Notes

Metacognition, achievement goals, study strategies and academic achievement: pathways to achievement
http://link.springer.com/article/10.1007/s11409-008-9022-4

Self-regulation is an important aspect of student learning in academic performance. According to Wolters (2003) ‘Self-regulated learners are autonomous, reflective and efficient learners, and have the cognitive and metacognitive abilities as well as the motivational beliefs and attitudes needed to understand, monitor and direct their own learning (p. 189).

The major models of self-regulated learning differ in their specific perspectives on self-regulation and employ different terminologies (e.g. Boekaerts 1997; Pintrich 2004; Schunk 2001; Winne 2001; Zimmerman and Schunk 2001). However, there is consensus among theorists that self-regulated learning involves goal setting, metacognition and the use of (meta)cognitive strategies.



Working memory capacity and disfluency effect: an aptitude-treatment-interaction study
http://link.springer.com/article/10.1007/s11409-015-9149-z

desirable difficulties:
- Tverski & Kahneman -two distinct processing systems in the working memory: System 1, which leads to a quick and effortless, more associative and intuitive processing, and System 2, which leads to a slow and effortful, more analytic and deliberate processing. Whereas perceiving information processing as easy activates System 1, perceiving information processing as difficult activates System 2. Thus, increasing the perceived difficulty associated with a cognitive task (i.e., disfluency) stimulates deeper processing and a more analytic and elaborative thinking rather than a heuristic and intuitive reasoning

cognitive load theory: intrinsic (ICL), extraneous (ECL), and germane CL (GCL).
ICL - fixed by a given task. depends on the learner’s prior knowledge. With more expertise a learner is able to construct meaningful chunks of information, reducing the amount of single unrelated elements in working memory and therefore will experience less ICL.
ECL - cognitive resources that are not directed to the learning task itself, but to additional demands like navigating, searching etc.
GCL - learner’s activities which contribute to a deeper comprehension of instructional material by processing, construction, and automation of schemas. desirable to increase this type of load, e.g., by activating the learner with encouraging and motivating tasks.
All three types of load are additive, e.g., together they constitute the overall amount of CL a learner is experiencing during a learning task. This CL burdens the working memory whose capacity is limited (Cowan 2001; Hasselhorn and Gold 2009; Miller 1994), To prevent an overload which would inhibit learning a lot, it would be most efficient to reduce ECL and to enhance GCL to foster learning

disfluency effect and its additional load is contradictory to the Cognitive Load Theory

Towards efficient measurement of metacognition in mathematical problem solving
http://link.springer.com/article/10.1007/s11409-012-9088-x

- metacognition probably is quite domain-specific (Veenman and Spaans 2005). The regulation of cognitive activities useful in one domain (e.g. making a summary when reading) may not be directly transferable to another domain (e.g. solving a math problem).

-Metacognition measured on-line of (during - e.g. think aloud) the learning process typically explains about 37 percent of the variance in learning
- One of the most frequently used categories of off-line measures is self-report questionnaires in which students are asked to report on their own metacognition. Some examples of frequently used questionnaires are the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich and De Groot 1990), the Learning and Study Strategies Inventory (LASSI; Weinstein et al. 1988) and the Metacognitive Awareness Inventory (MAI; Schraw and Dennison 1994).
- students are typically quite inaccurate in reporting their own metacognitive behavior. In a review of 21 studies using self-report questionnaires, the mean variance explained by metacognition in learning performance did not exceed 3 %
-off-line, generally formulated metacognitive questionnaires may be more adequate to assess metacognitive knowledge as opposed to metacognition applied during the learning process (Desoete 2007; Greene and Azevedo 2010).

On-line measures on the other hand have the advantage of measuring metacognition concurrent with the learning behavior, thus giving more insight in the actual use of metacognition affecting learning behavior. One way to infer on-line information about students’ metacognition, apart from using think-aloud protocols as discussed before, is to assess the actions or observable occurrences of events that a student performs such as drawing schemes, taking notes or clicking a button
e.g. instructing students to make a drawing, can clarify how they think about solving a word problem

Students’ problem visualizations in a drawing can be either schematic or pictorial. In schematic visualizations the structural relationships between variables in a problem are provided in a sketch, diagram or schema. In pictorial visualizations the elements in a problem are depicted without any relevant relationships between the elements. Pictorial visualizations show a student does not yet know how to explore the problem towards a useful solution, thus indicating low metacognitive regulation. Visualizations that schematize problem situations on the other hand, are an expression of sophisticated metacognitive regulation in mathematical problem solving, especially giving insight in the episodes of analyzing and exploring a problem

The correlation between the use of schematic visualizations and problem solving in mathematics ranges from about r = 0.3 (explained variance 9 %) (Edens and Potter 2007) to about r = 0.7 (explained variance 49 %)

Another way to collect information on metacognitive processes on-line of the learning task is through performance (or calibration) judgments (Schraw 2009). More specifically, by assessing the accuracy of students’ judgments of their own performance. The ability to judge one’s performance has been conceptualized as an expression of metacognitive monitoring behavior (Boekaerts and Rozendaal 2010; Efklides 2006). When making on-line prediction judgments, that is to say estimations about performance before solving a problem, a student is especially concerned with the question whether he/she can analyze and categorize a problem. This gives the student a general idea whether he/she will be able to solve the problem or not

In the literature, correlations between judgments of performance and mathematics performance range from about r = 0.4 to 0.6 (explained variance 16 % to 36 %)

Think aloud protocols (Veenman)

VisA instrument - students are asked to divide their problem solving over various steps:
1) Read the problem and rate your confidence for finding the correct answer (without calculating the answer);
2) Make a sketch which can help you solve the problem;
3)Solve the problem and fill in the answer;
4)Rate your confidence for having found the correct answer;

scoring:1)If students’ prediction judgments are correct (i.e. students predicted they could solve the problem correct and indeed did; or they predicted they could not solve the problem and indeed gave the wrong answer) students get 1 point. If students’ predictions are uncertain (orange traffic light) or incorrect (i.e. they predicted they could solve the problem correctly but in fact give the wrong answer; or they predicted they could not solve the problem but solved the problem correctly) they score 0 points.

2)For the visualization of the problem, students get 0 points if they made a pictorial sketch not depicting any of the important relationships in the problem, 0.5 point is awarded to sketches which are partly pictorial but have some schematic or mathematical features, and 1 point is given to primarily schematic visualizations.

3)The postdiction judgments of the students are scored in the same manner as step 1. Thus, students get 1 point when the postdiction is correct and 0 points when the postdiction does not match the answer.





Improving metacognition in the classroom through instruction, training, and feedback
https://link.springer.com/article/10.1007/s11409-015-9142-6
focusing on calibration
training and practice can be ineffective - requires feedback.
lower-performing students were capable of improving calibration, but only when incentives

lower performing students are more overconfident than higher performing students. Further, the results suggest that instruction on the concepts of metacognition and calibration (including practice making judgments and receiving feedback) is associated with improved calibration on subsequent exams. Study 2 showed differences in judgments and performance across exams for the class that received feedback on the first exam compared to the class that did not receive feedback. Therefore, we suggest that feedback could be an important factor in improving students’ metacognition in the classroom. Calibration was poorer for Exam 1; it was not until the students took the first exam (and received the incentives and feedback) that they were able to attenuate the overconfidence effect. Study 2 showed that those who received feedback made more changes than those who did not, particularly those in the D/F group. Importantly, the changes in metacognition were due to changes in both performance and judgments, both of which contribute to calibration. Feedback can change both of these components by providing an indication that additional study time is warranted as well as information about the accuracy of the judgment.

Metacognition is often resistant to improvements because students have difficulty translating an awareness of learning deficits into increased self-regulation (Cao and Nietfeld 2007). Lower performing students are typically at a greater disadvantage because they lack both the awareness of their own learning deficits (Hacker et al. 2000; Miller and Geraci 2011b) and the skills and knowledge necessary to engage in metacognitive processes (Veenman et al. 2006). However, the current studies suggest that lower performing students can improve both performance and judgments when sufficiently trained and motivated to do so.

In order to improve calibration, and ultimately performance, it is necessary to provide students with both the knowledge of what calibration is as well as the proper motivation to be accurately calibrated in addition to providing feedback. The data reveal that students are able to regulate their study behaviors resulting in improved performance and calibration. Thus, it appears that making students aware of their misconceptions about their performance through education and feedback can positively affect their performance in the classroom. Psychology instructors are uniquely equipped and have the opportunity to provide instruction on these topics as well as opportunities for students to practice metacognitive judgments. Although a lecture on the topic as carried out in this study may not be possible in every psychology course, basic instruction on the concepts in combination with the feedback and incentives may be able to improve calibration and performance, when possible, for learners of all abilities.