Perception and Attitude Toward Self-Regulated Learning in Educational Data Mining

2019 ◽  
Author(s):  
Pratya Nuankaew ◽  
Direk Teeraputon ◽  
Wongpanya Nuankaew ◽  
Kanakarn Phanniphong ◽  
Sasithon Imwut ◽  
...  
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Self Regulated Learning ◽  
Regulated Learning

A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments

2021 ◽  
pp. 278-292
Author(s):  
Eric Araka ◽  
Robert Oboko ◽  
Elizaphan Maina ◽  
Rhoda K. Gitonga
Keyword(s):  
Data Mining ◽  
Online Learning ◽  
Learning Environments ◽  
Domain Knowledge ◽  
Educational Data Mining ◽  
Online Learning Environments ◽  
Student Activities ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
E Learning

Self-regulated learning is attracting tremendous researches from various communities such as information communication technology. Recent studies have greatly contributed to the domain knowledge that the use self-regulatory skills enhance academic performance. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Modern learning management systems (LMS) allow storage of datasets on student activities. Subsequently, it is now possible to use Educational Data Mining to extract learner patterns which can be used to support SRL. This chapter discusses the current tools for measuring and promoting SRL on e-learning platforms and a conceptual model grounded on educational data mining for implementation as a solution to promoting SRL strategies.

scholarly journals Perception and Attitude Toward Self-Regulated Learning of Thailand’s Students in Educational Data Mining Perspective

2019 ◽  
Vol 14 (09) ◽  
pp. 34 ◽  
Author(s):  
Wongpanya Sararat Nuankaew ◽  
Pratya Nuankaew ◽  
Direk Teeraputon ◽  
Kanakarn Phanniphong ◽  
Sittichai Bussaman
Keyword(s):  
Higher Education ◽  
Learning Styles ◽  
Learning Strategies ◽  
Undergraduate Students ◽  
Learning Strategy ◽  
Educational Data Mining ◽  
Learning Technology ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Modern Learning

The Self-Regulated Learning (SRL) strategies can be the best. It can be achieved by a sub-goal that will be more important in the younger generation. This paper proposes the process of developing factors (attributes) which are related to the development of learning styles through self-regulated strategies. The objectives of this paper are (1) to study the perception and attitude toward the attributes of students with self-regulated learning of the students in higher education, and (2) to find the level of acceptance towards the factor of SRL using applied statistics and machine learning technology. The results show that two tools have proved the respondents and the factors of SRL in the accepted level. Besides, the results found that Thai higher education students still focus on formal learning, which conflicts with the behavior and us-age of Internet and telephone in the classroom. In future work, the author is committed to develop and apply a self-regulated learning strategy model with a combination of collaborative learning strategies of blended learning. Also, it supports undergraduate students in analyzing the factors and studying the behavior patterns of learners in suitable modern learning.

Predicting Students’ Achievement in a Hybrid Environment Through Self-Regulated Learning, Log Data, and Course Engagement: A Data Mining Approach

2021 ◽  
pp. 073563312110561
Author(s):  
Amira D. Ali ◽  
Wael K. Hanna
Keyword(s):  
Data Mining ◽  
Log Data ◽  
Communication Course ◽  
Face To Face ◽  
Self Regulated Learning ◽  
Self Reports ◽  
Regulated Learning ◽  
Course Engagement ◽  
Engagement Factors ◽  
Using Data

With the spread of the Covid-19 pandemic, many universities adopted a hybrid learning model as a substitute for a traditional one. Predicting students’ performance in hybrid environments is a complex task because it depends on extracting and analyzing different types of data: log data, self-reports, and face-to-face interactions. Students must develop Self-Regulated Learning (SRL) strategies to monitor their learning in hybrid contexts. This study aimed to predict the achievement of 82 undergraduates enrolled in a hybrid English for Business Communication course using data mining techniques. While clustering techniques were used to understand SRL patterns through classifying students with similar SRL data into clusters, classification algorithms were utilized to predict students' achievement by integrating the log files and course engagement factors. Clustering results showed that the group with high SRL achieved higher grades than the groups with medium SRL and low SRL. Classification results revealed that log data and engagement activities successfully predicted students’ academic performance with more than 88% accuracy. Therefore, this study contributes to the literature of SRL and hybrid classrooms by interpreting the predictive power of log data, self-reports, and face-to-face engagement to predict students’ achievement, a relatively unexplored area. This study recommended practical implications to promote students’ SRL and achievement in hybrid environments.

How Immersive Virtual Environments Foster Self-Regulated Learning

2018 ◽  
pp. 28-54 ◽  
Author(s):  
Yang Jiang ◽  
Jody Clarke-Midura ◽  
Ryan S. Baker ◽  
Luc Paquette ◽  
Bryan Keller
Keyword(s):  
Academic Success ◽  
Virtual Environments ◽  
Educational Data Mining ◽  
Science Inquiry ◽  
Sources Of Information ◽  
School Students ◽  
Immersive Virtual Environments ◽  
Self Regulated Learning ◽  
Immersive Virtual Environment ◽  
Regulated Learning

Over the past decade, immersive virtual environments have been increasingly used to facilitate students' learning of complex scientific topics. The non-linearity and open-endedness of these environments create learning opportunities for students but can also impose challenges in terms of extraneous cognitive load and greater requirements for self-regulated learning (SRL). SRL is crucial for academic success in various educational settings. This chapter explores how the immersive virtual assessments (IVAs), an immersive virtual environment designed to assess middle school students' science inquiry skills, fostered SRL. The analyses combining educational data mining techniques with multilevel analysis indicated that students developed self-regulatory behaviors and strategies as they used IVAs. Experience with IVAs prepared students to adopt more efficient note-taking and note-reviewing strategies. Students also learned to exploit more available sources of information by taking and reviewing notes on them in order to either solve inquiry problems or to monitor their solutions.

Development of the Teacher Attitudes Towards Self-Regulated Learning Scale

2018 ◽  
Vol 34 (3) ◽  
pp. 193-205 ◽  
Author(s):  
Julia Steinbach ◽  
Heidrun Stoeger
Keyword(s):  
Factor Analysis ◽  
Primary School ◽  
Factor Structure ◽  
Concurrent Validity ◽  
Binary Logistic Regression ◽  
Affective Component ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Teachers Attitudes ◽  
Development And Validation

Abstract. We describe the development and validation of an instrument for measuring the affective component of primary school teachers’ attitudes towards self-regulated learning. The questionnaire assesses the affective component towards those cognitive and metacognitive strategies that are especially effective in primary school. In a first study (n = 230), the factor structure was verified via an exploratory factor analysis. A confirmatory factor analysis with data from a second study (n = 400) indicated that the theoretical factor structure is appropriate. A comparison with four alternative models identified the theoretically derived factor structure as the most appropriate. Concurrent validity was demonstrated by correlations with a scale that measures the degree to which teachers create learning environments that enable students to self-regulate their learning. Retrospective validity was demonstrated by correlations with a scale that measures teachers’ experiences with self-regulated learning. In a third study (n = 47), the scale’s concurrent validity was tested with scales measuring teachers’ evaluation of the desirability of different aspects of self-regulated learning in class. Additionally, predictive validity was demonstrated via a binary logistic regression, with teachers attitudes as predictor on their registration for a workshop on self-regulated learning and their willingness to implement a seven-week training program on self-regulated learning.

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