Metacognition, achievement goals, study strategies and academic achievement: pathways to achievement

By Anneke Vrugt, F. Oort. 2008

411 citations recorded by the source corpus.

The purpose of this research was to develop and test a model of effective self-regulated learning. Based on effort expenditure we discerned effective self-regulators and less effective self-regulators. The model comprised achievement goals (mastery, performance-approach and -avoidance goals), metacognition (metacognitive knowledge, regulation and experience), study strategies (metacognitive, deep cognitive, surface cognitive and resource management strategies) and academic achievement. The relationships in the model were tested with controlling for intellectual ability, gender and age. The results showed that effective self-regulated learning involved two pathways: a metacognitive and a strategy pathway. The first pathway involved a positive relationship of mastery goals and a negative relationship of performance-avoidance goals with metacognition. Metacognition positively affected the use of the four study strategies. The strategy pathway involved positive effects of mastery and performance-approach goals on the use of metacognitive and deep cognitive strategies. Further, performance-approach goals positively affected the use of surface cognitive and resource management strategies. The use of metacognitive and resource management strategies had a positive and the use of surface cognitive strategies had a negative effect on exam scores.

Subfields: Developmental and Educational Psychology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/d5e8b0245d8be8362fd4548735e29843d89b0f5d

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In Developmental and Educational Psychology

In Experimental and Cognitive Psychology

Citations in the corpus

Sense of Belonging and Self-Regulated Learning: Testing Achievement Goals as Mediators (2018, background): “In comparison, performance-avoidance goals have been negatively associated with deep processing but positively associated with surface cognitive strategies, task disengagement, disorganization, and procrastination (Elliot et al., 1999; Liem et al., 2008; Vrugt & Oort, 2008; Wolters, 2004).”
Beyond intelligence: a meta-analytic review of the relationship among metacognition, intelligence, and academic performance (2018, background): “Previous studies have shown that metacognition was related to the use of numerous cognitive strategies, which were related to academic achievement (e.g., Pintrich and De Groot 1990; Pintrich et al. 1993; Sperling et al. 2004; Vrugt and Oort 2008; Wolters 2004).”
Goals, Efficacy and Metacognitive Self-Regulation A Path Analysis (2010, background): “Regulation of cognition refers to a set of activities that help students control their learning (Vrugt & Oort, 2008).”
Self-regulated learning: the role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course (2015, background): “Many SRL researchers have examined how motivational variables predict the use of learning strategies (Pintrich, 1989, 1999) or how motivation and learning strategies are related to student achievement (Vrugt & Oort, 2008; Yukselturk & Bulut, 2007; Zimmerman & Martinez-Pons, 1988).”
The Influence of Attitudes towards Mathematics, and Metacognitive Awareness on Mathematics Achievements (2017, background): “Students with high skills on metacognitive knowledge and metacognitive regulation will be motivated as they have intrinsic motivation, high self-efficacy, and orient towards understanding the task (Vrugt & Oort, 2008; Coutinho & Neuman, 2008).”
Evaluating the metacognitive awareness inventory using empirical factor-structure evidence (2018, background): “…cogent arguments that students who regulate their cognition tend to perform well in problembased learning (Hmelo-Silver 2004; Rozencwajg 2003), expert learning (Bransford et al. 2000; Sternberg 1998), and overall academic achievement (Peverly et al. 2003; Vrugt and Oort 2008; Winston et al. 2010).”
Developing Resilient Agency in Learning: The Internal Structure of Learning Power (2015, background): “…literature (Andrade, 1999; Blakey and Spence, 1990; Coutinho and Neuman, 2008; Dunlosky and Metcalfe, 2009; Kitchener, 1983, Veenman et al., 2006, Vrugt and Oort, 2008) that deals with these from the perspective of heuristics linked to specific disciplines through to the underlying neuroscience.”
Integrating the regulation of affect, behavior, and cognition into self-regulated learning paradigms among secondary and post-secondary students (2015, methodology): “Our definition for self-regulated learning strategies draws on early work in educational psychology that described deep and surface processing of the materials, organization, and overall engagement as crucial for learning (Corno 1986; Corno andMandinach 1983; Pintrich 2000; Vrugt and Oort 2008; Zimm”
How Does Metacognition Contribute to the Regulation of Learning? An Integrative Approach (2014, background): “Vrugt and Oort (2008) found that students in SRL follow two paths: one from motivation to metacognition to study strategies and one from motivation to strategy use directly.”
Interactions of Metacognition With Motivation and Affect in Self-Regulated Learning: The MASRL Model (2011, background): “However, motivation can also be related to MK. Specifically, in the expectancy-value theory of motivation (Eccles & Wigfield, 2002; Wigfield & Eccles, 2000) expectancy of success is based in part on self-perceptions of ability, on perceptions of tasks and their demands, and perceptions of others’ be”
Towards an Understanding of STEM Engagement: a Review of the Literature on Motivation and Academic Emotions (2019, background): “…whereas others found performance approach goals to be related to deep learning strategies, self-regulation, effort (Meece et al., 1988; Urdan, 2004; Vrugt & Oort, 2008), as well as hope and pride (Pekrun et al., 2006, 2009), higher self-efficacy, interest, and performance (Durik et al., 2009;…”