Effectiveness of motivational regulation: Dependence on specific motivational problems

Engelschalk, Tobias; Steuer, Gabriele; Dresel, Markus

doi:10.1016/j.lindif.2016.10.011

Cited by 40 publications

(44 citation statements)

References 31 publications

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“…Nevertheless, when discussing these findings, it is to be kept in mind that in this study we focused motivational regulation on the regulation of one's task value. Future research might expand motivational regulation prompts by investigating other motivational regulation strategies (e.g., goal based or expectancy-focussed strategies; Engelschalk, Steuer & Dresel, 2016;Schwinger & Otterpohl, 2017) that might have different effects. Therefore, an investigation of such strategies would contribute to a more detailed understanding of the potential of motivational regulation prompts.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, this assumption can be theoretically decomposed in two presumptions that are well supported by theoretical and empirical work as described above: (a) prompting is an effective measure to enhance strategy application while learning with digital media, and (b) task value can be regulated effectively by applying motivational regulation strategies (e.g., Bannert & Devolder et al, 2012;Engelschalk et al, 2016;Schwinger et al, 2009;Wolters, 2003).…”

Section: Srl With Digital Mediamentioning

confidence: 99%

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Supporting Self-Regulated Learning With Digital Media Using Motivational Regulation and Metacognitive Prompts

Daumiller

Dresel

2018

The Journal of Experimental Education

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Studying with digital media, learners often struggle because of inadequate self-regulation. Previous research presented clear evidence of metacognitive prompts being effective in supporting learning with digital media. This study examines the potential of motivational regulation prompts, which are assumed to additionally support self-regulated learning. During a 50-min learning session in a digital media learning environment, 215 university students received either no prompts, only metacognitive prompts, only motivational regulation prompts, or both types of prompts. Task value, metacognitive control, task-related learning activities, and knowledge were assessed at a pretest, posttest, and follow-up. The results replicated known positive effects of metacognitive prompts and revealed additional supportive effects of motivational regulation prompts on all dependent variables. Path modeling of the experimentally induced changes was in line with a theoretical model specifying proximal and distal effects of both prompts. Altogether, this indicates that especially motivational regulation prompts could be an effective scaffold to support SRL with digital media.

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Section: Discussionmentioning

confidence: 99%

Section: Srl With Digital Mediamentioning

confidence: 99%

Supporting Self-Regulated Learning With Digital Media Using Motivational Regulation and Metacognitive Prompts

Daumiller

Dresel

2018

The Journal of Experimental Education

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“…Motivation represents beliefs and attitudes that influence the use and development of cognition and metacognition (Schraw et al 2006). Motivation regulation strategies directly address specific motivational problems: for example, if a task is too difficult or boring for a student (Engelschalk et al 2016;Wolters and Rosenthal 2000). Accordingly, motivation regulation strategies are task-specific (e.g., Donker et al 2014) because they always focus on a particular motivational problem (Wolters and Rosenthal 2000).…”

Section: The Role Of Metacognitive Skills In Self-regulated Learningmentioning

confidence: 99%

Transfer of metacognitive skills in self-regulated learning: an experimental training study

Schuster

Stebner

Leutner

et al. 2020

Metacognition Learning

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Training interventions for self-regulated learning foster the use of strategies and skills as well as their transfer to new learning tasks. Because cognitive strategies or motivation regulation strategies are task-specific, their transfer is limited. In contrast, metacognitive skills are task-general and transferable to a wide variety of learning tasks. Questions arise, therefore, as to whether students transfer metacognitive skills spontaneously and how to support metacognitive skill transfer. Previous research shows that hybrid training, which addresses both metacognitive skills and cognitive strategies, supports near transfer. However, it is not clear whether hybrid training also fosters far transfer of metacognitive skills. In investigating this research question, 233 fifth-grade students were randomly assigned to six different conditions: two hybrid-training conditions (metacognitive skills and one out of two cognitive strategies), two non-hybrid training conditions (“only” one out of two cognitive strategies), and two control training conditions (neither metacognitive skills nor cognitive strategies). After 15 weeks of training, transfer of metacognitive skills to learning tasks similar to training tasks (near transfer) was tested. In the following 15 weeks, all students received a second, non-hybrid training involving a new cognitive strategy. Far transfer of metacognitive skills to the new cognitive strategy was tested afterward. The results show that hybrid training, compared to non-hybrid and control training, improved both students’ near and far transfer of metacognitive skills. Moreover, cognitive strategy use increased in at least one of the hybrid-training conditions. However, since the level of metacognitive skills use remained low, further means to support transfer are discussed.

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“…Motivation in (self-regulated) learning processes Motivation is considered to be a result of an interaction between environmental and individual factors (Cook and Artino Jr 2016;Hartnett et al 2011;Keller 2008b;Svinicki and Vogler 2012). Thus, internal as well as external factors can influence a person's motivation, such as self-efficacy beliefs (Bandura 1977;Zimmerman et al 2017), perceived autonomy (Deci and Ryan 2008;Deci et al 1996), attributions (Schunk 2008;Weiner 1985), value of the task, and expected difficulty in reaching the goal (Eccles and Wigfield 2002;Engelschalk et al 2016;Wigfield et al 2009), goal orientations (Elliot 2005;Elliot and Hulleman 2017), academic self-concept, or the design of the learning environment (Keller and Suzuki 2004).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

The importance of students’ motivational dispositions for designing learning analytics

Schumacher

2018

J Comput High Educ

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Depending on their motivational dispositions, students choose different learning strategies and vary in their persistence in reaching learning outcomes. As learning is more and more facilitated through technology, analytics approaches allow learning processes and environments to be analyzed and optimized. However, research on motivation and learning analytics is at an early stage. Thus, the purpose of this quantitative survey study is to investigate the relation between students' motivational dispositions and the support they perceive through learning analytics. Findings indicate that facets of students' goal orientations and academic self-concept impact students' expectations of the support from learning analytics. The findings emphasize the need to design highly personalized and adaptable learning analytics systems that consider students' dispositions and needs. The present study is a first attempt at linking empirical evidence, motivational theory, and learning analytics.

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Effectiveness of motivational regulation: Dependence on specific motivational problems

Cited by 40 publications

References 31 publications

Supporting Self-Regulated Learning With Digital Media Using Motivational Regulation and Metacognitive Prompts

Supporting Self-Regulated Learning With Digital Media Using Motivational Regulation and Metacognitive Prompts

Transfer of metacognitive skills in self-regulated learning: an experimental training study

The importance of students’ motivational dispositions for designing learning analytics

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