyze the relative impact of LMS quality recognized by learners in university distance education on learner satisfaction, and to analyze whether there were differences depending on the media used. To this end, a survey was conducted on 669 D university students, and multiple regression analysis was conducted. The results of the study that analyzed the data are as follows. First, it was found that there was a positive correlation among LMS system quality, information quality, service quality, and learner satisfaction. Second, the three factors explain 73.9% of

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Jianfeng, Cheng, Gerard Sagaya Raj, and Joanna Tan Tjin Ai. "The Relationships Among Language Learning Strategy, Motivation, Anxiety and Autonomy in Chinese EFL Learners." International Journal of Engineering & Technology 7, no. 3.21 (2018): 508. http://dx.doi.org/10.14419/ijet.v7i3.21.17224.

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This study attempted to explore the relationships among language learning strategy, motivation, anxiety and learner autonomy. Cluster sampling method was adopted to select 600 non-English major students as subjects at three universities in Henan province, China. Quantitative data collected from questionnaires was analyzed by SPSS 19.0. Results from Pearson correlation analysis showed that among the three investigated variables, language learning strategy and motivation had a significant positive relationship with learner autonomy in a decreasing order, but anxiety was significantly and negativ

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Lester, Nicholas A. "That’s hard." International Journal of Learner Corpus Research 5, no. 1 (2019): 1–32. http://dx.doi.org/10.1075/ijlcr.17013.les.

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Abstract Language learners are highly sensitive to statistical patterns in the input. When a target language provides the option to include or omit a grammatical form, learners have been shown to make decisions quite similar to native speakers. For example, learners opt to include or omit the complementizer that (as in I know (that) Steffi likes hot tea). This phenomenon has been explained in terms of a universal suite of cognitive mechanisms which support native and learner performance alike. Both learners and native speakers choose to include the complementizer when they are producing more c

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Sitienei, Miriam, Ayubu Anapapa, and Argwings Otieno. "Application of XGBoost Regression in Maize Yield Prediction." Asian Journal of Probability and Statistics 24, no. 1 (2023): 1–9. http://dx.doi.org/10.9734/ajpas/2023/v24i1513.

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Artificial Intelligence (AI) is the human-like intelligence imbued in machines so that they can perform tasks that normally require human intelligence. Machine learning is an AI technique which carries on the concepts of predictive analytics with one important distinction: the AI system can make assumptions, test hypotheses, and learn independently. XGBoost, Extreme gradient boosting, is a popular machine-learning library for regression tasks. It implements the gradient-boosting decision tree algorithm, which combines several feeble decision trees to produce a robust predictive model. In Boost

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Khutsafalo, Diketso Kelebemang, and Ushe Makambe. "Pedagogical strategies and Motivation of Hospitality Management Learners in a Higher Education Institution in Botswana." East African Journal of Education Studies 2, no. 1 (2020): 197–210. http://dx.doi.org/10.37284/eajes.2.1.257.

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The Botswana higher education system has been producing low-quality unemployable graduates and the low levels of learner motivation is believed to be a major factor. Premised on the Self-Determination Theory, the purpose of this study was to explore the nexus between pedagogical strategies and motivation of hospitality management learners in a selected higher education institution in Botswana. A descriptive survey design was adopted while the quantitative methodology was employed. Descriptive and inferential statistics such as the mean, standard deviation, correlation and regression, which wer

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Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the National Academy of Sciences 116, no. 10 (2019): 4156–65. http://dx.doi.org/10.1073/pnas.1804597116.

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There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgori

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Suresh, Adithya. "Advanced Ensemble Framework for Diabetes Outcome Forecasting." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 3156–63. http://dx.doi.org/10.22214/ijraset.2024.62259.

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Abstract: Diabetes is one of the chronic diseases, which is increasing from year to year. Developing an automated system that can detect diabetes patients plays an important role in medical science. A stack classifier model is designed for the detection of diabetes by combining three base estimators such as Random Forest Classifier, LightGBM Classifier, and K-Nearest Neighbors Classifier, and Logistic Regression as meta-classifiers. The data preprocessing includes transforming categorical variables into numerical format. Each base learner is trained on the preprocessed data and predictions are

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Noorwali, Safa, and Mona Sabir. "Investigating Technology Use and Learners’ Autonomy among Saudi EFL Learners." Arab World English Journal 11 (July 26, 2025): 126–43. https://doi.org/10.24093/awej/call11.8.

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Limited learner autonomy continues to hinder English language achievement among students in Saudi Arabia. This study investigates how first-year university students manage their English learning independently and explores the role of technology in supporting autonomous learning beyond the classroom. The central research question is: To what extent do first-year university students demonstrate autonomy in their English language learning processes? This inquiry is significant for educators and learners, offering insight into how digital tools can enhance learner independence and improve outcomes

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Kotoka, Love, and Jeanne Kriek. "PREDICTION OF THE CORRELATION BETWEEN PROBLEM-SOLVING SKILLS AND CONCEPTUAL REASONING IN STOICHIOMETRY." Journal of Baltic Science Education 21, no. 4 (2022): 615–37. http://dx.doi.org/10.33225/jbse/22.21.615.

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Learners underperform in stoichiometry as they lack conceptual reasoning of the underlying concepts and the ability to solve stoichiometric problems. Therefore, it was necessary to determine if there is a statistical correlation between problem-solving skills and conceptual reasoning in stoichiometry and if so, whether one can significantly predict the other. The theoretical framework is the cognitive load theory (CLT). This theory expects teachers to know where to focus their teaching and how to assess their learners’ work to avoid unnecessary overloading of the working memory, which might af

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Kiruma, Joyce Wagithi, Ann Aseey, Dorothy Kyalo, and Naomi Mwangi. "The Moderating Influence of Learner Diversity on the Relationship between Institutional Readiness and Learner Participation in Distance Learning in Kenya." SCIENCE MUNDI 3, no. 1 (2023): 102–10. http://dx.doi.org/10.51867/scimundi.3.1.10.

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The objective of this study was to establish the moderating influence of learner diversity on the relationship between institutional readiness and learner participation in distance learning in Kenya. The study adopted a mixed-methods research. The target population included 916 learners enrolled in bachelor of education programs, with a representative sample of 180 students selected. A structured survey questionnaire was utilized to collect data. Open-ended survey questions provided qualitative insights, enriching the data. The qualitative data were analyzed thematically. Descriptive statistic

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Husin, Husna Sarirah, Siti Haryani Shaikh Ali, Ummul Fahri Abdul Rauf, Shahrinaz Ismail, and Evi Yulianti. "Multiple Regression Analysis for Online Teaching Effectiveness." Journal of Advanced Research in Applied Sciences and Engineering Technology 62, no. 2 (2024): 56–65. https://doi.org/10.37934/araset.62.2.5665.

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The vast body of research on online teaching has changed dramatically over time due to several discoveries on online teaching opportunities and challenges, the status of these issues now, and general best practices. This paper reports on a multiple regression analysis to address the research questions of whether the personal factor, the technology, the learner factor, or the administrative factor would affect teaching effectiveness. Results indicated that the main challenge is the slow internet connection. Analysis also revealed that technology factor has the largest value among other factors

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Yoon, Taeyoung, and Daesung Kang. "Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases." Journal of Personalized Medicine 13, no. 2 (2023): 373. http://dx.doi.org/10.3390/jpm13020373.

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Background: Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Deep learning methods have been widely used in the field of medical image analysis and have shown promising results in the diagnosis of CVDs. Methods: Experiments were performed on 12-lead electrocardiogram (ECG) databases collected by Chapman University and Shaoxing People’s Hospital. The ECG signal of each lead was converted into a scalogram image and an ECG grayscale image and used to fine-tune the pretrained ResNet-50 model of each lead. The ResNet-50 model was used as a base learner for the stacking ensembl

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Lei, Jun, and Teng Lin. "Emergency Online Learning: The Effects of Interactional, Motivational, Self-Regulatory, and Situational Factors on Learning Outcomes and Continuation Intentions." International Review of Research in Open and Distributed Learning 23, no. 3 (2022): 43–60. http://dx.doi.org/10.19173/irrodl.v23i3.6078.

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This study investigated the effects of interactional, motivational, self-regulatory, and situational factors on university students’ online learning outcomes and continuation intentions during the COVID-19 pandemic. Data were collected from 255 students taking a business course at a university in southern China. Hierarchical multiple regression analyses revealed that while family financial hardship caused by COVID-19 was a marginally significant negative predictor of students’ learning outcomes, learner–content interaction; instructors’ provision of e-resources, course planning, and organisati

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Chou, Jui-Sheng, Dinh-Nhat Truong, and Chih-Fong Tsai. "Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics." Mathematics 9, no. 6 (2021): 686. http://dx.doi.org/10.3390/math9060686.

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Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly

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Paulanka, Betty J. "A regression model to identify successful learner traits with CAI." Journal of Medical Systems 10, no. 2 (1986): 121–38. http://dx.doi.org/10.1007/bf00993119.

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Li, Zhaohui, Lin Wang, Deyao Wang, Ming Yin, and Yujin Huang. "Short-Term Traffic-Flow Forecasting Based on an Integrated Model Combining Bagging and Stacking Considering Weight Coefficient." Electronics 11, no. 9 (2022): 1467. http://dx.doi.org/10.3390/electronics11091467.

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This work proposed an integrated model combining bagging and stacking considering the weight coefficient for short-time traffic-flow prediction, which incorporates vacation and peak time features, as well as occupancy and speed information, in order to improve prediction accuracy and accomplish deeper traffic flow data feature mining. To address the limitations of a single prediction model in traffic forecasting, a stacking model with ridge regression as the meta-learner is first established, then the stacking model is optimized from the perspective of the learner using the bagging model, and

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Wang, Hao. "Improving Neural Network Generalization on Data-Limited Regression with Doubly-Robust Boosting." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 20821–29. http://dx.doi.org/10.1609/aaai.v38i18.30071.

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Enhancing the generalization performance of neural networks given limited data availability remains a formidable challenge, due to the model selection trade-off between training error and generalization gap. To handle this challenge, we present a posterior optimization issue, specifically designed to reduce the generalization error of trained neural networks. To operationalize this concept, we propose a Doubly-Robust Boosting machine (DRBoost) which consists of a statistical learner and a zero-order optimizer. The statistical learner reduces the model capacity and thus the generalization gap;

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Mutende, Dr Rose Atieno. "Influence of Teacher Engagement with PhET Simulations on Learner Engagement and Conceptual Understanding in Science." IOSR Journal of Research & Method in Education 15, no. 3 (2025): 18–26. https://doi.org/10.9790/7388-1503061826.

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This study investigated the influence of teacher engagement with PhET Interactive Simulations on learner engagement and conceptual understanding in secondary school science classrooms within rural, underresourced settings in Western Kenya. Anchored in constructivist and social constructivist theories, the research adopted a convergent parallel mixedmethods design involving teacher and learner questionnaires, classroom observations, focus group discussions, and pre- and post-tests. The study was conducted across six public secondary schools in Bungoma and Kakamega counties, with 24 science teac

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Lee, MinWoo. "Analyzing the Error Types of Korean Language Learners Using Multinomial Logistic Regression." Korean Society of Bilingualism 79 (June 30, 2020): 135–60. https://doi.org/10.17296/korbil.2020..79.135.

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This study attempted to grasp the characteristics of errors by statistically analyzing the corpus of errors in large-scale learners. This study examined the characteristics of language-specific errors in the location, pattern, and level of errors in the learner’s corpus using the multinominal logistic regression model along with the occurrence rate. As a result, it was possible to identify characteristic error types with high or low probability of occurrence by student’s language. Through this study, the results that can be referred to in order to summarize the characteristic types of language

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Munkhdalai, Lkhagvadorj, Tsendsuren Munkhdalai, Pham Van Van Huy, Jang-Eui Hong, Keun Ho Ryu, and Nipon Theera-Umpon. "Neural Network-Augmented Locally Adaptive Linear Regression Model for Tabular Data." Sustainability 14, no. 22 (2022): 15273. http://dx.doi.org/10.3390/su142215273.

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Creating an interpretable model with high predictive performance is crucial in eXplainable AI (XAI) field. We introduce an interpretable neural network-based regression model for tabular data in this study. Our proposed model uses ordinary least squares (OLS) regression as a base-learner, and we re-update the parameters of our base-learner by using neural networks, which is a meta-learner in our proposed model. The meta-learner updates the regression coefficients using the confidence interval formula. We extensively compared our proposed model to other benchmark approaches on public datasets f

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Rahmanda Putri, Endin, Dwi Arman Prasetya, and Achmad Junaidi. "KLASIFIKASI PERULANGAN KANKER TIROID MENGGUNAKAN STACK ENSEMBLE DAN SMOTE." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 3 (2025): 4211–16. https://doi.org/10.36040/jati.v9i3.13616.

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Kanker tiroid berdiferensiasi (Differentiated Thyroid Cancer/DTC) memiliki tingkat perulangan sekitar 20%, sehingga identifikasi sejak dini menjadi krusial untuk intervensi dan rencana perawatan terhadap kekambuhan. Penelitian ini menggunakan dataset dari UCI Machine Learning Repository yang berisi 17 atribut klinis pasien dengan proporsi data latih dan uji 80:20. Untuk menangani ketidakseimbangan kelas, diterapkan Synthetic Minority Over-sampling Technique (SMOTE). Model Decision Tree, Support Vector Machine (SVM), dan Logistic Regression digunakan sebagai base learner, sementara meta learner

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Stover, Sheri, Sharon Heilmann, and Amelia Hubbard. "Learner-Centered Design." Journal of Effective Teaching in Higher Education 1, no. 1 (2018): 1–19. http://dx.doi.org/10.36021/jethe.v1i1.16.

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This quantitative research study examined one instructor’s redesign of her introductory Anthropology course (N = 265) from Teacher-Centered (TC) to Learning-Centered (LC) and the resulting impact on her students’ perceptions of Teaching Presence (TP), Social Presence-Interaction (SP-I), Social Presence- Participation (SP-P), Cognitive Presence (CP), and Satisfaction (SAT). Using the Community of Inquiry (CoI) survey (Swan, Richardson, Ice, Garrison, Cleveland-Innes, & Arbaugh, 2008) in a face-to-face classroom environment; results indicated that implementing a LC classroom compared to a TC

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Abdullahi, Nimota Jibola Kadir. "https://ejournal.upsi.edu.my/index.php/SAECJ/article/view/3331." Southeast Asia Early Childhood Journal 9, no. 1 (2020): 59–74. http://dx.doi.org/10.37134/saecj.vol9.1.5.2020.

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This study embarked on managing teaching approach in early childhood care education towards skill development in Nigeria. This study determined the relationship between learner-centered approach, active learning, higher-order thinking and skill development of early childhood care education learners in North-central zone, Nigeria. Quantitative research designed was used for this study. Stratified random sampling technique was used to select 338 head teachers and 361 teachers making a total number of 699 participants in public primary school with early childhood care education centre. Data was c

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Muhia, Margaret Wambui, Victor Cole, and John Kitur. "Contribution of Critical Thinking to Effective Learner Transformation in Teaching of Bible-Based Courses in Chartered Christian Universities in Nairobi County, Kenya." Journal of Education and Learning (JEL) 3, no. 1 (2024): 19–30. http://dx.doi.org/10.51317/jel.v3i1.533.

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This paper aimed to assess the contribution of critical thinking in teaching Bible-based courses for learner transformation in charted Christian universities in Nairobi. Considerable resources have been allocated to train lecturers in using critical thinking in their teaching methods. However, there needs to be more knowledge regarding the actual impact of this training. Biblical worldview and transformative learning theory underpinned this study. Descriptive analysis was employed to establish the relationship between using critical thinking in teaching Bibe-based courses and learner transform

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Zahler, Sara. "The second language development of past perfective forms in Spanish." Borealis – An International Journal of Hispanic Linguistics 12, no. 1 (2023): 207–29. http://dx.doi.org/10.7557/1.12.1.6725.

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Research studies on second language (L2) morphosyntactic variation that include multiple learner course or experience levels often conduct separate statistical analyses of the factors affecting variation, one per learner level, and compare significant constraints and their coefficients across these groups as an indication of L2 development. This method of comparing across multiple regression analyses can lead to the perception of differences across participant groups that may not be statistically significant. Thus, the current study reanalyzes the data from Author (forthcoming), who investigat

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Konstantinov, Andrei, Stanislav Kirpichenko, and Lev Utkin. "Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya–Watson Regression." Algorithms 16, no. 5 (2023): 226. http://dx.doi.org/10.3390/a16050226.

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A new method for estimating the conditional average treatment effect is proposed in this paper. It is called TNW-CATE (the Trainable Nadaraya–Watson regression for CATE) and based on the assumption that the number of controls is rather large and the number of treatments is small. TNW-CATE uses the Nadaraya–Watson regression for predicting outcomes of patients from control and treatment groups. The main idea behind TNW-CATE is to train kernels of the Nadaraya–Watson regression by using a weight sharing neural network of a specific form. The network is trained on controls, and it replaces standa

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Aphrodis, Nyandwi, and Hesbon Opiyo Andala PhD. "SCHOOL CULTURAL PRACTICES AND LEARNERS ACADEMIC ACHIEVEMENT, IN PUBLIC PRIMARY SCHOOLS IN RWANDA: A CASE OF GASABO DISTRICT." International Journal of Advanced Research 12, no. 08 (2024): 1616–20. http://dx.doi.org/10.21474/ijar01/19409.

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This study investigates the impact of positive cultural practices in schools on the academic performance of learners in public primary schools in Gasabo District, Rwanda. It focuses on three key objectives: identifying the cultural practices upheld by school leaders, assessing the correlation between these practices and learners academic outcomes, and determining the overall influence of these practices on academic achievement. The research adopts both descriptive and correlational research designs, with data collected from a sample of 168 respondents through questionnaires and interviews. The

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Torres, Julio, Ricardo Estremera, and Sherez Mohamed. "THE CONTRIBUTION OF PSYCHOSOCIAL AND BIOGRAPHICAL VARIABLES TO HERITAGE LANGUAGE LEARNERS’ LINGUISTIC KNOWLEDGE OF SPANISH." Studies in Second Language Acquisition 41, no. 04 (2019): 695–719. http://dx.doi.org/10.1017/s0272263119000184.

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AbstractIndividual differences (IDs) largely contribute to success in adult second language attainment (e.g., Dörnyei, 2006). Heritage language (HL) studies have also explored the role of IDs, namely psychosocial variables, and biographical factors with an adult HL learner population. However, the specific contribution of these variables to HL learners' performance on linguistic tests that differ in degree of explicitness and modality remains unknown. Therefore, the current study tested 103 adult HL learners of Spanish who completed a spoken elicited imitation task (EIT) and a written untimed

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Dewaele, Jean-Marc. "The predictors of exam performance of Kazakh university students and secondary school pupils learning Turkish." ITL - International Journal of Applied Linguistics 173, no. 1 (2021): 94–119. http://dx.doi.org/10.1075/itl.21019.dew.

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Abstract The present study investigates the effect of sociobiographical, emotional, attitudinal characteristics and teacher perceptions of 275 Kazakh secondary school pupils and 317 university students on their exam performance in Turkish as a foreign language (FL). Multiple regression analyses reveal that exam results in Turkish of secondary school pupils are predicted by teacher gender, participant’s age, attitude towards the FL and FL Classroom Anxiety. A very different picture emerges for university students, where FL level, participant’s gender, FL Enjoyment, FL Classroom Anxiety and teac

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Mathan, N., Aneesh Wunnava, Sumukh Pandey, M. R. Tejeshwari, and Lokesh Verma. "Information-Seeking Behavior among Non-Traditional Learners in Online Education Platforms." Indian Journal of Information Sources and Services 15, no. 2 (2025): 160–67. https://doi.org/10.51983/ijiss-2025.ijiss.15.2.22.

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Aim: As online education continues to evolve, it increasingly attracts non-traditional learner’s individuals who diverge from the conventional college-aged, full-time student demographic- including adult learners, part-time students, career-switchers, and lifelong learners. Understanding how these learners seek, evaluate, and utilize information within online learning platforms is essential for enhancing educational design and learner engagement. It investigates the information-seeking behavior of non-traditional learners across various online education platforms, learning management systems,

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Esfandiari, Rajab, and Sepideh Hesani. "Willingness to Communicate, Possible Selves, Learner Autonomy, and Academic Target Needs: Implications for Sustainable Development in L2 Pedagogy." Journal of Teacher Education for Sustainability 21, no. 2 (2019): 121–38. http://dx.doi.org/10.2478/jtes-2019-0021.

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Abstract Life-long learning requires that language learners be well prepared for the target situations they will be operating in. To achieve this goal and to improve education for sustainable development, language teachers and language educators need to motivate language learners to participate in the target language. The present study, therefore, investigated the relationships between willingness to communicate (WTC) and possible selves, WTC and learner autonomy, and WTC and academic target needs in an English for Academic Purposes (EAP) context to open up new horizons for sustainable L2 lear

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Muhia, Margaret Wambui, John Kitur, and Victor Cole. "(1.2.3) Africa International University, Kenya. Cite this article in APA Muhia, M. W., Cole, V, & Kitur, J. (2024). Contribution of authentic relationships to effective learner transformation in teaching of bible-based courses in chartered Christian uni." Journal of Pedagogy and Curriculum (JPC) 3, no. 1 (2024): 1–17. http://dx.doi.org/10.51317/jpc.v3i1.530.

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The purpose of this study was to assess the contribution of authentic relationships to effective learner transformation in the teaching of Bible-based courses in chartered Christian universities in Nairobi County, Kenya. A lot of resources have been used in training the lecturers to use authentic relationships in teaching, yet little is known about the training impact. The study was based on transformative learning and Biblical worldview theories. The study utilised a descriptive research design to establish the relationship between the use of authentic relationships in the teaching of Bible-b

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Moradi, Sholeh, Shima Ghahari, and Mohammad Abbas Nejad. "Learner- vs. expert-constructed outlines: Testing the associations with L2 text comprehension and multiple intelligences." Studies in Second Language Learning and Teaching 10, no. 2 (2020): 359–84. http://dx.doi.org/10.14746/ssllt.2020.10.2.7.

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Cognitive organizers (COs) are text aids which represent objects, concepts, and their relations by the use of symbols and spatial arrangements without adding to semantic content. The present study examines language learners’ text comprehension through outlines, a popular CO, compared with text-only condition, and further investigates the effect of learner-constructed outlines (i.e., systematic note-taking) and expert-constructed outlines (i.e., readymade displays) on comprehension. Finally, the predictive power of multiple intelligences (MI) across different input modalities is scrutinized. Fo

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Prachi, P. More* Kavita M. Kelkar. "DESIGN OF E-LEARNING SYSTEM FOR AFFECTIVE LEARNER MODEL USING REGRESSION TECHNIQUE A PROPOSED SYSTEM FOR LEARNER MODEL FOR SENTIMENTAL ANALYSIS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 5 (2017): 592–97. https://doi.org/10.5281/zenodo.203854.

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This paper speaks about the proposed system for designation of the E-Learning system of the affective learner with the help of Regression Technique. Affective Computing is a upcoming and fast growing field in computer science in past few years. The paper is a proposed system for the design of E-Learning system so that the Emotions can be detected or predicted with the help of regression technique. The various Invasion Techniques are also discussed. Not to affect the privacy of the Learner being the main motto of this research and proposed system.

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