An active e-book to foster self-regulation in music education

2014 ◽  
Vol 11 (4) ◽  
pp. 254-269 ◽  
Author(s):  
Luca Andrea Ludovico ◽  
Giuseppina Rita Mangione
Keyword(s):  
Music Education ◽  
Instrumental Music ◽  
Self Regulation ◽  
Cognitive Strategies ◽  
Theoretical Research ◽  
Web Based ◽  
Content Type ◽  
Self Regulated Learning ◽  
Instrumental Music Education ◽  
Regulated Learning

Purpose – The purpose of this work is to analyze the concept of self-regulated learning and applying it to a web-based interface for music teaching. Design/methodology/approach – This work starts from a systematic review about music education and self-regulation during learning processes. Then, the paper identifies those meta-cognitive strategies that music students should adopt during their instrumental practice. The goal is applying such concepts to rethink the structure of a didactic e-book for instrumental music education. Thanks to the adoption of the Institute of Electrical and Electronics Engineers (IEEE) 1599 standard, the paper outlines a model of active e-book able to improve learners’ performances through proper cognitive and multi-modal scaffolds. In the last section, the design principles for an implementation will be proposed. Findings – This work applies theoretical research on self-regulated learning to the design and implementation of a working prototype. Research limitations/implications – A limitation is the lack of experimentation data, required to test the efficacy and effectiveness of the proposed e-book model and its impact on self-regulated music abilities. A validation strategy – e.g. based on scenarios – will be proposed in our future works, thanks to the support of music learning centres and focus groups composed by young Italian students. Originality/value – This work has been invited as an extension of the paper presented by the authors at EL2014 International Conference held in Lisbon. The previous work has been awarded as the best paper of the conference. In this extension, the authors provide further details about the proposed framework, highlighting in particular the implementation of scaffolds in the interface.

How instructors can use case based learning (CBL): CBL approach can inspire more self-regulated learning in students

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Keyword(s):  
Situated Learning ◽  
Design Methodology ◽  
Self Regulation ◽  
Use Case ◽  
Content Type ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Case Based Learning ◽  
Based Instruction ◽  
Case Based

Purpose The purpose of this paper is to demonstrate the linkage of case-based instruction with the enhancement of self-regulated learning of employees. Design/methodology/approach The authors carried out a literature review of SRL and CBL, including reviewing the theories of situated learning and constructivism. They then provided a detailed design presentation for using CBL with trainees. Findings The findings of the analysis enable a full, detailed approach to the application of CBL for practitioner use Originality/value Case-based instruction has not previously been directly linked to the self-regulation of learning.

Stimulating employee learning: the confluence of case-based and self-regulated learning

2020 ◽  
Vol 52 (3) ◽  
pp. 171-183 ◽  
Author(s):  
Paul Lyons ◽  
Randall Paul Bandura
Keyword(s):  
Self Regulation ◽  
The Self ◽  
Content Type ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Analysis And Synthesis ◽  
Case Based Learning ◽  
Based Instruction ◽  
Employee Learning ◽  
Case Based

Purpose The purpose of this paper is to demonstrate the linkage of case-based instruction with the enhancement of self-regulated learning of employees. Specific objectives are to review and describe: some characteristics of adult learning, features and functions of self-regulated learning (SRL) and the instructional approach, case-based learning (CBL), followed by a synthesis of the information in the form of a detailed, CBL approach for practitioner use. Design/methodology/approach The approach used in this work is represented in the following phases. First, conduct a literature review of each of these areas: characteristics of adult learners, details of SRL and its components, accompanied by the expression of linkages among the concepts and CBL. This includes exploration of metacognition, as it relates to the self-regulation of learning. Also reviewed was CBL, including theory background. Literature reviews were aided by databases: Education Research Complete, Psych Info and ScienceDirect. The second substantive phase was to synthesize the information obtained in the reviews and analysis and create a detailed design presentation for using CBL with trainees. Findings The findings of the analysis and synthesis of information enable the expression of a complete, detailed approach to the application of CBL for practitioner use. A review of practices in CBL augmented with examination of important theories of situated cognition and constructivism assist in building a rationale as to how case-based instruction may improve individual and team/group self-regulation of learning. Practical implications Presented in the paper is a complete model of case-based instruction for practitioner use and refinement. Originality/value Case-based instruction has not been directly linked to the self-regulation of learning. This paper makes a contribution to the literature of CBL as well as to situational cognition.

Supporting Learning Self-Regulation through a PLE

2016 ◽  
pp. 1129-1156
Author(s):  
Iolanda Garcia ◽  
Begoña Gros ◽  
Ingrid Noguera
Keyword(s):  
Lifelong Learning ◽  
Time Management ◽  
Knowledge Society ◽  
Self Regulation ◽  
Self Directed Learning ◽  
Web Based ◽  
Virtual Spaces ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Directed Learning

In the knowledge society, autonomous and Self-Directed Learning (SDL) have become particularly important for professional development and lifelong learning. This kind of learning can take place in physical and virtual spaces that may belong to formal institutions but also to extended communities and networks. In virtual spaces, self-directed learning and self-regulation skills and capacities play an important role in learners' performance. For this reason, it is highly recommended to empower students to design and deploy educational spaces and projects able to fuse formal and informal contexts. The use of Personal Learning Environments (PLEs) can support learners to gain control over their experiences through Web-based tools and a task-orientated environment. It is known that time management is one relevant component of self-regulated learning. There are many Web-based tools that can be used to control time investment and promote planning but little research that takes into account time management in the design and use of PLEs. This chapter describes the results of the Just4me project1, aimed at designing and developing a PLE to support self-regulated learning dealing with time management as an important dimension in lifelong learning. From this perspective, this chapter contributes to the operationalization and analysis of the time factor in online learning regarding time management in self-regulated learning processes supported by PLEs.

Self-Regulated Music Practice: Microanalysis as a Data Collection Technique and Inspiration for Pedagogical Intervention

2018 ◽  
Vol 66 (3) ◽  
pp. 295-319 ◽  
Author(s):  
Peter Miksza ◽  
Jennifer Blackwell ◽  
Nicholas E. Roseth
Keyword(s):  
Music Education ◽  
Instrumental Music ◽  
Self Regulation ◽  
Daily Practice ◽  
Data Sources ◽  
Practice Session ◽  
Music Practice ◽  
Instrumental Music Education ◽  
Data Collection Technique ◽  
Music Education Majors

The purpose of this study was to explore a microanalysis technique for measuring instrumentalists’ self-regulation tendencies during music practice. A secondary purpose was to investigate whether an intervention informed by the features of the microanalysis technique would increase the students’ self-regulated learning tendencies. Three undergraduate instrumental music education majors volunteered to participate in this study. This study was designed as a multiple-baselines experiment spanning 15 consecutive days. Data sources included (a) entrance interviews; (b) daily practice efficacy ratings; (c) data gathered from pre- and posttest microanalysis sessions; (d) detailed behavioral analyses of video-recorded, pre- and posttest practice sessions; and (e) a focus group exit interview. The microanalytic intervention designed for this study involved a coaching session in which a member of the research team explicitly drew attention to the affective, behavioral, and metacognitive qualities related to effective practicing during a student’s practice session. The pretest microanalysis data revealed distinct learning profiles for each student that were corroborated with information from the other data sources. The intervention had modest effects that varied across participants, suggesting that it was useful for bringing to light and addressing individuals’ specific self-regulatory deficiencies in a manner respective to their needs.

scholarly journals The Effects of Self-regulated Learning Strategies on Computer Programming Achievement in Teacher Education

2021 ◽  
Vol 4 (4) ◽  
pp. p37
Author(s):  
Gary Cheng
Keyword(s):  
Learning Strategies ◽  
Help Seeking ◽  
Computer Programming ◽  
Self Regulation ◽  
Management Strategies ◽  
Cognitive Strategies ◽  
Strategy Training ◽  
Self Regulated Learning ◽  
Java Programming ◽  
Regulated Learning

This study investigates the effects of student use of self-regulated learning (SRL) strategies on their computer programming achievement. Ninety-six students from undergraduate teacher training programmes offered by a Hong Kong university voluntarily participated in the study. Sixty-six of them were first-year students enrolling on an introductory Java programming course, while 30 were second-year students enrolling on an advanced Java programming course. The SRL strategies adopted by participants were measured by the Motivated Strategies for Learning Questionnaire (MSLQ) and were exemplified from the reflective writing of their electronic portfolios. Their achievement in computer programming was evaluated using continuous and end of course assessments. The findings of this study suggest that higher-order cognitive strategies (i.e. elaboration, organization, critical thinking), metacognitive control strategies (i.e. self-regulation) and resource management strategies (i.e. time and study environment management, help seeking) are likely to facilitate a prolonged achievement of computer programming for both novices and non-novices. They can provide insights into designing adequate SRL strategy training to support student learning in computer programming.

Supporting Learning Self-Regulation through a PLE

2018 ◽  
pp. 1018-1046
Author(s):  
Iolanda Garcia ◽  
Begoña Gros ◽  
Ingrid Noguera
Keyword(s):  
Lifelong Learning ◽  
Time Management ◽  
Knowledge Society ◽  
Self Regulation ◽  
Self Directed Learning ◽  
Web Based ◽  
Virtual Spaces ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Directed Learning

In the knowledge society, autonomous and Self-Directed Learning (SDL) have become particularly important for professional development and lifelong learning. This kind of learning can take place in physical and virtual spaces that may belong to formal institutions but also to extended communities and networks. In virtual spaces, self-directed learning and self-regulation skills and capacities play an important role in learners' performance. For this reason, it is highly recommended to empower students to design and deploy educational spaces and projects able to fuse formal and informal contexts. The use of Personal Learning Environments (PLEs) can support learners to gain control over their experiences through Web-based tools and a task-orientated environment. It is known that time management is one relevant component of self-regulated learning. There are many Web-based tools that can be used to control time investment and promote planning but little research that takes into account time management in the design and use of PLEs. This chapter describes the results of the Just4me project1, aimed at designing and developing a PLE to support self-regulated learning dealing with time management as an important dimension in lifelong learning. From this perspective, this chapter contributes to the operationalization and analysis of the time factor in online learning regarding time management in self-regulated learning processes supported by PLEs.

Supporting Learning Self-Regulation through a PLE

2013 ◽  
pp. 127-162
Author(s):  
Iolanda Garcia ◽  
Begoña Gros ◽  
Ingrid Noguera
Keyword(s):  
Lifelong Learning ◽  
Time Management ◽  
Knowledge Society ◽  
Self Regulation ◽  
Self Directed Learning ◽  
Web Based ◽  
Virtual Spaces ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Directed Learning

In the knowledge society, autonomous and Self-Directed Learning (SDL) have become particularly important for professional development and lifelong learning. This kind of learning can take place in physical and virtual spaces that may belong to formal institutions but also to extended communities and networks. In virtual spaces, self-directed learning and self-regulation skills and capacities play an important role in learners’ performance. For this reason, it is highly recommended to empower students to design and deploy educational spaces and projects able to fuse formal and informal contexts. The use of Personal Learning Environments (PLEs) can support learners to gain control over their experiences through Web-based tools and a task-orientated environment. It is known that time management is one relevant component of self-regulated learning. There are many Web-based tools that can be used to control time investment and promote planning but little research that takes into account time management in the design and use of PLEs. This chapter describes the results of the Just4me project1, aimed at designing and developing a PLE to support self-regulated learning dealing with time management as an important dimension in lifelong learning. From this perspective, this chapter contributes to the operationalization and analysis of the time factor in online learning regarding time management in self-regulated learning processes supported by PLEs.

Breaking the code: barriers affecting inclusive thinking-HETL Scotland 2017

2018 ◽  
Vol 10 (2) ◽  
pp. 118-129
Author(s):  
Linda Carol Algozzini ◽  
Valencia Lavon Gabay ◽  
Shannon D. Voyles ◽  
Kimberly Bessolo ◽  
Grady Batchelor
Keyword(s):  
Higher Education ◽  
First Century ◽  
Individual Student ◽  
Change Model ◽  
Participatory Action ◽  
Content Type ◽  
Self Regulated Learning ◽  
Group Coaching ◽  
Regulated Learning

Purpose This case study reviews a group coaching and mentoring (GCM) change model and its significance in dissolving barriers and promoting equity in virtual learning environments. The purpose of this paper is to examine the model’s approach to shifting instructor mindsets to align with institutional core values and initiatives that best serve a twenty-first century adult learner. Design/methodology/approach The change model, grounded in GCM, metacognition, self-regulated learning, and community of practice theory, incorporates participatory action research design focusing on cycles of action, reflection, and evaluation. Findings This study illustrates the change model’s success in moving educators toward deeper understanding of self and individual student differences. It further showcases how professionals adapt and improve practices using self-regulated learning and metacognition to better serve the population they teach. Practical implications The GCM framework improved engagement. The design, while implemented in a higher education arena, is applicable to other entities seeking to bridge gaps using metacognition and self-regulated learning to become adaptable and inclusive. Originality/value The change model, recipient of one of this year’s Effective Practice Awards from the Online Learning Consortium (2017), is recognized for innovation and replicability in and beyond higher education.

scholarly journals The Role of Mental Effort in Fostering Self-Regulated Learning with Problem-Solving Tasks

2020 ◽  
Vol 32 (4) ◽  
pp. 1055-1072 ◽  
Author(s):  
Tamara van Gog ◽  
Vincent Hoogerheide ◽  
Milou van Harsel
Keyword(s):  
Problem Solving ◽  
Self Regulation ◽  
Mental Effort ◽  
Future Research ◽  
Task Sequence ◽  
Self Monitoring ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
And Mathematics

Abstract Problem-solving tasks form the backbone of STEM (science, technology, engineering, and mathematics) curricula. Yet, how to improve self-monitoring and self-regulation when learning to solve problems has received relatively little attention in the self-regulated learning literature (as compared with, for instance, learning lists of items or learning from expository texts). Here, we review research on fostering self-regulated learning of problem-solving tasks, in which mental effort plays an important role. First, we review research showing that having students engage in effortful, generative learning activities while learning to solve problems can provide them with cues that help them improve self-monitoring and self-regulation at an item level (i.e., determining whether or not a certain type of problem needs further study/practice). Second, we turn to self-monitoring and self-regulation at the task sequence level (i.e., determining what an appropriate next problem-solving task would be given the current level of understanding/performance). We review research showing that teaching students to regulate their learning process by taking into account not only their performance but also their invested mental effort on a prior task when selecting a new task improves self-regulated learning outcomes (i.e., performance on a knowledge test in the domain of the study). Important directions for future research on the role of mental effort in (improving) self-monitoring and self-regulation at the item and task selection levels are discussed after the respective sections.

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