Self-explaining steps in problem-solving tasks to improve self-regulation in secondary education.

2018 ◽  
Vol 110 (4) ◽  
pp. 578-595 ◽  
Author(s):  
Martine Baars ◽  
Claudia Leopold ◽  
Fred Paas
Keyword(s):  
Secondary Education ◽  
Problem Solving ◽  
Self Regulation

El acceso a la función pública docente en Educación Infantil, Primaria y Secundaria

2020 ◽  
pp. 138-159
Author(s):  
Goiatz Aramendi Lekuona ◽  
Pello Aramendi Jauregi
Keyword(s):  
Secondary Education ◽  
Teaching Strategies ◽  
Civil Service ◽  
Self Regulation ◽  
Learning To Learn ◽  
Primary And Secondary Education ◽  
Theoretical Part ◽  
Close Relationship ◽  
Attention To Diversity ◽  
Diversity Evaluation

The research presented below aims to describe and analyse the teaching strategies and supports obtained by teachers in the province of Gipuzkoa who sit competitive examinations to access the teaching civil service in Infant, Primary and Secondary Education. This study opted for a sequential explanatory design with 469 candidates. Teachers who have passed the official examinations placed special emphasis on the first test (theoretical part and practical exercise), took into account the criteria of evaluation of the examinations and prioritised issues such as attention to diversity, evaluation, the competence of learning to learn, self-regulation of learning and the design of teaching units. In addition, they received valuable help from relatives, people linked to teaching with whom they have a close relationship, and work colleagues.

scholarly journals The Role of Mental Effort in Fostering Self-Regulated Learning with Problem-Solving Tasks

2020 ◽  
Vol 32 (4) ◽  
pp. 1055-1072 ◽  
Author(s):  
Tamara van Gog ◽  
Vincent Hoogerheide ◽  
Milou van Harsel
Keyword(s):  
Problem Solving ◽  
Self Regulation ◽  
Mental Effort ◽  
Future Research ◽  
Task Sequence ◽  
Self Monitoring ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
And Mathematics

Abstract Problem-solving tasks form the backbone of STEM (science, technology, engineering, and mathematics) curricula. Yet, how to improve self-monitoring and self-regulation when learning to solve problems has received relatively little attention in the self-regulated learning literature (as compared with, for instance, learning lists of items or learning from expository texts). Here, we review research on fostering self-regulated learning of problem-solving tasks, in which mental effort plays an important role. First, we review research showing that having students engage in effortful, generative learning activities while learning to solve problems can provide them with cues that help them improve self-monitoring and self-regulation at an item level (i.e., determining whether or not a certain type of problem needs further study/practice). Second, we turn to self-monitoring and self-regulation at the task sequence level (i.e., determining what an appropriate next problem-solving task would be given the current level of understanding/performance). We review research showing that teaching students to regulate their learning process by taking into account not only their performance but also their invested mental effort on a prior task when selecting a new task improves self-regulated learning outcomes (i.e., performance on a knowledge test in the domain of the study). Important directions for future research on the role of mental effort in (improving) self-monitoring and self-regulation at the item and task selection levels are discussed after the respective sections.

Collaborative Solution Discovery: A Problem Solving Process

2019 ◽  
pp. 100-103
Author(s):  
Katharine Clemmer
Keyword(s):  
Problem Solving ◽  
Learning Environment ◽  
Real Time ◽  
School System ◽  
Mathematical Thinking ◽  
Self Regulation ◽  
Math Learning ◽  
New Approach ◽  
Problem Solving Process ◽  
The Way

Loyola Marymount University (LMU) has developed a new approach to problem solving, Collaborative Solution Discovery (CSD), to help practitioners in a school system leverage their individual passions in a way that grows students’ positive math identity through mathematical thinking, problem solving, and self-regulation. By focusing on how students and teachers interact with each other in real-time in an ideal classroom, practitioners take ownership of a process to guide their students in growing their positive math identity and thus taking ownership of their own math learning. Practitioners measure progress along the way through metrics that are created, defined, used, and continually refined by themselves to attain their ideal math learning environment. The entire CSD process results in a system that owns ist improvement efforts—improvement efforts that are flexible, adaptable, and sustainable.

Assessing Self-regulation Development through Sharing Feedback in Online Mathematical Problem Solving Discussion

2010 ◽  
pp. 232-247 ◽  
Author(s):  
Bracha Kramarski
Keyword(s):  
Problem Solving ◽  
Mathematical Reasoning ◽  
Mathematical Problem ◽  
Self Regulation ◽  
Sixth Grade ◽  
Mathematical Problem Solving ◽  
Social Feedback ◽  
Self Regulated Learning ◽  
Metacognitive Teaching ◽  
Online Feedback

This study examined the relative efficacies of two different metacognitive teaching methods – problem solving (M_PS) and sharing knowledge (M_SK). Seventy-two Israeli sixth-grade students engaged in online mathematical problem solving and were each supported using one of the two aforementioned methods. M_PS students used a problem-solving and feedback process based on the IMPROVE model (Kramarski & Mevarech, 2003). In contrast, M_SK participants were instructed to reflect and provide feedback on the solution without an explicit model. This study evaluated each method‘s impact on the students’ mathematical online problem solving. It also examined self-regulated learning (SRL) processes by assessing students‘ online feedback using a rubric scheme. Findings indicated that M_PS students outperformed the M_SK students in algebraic knowledge and mathematical reasoning, as well as on various measures of sharing cognitive and metacognitive feedback. The M_SK students outperformed the M_PS students on measures of sharing motivational and social feedback.

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