Relation between students' expectations about their grade and metacognitive monitoring and a deeper understanding of metacognitive judgments
| Autor | Antonio P. Gutierrez de Blume, Diana Montoya |
| Cargo | Associate Professor in the Department of Curriculum, Foundations, and Reading, Georgia Southern University, United States. ORCID: https://orcid. org/0000-0001-6809-1728 P.O. Box 8144, Statesboro, GA 30460-8144, United States. Doctor of Philosophy in Educational Psychology from the University of Nevada, Las Vegas (United States). Email:... |
| Páginas | 13-31 |
14
| Universidad de san BUenaventUra, sede Bogotá | Psychologia: avances de la disciPlina | FacUltad de Psicología |
antonio P. gUtierrez de BlUme , diana marcela montoya londoño
planning, and evaluation. Educators should explicitly teach metacognitive monitoring skills to improve students’ self-
regulated learning.
Key words. Metacognition; Absolute accuracy; Absolute bias; Mixed method (Source: PsycINFO Thesaurus).
relaciÓN eNtre las eXPectatiVas De los
estuDiaNtes soBre su Nota y el moNitoreo
metacogNitiVo y uNa comPreNsiÓN más ProFuNDa
De los Juicios metacogNitiVos
Resumen
La metacognición es un proceso importante de pensamiento de orden superior para un aprendizaje exitoso. El
presente estudio investigó la relación entre las expectativas de los estudiantes sobre su nota (expresadas como puntua-
ciones de diferencia entre la nota esperada y la nota real) (N = 65) y su precisión y sesgo de monitoreo metacognitivo
y el grado en que estas diferencias en la nota esperada versus la nota real predijeron la precisión y el sesgo, empleando
un diseño de investigación secuencial explicativo cuantitativo-CUALITATIVO de método mixto. El estudio también exploró
cómo los estudiantes desarrollan y refinan juicios metacognitivos y los tipos de estrategias que emplean durante este
proceso. Los resultados revelaron que había relaciones significativas entre las diferencia de puntajes en la nota esperada
versus la nota real y la precisión y el sesgo (r = .02 to r = .89, en valor absoluto), y que estas diferencia de puntajes
predijo significativamente tanto la precisión (R2 = .52) como el sesgo (R2 = .69). Además, los hallazgos cualitativos
revelaron que había diferencias en la forma en que los estudiantes desarrollaban y refinaban juicios metacognitivos en
función de cuatro aspectos del aprendizaje: esfuerzo / preparación, selección / implementación de estrategias, planifi-
cación y evaluación. Los docentes deben enseñar explícitamente habilidades de monitoreo metacognitivo para mejorar
el aprendizaje autorregulado de los estudiantes.
Palabras clave: metacognición; precisión absoluta; sesgo absoluto; método mixto; desempeño (Fuente: PsycINFO
Tesauro).
Introduction
Research on metacognition has involved two
main trends. Some studies focus on the two classic com-
ponents of metacognition, metacognitive knowledge and
regulation. Others shifted to a new paradigm that recog-
nizes individual differences in metacognitive behavior
and the development of metacognitive profiles, which has
allowed researchers to better understand the importan-
ce of relatively obscure aspects such as metacognition’s
relation to personality, self-concept, types and levels of
processing, rhythms in learning, and even locus of con-
trol, among other aspects (Gutierrez de Blume & Monto-
ya, 2020; Gutierrez de Blume et al., in press). However,
both trends help advance researchers’ understanding of
how metacognition operates in the learning, problem
solving, and reasoning skills in students of all ages, do-
mains, tasks, and contexts (Azevedo, 2020).
The latest research on metacognition is oriented
to the development of research more focused on not only
explaining, but also understanding the different mecha-
nisms regarding how students learn from a metacognitive
perspective. This orientation examines monitoring as a
complex, multilevel process with different layers predi-
cated on a theoretical model that explains the nuanced
role of metacognitive monitoring accuracy and error in
learning-judgment development (Gutierrez de Blume,
2020; Gutierrez et al., 2016; Gutierrez de Blume et al.,
2021). Thus, research underscores the relevance of spe-
cifying the underlying aspects of monitoring and control
processes in the development of first- and second-order
metacognitive judgments such as predictive, concurrent,
15
Grade expectations and metacoGnitive monitorinG
| psychol. | BoGotá, colomBia | vol. 15 | n.° 2 | p. 13-31 | Julio - diciemBre| 2021 | issn 1900-2386 |
and postdictive. Further, research exists that links “war-
mer” aspects of cognition, including variables such as
motivation, attributional style (Gutierrez & Price, 2017),
and affect and personality (Gutierrez de Blume & Mon-
toya, 2020) that seem to guide the level of metacognitive
awareness during learning. Increasing understanding of
potentially generalizable metacognitive skills, applicable
in any domain, promises to benefit students in many
areas of knowledge acquisition and in everyday life. This
is the case because being able to accurately monitor one’s
progress towards a learning goal and clearly understan-
ding task demands, as a form of formative-continuous
evaluation, can, presumably, improve the effectiveness of
later learning episodes (Dunlosky & Rawson, 2019).
Interestingly, many studies regarding metacogni-
tive monitoring and the development of metacognitive
judgments have employed a quantitative approach focu-
sed on investigating relative or absolute monitoring jud-
gments. These types of monitoring judgments describe
the relation between performance in an evaluation task
and learners’ confidence in performance judgments
(Schraw et al., 2013; Gutierrez de Blume et al., 2021).
These concepts are explored next.
Research on metacognitive monitoring has focused
on estimating the level of performance, accuracy, and con-
fidence with various measures. Metacognitive judgments
can be understood, for instance, in terms of absolute and
relative accuracy (Schraw, 2009a, 2009b; Schraw et al.,
2013), as well as how accuracy is determined such as the
Goodman-Kruskal gamma correlation (Nelson, 1996).
Regardless of how researchers measure monitoring or how
accuracy is determined, measurement follows a typical
format in which learners answer a test item and provide
a confidence rating on performance on that item (local),
or they provide confidence ratings holistically for an enti-
re assessment to compare against overall performance on
the assessment (global), which can also be done prior to
(predictions) and/or after (postdictions) the assessment
itself (Follmer & Clariana, 2020; Schraw, 2009a, 2009b).
Monitoring accuracy is subsequently calculated based on
different computational formulas using frequencies of two
or more of the four mutually exclusive cells in a 2x2 data
matrix, where cell a corresponds to correct performan-
ce that is judged to be correct; cell b corresponds to in-
correct performance that is judged to be correct; cell c
corresponds to correct performance that is judged to be
incorrect; and cell d corresponds to incorrect performan-
ce that is judged to be incorrect (Gutierrez et al., 2016;
Gutierrez de Blume et al., 2021; Schraw et al., 2013).
Thus, cells a and d in this framework correspond to ac-
curate monitoring whereas cells b (referred to variously as
overconfidence or an illusion of knowing [Serra & Metcalfe,
2009]) and c (referred variously as underconfidence or an
illusion of not knowing [Serra & Metcalfe, 2009]) correspond
to erroneous monitoring.
Schraw and his colleagues (Gutierrez et al., 2016;
Schraw et al., 2013; Schraw et al., 2014) examined di-
fferent statistical measures and how they relate. These
included parametric monitoring indices like sensitivity,
specificity, d’ (“d prime”), and the G-index, and non-
parametric ones such as the odds-ratio, gamma, kappa,
and Sokal distance. The main objective of this series of
studies was to determine not only different latent di-
mensions of the metacognitive monitoring process, but
also to uncover which of these measures explain more
variation in the data, and if the use of multiple measures
provides a more complete result. Results of these stu-
dies provided a greater understanding of the underlying
mechanisms of metacognitive monitoring. Nevertheless,
a major shortcoming of these works is that they were
all quantitative in nature, thereby prohibiting a deeper,
richer understanding of metacognitive monitoring due to
a lack of process-oriented data. This necessitates addi-
tional research employing qualitative and mixed method
research designs.
However, relatively few studies exist that employ
alternate research designs. Exceptions to this include an
investigation carried out in South Africa with a group of
students of a basic chemistry course (Mathabathe, 2019;
Mathabathe & Potgieter, 2014). Mathabathe and Potgie-
ter (2014) sought to establish whether students’ over-
confidence before instruction was adjusted after instr uc-
tion. Results revealed that most of the students were too
confident in their judgments of performance. In both
the pre- and post-tests, the quantitative results showed
that students with little preparation were slow to develop
accurate metacognitive monitoring skills within the clas-
sroom environment that did not include instruction fo-
cused on the development of such skills. Along a similar
vein, Mathabathe (2019) explored the justifications that
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