Motivation From a Self-Regulated Learning Perspective: Application to School Psychology

Self-regulation of learning involves developing metacognitive awareness (planning, monitoring, and evaluating) of (a) cognition—motivational beliefs, (b) behaviors—persistence, effort, engagement, and (c) affect—enjoyment, interest, and other emotions. Metacognitive awareness creates opportunities to exert metacognitive control as needed, which may involve sustaining or manipulating motivational cognition, behavior, and affect. By adopting a self-regulation perspective, this paper discusses the ways motivation develops within and across academic tasks and situations, as well as the ways learners can be supported to take control of their motivation in those contexts. Applying self-regulation principles in the practice of School Psychology means to consider the role of situation, context, and learners’ socio-historical experiences while empowering learners to focus attention on things they can control.

A Metacognitive Instructional Guide to Support Effective Studying Strategies

Metacognition—the processes whereby learners assess and monitor their progress in learning (metacognitive monitoring, MM) and use these judgements of learning to make choices about what to study in the future (metacognitive control, MC)—has been shown to be beneficial to learning. However, effective learning also relies on metacognitive knowledge (MK)—that is, students’ knowledge about effective study strategies and how to employ them. Few students receive explicit in-class instruction on these topics. Here, we explore if an online instructional guide, which includes information about evidence-based study strategies, example questions for self-testing, and a study calendar to help regulate timing of studying can effectively teach MK to improve performance.While it is unclear if the online instructional guide was related to increases in MK, MM, and MC, we did observe benefits to student performance, particularly in highly anxious students on high-stake assessments such as the final examination. Future research should seek to understand how students were engaging with the guide and how the nature of the engagement impacted their study strategies.

Cognition, Metacognition, and Self-Regulated Learning

Psychology’s attention to mental events took root in the middle of the 19th century and grew through studies of learning, forgetting, and problem solving. Following several decades during which behaviorism dominated the field, cognitive studies of learning rapidly expanded after the mid-1960s. Foci for research concerned how learners acquire different kinds information, particularly declarative knowledge, procedural knowledge, and schemas, and identifying cognitive operations learners can apply to transform experience into knowledge. What learners know significantly shapes what they learn. Prior knowledge often benefits learning, but inaccurate knowledge, called misconceptions, and skills applied indiscriminately can impede it. Effort to learn, called cognitive load, is not a unary concept. Designing learning tasks to focus cognition in ways germane to content is one key to effective instruction. Learners can think about their cognition and its properties. This is metacognition. Examples include judgments of whether and what is learned, planning shaped by the relative success in tasks and affective experiences, and decisions to abandon risky or unproductive tasks. Measures of metacognition, predominantly learners’ reports as opposed to direct indicators, correlate modestly with achievement, but this may reflect that students are not often educated in study tactics and learning strategies. Metacognition is a key factor in learners’ decisions about which study tactics and learning strategies they use, and a challenge learners face is overcoming overconfidence about what they know. The metacognitive decision-making event is modeled as an If–Then production. Metacognitive control of how learners choose to go about learning is conditional on metacognitive monitoring of conditions the learner believes will influence learning processes and outcomes. When learners experiment with approaches to learning, they engage in self-regulated learning (SRL). SRL is a very energetic area of research that spans investigations into learners’ metacognition about conditions for learning, operations on information, products resulting from those operations, and evaluations of products in terms of standards the learner holds; the COPES model. Like its foundation in metacognition, SRL also correlates modestly with achievement and is similarly challenged by relying on learners’ self-reports about SRL. However, learners can be taught how to better apply SRL which may realize benefits to achievement.

Learning strategies in primary school-age students

The purpose of this research was to examine the relationship between personality traits, motivation and learning strategies of primary school students. A total of 193 students filled out Personality Traits Questionnaire for Children, Goal Orientations Questionnaaire and The Learning Strategies Questionnaire. Results showed that male students, compared to female students, were more inclined to goals aimed at others and non-academic goals. Female students, compared to male students, used meta-cognitive control and deep processing more often. Younger students rated goals aimed at themselves and others more important, and used all three types of learning strategies more often. Regression analyses showed that both personality traits and students’ goal orientations significantly contributed to all of the learning strategies variance. Conscientiousness and emotional stability were the most important predictors among Big five personality traits. Self-oriented goals were significant predictors of metacognitive control and deep processing, while goals oriented at others significantly predicted surface processing.

A Post-Encoding Pre-Production Reinstatement (PEPPR) Model of Dual-List Free Recall

Recent events are easy to recall, but they also interfere with recall of more distant, non-recent events. Many computational models recall non-recent memories by using the context associated with those events as a cue. But some models do little to explain how people initially activate non-recent contexts in the service of accurate recall. We addressed this limitation by evaluating two candidate mechanisms within the Context-Maintenance and Retrieval model. The first is a Backward-Walk mechanism that iteratively applies a generate/recognize process to covertly retrieve progressively less recent items. The second is a Post-Encoding Pre-Production Reinstatement (PEPPR) mechanism that formally implements a metacognitive control process that reinstates non-recent contexts prior to retrieval. Models including these mechanisms make divergent predictions about the dynamics of response production and monitoring when recalling non-recent items. Before producing non-recent items, Backward-Walk cues covert retrievals of several recent items, whereas PEPPR cues few, if any, covert retrievals of that sort. We tested these predictions using archival data from a dual-list externalized free recall paradigm that required subjects to report all items that came to mind while recalling from the non-recent list. Simulations showed that only the model including PEPPR accurately predicted covert recall patterns. That same model fit the behavioral data well. These findings suggest that self-initiated context reinstatement plays an important role in recall of non-recent memories and provides a formal model that uses a parsimonious non-hierarchical context representation of how such reinstatement might occur.

Metacognition: ideas and insights from neuro- and educational sciences

Metacognition comprises both the ability to be aware of one’s cognitive processes (metacognitive knowledge) and to regulate them (metacognitive control). Research in educational sciences has amassed a large body of evidence on the importance of metacognition in learning and academic achievement. More recently, metacognition has been studied from experimental and cognitive neuroscience perspectives. This research has started to identify brain regions that encode metacognitive processes. However, the educational and neuroscience disciplines have largely developed separately with little exchange and communication. In this article, we review the literature on metacognition in educational and cognitive neuroscience and identify entry points for synthesis. We argue that to improve our understanding of metacognition, future research needs to (i) investigate the degree to which different protocols relate to the similar or different metacognitive constructs and processes, (ii) implement experiments to identify neural substrates necessary for metacognition based on protocols used in educational sciences, (iii) study the effects of training metacognitive knowledge in the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature from educational sciences regarding the domain-generality of metacognition.

Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning

The importance of learning design in education is widely acknowledged in the literature. Should learners make effective use of opportunities provided in a learning design, especially in online environments, previous studies have shown that they need to have strong skills for self-regulated learning (SRL). The literature, which reports the use of learning analytics (LA), shows that SRL skills are best exhibited in choices of learning tactics that are reflective of metacognitive control and monitoring. However, in spite of high significance for evaluation of learning experience, the link between learning design and learning tactics has been under-explored. In order to fill this gap, this paper proposes a novel learning analytic method that combines three data analytic techniques, including a cluster analysis, a process mining technique, and an epistemic network analysis. The proposed method was applied to a dataset collected in a massive open online course (MOOC) on teaching in flipped classrooms which was offered on a Chinese MOOC platform to pre- and in-service teachers. The results showed that the application of the approach detected four learning tactics (Search oriented, Content and assessment oriented, Content oriented and Assessment oriented) which were used by MOOC learners. The analysis of tactics’ usage across learning sessions revealed that learners from different performance groups had different priorities. The study also showed that learning tactics shaped by instructional cues were embedded in different units of study in MOOC. The learners from a high-performance group showed a high level of regulation through strong alignment of the choices of learning tactics with tasks provided in the learning design. The paper also provides a discussion about implications of research and practice.