The Autonomous Learner Model

2021 ◽  
pp. 91-114
Author(s):  
George T. Betts ◽  
Robin J. Carey ◽  
Blanche M. Kapushion
Keyword(s):  
Learner Model ◽  
Autonomous Learner

scholarly journals The Effects of Applying Betts’ Autonomous Learner Model on Iranian Students

2012 ◽  
pp. 310-321
Author(s):  
Nahid Yarahmadzehi ◽  
Elham Bazleh
Keyword(s):  
Language Learning ◽  
Language Teaching ◽  
Learner Autonomy ◽  
Control Group ◽  
Self Directed Learning ◽  
Learning Readiness ◽  
Learner Model ◽  
Directed Learning ◽  
Autonomous Learner ◽  
Iranian Students

Classroom-based, teacher-directed language learning has been dominant in language teaching and learning for decades; however, the notion of autonomy is not novel to language teachers. Since the publication of Holec’s book, Autonomy and Foreign Language Learning (1981), autonomy in language learning has been a significant issue for discussion in relation to language learning practices and language teaching principles. Many ESL researchers have turned their attention to learner autonomy in classroom settings; however, learner autonomy in the Iranian context within self-access settings, classroom settings, and school curriculum has not been adequately addressed in the literature. To fill the research gap mentioned above, the present study aims to determine: 1. if Betts’s Autonomous Learner Model (Betts & Kercher, 1999) has any significant effect in terms of students’ self-directed learning readiness, and 2. if Betts’s Autonomous Learner Model has any significant effect on students’ English language proficiency. Adopting a quasi-experimental design, the study involved a comparison between the experimental and the control group. Two instruments were used: Gugliemino’s (1977) Self-Directed Learning Readiness Scale (SDLRS); and standardized TOEFL test. 30 students (group A) were taught English based on a pedagogical model, which blended Betts’s ALM with classroom instruction and 30 students (group B) were taught through a traditional teacher-directed method. Finally, after six months of treatment, TOEFL test and SDLRS test were administered as the post-test and the results were analyzed by means of SPSS software. The results showed that ALM can work with Iranian students as evidenced by generally average performance on SDLRS and TOEFL post-tests.

Autonomous Learner Model Resource Book

2021 ◽  
Author(s):  
George T. Betts ◽  
Robin J. Carey ◽  
Blanche M. Kapushion
Keyword(s):  
Learner Model ◽  
Autonomous Learner

The Autonomous Learner Model

2016 ◽  
pp. 201-220 ◽  
Author(s):  
George Betts ◽  
Blanche Kapushion ◽  
Robin J. Carey
Keyword(s):  
Learner Model ◽  
Autonomous Learner

A Paradox of Exceptionalities

1993 ◽  
Vol 16 (1) ◽  
pp. 46-49 ◽  
Author(s):  
Janis Fall ◽  
Linda Nolan
Keyword(s):  
Special Education ◽  
Gifted And Talented ◽  
Learning Difficulties ◽  
Learning Disabled ◽  
Learner Model ◽  
Significant Deficit ◽  
Autonomous Learner ◽  
Talented Students

The term gifted learning disabled (GLD) is a relatively new idea in special education. The term elicits confusion among parents, educators, and the students themselves. A student with outstanding skills in one area and a significant deficit in another may not be succeeding in school, but still have talents far beyond his peers. This dichotomy leaves the student frustrated, his parents puzzled, and his teachers feeling helpless. There are programming strategies that can be effective for this type of student. The Autonomous Learner Model (Betts, 1985) has been adapted to provide activities to meet the diversified needs of these gifted and talented students with learning difficulties.

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Yan Hu ◽  
Guangya Zhou ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Qin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecule ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Learner Model ◽  
Mao B ◽  
Number Of Patients ◽  
Sar Model ◽  
Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

scholarly journals A Learner Model Integrating Cognitive and Metacognitive And Its Application on Scratch Programming Projects

2020 ◽  
Vol 53 (5) ◽  
pp. 644-649
Author(s):  
Sifeng Jing ◽  
Ying Tang ◽  
Xiwei Liu ◽  
Xiaoyan Gong
Keyword(s):  
Learner Model
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