Ace Your Self-Study: A Mobile Application to Support Self-Regulated Learning.

Without guidance, students typically overestimate their understanding and memory of learning materials, which can have detrimental effects on the learning process. However, most students do not receive guidance or instruction about how to study. Moreover, students are largely unaware of strategies to self-regulate their learning and study effectively. Research has shown that prompting both cognitive and metacognitive strategies is effective to support self-regulated learning (SRL). Therefore we developed a mobile application, the Ace your self-study app, to prompt both cognitive and metacognitive strategies to support learning processes. In this article a theoretical background, description of the app's features and design choices are presented. Also, data from the application in presented to give provide an idea of how the app has been used.

Exploring Students' Use of a Mobile Application to Support Their Self-Regulated Learning Processes.

Being able to self-regulate one's learning is essential for academic success but is also very difficult for students. Especially first year students can be overwhelmed with the high study load and autonomy in higher education. To face this challenge, students' monitoring and self-regulated learning (SRL) processes are crucial. Yet, often students are not aware of effective SRL strategies or how to use them. In this study, the use of a mobile application with gamification elements (i.e., Ace Your Self-Study App) to support first-year university students' SRL processes was investigated. In Study 1a, the Ace your self-study app was implemented in a first-year psychology course, and students' SRL skills, motivation, self-efficacy, app use and satisfaction, and performance were measured. The results showed a significant increase in autonomous motivation, controlled motivation, and metacognitive self-regulation skills (MSR-R) across the 5-week course. Moreover, students who used the mobile application with gamified elements showed higher autonomous motivation. Nevertheless, most students used the app only for a limited number of self-study sessions. In Study 1b, students' self-study experiences were captured using focus group interviews to shed some more light on why students did or did not use the app. The results show that if students feel they do not need support for their SRL processes during self-study, they are less inclined to use the app. Specifically, regarding using study strategies, it was found that only if students' strategies do not work well in their perception, they feel the need to change their way of studying and choose another strategy. These results are discussed in the context of theory on SRL and how to support it.

Establishing a Scientific Consensus on the Cognitive Benefits of Physical Activity.

Research suggests that physical activity can be used as an intervention to increase cognitive function. Yet, there are competing views on the cognitive effects of physical activity and it is not clear what level of consensus exists among researchers in the field. The purpose of this study was two-fold: Firstly, to quantify the scientific consensus by focusing on the relationship between physical activity and cognitive function. Secondly, to investigate if there is a gap between the public's and scientists' interpretations of scientific texts on this topic. A two-phase study was performed by including 75 scientists in the first phase and 15 non-scientists in the second phase. Participants were asked to categorize article abstracts in terms of endorsement of the effect of physical activity on cognitive function. Results indicated that there was a 76.1% consensus that physical activity has positive cognitive effects. There was a consistent association between scientists' and non-scientists' categorizations, suggesting that both groups perceived abstracts in a similar fashion. Taken together, this study provides the first analysis of its kind to evaluate the level of consensus in almost two decades of research. The present data can be used to inform further research and practice.

Training self-assessment and task-selection skills to foster self-regulated learning: Do trained skills transfer across domains?

Students' ability to accurately self-assess their performance and select a suitable subsequent learning task in response is imperative for effective self-regulated learning. Video modeling examples have proven effective for training self-assessment and task-selection skills, and-importantly-such training fostered self-regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task-selection rule or a more general heuristic task-selection rule in biology would transfer to self-regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task-selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self-regulated learning in math. Future research should investigate how to support transfer of task-selection skills across domains.

The Association between Motivation, Affect, and Self-regulated Learning When Solving Problems.

Self-regulated learning (SRL) skills are essential for learning during school years, particularly in complex problem-solving domains, such as biology and math. Although a lot of studies have focused on the cognitive resources that are needed for learning to solve problems in a self-regulated way, affective and motivational resources have received much less research attention. The current study investigated the relation between affect (i.e., Positive Affect and Negative Affect Scale), motivation (i.e., autonomous and controlled motivation), mental effort, SRL skills, and problem-solving performance when learning to solve biology problems in a self-regulated online learning environment. In the learning phase, secondary education students studied video-modeling examples of how to solve hereditary problems, solved hereditary problems which they chose themselves from a set of problems with different complexity levels (i.e., five levels). In the posttest, students solved hereditary problems, self-assessed their performance, and chose a next problem from the set of problems but did not solve these problems. The results from this study showed that negative affect, inaccurate self-assessments during the posttest, and higher perceptions of mental effort during the posttest were negatively associated with problem-solving performance after learning in a self-regulated way.