scholarly journals Examining positive emotions, autonomy support and learning strategies: Self-directed versus teacher-directed learning environments

2021 ◽  
Author(s):  
Sabine Schweder ◽  
Diana Raufelder
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Learning Strategies ◽  
Autonomy Support ◽  
Structural Equation ◽  
Positive Emotions ◽  
The Self ◽  
Self Directed Learning ◽  
Directed Learning ◽  
The Relationship

AbstractAn increasing number of German schools have suspended teacher-directed learning (TL) in favor of self-directed learning (SL) modules. We used the broaden-and-build theory and self-determination theory as a theoretical framework to determine whether students in self-directed and teacher-directed learning environments differ in the interplay of positive emotions and learning strategies of students in secondary education. The study also compares the mediating role of autonomy support on the relationship between positive emotions and learning strategies. Questionnaire data from 787 German secondary school students in the sixth and seventh grades were analyzed. The results of the latent mean comparison indicated that students in the self-directed learning environment demonstrated more adaptive learning behaviors. Further, a multigroup structural equation model identified strong differences in the interplay of the variables between students in the self-directed and teacher-directed learning environments. In the teacher-directed learning environment, autonomy support was not found to mediate the relationship between positive emotions and learning strategies.

scholarly journals The Relationship between Self-efficacy and Learning Environment with Students' Self-directed Learning

2020 ◽  
Vol 4 (1) ◽  
pp. 37-42
Author(s):  
Nanda Mirzawati ◽  
Neviyarni Neviyarni ◽  
Rusdinal Rusdinal
Keyword(s):  
Learning Environment ◽  
Significant Relationship ◽  
Self Efficacy ◽  
Random Sampling ◽  
The Self ◽  
Sampling Techniques ◽  
Self Directed Learning ◽  
Directed Learning ◽  
Correlational Method ◽  
The Relationship

The lack of self-directed learning of students shown by students is one problem associated with self-efficacy and the learning environment. The purpose of the research is to reveal the relationship between self-efficacy and the learning environment, and self-directed learning. This study uses the correlational method and data collected from a sample of 198 students, which were found using proportional random sampling techniques. Data were collected using a self efficacy scale (24 valid items = 0.851), a learning environment scale (32 valid items = 0.830), and a self-directed learning scale (64 valid items = 0.936). Data were then analyzed using multiple regression. The results showed the correlation coefficient r = 0.535 and p = 0.00 (p> 0.05), which showed that there was a positive and significant relationship between self-efficacy and the learning environment together with self-directed learning. So it can be explained that the higher the self-efficacy and learning environment, the higher the student's self-directed learning.

Undergraduate Student Perception of Caring and Trust

2019 ◽  
pp. 23-43
Author(s):  
Pamela Lee Grant
Keyword(s):  
Adult Learning ◽  
Student Learning ◽  
Undergraduate Student ◽  
Student Perception ◽  
The Self ◽  
Self Directed Learning ◽  
Learning Framework ◽  
Directed Learning ◽  
The Relationship

The purpose of this chapter is to provide information about the use of caring and trust within the undergraduate classroom as it may apply to self-directed learning. Some evidence of the relationship between caring and trust is provided through a recent study by the author. Malcolm S. Knowles' Designs for Adult Learning demonstrates the use of both caring and trust within the self-directed learning framework. The method used by Knowles takes a caring approach to student learning that is based in trust between the educator and student.

scholarly journals THE RELATIONSHIP BETWEEN LANGUAGE LEARNING STRATEGIES AND VOCATIONAL LEARNERS’ EFL PROFICIENCY

2020 ◽  
Vol 8 (2) ◽  
pp. 39
Author(s):  
Hilda _ Yustitiasari
Keyword(s):  
Language Learning ◽  
Learning Strategies ◽  
Learning Strategy ◽  
English Proficiency ◽  
Language Learning Strategies ◽  
Self Directed Learning ◽  
Vocational College ◽  
Language Learning Strategy ◽  
Directed Learning ◽  
The Relationship

Abstract Language learning strategies (LLS) are perceived as tools for teacher to educate the learners behave self-directed learning. It also known, improving language proficient. In the case of vocational college students, they practically use self-directed learning in learning English outside classroom since they should pass the TOEFL test after the internship as the requirement of graduation. Hereby, this study identifies: (1) the frequently language learning strategy used by vocational college learners, (2) the relationship between language learning strategy and English proficiency, (3) the strategy used by learners which could predict learners proficient. The participant is 52 students of Politeknik Kota Malang in 2016- 2017 academic year which done the TOEFL test and willing to complete the Strategy Inventory of Language Learning (SILL). This study employed quantitative approach and correlational research as the research design to identify the relationship between variables. There are six strategies of language learning strategies by Oxford (1990) as the independent variables (predictors) and English proficiency (criterion) as the dependent variable. Therefore, multiple regression analysis employed in this study. The result showed that metacognitive strategy reported highly used by learners. While compensation, cognitive, social strategies reported medium used by learners. The low strategy used was affective and memory strategies. Although, language learning strategy cannot simultaneously predict the English proficiency there is one strategy that can be used to predict English proficiency, namely compensation strategy. Keywords: Language Learning Strategies, EFL Learners, English Proficiency.

scholarly journals Development of self-rating scale instrument of self-directed learning skills for high school students

2019 ◽  
Vol 23 (1) ◽  
pp. 1-11
Author(s):  
Intan Febry Sulasiwi ◽  
Supriyono Koes Handayanto ◽  
Wartono Wartono
Keyword(s):  
High School Students ◽  
Learning Strategies ◽  
Rating Scale ◽  
The Self ◽  
Self Directed Learning ◽  
School Students ◽  
Self Rating ◽  
Factors Analysis ◽  
Five Factors ◽  
Directed Learning

This study aims to develop a valid and reliable self-rating scale instrument for measuring Self-Directed Learning (SDL) skills. This DDI study follows the steps of Hinkin’s development (1995) which consists of five stages: creating an item pool, expert conclusion, implementation, confirmatory factor analysis, and reliability analysis. The self-rating scale developed in this study consisted of sixty statements accompanied by a 1-5 Likert scale. Based on the factors analysis, 16 items were still in the draft and 44 items were declared valid and reliable. Five factors that are determined are: awareness (8 items, α = 0.717), learning strategies (9 items, α = 0.806), and learning activities (7 items, α = 0.777), evaluation (8 items, α = 0.790), and interpersonal skills (12 items, α = 0.907). The reliability coefficient (Cronbach Alpha) of the self-rating scale is α = 0.933, with the required reliability criteria is 0.5. On a scale conversion of 1-100, the student’s highest score of SDL skills is 93, and the lowest score SDL skills are 31 (SD = 20.334).

The Role of Metacogntion and Knowledge Transfer in Self-Directed Learning

2018 ◽  
pp. 141-159
Author(s):  
Gina J. Mariano ◽  
Kirsten Batchelor
Keyword(s):  
Critical Thinking ◽  
Knowledge Transfer ◽  
Learning Strategies ◽  
Albert Einstein ◽  
Multiple Perspectives ◽  
Self Directed Learning ◽  
Directed Learning ◽  
Metacognitive Learning ◽  
The Relationship

Education is what remains after one has forgotten everything he learned in school. This quote by Albert Einstein embodies the essence of the relationship between metacognition and self-directed learning. It is important for students to remember what they learn in school, but many forget the information because they have not been taught metacognitive learning strategies. The learning strategies we teach students supports them in their effort to become good learners. In this chapter, we discuss the relationships between metacognition and knowledge transfer, critical thinking and self-directed learning. It brings together multiple perspectives on metacognition and knowledge transfer and discusses instructor strategies to engage students in metacognitive learning strategies.

Personal Learning Environments

2017 ◽  
pp. 236-248 ◽  
Author(s):  
Mary Hricko
Keyword(s):  
Learning Environment ◽  
Transformative Learning ◽  
Learning Environments ◽  
Learning Needs ◽  
Self Directed Learning ◽  
Personal Learning ◽  
Personal Learning Environment ◽  
Personal Learning Environments ◽  
Directed Learning ◽  
Future Of Education

A personal learning environment (PLE) is a construct designed to facilitate the process of learning and knowledge management. As a multidimensional system, a personal learning environment enables users to control the content and process of learning through the selection of resources, applications, and activities that best serve the learning needs. Personal learning environments exist as transformative learning spaces that differentiate to the users' ongoing personal interests and needs. Personal learning environments will continue to transform the educational landscape as technology continues to impact our culture. New modalities of learning will be needed to meet the needs of individuals who wish to pursue education in a manner that best serves their needs. Self-directed learning will require flexible landscapes that can coexist with traditional educational platforms; personal learning environments, if implemented effectively, can meet the emerging challenges in the future of education.

scholarly journals Validation of Internal structure of Self-Directed Learning Readiness Scale among Indian Medical Students using factor analysis and the Structural equation Modelling Approach

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Archana Prabu Kumar ◽  
Abirami Omprakash ◽  
Prabu Kumar Chokkalingam Mani ◽  
Narasimman Swaminathan ◽  
K. Maheshkumar ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Medical Students ◽  
Internal Structure ◽  
Structural Equation ◽  
The Self ◽  
Model Fit ◽  
Self Directed Learning ◽  
Indian Medical ◽  
Equation Modelling ◽  
Directed Learning

Abstract Background The Self-Directed Learning Readiness Scale (SDLRS) is a tool that helps in the assessment of the readiness of the students to pursue Self-Directed Learning (SDL). There are no documented studies on the validation of internal structure of the SDLRS among Indian medical students. Hence, the objective of this study is to validate the internal structure of SDLRS among Indian medical students using factor analysis and the Structural Equation Modelling (SEM) approach. Methods We administered Fisher’s 40-item SDLRS to 750 students after receiving the ethics clearance and the author’s permission and taking written informed consent from all the study participants (response rate: 92%). The exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Cronbach’s alpha were performed using SPSS version 25 and the Lavaan package of R version 3.1.2. Results The values of the comparative fit index (CFI), standardised root-mean-square residual (SRMR), and root mean square error of approximation (RMSEA) were ≥ 0.9, ≤ 0.08, and ≤ 0.08, respectively, for a model fit to be acceptable. EFA showed that except for Q2 (loading score: 0.210), Q12 (loading score: 0.384), Q13 (loading score: 0.362), and Q25 (loading score: -0.219), all the items loaded well. After the exclusion of the aforementioned items, the factor loading scores for the items in the self-management, desire for learning, and self-control factors ranged from 0.405 to 0.753 (Cronbach α: 0.775), 0.396 to 0.616 (Cronbach α: 0.730), and 0.427 to 0.556 (Cronbach α: 0.799), respectively. The updated model was used for CFA, which displayed a good model fit. Conclusions The resultant model consisting of 36 items is shown to have internal structure validity for Indian version of SDLRS, which can be used to assess medical students.

Self-Directed Learning Strategies in Adult Educational Contexts

2019 ◽  
pp. 124-137
Author(s):  
Roaa Aljafari
Keyword(s):  
Adult Education ◽  
Learning Strategies ◽  
Learner Control ◽  
Training Model ◽  
The Self ◽  
Individual Autonomy ◽  
Self Directed Learning ◽  
Learning Content ◽  
Directed Learning ◽  
The Impact

Self-directed learning is a teaching model where the learning content which is predetermined by the educator as well as the students tends to learn at their own pace to enable them to master the content provided. The features of self-directed learning may include individual autonomy, learner control, and autoindexing. In addition, self-directed learning has grown to become a generic training model for medicine, business, and adult education. Problem-based learning also tends to involve elements of self-directed instructions in its model. Self-directed learning also has evaluation tools that enable it to measure the impact of the self-directed learning on the students such as the ability for the students to perceive themselves as having the skills as well as attitudes required for successful learning.

Sign in / Sign up

Export Citation Format

Share Document