1. Key Overlaps and Contradictory Findings in the Three Studies
1.1. Common FIndings
1.1.1. Self-Regulated Learning (SRL) as the Core Framework
1.1.1.1. Silva et al. (2023) demonstrate SRL in a real-world challenge (100 Days of Practice), showing how musicians adjust their learning strategies dynamically.
1.1.1.2. Upitis (2017) highlights that students who engaged deeply with SRL strategies in Cadenza showed higher motivation and practice efficiency.
1.1.1.3. Wan (2022) frames SRL as a social-cognitive process that develops over time.
1.1.1.4. All three studies use Zimmerman’s SRL model as the theoretical foundation, particularly focusing on goal-setting, self-monitoring, reflection, and feedback as essential for effective practice.
1.1.2. Digital Tools Can Enhance Self-Regulated Learning
1.1.2.1. Wan (2022) argues that digital technology (DT) has the potential to scaffold SRL, though research in this area is still underdeveloped.
1.1.2.2. Upitis (2017) finds that Cadenza effectively supports self-regulation, motivation, and structured practice.
1.1.2.3. Silva et al. (2023) extend this idea by showing how social media (Instagram) can serve as an external motivator for self-regulation through public accountability and community engagement.
1.1.3. Mid-Week Feedback and Teacher Interactions Are Valuable
1.1.3.1. In both Wan (2022) and Upitis (2017), mid-week interactions with teachers and structured digital feedback (e.g., annotations, goal-tracking) improved students’ practice effectiveness.
1.1.3.2. Silva et al. (2023) demonstrate a similar process but through social feedback from Instagram followers, which increased motivation and accountability.
1.1.4. Video Recording as a Self-Reflection Tool
1.1.4.1. All three studies emphasize the role of video in self-regulated learning:
1.1.4.1.1. Cadenza in Upitis (2017) includes a media annotation tool that helps students track their progress.
1.1.4.1.2. Silva et al. (2023) used daily self-recordings to analyze practice habits and adjust strategies.
1.1.4.1.3. Wan (2022) suggests that self-recording and reflection can enhance metacognitive skills in instrumental learning.
1.1.5. Different Types of Learners Engage with SRL Tools in Different Ways
1.1.5.1. Upitis (2017) identified five student profiles based on how they used Cadenza (from highly engaged students to those who found it unhelpful).
1.1.5.2. Silva et al. (2023) found that self-regulation strategies evolve over time and must be adapted for different situations.
1.1.5.3. Wan (2022) acknowledges that younger students struggle with self-regulation and need external scaffolding, which aligns with the findings in both studies.
1.2. Contradictory Findings
1.2.1. Effectiveness of Digital Tools for Different Learners
1.2.1.1. Wan (2022) argues that digital tools may not benefit all students equally and that more research is needed to understand their role in SRL.
1.2.1.2. Upitis (2017) found that Cadenza was transformative for some students but ineffective for others, suggesting that motivation and engagement levels determine digital tool success.
1.2.1.3. Silva et al. (2023) show that social media-driven practice (Instagram) was highly beneficial, even for an advanced musician. However, this may not generalize to all learners, especially those who struggle with self-motivation.
1.2.2. The Role of Social Influence in SRL
1.2.2.1. Wan (2022) and Upitis (2017) emphasize teacher guidance as a crucial component of SRL development.
1.2.2.2. Silva et al. (2023), in contrast, show that peer interactions and social media accountability can replace traditional teacher feedback in fostering self-regulation.
1.2.3. Challenges and Limitations of Digital Practice Tools
1.2.3.1. Wan (2022) highlights that current digital tools mainly focus on technical aspects (e.g., pitch accuracy) rather than fostering deep learning strategies.
1.2.3.2. Upitis (2017) found that while Cadenza improved SRL, some students found it unnecessary or overwhelming.
1.2.3.3. Silva et al. (2023) faced challenges with self-imposed pressure and the anxiety of public performance on Instagram, suggesting that social media-driven practice can introduce stress and unrealistic expectations.
1.2.4. Impact of External Motivation vs. Internal SRL Development
1.2.4.1. Wan (2022) emphasizes gradual development of self-regulation through teacher scaffolding and metacognitive awareness.
1.2.4.2. Upitis (2017) argues that external rewards (points, badges, teacher encouragement) can help students regulate their practice.
1.2.4.3. Silva et al. (2023) found that public accountability on Instagram was a strong motivator, but its effectiveness relied on the participant’s intrinsic motivation to complete the challenge.
1.3. How this relates to my research
1.3.1. You can explore different digital tools (apps vs. social media) for SRL facilitation.
1.3.1.1. Your research could compare structured digital practice tools like Cadenza with unstructured platforms like Instagram to determine which is more effective for different learner types.
1.3.2. Consider how different types of learners engage with digital tools.
1.3.2.1. Just as Upitis (2017) identified five student profiles, you could investigate how different learners (beginners vs. advanced musicians) develop SRL with technology.
1.3.3. Investigate the balance between external motivation and intrinsic SRL development.
1.3.3.1. Wan (2022) suggests SRL should be nurtured gradually, while Silva et al. (2023) found that public accountability boosted short-term motivation. Your study could explore how digital tools support long-term SRL growth.
1.3.4. Assess the role of feedback (teacher, peer, or self-reflection) in SRL.
1.3.4.1. While Upitis (2017) found teacher feedback essential, Silva et al. (2023) showed that social media could substitute for it. Your research could analyze how different feedback mechanisms impact SRL development.
2. Key Overlaps and Contradictions in the Findings
2.1. Common Findings Across the Three Articles
2.1.1. Self-Regulation Enhances Academic Performance
2.1.1.1. All four articles agree that SRL processes (goal setting, planning, monitoring, reflection) are crucial for improving learning outcomes:
2.1.1.1.1. Donker et al. (2014) found SRL strategy instruction led to significant academic gains across subjects
2.1.1.1.2. Isaacson (2006) showed high-performing students were more accurate in monitoring their learning and goals, reflecting effective SRL use
2.1.1.1.3. Zimmerman (2000) argues SRL enables students to regulate behavior and cognition in pursuit of goals
2.1.1.1.4. Greene (2011) confirms that SRL is a dynamic, task-specific process that requires active engagement to be effective
2.1.2. 2. Self-Efficacy is a Key Motivational Driver of SRL
2.1.2.1. All authors highlight self-efficacy as a predictor and mediator of students’ motivation and use of SRL strategies:
2.1.2.1.1. Zimmerman (2000): Self-efficacy strongly predicts effort, persistence, and achievement more than self-concept or outcome expectations
2.1.2.1.2. Isaacson (2006): High-achieving students showed greater alignment between confidence and performance, demonstrating stronger self-efficacy and better self-monitoring
2.1.2.1.3. Greene (2011) discusses how self-efficacy beliefs guide how often and in what context students engage in SRL behaviors, especially when using real-time assessment tools like think-aloud protocols
2.1.2.1.4. Donker et al. (2014) found that motivational factors (including self-efficacy) must be taught alongside cognitive strategies for SRL interventions to be most effective
2.1.3. 3. Metacognitive Monitoring is Essential to SRL
2.1.3.1. Accurate self-monitoring (knowing what one knows and doesn’t know) is fundamental to adjusting learning behavior:
2.1.3.1.1. Isaacson (2006): Students who could predict their test scores more accurately used SRL strategies more effectively and succeeded academically
2.1.3.1.2. Greene (2011): Emphasizes using methods like think-aloud protocols to capture real-time metacognitive decisions, rather than relying on after-the-fact self-reports
2.1.3.1.3. Donker et al. (2014): Instruction in metacognitive strategies (planning, monitoring, evaluation) consistently improved outcomes in multiple domains
2.1.4. 4. Instruction in SRL and Self-Efficacy Skills Can Be Taught
2.1.4.1. All authors agree that these skills are not innate but can be explicitly taught and developed:
2.1.4.1.1. Donker et al. found that SRL strategy instruction improves academic outcomes, even among low-ability students
2.1.4.1.2. Zimmerman (2000) and Isaacson (2006) both argue for intentional SRL instruction to boost self-efficacy and student autonomy
2.2. Contradictory Findings
2.2.1. 1. Effectiveness of Self-Report vs. Real-Time Assessment
2.2.1.1. Greene (2011) criticizes traditional self-report measures of SRL (e.g., MSLQ) for inaccurately capturing SRL processes, as they rely on memory and generic prompts
2.2.1.2. In contrast, Isaacson (2006) used self-report prediction and postdiction in class and found strong correlations with academic success, suggesting that when designed carefully, self-reports can be informative
2.2.1.3. 🔁 Resolution: Both acknowledge the need for context-specific, task-embedded assessments, though Greene emphasizes the superiority of think-aloud methods.
2.2.2. 2. Role of Prior Ability in SRL Outcomes
2.2.2.1. Donker et al. (2014) found no significant differences in SRL strategy effectiveness across student ability levels, suggesting SRL is universally beneficial
2.2.2.2. However, Isaacson (2006) found that high-performing students had better metacognitive accuracy and strategy use, suggesting a possible performance-based differentiation
2.2.2.3. 🔁 Resolution: While SRL instruction benefits all learners, higher-performing students may have more refined SRL skills, but this gap can be closed with targeted support.
2.2.3. 3. Emphasis on Cognitive vs. Motivational Components
2.2.3.1. Zimmerman (2000) and Isaacson (2006) emphasize self-efficacy and motivation as central to learning success.
2.2.3.1.1. Why ?
2.2.3.2. Donker et al. (2014) focus more heavily on strategy instruction, with motivation treated as one component among many
2.2.3.2.1. why?
2.2.3.3. 🔁 Resolution: All agree both are essential, but differ slightly in how central they position motivation/self-efficacy vs. strategic behavior/metacognition.
2.3. ✅ Summary for My Research
2.3.1. These articles provide strong, overlapping evidence that:
2.3.1.1. Self-efficacy beliefs are essential to the effective use of SRL strategies.
2.3.1.2. SRL involves metacognitive, motivational, and behavioral processes, best captured in dynamic, context-sensitive assessments (e.g., TAPs).
2.3.1.3. Teaching SRL strategies explicitly improves academic outcomes and is crucial for developing independent, reflective learners.
3. Academic Domains
3.1. Donker, A. S., de Boer, H., Kostons, D., Dignath van Ewijk, C. C., & van der Werf, M. P. C. (2014). Effectiveness of learning strategy instruction on academic performance: A meta-analysis. Educational Research Review, 11, 1-26. http://dx.doi.org/10.1016/j.edurev.2013.11.002
3.1.1. Research Type:
3.1.1.1. Literature Meta-analysis
3.1.1.1.1. A total of 58 peer-reviewed articles from 2000-2012 including 95 strategy-interventions
3.1.1.1.2. Mathematics, comprehensive reading, writing and science
3.1.1.1.3. included three types of learnign strategies: "Metacognitive, cognitive, and management strategies, and their related motivational aspects and metacognitive knowledge."
3.1.2. Objectives/Research Questions:
3.1.2.1. “Which strategies instructed are the most effective in improving academic performance?”
3.1.2.2. "Do the effects of the strategies instructed differ for different types of students?"
3.1.3. Parcitipants/Population:
3.1.3.1. primary and secondary school, including 12th grade, and children with learning difficulties and disabilities
3.1.3.2. Profiles - four groups: regular/representitive students , children with low SES, students with learning difficulties, gifted/high SES
3.1.4. Methodology:
3.1.5. General Findings:
3.1.5.1. Metacognitive knowledge on planning ad monitoring
3.1.5.1.1. are the most addressed components
3.1.5.2. cognitive strategy - elaboration
3.1.5.2.1. the most trained sub-strategy
3.1.5.3. management strategies
3.1.5.3.1. are addressed less
3.1.5.4. motiovational aspects
3.1.5.4.1. were addressed the least
3.1.6. Special Findings:
3.1.6.1. Metacognitive knowledge directly postively affect academic achievenment
3.1.6.1.1. Reason:
3.1.6.2. No specific correlations between the effectiveness of strategies and different types of students.
3.1.6.2.1. BUT
3.1.7. Key Terms:
3.1.7.1. Cognition strategies
3.1.7.2. Metacognitive strategies
3.1.7.2.1. Metacognitive knowledge
3.1.7.3. Management Strategies
3.1.7.4. SES
3.2. Greene, J. A., Robertson, J., & Costa, L. J. C. (2011). Assessing self-regulated learning using think-aloud methods. In: Schunk, D. H., & Zimmerman, B. (Eds.). Handbook of self-regulation of learning and performance (pp.313-328). Routledge. http://ebookcentral.proquest.com/lib/mcgill/detail.action?docID=668792
3.2.1. Research Type:
3.2.1.1. Conceptual and methodological review.
3.2.1.1.1. Data Collection Method:
3.2.1.1.2. Data Analysis:
3.2.2. Objectives/Research Questions:
3.2.2.1. Research Questions
3.2.2.1.1. What are the limitations of traditional SRL assessment methods (e.g., self-report questionnaires)?
3.2.2.1.2. Does verbalizing one’s thought process interfere with self-regulation, or does it accurately reflect real-time SRL processing?
3.2.2.1.3. How can Think-Aloud Protocols (TAPs) capture SRL more effectively than self-report methods?
3.2.2.2. Objectives
3.2.2.2.1. To critically examine the weaknesses of self-report methods for assessing SRL.
3.2.2.2.2. To advocate for TAPs as a real-time and more accurate measure of self-regulated learning.
3.2.2.2.3. To explain how TAPs should be implemented to capture SRL without interfering with cognition.
3.2.3. Parcitipants/Population:
3.2.3.1. No direct participants, as this is a review study.
3.2.3.2. Studies reviewed included data from middle school, high school, and undergraduate students who participated in SRL research using TAPs.
3.2.4. Special Findings:
3.2.4.1. Criticism of Self-Report SRL Measures
3.2.4.1.1. Self-report methods (e.g., MSLQ) are flawed because they require learners to recall past SRL behaviors, often inaccurately.
3.2.4.1.2. Learners struggle to accurately report their SRL strategies, even immediately after learning tasks.
3.2.4.1.3. Self-report instruments force learners to choose from predetermined options, which may not reflect their actual cognitive processes.
3.2.4.2. Think-Aloud Protocols as a More Effective SRL Assessment Tool
3.2.4.2.1. TAPs allow researchers to capture SRL behaviors as they occur in real time.
3.2.4.2.2. Unlike retrospective self-reports, TAPs don’t rely on memory but document cognitive/metacognitive processes directly.
3.2.4.2.3. TAPs allow for open-ended verbalizations, ensuring that unexpected SRL strategies are not ignored.
3.2.4.3. TAPs Can Capture a Wide Range of SRL Processes
3.2.4.3.1. Cognitive strategies (e.g., problem-solving, information processing).
3.2.4.3.2. Metacognitive strategies (e.g., self-monitoring, planning, self-evaluation).
3.2.4.3.3. Motivational processes (e.g., self-efficacy, persistence, emotional regulation).
3.2.4.3.4. Behavioral processes (e.g., time management, help-seeking, task persistence).
3.2.4.4. Challenges and Limitations of TAPs
3.2.4.4.1. Verbalization can be unnatural for some learners: Not all students are comfortable thinking aloud.
3.2.4.4.2. Potential cognitive interference: Asking students to verbalize thoughts might alter their natural SRL behaviors.
3.2.4.4.3. Coding and analyzing TAP data is labor-intensive: Requires rigorous segmentation and categorization of verbal responses.
3.2.4.5. Empirical Evidence Supporting TAPs for SRL Assessment
3.2.4.5.1. TAPs have been used successfully in science learning (Azevedo, 2005), history (Greene et al., 2010), and mathematics (Muis, 2008).
3.2.4.6. Future Research Directions
3.2.4.6.1. Further studies are needed to determine whether TAPs interfere with cognitive processing (reactivity issue).
3.2.4.6.2. TAPs should be adapted to assess self-regulation across different academic and real-world learning environments.
3.2.4.6.3. Exploring how TAPs can be combined with digital tools (e.g., online learning platforms, AI-driven feedback).
3.2.5. Key Terms:
3.2.5.1. Think-Aloud Protocols (TAPs):
3.2.5.1.1. A method where participants verbalize their thoughts in real-time while completing a task.
3.2.5.2. Protocol Analysis:
3.2.5.2.1. A systematic method for analyzing verbal reports to understand cognitive processes. (Ericsson and Simon 1993)
3.2.5.3. Reactivity Issue:
3.2.5.3.1. The concern that verbalizing thoughts may alter natural cognitive behaviors.
3.2.6. Connections to my topic:
3.2.6.1. Alternative SRL Assessment Methods for Music Practice
3.2.6.1.1. Many studies in music education use self-reports (practice diaries, surveys) to assess practice behaviors.
3.2.6.1.2. Greene et al. argue that self-reports may be inaccurate, and TAPs could be a better way to assess how musicians engage in self-regulation during practice.
3.2.6.1.3. look into existing music research think-aloud methods to capture real-time decision-making in practice. (ex: Bathgate 2012)
3.2.6.2. Role of Metacognition in Music Learning
3.2.6.2.1. My research focuses on how musicians develop metacognitive skills (e.g., self-monitoring, goal-setting, error correction).
3.2.6.2.2. TAPs have been shown to successfully capture metacognitive strategies in other domains (e.g., science, math, history).
3.2.6.2.3. Applying TAPs in music practice could provide deeper insights into how musicians refine their learning strategies.
3.2.6.3. Combining TAPs with Digital Tools for Practice Analysis
3.2.6.3.1. Previous studies (Wan, 2022; Upitis, 2017; Silva et al., 2023) focused on digital tools for SRL (Cadenza, social media, practice apps).
3.2.6.3.2. Greene et al. (2011) suggest that TAPs could be integrated into digital platforms to provide real-time SRL assessments.
3.2.6.3.3. could explore whether musicians using TAPs in conjunction with digital tools show enhanced self-regulation skills.
3.2.6.4. Examining Reactivity in SRL Research on Music Practice
3.2.6.4.1. major concern in TAP research: whether verbalizing thoughts alters natural learning behaviors.
3.2.6.4.2. In music, this could be investigated by comparing musicians who verbalize practice strategies vs. those who do not.
3.2.6.4.3. Your study could address whether verbal self-explanations improve or disrupt self-regulation in instrumental practice.
3.3. Isaacson, R. M., & Fujita, F. (2006). Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning. Journal of the Scholarship of Teaching and Learning, 6(1), 39-55. https://doi.org/10.4236/psych.2017.812125
3.3.1. Research Type:
3.3.1.1. Longitudinal study tracking students' performance over 10 weeks.
3.3.1.1.1. Participants
3.3.1.1.2. Data Collection:
3.3.1.1.3. Data Analysis:
3.3.2. Objectives/Research Questions:
3.3.2.1. explores the relationship between metacognitive knowledge monitoring (MKM) and self-regulated learning (SRL), focusing on how students' awareness of their knowledge affects their academic success.
3.3.2.1.1. Research Questions
3.3.2.1.2. Objectives
3.3.3. Special Findings:
3.3.3.1. Choosing the Right Questions Reflects Metacognitive Ability
3.3.3.1.1. Students with strong MKM were more selective about answering difficult questions when confident in their knowledge.
3.3.3.1.2. Low MKM students guessed more often and made poor question choices, lowering their scores.
3.3.3.2. High-Achieving Students Have Better Metacognitive Awareness
3.3.3.2.1. Accurate Test Predictions:
3.3.3.2.2. Strategic Question Selection:
3.3.3.3. Self-Regulated Learning Involves Continuous Adjustments on Strategies and Self-estimation
3.3.3.3.1. high-achieving students constantly adjusted their study strategies based on feedback.
3.3.3.3.2. Low-achieving students overestimated their performance, failing to modify study habits to improve.
3.3.3.4. Metacognitive Awareness Correlates with Effective Goal Setting
3.3.3.4.1. High achievers had realistic goals that matched their actual performance.
3.3.3.4.2. Low achievers set unrealistically high goals without aligning them with study behaviors.
3.3.3.5. Three Types of Intra-Individual Differences in SRL Over Time
3.3.3.5.1. Relative Postdiction Accuracy:
3.3.3.5.2. Self-Efficacy Constancy:
3.3.3.5.3. Reliance on Effort:
3.3.3.6. Self-Efficacy and SRL Are Interrelated
3.3.3.6.1. Over time, students who adjusted their self-efficacy based on feedback improved their SRL skills.
3.3.3.6.2. Students who maintained unrealistic self-efficacy beliefs struggled to improve.
3.3.3.7. Challenges in Teaching Metacognitive Knowledge Monitoring (MKM)
3.3.3.7.1. Some students resisted modifying their study habits, continuing ineffective strategies despite repeated failures. (same as Bathgate 2012)
3.3.3.7.2. MKM skills may need explicit teaching to help students become aware of their own learning processes.
3.3.4. Key Terms:
3.3.4.1. Metacognitive Knowledge Monitoring (MKM):
3.3.4.1.1. The ability to accurately assess one’s own knowledge and learning progress.
3.3.4.2. Postdiction Accuracy:
3.3.4.2.1. The ability to predict test performance after taking a test but before grading.
3.3.4.3. Self-Efficacy Constancy:
3.3.4.3.1. The extent to which students adjust their confidence based on feedback.
3.3.4.4. Reliance on Effort:
3.3.4.4.1. When students judge learning only by time spent studying, rather than comprehension.
3.3.4.5. Question Dependency (QD):
3.3.4.5.1. A measure of how well students choose the right test questions based on their knowledge.
3.3.5. Connections to my topic:
3.3.5.1. Isaacson & Fujita’s (2006) study provides strong empirical support for your research on self-regulated learning (SRL) and metacognitive awareness in musicians' practice habits. It aligns with your focus on how metacognition and self-efficacy shape SRL strategies in professional musicians.
3.3.5.1.1. MKM and SRL in Instrumental Learning - self-monitoring skills
3.3.5.1.2. Self-Efficacy in SRL
3.3.5.1.3. The Relationship Between Goal-Setting and Practice Quality
3.3.5.1.4. How Musicians Adjust SRL Strategies Over Time
3.3.5.1.5. Teaching MKM and SRL Strategies to Musicians
3.4. Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25, 82-91. https://doi.org/10.1006/ceps.1999.1016
3.4.1. Research Type:
3.4.1.1. Theoretical and empirical review.
3.4.1.1.1. Data Sources:
3.4.1.1.2. Analysis Approach:
3.4.2. Objectives/Research Questions:
3.4.2.1. explores how self-efficacy influences motivation and self-regulated learning
3.4.2.1.1. Research Questions
3.4.2.1.2. Objectives
3.4.3. Parcitipants/Population:
3.4.3.1. Studies reviewed included data from students across various educational levels, primarily focusing on school and college students.
3.4.4. Special Findings:
3.4.4.1. Self-Efficacy is a Stronger Predictor of Academic Success than Related Constructs
3.4.4.1.1. While outcome expectations, self-concept, and perceived control influence motivation, self-efficacy is more predictive of academic performance.
3.4.4.1.2. Example: A student may believe math is important (outcome expectation) but still lack confidence in solving problems (low self-efficacy), leading to poor performance.
3.4.4.2. Self-Efficacy Directly Affects Learning and Motivation
3.4.4.2.1. High self-efficacy leads to:
3.4.4.2.2. Low self-efficacy leads to avoidance behaviors, lack of persistence, and negative emotional responses.
3.4.4.3. Self-Efficacy and Self-Regulated Learning (SRL) are Interdependent
3.4.4.3.1. Students with high self-efficacy are more likely to:
3.4.4.4. Self-Efficacy is Highly Sensitive to Context and Instruction
3.4.4.4.1. Self-efficacy is not a fixed trait but changes based on experiences, feedback, and learning environments.
3.4.4.4.2. Instructional strategies that improve self-efficacy include:
3.4.5. Key Terms:
3.4.5.1. Self-Efficacy (task specific):
3.4.5.1.1. A person’s belief in their ability to successfully perform a task.
3.4.5.2. Outcome Expectation:
3.4.5.2.1. Belief that an action will lead to a particular result (e.g., "If I study, I'll get good grades").
3.4.5.3. Self-Concept/self-esteem:
3.4.5.3.1. A broader self-perception that includes self-esteem and identity (e.g., "I’m a good student").
3.4.5.4. Perceived Control:
3.4.5.4.1. Belief in one’s ability to influence outcomes through effort (similar to locus of control).
3.4.5.5. Intrinsic Motivation
3.4.5.5.1. Definition: Engaging in a task for the inherent enjoyment or challenge it provides.
3.4.5.6. Extrinsic Motivation
3.4.5.6.1. Definition: Engaging in a task due to external rewards or pressures (e.g., grades, praise, competitions).
3.4.5.7. Vicarious Learning: Observing and modeling the behaviors of successful peers.
3.4.5.8. Verbal Persuasion: Encouragement from others to build confidence.
3.4.6. Connections to my topic:
3.4.6.1. foundational source- aligns with your focus on how self-efficacy influences musicians’ motivation, engagement, and practice quality.
3.4.6.1.1. Self-Efficacy as a Core Factor in Self-Regulated Learning and motivation and task engagement
3.4.6.1.2. Instructional Strategies to Enhance Self-Efficacy in Musicians
3.4.6.1.3. Measuring Self-Efficacy in Musical Learning Contexts
4. Educational Interventions
4.1. Bathgate, M., Sims-Knight, J., & Schunn, C. (2012). Thoughts on thinking: Engaging novice music students in metacognition. Applied Cognitive Psychology, 26(3), 403-409. https://doi.org/10.1002/acp.1842
4.1.1. Research Type:
4.1.1.1. A within-subjects experimental design where students were exposed to both traditional and metacognitive teaching conditions.
4.1.1.1.1. Participants:
4.1.1.1.2. Teaching Conditions:
4.1.1.1.3. Data Collection Methods:
4.1.1.1.4. Data Analysis:
4.1.2. Objectives/Research Questions:
4.1.2.1. This study aimed to investigate how explicit metacognitive instruction can improve musical practice and performance among novice students.
4.1.2.1.1. Research Questions
4.1.2.1.2. Objectives
4.1.3. Special Findings:
4.1.3.1. Metacognitive Instruction Led to Significant Performance Gains
4.1.3.1.1. Students who received metacognitive training performed better in rhythm and musicality than those in the control group.
4.1.3.1.2. Improvements in performance were maintained even after returning to traditional instruction.
4.1.3.2. Metacognitive Awareness in Novices Can Be Developed
4.1.3.2.1. Many novice students do not naturally engage in reflective practice and lack structured strategies for improvement.
4.1.3.2.2. Through explicit instruction, students became more aware of practice strategies, leading to more effective and structured practice.
4.1.3.3. Metacognition Led to More Efficient (Not Longer) Practice
4.1.3.3.1. Students in both conditions practiced for the same amount of time, meaning performance gains were due to better strategy use, not increased practice time.
4.1.3.3.2. This suggests that structured reflection can maximize the effectiveness of limited practice time.
4.1.3.4. Mixed Results for Self-Efficacy
4.1.3.4.1. Contrary to expectations, self-efficacy did not significantly improve following metacognitive training.
4.1.3.5. Teacher and Student Perceptions of Metacognitive Instruction Differed
4.1.3.5.1. Some instructors believed they were already incorporating metacognitive techniques, but students did not perceive these strategies as explicit.
4.1.3.5.2. Students needed structured and repeated exposure to reflective strategies before they internalized them.
4.1.3.6. Challenges in Implementing Metacognitive Strategies
4.1.3.6.1. Some students resisted breaking music into sections for focused practice, preferring to play pieces straight through.
4.1.3.6.2. Others embraced the process and found it transformative, particularly those struggling with progress before the intervention.
4.1.4. Key Terms:
4.1.4.1. Metacognition:
4.1.4.1.1. The ability to reflect on, monitor, and regulate one’s own learning process.
4.1.4.2. Self-Regulated Learning (SRL):
4.1.4.2.1. The ability to set goals, plan strategies, monitor progress, and adjust approaches based on feedback.
4.1.4.3. Deliberate Practice:
4.1.4.3.1. A structured approach to practice that focuses on problem-solving and targeted improvement rather than mere repetition.
4.1.4.4. Performance Evaluation:
4.1.4.4.1. The assessment of musical performance based on rhythm, accuracy, and musicality.
4.1.5. Connections to my topic:
4.1.5.1. Metacognitive Awareness as a Driver of Effective Practice
4.1.5.1.1. My research: how metacognitive awareness enhances learning engagement and self-reflection in musicians.
4.1.5.1.2. Bathgate et al. (2012): explicit metacognitive instruction significantly improves performance outcomes, suggesting that musicians who actively plan, self-monitor, and evaluate their practice sessions achieve greater efficiency and musical progress.
4.1.5.2. The Role of SRL in Structuring Practice for Young Professional Musicians
4.1.5.2.1. My study: focuses on young professional musicians, who, like the students in Bathgate et al.’s study, may benefit from structured self-regulated learning strategies.
4.1.5.2.2. Bathgate: reinforce that novice musicians often lack deliberate, structured practice approaches, and explicit training in metacognitive reflection can bridge this gap, leading to more effective learning.
4.1.5.2.3. I could: exploring how professional musicians integrate metacognitive strategies into long-term practice habits and whether such strategies continue to shape their development over time.
4.1.5.3. Self-Efficacy and Its Influence on Self-Regulated Learning Behaviors
4.1.5.3.1. One of my research questions explores how self-efficacy beliefs impact motivation, engagement, and SRL behaviors.
4.1.5.3.2. Bathgate et al. (2012) expected self-efficacy to increase following metacognitive instruction but found no significant changes.
4.1.5.3.3. This suggests that while metacognition enhances performance, it may not immediately influence self-belief—
4.1.5.4. Bridging the Gap Between Explicit Instruction and Self-Regulated Learning in Real-World Practice
4.1.5.4.1. Bathgate et al. (2012) highlight a disconnect between teachers’ perceptions of their metacognitive instruction and students’ actual engagement with these strategies.
4.1.5.4.2. I could address: young musicians internalize and sustain metacognitive strategies beyond structured lessons, identifying effective methods for integrating SRL into independent practice settings.
4.2. McPherson, G. E. (2022). Self-regulated learning music microanalysis. In G. E. McPherson (Ed.), The Oxford Handbook of Music Performance, Volume 1 (pp. 553-575). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190056285.013.23
4.2.1. Research Type:
4.2.1.1. applies SRL microanalysis, a structured technique for examining musicians' cognitive, behavioral, and emotional regulation during practice.
4.2.2. Objectives/Research Questions:
4.2.2.1. Research Questions:
4.2.2.1.1. How can self-regulated learning (SRL) microanalysis be applied to optimize music practice?
4.2.2.1.2. What specific SRL strategies enhance musicians’ self-monitoring, goal setting, and strategic planning?
4.2.2.1.3. How do different phases of Zimmerman’s SRL model (forethought, performance, and self-reflection) interact in musical practice?
4.2.2.2. Objectives:
4.2.2.2.1. To explore the application of SRL microanalysis in musicians’ practice.
4.2.2.2.2. To identify key SRL strategies that improve musicians' engagement and effectiveness in practice.
4.2.2.2.3. To assess the role of self-efficacy, motivation, and metacognitive awareness in learning music.
4.2.3. Parcitipants/Population:
4.2.3.1. The study mainly focuses on conservatory-level music students and professional musicians engaging in structured practice.
4.2.4. Special Findings:
4.2.4.1. Musicians with higher self-regulation engage in goal-oriented practice, emphasizing planning and monitoring rather than passive repetition.
4.2.4.2. Self-efficacy plays a critical role in effective learning—those who believe in their ability to improve set more challenging goals and persist longer in practice.
4.2.4.3. Forethought phase strategies (planning, strategic goal setting) were more predictive of effective practice than reactive, post-performance reflection.
4.2.4.4. Microanalysis intervention techniques help musicians develop systematic practice habits, fostering independence and metacognitive awareness.
4.2.5. Key Terms:
4.2.5.1. Microanalysis – A real-time assessment method for tracking SRL behaviors in practice.
4.2.5.2. Self-Efficacy – Belief in one’s ability to achieve specific goals.
4.2.5.3. Goal Orientation – The motivational drive behind learning and practice habits.
4.2.5.4. Strategic Planning – The process of selecting effective learning strategies to optimize performance.
4.2.6. Connections to my research
4.2.6.1. Alignment with SRL Framework
4.2.6.1.1. Supports my focus on how metacognitive awareness enhances musicians' self-regulated learning (SRL) strategies.
4.2.6.2. Empirical Support for SRL in Music Practice
4.2.6.2.1. Demonstrates that musicians with structured SRL strategies achieve more effective, goal-oriented practice.
4.2.6.2.2. Confirms that self-monitoring and strategic planning lead to improved engagement and performance outcomes.
4.2.6.3. Role of Self-Efficacy in Music Learning
4.2.6.3.1. Validates my research on how self-efficacy beliefs influence motivation, persistence, and learning behaviors.
4.2.6.3.2. Shows that high self-efficacy musicians engage in more challenging, deliberate practice strategies.
4.2.6.4. Microanalysis as a Method for Studying SRL in Musicians
4.2.6.4.1. Provides a real-time assessment approach to studying SRL behaviors in practice, which could be integrated into your methodology.
4.2.6.4.2. Suggests that analyzing musicians’ practice behaviors in real-time can yield deeper insights into self-regulation processes.
5. Topic 2 Technologies for fostering SRL
5.1. Academic Domains
5.1.1. Bartolomé, A., & Steffens, K. (2011). Technologies for self-regulated learning. In R. Carneiro, P. Lefrere, K. Steffens, & J. Underwood (Eds.), Self-Regulated Learning in Technology Enhanced Learning Environments (pp. 21–31). Sense Publishers.
5.1.1.1. Research Type:
5.1.1.1.1. Literature Review & Theoretical Analysis:
5.1.1.1.2. Conceptual Framework Development:
5.1.1.2. Objectives/Research Questions:
5.1.1.2.1. To analyze the relationship between technology and SRL, particularly how digital tools can support learners' autonomy.
5.1.1.2.2. To identify key characteristics that TELEs should possess to foster self-regulated learning.
5.1.1.2.3. To examine whether existing technologies effectively support self-regulated learning behaviors.
5.1.1.3. Parcitipants/Population:
5.1.1.3.1. No empirical study was conducted, but prior research on technology-enhanced learning environments was reviewed.
5.1.1.3.2. The discussion is theoretical, focusing on the design and potential benefits of TELEs.
5.1.1.4. General Findings:
5.1.1.4.1. Three Key Characteristics of SRL-Supporting Technologies
5.1.1.4.2. Comparison of Different TELEs
5.1.1.4.3. Connectivism as a Learning Model for SRL
5.1.1.4.4. The Role of Teachers in SRL Technologies
5.1.1.5. Connections to my research topic:
5.1.1.5.1. 1.First Research Question: Can Digital Technologies Support SRL?
5.1.1.5.2. 2. Alignment with the second Research Question: Challenges and Barriers to Technology Adoption
5.1.1.6. Key Terms:
5.1.1.6.1. Cognitive Apprenticeship Model
5.1.1.6.2. Connectivism
5.1.2. Kitsantas, A. (2013). Fostering college students' self-regulated learning with learning technologies. Hellenic Journal of Psychology, 10, 235-252.
5.1.2.1. Research Type:
5.1.2.1.1. Literature Review & Theoretical Analysis:
5.1.2.1.2. Instructional Model for SRL Development:
5.1.2.2. Objectives/Research Questions:
5.1.2.2.1. To examine the role of learning technologies in promoting self-regulation in online and hybrid learning environments.
5.1.2.2.2. To identify instructional strategies that help college students develop goal-setting, self-monitoring, and self-evaluation skills.
5.1.2.2.3. To provide guidelines for educators on how to design technology-enhanced courses that support SRL.
5.1.2.3. Parcitipants/Population:
5.1.2.3.1. No direct experimental study; the paper synthesizes research and proposes instructional models.
5.1.2.3.2. The primary focus is on college students in online and hybrid learning environments.
5.1.2.4. General Findings
5.1.2.4.1. Technology Can Engage Students in All Three SRL Phases
5.1.2.4.2. Social Networks and Virtual Learning Can Boost SRL
5.1.2.5. Special Findings
5.1.2.5.1. The Role of the Instructor in Scaffolding SRL
5.1.2.5.2. Self-Efficacy is Key to SRL in Digital Learning
5.1.2.6. Connections to my topic
5.1.2.6.1. 📌 Directly supports your investigation into how digital technologies enhance self-regulated learning in music education.
5.1.2.6.2. 📌 Highlights the role of digital tools in fostering metacognitive awareness, goal-setting, and self-monitoring.
5.1.2.6.3. 📌 Suggests that integrating structured SRL support into digital platforms (e.g., practice logs, self-evaluation rubrics) could improve musicians’ learning efficiency.
5.1.2.7. Key Terms:
5.1.3. Yot-Domínguez, C., & Marcelo, C. (2017). University students’ self-regulated learning using digital technologies. International Journal of Educational Technology in Higher Education, 14(38), 1-18. https://doi.org/10.1186/s41239-017-0076-8
5.1.3.1. Research Type:
5.1.3.1.1. Survey-Based Study:
5.1.3.1.2. Participants:
5.1.3.1.3. Factor analysis was conducted to group SRL strategies into distinct categories.
5.1.3.2. Objectives/Research Questions:
5.1.3.2.1. To examine how university students integrate digital technologies into their self-regulated learning processes.
5.1.3.2.2. To determine if students actively engage in self-regulated learning strategies using digital resources.
5.1.3.2.3. To identify patterns in students’ technology use for planning, monitoring, and evaluating their learning.
5.1.3.3. Special Findings:
5.1.3.3.1. Students Use Technology, But Not for Self-Regulation
5.1.3.3.2. SRL Strategies Are Mostly Social, Not Individual
5.1.3.3.3. Limited Use of Digital Technologies for SRL
5.1.3.3.4. Two Distinct Student Profiles Identified
5.1.3.4. Key Terms:
5.1.3.4.1. Digital Native Myth –
5.1.3.4.2. Social Support in SRL –
5.1.3.4.3. Cognitive Regulation –
5.1.3.5. Connections to my topic
5.1.3.5.1. 📌 Suggests that students underutilize technology for self-reflection and goal-setting, which may also apply to music students’ practice behaviors.
5.1.3.5.2. 📌 Highlights barriers to adopting technology for SRL, which aligns with your research question on challenges in digital tool adoption.
5.1.3.5.3. 📌 Findings suggest that strategic interventions (e.g., guided self-monitoring, teacher support) are needed to promote effective SRL habits.
5.2. Music Practice
5.2.1. Wan, L., Crawford, R., & Jenkins, L. (2022). Facilitating self-regulation of instrumental practice with digital technology: A framework, a synthesis of literature, and a call for research. Australian Journal of Music Education, 83, 54-94.
5.2.1.1. Research Type:
5.2.1.1.1. Semi-systematic literature review:
5.2.1.2. Objectives/Research Questions:
5.2.1.2.1. Research Questions
5.2.1.2.2. Objectives
5.2.1.3. Parcitipants/Population:
5.2.1.3.1. No empirical study was conducted; this is a theoretical review.
5.2.1.3.2. Reviewed studies cover a wide range of participants, including children, adolescents, and adult musicians.
5.2.1.4. Special Findings:
5.2.1.4.1. Lack of a Unified Model for Self-Regulation in Instrumental Practice
5.2.1.4.2. Self-Regulated Musicians Utilize Strategic Learning Phases
5.2.1.4.3. Technology Can Bridge Gaps in SRL Development
5.2.1.4.4. Limited Empirical Research on Technology’s Role in Instrumental SRL
5.2.1.4.5. Technology Should Be Integrated with Pedagogical Approaches
5.2.1.5. Connections to my topic
5.2.1.5.1. 📌 Strong alignment with my research topic on digital tools for SRL in music education.
5.2.1.5.2. 📌 Reinforces my focus on motivation, goal-setting, and self-reflection in digital learning environments.
5.2.1.5.3. 📌 Identifies gaps in research (e.g., lack of empirical studies on student-driven digital SRL tools), which I could address in my work.
5.2.2. Upitis, R. (2017). Student experiences with a digital tool for music practice and learning. Music Education Research, 19(3), 292-310. https://doi.org/10.15405/ejsbs.2017.08.issue-3
5.2.2.1. Research Type:
5.2.2.1.1. Research Design: Case study approach
5.2.2.1.2. Duration: 10 months
5.2.2.1.3. Data Collection:
5.2.2.1.4. Data Analysis: Qualitative thematic analysis following protocols established by Creswell (2012) and Yin (2009).
5.2.2.2. Objectives/Research Questions:
5.2.2.2.1. Objectives
5.2.2.2.2. Research Questions
5.2.2.3. Parcitipants/Population:
5.2.2.3.1. Teachers: 3 music teachers selected based on:
5.2.2.3.2. Students: 104 students across different studios:
5.2.2.4. Special Findings:
5.2.2.4.1. Varied Impact of Cadenza
5.2.2.4.2. Five Student Profiles Identified
5.2.2.4.3. Key Features of Cadenza That Contributed to Student Success
5.2.2.4.4. Teachers Adjusted Expectations for Cadenza Users
5.2.2.5. Key Terms:
5.2.2.5.1. Digital Music Tools: Software designed to assist in musical learning and practice.
5.2.2.5.2. Cadenza: The specific digital tool examined in the study, designed to facilitate practice between lessons.
5.2.2.6. connections to my topic:
5.2.2.6.1. Impact of Digital Tools:
5.2.2.6.2. Differentiated Student Profiles:
5.2.2.6.3. Teacher's Role in SRL Development:
5.2.3. Silva, C. dos S., Marinho, H., & Fiorini, C. (2023). Selection and adaptation of self-regulated learning strategies in an online music performance challenge. Psychology of Music, 51(3), 667–681. https://doi.org/10.1177/03057356221108762
5.2.3.1. Research Type:
5.2.3.1.1. Research Design:
5.2.3.1.2. Methodological Framework:
5.2.3.1.3. Data Collection:
5.2.3.2. Objectives/Research Questions:
5.2.3.2.1. Objectives
5.2.3.2.2. Research Questions:
5.2.3.3. Parcitipants/Population:
5.2.3.3.1. Single participant:
5.2.3.3.2. Social Media Audience:
5.2.3.4. Special Findings:
5.2.3.4.1. Social Media as an External Motivator for Practice
5.2.3.4.2. Self-Regulation Processes Were Interdependent
5.2.3.4.3. New Self-Regulation Strategies Emerged
5.2.3.4.4. Social Media-Driven Practice Was Both Beneficial and Challenging
5.2.3.4.5. Self-Monitoring via Video Feedback was Transformative
5.2.3.4.6. The Physical Practice Environment Had a Notable Impact
5.2.3.4.7. The Challenge Encouraged Metacognitive Reflection
5.2.3.5. Key Terms:
5.2.3.5.1. Autoethnography: A research method that combines personal narrative with academic analysis.
5.2.3.5.2. Social Media Accountability: The idea that public sharing of learning experiences can enhance motivation and persistence.
5.2.3.5.3. Video Feedback: Using self-recorded practice sessions to assess and refine performance.
5.2.3.6. Connections to my topic:
5.2.3.6.1. Integration of SRL Theory into Instrumental Learning:
5.2.3.6.2. Digital Tools and SRL Development:
5.2.3.6.3. Longitudinal Perspective on Practice Habits:
5.2.3.6.4. The Role of Self-Reflection in Learning:
5.2.3.6.5. Interdependence of SRL Dimensions:
6. Key Overlaps and Contradictions in the Findings
6.1. Common Findings Across the Three Articles
6.1.1. Technology as a Potential SRL Facilitator
6.1.1.1. All three articles agree that digital technologies can support self-regulated learning (SRL) by enhancing goal-setting, self-monitoring, and self-reflection
6.1.1.2. Technologies such as learning management systems (LMS), blogs, e-portfolios, and social learning tools have been shown to facilitate planning, tracking progress, and self-evaluation
6.1.2. Importance of Instructor Guidance
6.1.2.1. Kitsantas (2013) and Bartolomé & Steffens (2011) emphasize that instructors play a crucial role in scaffolding SRL through technology
6.1.2.2. Simply providing students with digital tools is not enough; structured guidance is necessary to help them develop self-regulatory behaviors.
6.1.3. Variation in SRL Adoption Among Students
6.1.3.1. Yot-Domínguez & Marcelo (2017) found that while students frequently use digital tools, not all of them use technology specifically for self-regulated learning
6.1.3.2. This aligns with Kitsantas (2013), who found that only students with prior SRL training effectively integrate technology into their learning cycles
6.1.4. Blended and Online Learning Can Enhance SRL
6.1.4.1. All three articles highlight that hybrid and online learning environments provide opportunities for autonomous learning, where students must take control of their learning processes
6.1.4.2. However, this shift requires students to be more proactive in using technology to support their SRL.
6.2. Contradictory Findings
6.2.1. Effectiveness of Technology Without Direct Instruction
6.2.1.1. Bartolomé & Steffens (2011) argue that well-designed technology-enhanced learning environments (TELEs) inherently foster SRL, without necessarily needing instructor intervention
6.2.1.2. Kitsantas (2013) and Yot-Domínguez & Marcelo (2017) contradict this by showing that students often fail to self-regulate effectively without teacher support
6.2.2. Self-Efficacy and SRL with Technology
6.2.2.1. Kitsantas (2013) found that students with higher self-efficacy engaged in more SRL behaviors when using technology
6.2.2.2. Yot-Domínguez & Marcelo (2017), however, reported that many students lacked confidence in using technology for self-regulation, despite frequent digital tool use
6.2.3. Barriers to SRL Adoption with Technology
6.2.3.1. Kitsantas (2013) and Bartolomé & Steffens (2011) focused on the potential benefits of digital tools but did not extensively discuss barriers to adoption
6.2.3.2. Yot-Domínguez & Marcelo (2017) found that many students do not use digital tools for SRL despite their availability due to a lack of awareness, training, or perceived relevance
7. Music Education
7.1. Concina, E. (2019). The role of metacognitive skills in music learning and performing: Theoretical features and educational implications. Frontiers in Psychology, 10, 1583. https://doi.org/10.3389/fpsyg.2019.01583
7.1.1. Research Type:
7.1.1.1. Theoretical review of literature
7.1.1.1.1. Data Sources:
7.1.1.1.2. Population:
7.1.2. Objectives/Research Questions:
7.1.2.1. Research Questions
7.1.2.1.1. What are the key components of metacognitive skills in music learning and performance?
7.1.2.1.2. How do expert musicians apply metacognitive strategies in practice and performance?
7.1.2.1.3. How can metacognitive skills be developed in music students during their training?
7.1.2.1.4. What are the educational implications of integrating metacognition into music pedagogy?
7.1.2.2. Objectives
7.1.2.2.1. To explore the role of metacognition in music learning and performance.
7.1.2.2.2. To analyze how expert musicians use metacognitive strategies to regulate their practice and improve performance.
7.1.2.2.3. To examine the differences in metacognitive awareness between expert musicians and music students.
7.1.2.2.4. To propose educational strategies for fostering metacognitive skills in music learners.
7.1.3. Special Findings:
7.1.3.1. Expert musicians display higher metacognitive competence than students
7.1.3.1.1. They select and adapt learning strategies effectively.
7.1.3.1.2. They engage in self-reflection and deliberate practice, which leads to better problem-solving and learning efficiency.
7.1.3.2. Metagocginitive competence lead to higher self-efficacy
7.1.3.2.1. (p9, Nielsen (2004), who examined adult music students. A positive correlation between the use of cognitive and metacognitive strategies and the level of self- efficacy emerged, showing that students with a high sense of efficacy were more likely to adopt strategic behaviors in their musical learning.)
7.1.3.3. Music students struggle with metacognitive awareness
7.1.3.3.1. Many students lack structured practice strategies and rely on inefficient learning habits.
7.1.3.3.2. Explicit teaching of metacognitive skills can help students develop strategic learning approaches.
7.1.3.3.3. Teacher and paprents are the learning facilitor for students development of metacognitive competence.
7.1.3.4. Educational interventions can enhance metacognitive development in music students
7.1.3.4.1. Younger pupils should need more guidence on metacognitive awareness, compared to adults, who have established certain learning competence. The teaching methods should be different.
7.1.3.4.2. Teachers should explicitly discuss metacognitive strategies in lessons.
7.1.3.4.3. Structured reflection activities, such as practice diaries and self-assessment tools, improve students’ learning autonomy.
7.1.3.4.4. Encouraging peer learning and collaborative reflection fosters metacognitive growth.
7.1.4. Key Terms:
7.1.4.1. Metacognitive Strategies – Techniques used to enhance learning efficiency, such as goal setting, self-monitoring, and self-evaluation.
7.1.4.2. Expert vs. Novice Learners – Experts use adaptive, flexible learning strategies, whereas novices often rely on rigid or inefficient methods.
7.1.5. Connections to my topic:
7.1.5.1. Strong Alignment with SRL and Metacognitive Awareness in Musicians
7.1.5.1.1. Concina (2019) emphasizes the role of planning, self-monitoring, and self-reflection in music practice, which aligns directly with your focus on Zimmerman’s SRL framework.
7.1.5.2. Confirms the Importance of Self-Efficacy in Learning
7.1.5.2.1. Expert musicians display high self-efficacy, enabling them to persist in refining their skills.
7.1.5.2.2. My research can explore how self-efficacy interacts with metacognitive awareness in young professional musicians.
7.1.5.3. Highlights the Need for Explicit Metacognitive Instruction
7.1.5.3.1. Many music students struggle to self-regulate their practice effectively.
7.1.5.3.2. Your study can investigate how structured interventions (e.g., guided reflection, digital tools) improve metacognitive engagement in musicians.
7.1.5.4. Supports the Use of Reflection Tools for Practice Optimization
7.1.5.4.1. Concina (2019) suggests using practice diaries, self-assessment logs, and strategic goal-setting exercises to enhance learning.
7.1.5.4.2. my research could examine how digital tools or video feedback impact musicians' metacognitive development.
7.1.5.5. Bridges the Gap Between Novice and Expert Learners
7.1.5.5.1. Concina highlights the differences in metacognitive competence between expert musicians and students.
7.1.5.5.2. my study could analyze how young professionals transition from student learning habits to expert-level self-regulation.
7.2. Araújo, M. V. (2016). Measuring self-regulated practice behaviours in highly skilled musicians. Psychology of Music, 44(2), 278-292. https://doi.org/10.1177/0305735614567554
7.2.1. Research Type:
7.2.1.1. Quantitative survey research with statistical validation.
7.2.1.1.1. Instrument: A newly developed 22-item self-report questionnaire measuring SRL behaviors based on previous literature.
7.2.2. Objectives/Research Questions:
7.2.2.1. Research Questions
7.2.2.1.1. "(i) Can self-regulated practice behaviours be measured by a self-report questionnaire for advanced musicians?"
7.2.2.1.2. "(ii) To what extent does a sample of advanced musicians self-regulate, and what are the self- regulated practice behaviours they use most?"
7.2.2.1.3. "(iii) Is self-regulated practice predicted by age, gender, musical instruments and time spent practising?"
7.2.2.2. Objectives
7.2.2.2.1. To develop and validate a self-report questionnaire measuring self-regulated practice behaviors in highly skilled musicians.
7.2.2.2.2. To identify key factors that contribute to self-regulation in practice, including practice organization, personal resources, and external resources.
7.2.2.2.3. To analyze the relationship between self-regulated behaviors and variables like age, practice time, and social support.
7.2.2.2.4. To examine how self-regulation skills evolve over time in advanced musicians.
7.2.3. Special Findings:
7.2.3.1. Three Core Factors of Self-Regulated Music Practice
7.2.3.1.1. Practice Organization: Setting goals, structuring time, and planning practice.
7.2.3.1.2. Personal Resources: Metacognitive awareness, strategy use, and self-efficacy.
7.2.3.1.3. External Resources: Seeking help, using study materials, and social support.
7.2.3.2. Personal Resource Strategies are Most Critical for Advanced Musicians
7.2.3.2.1. The strongest predictor of effective practice was reliance on personal metacognitive resources (e.g., self-monitoring, problem-solving).
7.2.3.2.2. External resources (e.g., teacher support, study materials) became less important with experience.
7.2.3.3. Older Musicians Practice Less But More Efficiently
7.2.3.3.1. Younger musicians practiced longer but relied more on structured organization and external resources.
7.2.3.3.2. Older musicians compensated for less practice time with more refined, self-regulated strategies.
7.2.3.4. Practice Time is Positively Correlated with Self-Regulation, but Only to a Certain Extent
7.2.3.4.1. Musicians who practiced longer tended to have higher levels of self-regulation, but the relationship weakened at advanced levels.
7.2.3.4.2. Beyond a certain expertise level, self-regulation mattered more than raw practice time.
7.2.3.4.3. why?
7.2.3.5. Goal-Setting and Planning were Not Universally High:
7.2.3.5.1. Although SRL literature emphasizes structured planning as a key to success, some highly skilled musicians did not engage in detailed practice organization.
7.2.3.5.2. This suggests that expert musicians develop implicit learning structures rather than rigidly planning every session.
7.2.4. Key Terms:
7.2.4.1. Metacognitive Awareness – A musician’s ability to reflect on and control their learning process.
7.2.5. Connections
7.2.5.1. Reinforces the Role of Metacognitive Strategies in Expert Performance
7.2.5.1.1. Expert musicians rely less on external resources and more on personal strategies.
7.2.5.1.2. my research could analyze how musicians transition from external guidance to independent learning.
7.2.5.2. Highlights the Importance of Self-Efficacy in Music Practice
7.2.5.2.1. Musicians who rely on internal strategies and self-efficacy achieve more efficient learning.
7.2.5.2.2. my research can explore how self-efficacy influences musicians' long-term practice habits.
7.2.5.3. Implications for Music Education and Practice Optimization
7.2.5.3.1. Findings suggest that explicit SRL training can improve efficiency and reduce reliance on practice time alone.
7.2.5.3.2. i could explore how structured metacognitive interventions improve musicians’ self-regulation and engagement.
7.3. dos Santos Silva, C., & Marinho, H. (2024). Self-regulated learning processes of advanced musicians: A PRISMA review. Musicae Scientiae 0(0), 1-20. https://doi.org/10.1177/10298649241275614
7.3.1. Research Type:
7.3.1.1. A systematic review based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework.
7.3.1.1.1. Research designs categorized into observational studies, interventions, and descriptive-correlational studies
7.3.1.1.2. 48 studies (2001-2023) included were empirical, peer-reviewed, and examined SRL in music within higher education institutions or professional settings.
7.3.1.1.3. participants:
7.3.2. Objectives/Research Questions:
7.3.2.1. objectives
7.3.2.1.1. Identify trends in advanced students' practice behaviours.
7.3.2.1.2. To investigate how advanced musicians engage in self-regulated learning (SRL) strategies.
7.3.2.1.3. To examine the effectiveness of SRL training interventions.
7.3.2.1.4. To analyze existing research using Zimmerman’s SRL mode
7.3.2.2. questions
7.3.2.2.1. "How do advanced musicians plan and employ learning strategies?"
7.3.2.2.2. "Is there evidence of the benefits of SRL training for advanced musicians?"
7.3.3. General Findings:
7.3.3.1. Self-Efficacy in Advanced Musicians
7.3.3.1.1. Goal Setting and Self-Efficacy:
7.3.3.1.2. Self-Efficacy and SRL Cycle:
7.3.3.2. Metacognitive Instruction in Music Education
7.3.3.2.1. Underutilization of Metacognitive Strategies:
7.3.3.2.2. Impact of Metacognitive Training:
7.3.3.3. Teacher Involvement in SRL Training:
7.3.3.3.1. Only a few studies examined music educators’ role in promoting SRL.
7.3.3.4. Help-Seeking Behavior:
7.3.3.4.1. Professional musicians relied less on external feedback than students.
7.3.3.4.2. Seeking feedback from teachers and peers proved beneficial in improving musical performance and practice efficiency
7.3.3.5. Cyclical Nature of SRL:
7.3.3.5.1. Self-reflection played a key role in efficient practice and improved goal-setting.
7.3.3.5.2. Regular self-evaluation helped musicians plan future sessions more effectively
7.3.4. Special Findings:
7.3.4.1. Underutilization of Metacognitive Strategies:
7.3.4.1.1. Despite their advanced skill levels, many musicians relied heavily on passive rehearsal strategies like repetition, rather than strategic problem-solving approaches
7.3.4.1.2. Studies highlighted a gap between the metacognitive strategies musicians claimed to use and those they actually employed daily
7.3.4.2. Teacher Involvement in SRL Training:
7.3.4.2.1. Only a few studies examined music educators’ role in promoting SRL.
7.3.5. Key Terms:
7.3.5.1. Metacognitive Monitoring: The ability to observe and regulate one’s cognitive processes in learning.
7.4. Hallam, S. (2001). The development of metacognition in musicians: Implications for education. British Journal of Music Education, 18(1), 27–39. https://doi.org/10.1017/S0265051701000122
7.4.1. Research Type:
7.4.1.1. Qualitative study using semi-structured interviews and observational data.
7.4.1.1.1. Participants:
7.4.1.1.2. Data Analysis:
7.4.1.1.3. Participants
7.4.2. Objectives/Research Questions:
7.4.2.1. Questions
7.4.2.1.1. How do metacognitive and performance planning strategies develop in musicians from novice to professional levels?
7.4.2.1.2. What is the relationship between the development of expertise and the use of planning strategies in musicians?
7.4.2.2. Objectives
7.4.2.2.1. To examine how these strategies evolve from novice to professional levels and their impact on practice effectiveness.
7.4.2.2.2. To explore the development of metacognition and performance planning strategies among musicians.
7.4.3. Parcitipants/Population:
7.4.4. Methodology:
7.4.5. General Findings:
7.4.5.1. Professional Musicians Exhibit Highly Developed Metacognitive Skills
7.4.5.1.1. Strong self-awareness of strengths and weaknesses.
7.4.5.1.2. Advanced knowledge of practice strategies for technical mastery, interpretation, and self-monitoring.
7.4.5.1.3. Flexible and adaptive approaches to practice based on individual needs.
7.4.5.2. Novices Struggle with Structured Planning and Self-Evaluation
7.4.5.2.1. Novices lacked goal-oriented practice strategies and often practiced passively.
7.4.5.2.2. Many failed to identify errors efficiently or apply appropriate corrections.
7.4.5.2.3. Advanced pre-college students exhibited some self-regulation skills but lacked structured performance preparation.
7.4.5.3. Planning and Organization are Key Indicators of Expertise
7.4.5.3.1. Professionals had well-developed planning habits, ensuring efficient and focused practice.
7.4.5.3.2. Some novice musicians showed low organization, practicing without structure or motivation.
7.4.5.3.3. High levels of organization correlated with high concentration and better performance outcomes.
7.4.5.4. Metacognitive Skills Influence Performance Anxiety and Confidence
7.4.5.4.1. Highly metacognitive musicians developed strategies to manage nerves and optimize focus.
7.4.5.4.2. Some novices experienced severe performance anxiety and lacked preparation techniques to cope.
7.4.5.4.3. Professionals varied in their approach:
7.4.5.5. Diversity in Strategy Use Reflects Individual Learning Styles
7.4.5.5.1. No single "expert" approach to practice exists—successful musicians adapt strategies based on individual needs.
7.4.5.5.2. Some professionals favored analytical, slow practice, while others used intuitive, goal-oriented methods.
7.4.6. Special Findings:
7.4.7. Key Terms:
7.4.7.1. Performance Preparation Strategies: Techniques used to enhance concentration, reduce anxiety, and optimize performance.
7.4.7.2. Individualized Learning Approaches: Recognition that musicians vary in how they process, internalize, and refine skills.
7.4.8. Connections:
7.4.8.1. Strong Evidence for the Role of Planning and Self-Evaluation
7.4.8.1.1. Professional musicians engage in systematic goal-setting and reflection, while novices struggle with structured practice.
7.4.8.1.2. My study can examine how structured interventions (e.g., practice journals, video self-evaluation) enhance SRL in musicians.
7.4.8.2. Supports the Link Between Self-Efficacy and Learning Efficiency
7.4.8.2.1. Professionals show higher confidence and adaptability, while novices often lack self-belief.
7.4.8.2.2. My research will explore how self-efficacy beliefs influence engagement, persistence, and practice quality.
7.4.8.3. Highlights the Importance of Individualized Learning Strategies
7.4.8.3.1. Hallam’s study suggests that effective practice varies across individuals—this aligns with my research on adaptive learning techniques in SRL.
7.4.8.3.2. My study can further investigate how musicians personalize self-regulation strategies based on their strengths and weaknesses.
7.4.8.4. Bridges the Gap Between Novices and Experts
7.4.8.4.1. Novices lack structured reflection and planning skills, supporting my focus on teaching metacognitive awareness to developing musicians.
7.4.8.4.2. I could analyze how young professional musicians transition from novice learning habits to expert-level self-regulation.
7.5. Nielsen, S. (2001). Self-regulating learning strategies in instrumental music practice. Music Education Research, 3(2), 155–167. https://doi.org/10.1080/14613800120089223
7.5.1. Research Type:
7.5.1.1. Qualitative case study using video observation and verbal protocols.
7.5.1.1.1. Participants: 2 advanced organ students from the Church Music Program at the Norwegian Academy of Music.
7.5.1.1.2. Procedure:
7.5.1.1.3. Data were coded and analyzed thematically, following SRL frameworks (e.g., Zimmerman 1998, 2000).
7.5.2. Objectives/Research Questions:
7.5.2.1. questions:
7.5.2.1.1. Do advanced music students demonstrate skilled self-regulated learning during practice?
7.5.2.1.2. What self-regulatory strategies do they use during different phases of learning (e.g., early and late stages)?
7.5.2.1.3. How do students apply goal-setting, planning, monitoring, and strategy revision in real-time practice sessions?
7.5.2.2. objectives
7.5.2.2.1. To explore how advanced conservatory students self-regulate their learning during instrumental practice.
7.5.2.2.2. To investigate the microstructure of learning within practice sessions, especially strategy use across different phases.
7.5.3. General Findings:
7.5.3.1. 1. Extensive Self-Regulation Among Advanced Students
7.5.3.1.1. Students engaged in goal setting, strategic planning, self-instruction, and self-monitoring to optimize learning.
7.5.3.1.2. Their self-regulation strategies evolved cyclically, meaning their evaluation of practice shaped future learning decisions.
7.5.3.2. 2.Self-Monitoring and Self-Evaluation Were Highly Developed
7.5.3.2.1. Different Approaches to Goal Setting
7.5.3.2.2. Students set specific learning outcomes for each session, adjusting their criteria over time.
7.5.3.2.3. They assessed technical mastery vs. expressive interpretation, shifting focus dynamically.
7.5.3.3. 3. Use of Self-Instructions and Task Strategies
7.5.3.3.1. Students provided verbal self-guidance, reinforcing key learning principles.
7.5.3.3.2. Task simplification strategies were frequently used (e.g., isolating problem areas, slowing down difficult sections, practicing hands separately).
7.5.3.4. 4. Cyclical Process of Self-Regulated Learning
7.5.3.4.1. The study identified a four-phase cycle:
7.5.4. Special Findings:
7.5.4.1. First study to analyze SRL in instrumental music using real-time and retrospective think-aloud methods.
7.5.4.2. Developed a preliminary cyclic model of self-regulated learning, illustrating how musicians refine their strategies through practice.
7.5.4.3. Highlighted individual differences in how musicians structure their learning, emphasizing that SRL strategies vary based on personal goals and problem perceptions.
7.5.5. Key Terms:
7.5.5.1. Problem beliefs – Perceived difficulty or challenge influencing strategy use.
7.5.5.2. Cyclic SRL model – The conceptual model developed by Nielsen showing adaptive strategy shifts.
7.5.6. Important Quotes:
7.5.6.1. Problem belifs, metacognitive competence and self-efficacy beliefs may influence the strategy use.
7.5.6.1.1. student evaluate performance - problem beliefs - self-efficacy enhanced - positively influence use of strategies
7.5.6.1.2. student evaluate unsuccessful performance - self-efficacy decreased - negatively influence use of strategies "Their use of strategies may also be independent of metacognitive control."
7.6. Nielsen, S. G. (2004). Strategies and self-efficacy beliefs in instrumental and vocal individual practice: A study of students in higher music education. Psychology of Music, 32(4), 418-431. https://doi.org/10.1177/0305735604046099
7.6.1. Research Type:
7.6.1.1. Quantitative survey-based study using a Norwegian-adapted version of the Motivated Strategies for Learning Questionnaire (MSLQ).
7.6.1.1.1. The questionnaire assessed:
7.6.1.1.2. Rating scale: 7-point Likert scale for each of the 50 strategy and 8 self-efficacy items.
7.6.1.1.3. Participants
7.6.2. Objectives/Research Questions:
7.6.2.1. questions
7.6.2.1.1. To what extent do first-year music students use specific learning and study strategies in their practice?
7.6.2.1.2. What is the relationship between self-efficacy beliefs and strategy use in instrumental practice?
7.6.2.1.3. Are there any differences in strategy use or self-efficacy beliefs based on instrument, degree program, or gender?
7.6.2.2. objectives
7.6.2.2.1. To investigate the learning strategies (cognitive, metacognitive, resource management) used by advanced music students.
7.6.2.2.2. To explore the connection between students' self-efficacy beliefs and their use of self-regulated learning strategies.
7.6.2.2.3. To examine whether gender, main instrument, or degree program influence strategy use or self-efficacy.
7.6.3. Parcitipants/Population:
7.6.4. Methodology:
7.6.5. General Findings:
7.6.5.1. Strategy Use:
7.6.5.1.1. Students used a wide range of cognitive and metacognitive strategies, but resource management strategies (peer learning, help-seeking) were used less frequently.
7.6.5.1.2. Rehearsal, elaboration, and critical thinking were most frequently used.
7.6.5.2. Self-Efficacy and Strategy Use:
7.6.5.2.1. Self-efficacy scores were positively correlated with nearly all learning strategy types, especially metacognitive strategies.
7.6.5.2.2. Students who felt more capable reported greater use of planning, monitoring, and critical thinking strategies.
7.6.5.2.3. There was no correlation between self-efficacy and effort regulation, suggesting that belief in ability does not necessarily reduce avoidance or disengagement.
7.6.5.3. Gender and Program Differences:
7.6.5.3.1. Male students had higher self-efficacy scores overall and used more critical thinking strategies than females.
7.6.5.3.2. No significant differences were found in strategy use or self-efficacy across instrument groups or degree programs.
7.6.5.3.3. However, there was a significant interaction effect between gender and degree program on self-efficacy, indicating that contextual experience (e.g., pre-conservatory exposure) may shape students' beliefs.
7.6.5.4. Educational Implications:
7.6.5.4.1. Students might benefit from greater encouragement to engage in peer learning and help-seeking, which are underused but potentially powerful.
7.6.5.4.2. Teachers and institutions should be aware of gender differences and foster environments that build efficacy and promote strategic engagement.
7.6.6. Special Findings:
7.6.6.1. 1. Self-efficacy plays a major role in shaping learning behaviors, but students with low self-efficacy do not necessarily lack ability—rather, they use fewer strategic learning methods.
7.6.6.1.1. High efficacy
7.6.6.2. 2. Help-seeking and peer learning were notably low, suggesting that music students tend to approach practice in an individualistic, self-reliant manner rather than as a collaborative process.
7.6.6.3. 3. Contrary to expectations, effort regulation was not significantly correlated with self-efficacy, suggesting that motivation to persist in practice does not necessarily stem from confidence in one's abilities.
7.6.7. Key Terms:
7.6.7.1. Cognitive Strategies – Repetition, elaboration, organizing information, and critical thinking.
7.6.7.2. Metacognitive Strategies – Planning, monitoring, and regulating learning processes.
7.6.7.3. Resource Management Strategies – Time management, effort regulation, peer interaction, and help-seeking.
7.6.8. Important Quotes:
7.6.8.1. "Self-efficacy is defined as ‘people’s judgement of their capabilities to organize and execute the courses of action required to attain designated types of performances’ ((Bandura, 1986) p2