Scientia et Technica Año XXVI, Vol. 26, No. 04, diciembre de 2021. Universidad Tecnológica de Pereira. ISSN 0122-1701 y ISSN: 2344-7214
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Abstract—This article documents the construction of a taxonomy
of metacognitive activities that describes the metacognitive skills
of university engineering students during problem-solving
learning. The methodology used for the construction of the
taxonomy was developed considering some requirements raised
in the literature, the constant comparison method, and the
execution of seven steps. The construction of the taxonomy was
necessary given that the problem solving learning implies the
participation of metacognitive skills and these are the set of
activities that help the student to monitor and control their
learning. Metacognitive skills must be evaluated to provide
teachers with information to establish their instructing processes,
considering the characteristics of the students. It is important to
build a taxonomy of metacognitive activities to carry out an
appropriate assessment of metacognitive skills that allows
specifying the metacognitive behaviors of the students involved in
the learning process. The constructed taxonomy contains detailed
descriptions of metacognitive activities, facilitating that other
investigations use this instrument. The document is written in
such a way that it becomes a guide for future studies to have a
reference on how to design a taxonomy of metacognitive
activities.
Index Terms—Metacognitive activities, metacognitive skills,
problem solving, taxonomy.
Resumen—Este artículo documenta la construcción de la
taxonomía de actividades metacognitivas que describe las
habilidades metacognitivas de estudiantes universitarios de
ingeniería durante el aprendizaje de la resolución de problemas.
This manuscript was submitted on October 29, 2020, and accepted on
November 23, 2021.
The present study is derived from the doctoral dissertation “Participación
de las habilidades metacognitivas durante el aprendizaje de la resolución de
problemas en la asignatura de simulación de eventos discretos” with code 7-
19-3 from Universidad Tecnológica de Pereira of Doctorado en Didáctica.
M. E. Bernal-Loaiza, Professor in the Department of Industrial
Engineering, of the Universidad Tecnológica Pereira, from Pereira, Colombia
(email: mbernal@utp.edu.co).
M. Castaño-Ramirez, student in the Department of Industrial Engineering,
of the Universidad Tecnológica Pereira, from Pereira, Colombia (email:
manuela.castano@utp.edu.co).
M. Gomez-Suta, Professor in the Department of Industrial Engineering, of
the Universidad Tecnológica Pereira, from Pereira, Colombia (email:
madegomez@utp.edu.co).
R. Iodice, PhD Cognitive & Behavioral Neuroscience, Universidad
Católica de Pereira, from Pereira, Colombia (email:
rosario.iodice@ucp.edu.co).
La metodología usada para la construcción de la taxonomía se
desarrolló teniendo en cuenta algunos requisitos planteados en la
literatura, el método de comparación constante y la realización
de siete pasos. La construcción de la taxonomía fue necesaria
dado que, el aprendizaje de la resolución de problemas implica la
participación de las habilidades metacognitivas y estas son el
conjunto de actividades que ayudan al estudiante a monitorear y
controlar su aprendizaje. Las habilidades metacognitivas deben
ser evaluadas con el fin de brindar a los docentes información
para establecer sus procesos de enseñanza, considerando las
características de los alumnos. Es importante construir una
taxonomía de actividades metacognitivas para ejecutar una
apropiada evaluación de las habilidades metacognitivas que
permita especificar los comportamientos metacognitivos de los
alumnos involucrados durante el aprendizaje. La taxonomía
construida contiene descripciones detalladas de las actividades
metacognitivas, lo que facilita que otras investigaciones la
utilicen. El documento está redactado, de tal forma, que sea una
guía para que futuros estudios posean un referente de cómo
diseñar una taxonomía de actividades metacognitivas.
Palabras claves: Actividades metacognitivas, habilidades
metacognitivas, resolución de problemas, taxonomía.
I. INTRODUCTION
onstructing a taxonomy of metacognitive activities is
important, since it allows the assessment, in a suitable
way, of the metacognitive skills (MS) of students,
characterizing their metacognitive behaviors during the
problem solving (PS) learning.
In this order to construct a taxonomy of metacognitive
activities, it is important to understand that PS learning
requires that the student integrate his/her mathematical
knowledge and the way how to use it; nevertheless, this
integrated approach is a challenging task for students [1]. For
instance, [2] propose that “a student who knows the area
calculation formula of a parallelogram can easily solve a
problem that is aimed at directly calculating the area of a
parallelogram. However, when the student needs to calculate
the area of a parallelogram within a novel type of question,
she/he may fail to transfer prior knowledge to the task at hand
and may not be able to solve the problem”.
Therefore, MS play an important role in PS, since they
“pertain to the acquired repertoire of procedural knowledge
Construction of a Taxonomy of Metacognitive
Activities to Characterize Problem Solving
Learning
Construcción de Taxonomía de Actividades Metacognitivas para Caracterizar el
Aprendizaje de la Resolución Problemas
M. E. Bernal-Loaiza ; M. Castaño-Ramírez ; M. Gómez-Suta ; R. Iodice
DOI: https://doi.org/
Artículo de investigación científica y tecnológica
C
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for monitoring, guiding, and controlling one’s learning and
problem-solving behavior.” [3]. In this order, MS allow
students to interiorize knowledge and their learning activities,
with the purpose of adapting those activities to the situational
demands, thus optimizing their PS results [4], [5].
Nevertheless, students do not acquire MS naturally, either
because they lack opportunities or because they do not see the
importance of investing their efforts in the construction of
such skills [6]-[7]. Therefore, teachers can employ assessment
methods to adapt their teaching strategies according to the
students’ characteristics, in this way, instructors may foster the
MS use [8]-[9].
MS occur through cognitive activities [3], since “one cannot
engage in planning without carrying out cognitive activities,
such as generating problem-solving steps and sequencing
those steps” [10].
The simple fulfillment of cognitive activities does not lead
to having MS; on the contrary, MS occur when metacognitive
activities regulate cognitive activities [3]. In this context, [3]
states that the metacognitive activity resembles a General, and
cognitive activities resemble an army, where the General
cannot win the war without soldiers (cognitive) and neither
can a disorganized army.
The metacognitive activity is essential in novel situations or
when the automatic responses are not adaptive [11], [12].
Consequently, metacognitive activities follow the guidelines
of the metacognitive strategies that allow taking decisions in
compliance with a given objective, selecting pertinent
information, and organizing activities in a logical way [13].
Hence, metacognitive strategies are sequential processes
devoted to monitoring and controlling cognitive activities,
with the purpose of assuring the fulfillment of an objective
[14]. In this order, the metacognitive activity is an executive
function which comprises a set of essential cognitive
processes for the metacognitive regulation of learning [11],
[12].
Several authors [15], [16]-[17] claim that MS are: i)
planning, which is the selection of appropriate strategies, and
the localization of factors affecting performance, ii)
monitoring, which is the possibility to carry out, understand,
and modify the achievement of the task, and iii) evaluation,
which is the verification of the nature of the actions and
decisions taken by the student to identify their efficiency.
The accomplishment of MS involves cognitive and
metacognitive activities, as well as metacognitive strategies.
For this reason, a method which aims to characterize MS
should keep this relationship into account. For example, online
assessment facilitates the evaluation of MS, considering the
mentioned relationship.
The findings of online methods are strong predictors of
learning outcomes [18], since they assess students during PS,
as online assessment start from the actual student’s
performance during PS [6], [19]-[20]. In addition, they look
for information, considering the specific domain where the
students solve problems [21].
Likewise, online methods are thinking-aloud protocols
(TAP) and Logfiles. On the one hand, the student verbalizes
his/her thoughts while solving the task during TAP. On the
other hand, Logfiles provides detailed information of the
cognitive activities expressed by the student during the
execution of a cognitive challenge that implies the use of a
computer [22].
The TAP and Logfiles provide information that a group of
judges interprets and codifies through a system of categories
established in a taxonomy of metacognitive activities [19].
This taxonomy should describe the MS, regarding the
metacognitive activities that take part in the task resolution of
a specific domain [23].
Diverse taxonomies of metacognitive activities have been
proposed; for instance, in [24] the authors report a taxonomy
of metacognitive activities to analyze the learning process of
psychology university students in hypermedia environments.
In a similar way, in [23] a general taxonomy is proposed to
examine high school students during the reading of history
texts and solving physics problems. In addition, [19] expose a
taxonomy to describe activities used by kids while they solve
mathematics problems.
The taxonomies described above are an invaluable
contribution to the assessment of MS; nonetheless, they have
several deficiencies. First, these tools are designed only in the
English language, which means that their implementation in
Spanish language studies results in a process that requires an
adaptation and validation in this language.
A second deficiency is that, out of the authors mentioned
before, only [24] present descriptions of their metacognitive
activities; nevertheless, this information is not sufficient, since
it is not possible to identify which type of expressions (verbal
or nonverbal) performs the student when he/she utilizes a
particular activity. In consequence, it is difficult that other
investigations employ this taxonomy.
For the above reasons, those works that wish to apply the
taxonomy proposed by [24] may fall into two scenarios. In the
first, they could make research efforts to extend the taxonomy
descriptions, in this order to understand the meaning of each
metacognitive activity. In the second scenario, the research
studies would assume the ambiguity of the description, and
with only this information, they could analyze their students,
nonetheless, their results would be debatable since they
depend exclusively on the coder’s judgement.
It is worth highlighting that in [25] the authors of this paper,
reported a first version of the taxonomy with the use of
technological tools.
The aim of this paper is to document the method used by
the authors to construct the taxonomy of metacognitive
activities which describe the MS of engineering university
students during the PS learning.
The taxonomy presented here is within the limited field of
taxonomies designed in the Spanish language; this taxonomy
permits the creation of wide descriptions of metacognitive
behaviors. Hence, the disclosed tool may be used by other
research studies.
II. METHODOLOGY
This section provides a scenario which explains how to
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construct a taxonomy of metacognitive activities. First, the
authors expose the requirements that the taxonomy should be
expected to meet. Second, it presents the method used, and
finally, the steps created to build the tool. These steps are
explained in section III.
A. Requisites
This research draws from the three requisites proposed by
[23] to construct a taxonomy of metacognitive activities:
1) The taxonomy should expect the metacognitive activities
to be suitable to describe the students’ behavior in the interest
domain.
2) The taxonomy should be complete in terms of its
components, in order to cover declarations that go beyond
literal texts, that is, actions performed by the student which are
not possible to be detected in the recording.
3) The taxonomy should be related with other taxonomies
specialized in metacognitive activities and divulged in the
contemporaneous literature, with the purpose of allowing the
proposed taxonomy to have slight divergences with other
already existing taxonomies.
B. Method
The authors of the present work used the constant
comparison method to construct the taxonomy of
metacognitive activities that meet the three requisites outlined
before. This method has been utilized in other studies as in
[23] and in [26] to generate a system of categories.
The method is based on the comparison and systematic
analysis of information, to find verbal and nonverbal patterns,
and identify events through the saturation of data, and not the
test or verification of previously established hypothesis [27].
In this regard, this method highlights the importance of
analyzing and comparing information systematically with the
purpose of verifying common behaviors.
C. Steps created.
The meeting of the requisites and the utilization of the
constant comparison method allowed for the creation of steps
with which the taxonomy of metacognitive activities was
construct (see fig. 1).
The seven steps permit the construction of the taxonomy,
ending up with three main categories and 28 metacognitive
activities distributed in the following way: 6 metacognitive
activities in the planning category, 17 in monitoring, and 5 in
evaluation. Each activity relies on its respective description.
III. RESULTS
This section contains a detailed description of the way each
step of fig.1 was carried out; besides, the authors document
how to meet the specified requirements in [23].
Step 1: Gather metacognitive activities which occur in
contexts like those experienced during the PS learning.
The authors recovered specialized literature through a
snowball sampling technique; in this way, the following
documents were retrieved: [6], [19], [20], [23], [24], [26],
[28]. Later, the authors gathered metacognitive activities from
these texts.
Step 2: Selection of metacognitive activities that belong to
the planning, monitoring, and evaluation categories.
The authors filtered out the metacognitive activities
gathered in step 1, to keep those activities that suit the
categories of project.
This filtering considered the reports published in the
specialized literature; for example, the authors retrieved the
activity “explain and justify strategies” from [23] and assigned
this activity to the evaluation category, since in [23] they
report that this activity describes assessment MS.
Fig. 1. Steps for the construction of the taxonomy of metacognitive activities.
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Step 3: Gathering information in the field.
The authors applied six pilot tests of PS to students,
regarding that each student takes a PS test. The individuals
belong to the program of Industrial Engineering of the Faculty
of Business Sciences of the Universidad Tecnológica de
Pereira.
In some cases, the exams required to use ProModel
TM
software (version 8.6.2.1037), which is a computer tool that
helps the engineering students to solve complex problems,
then, it was necessary to employ the Logfiles to evidence the
PS process. TAP occurred in all the exams to video capture
the verbalization of the students’ thoughts.
Step 4: Transcription and codifying of gathered
information.
The researchers transcribed the TAP information of each
pilot test, considering the observations evidenced with the
Logfiles. After, auditors individually coded one transcription
at a time. The codifying regarded assigning metacognitive
activities to fragments of the transcription. The fig. 2 exposes
an example of the encoding result where the codes are in red,
green, and yellow colors, and the Spanish transcription is
surrounded by a blue box.
Step 5: Identification of convergences.
The researchers collected the codified fragments with the
same metacognitive activity, then identified the relevant terms
in the fragments or in the label of the metacognitive activity,
following their own perspective. The authors looked up these
words in the dictionary of the Spanish Royal Academy (Real
Academia Española - RAE). The Table I summarizes the
relevant words of the fragment and the metacognitive activity
(underlined terms), as well as the definitions extracted from
the RAE.
Step 6: Write a memo of semantic analysis.
In this step, the authors drew from the postulate of [29],
who state that the taxonomy is a key structure to categorize the
knowledge centered in semantics. Therefore, the researchers
analyzed semantically the fragment and the metacognitive
activity that had been previously enriched with the dictionary
information. Thus, the authors recognized behavior patterns of
the student during PS. The Table I exposes the memo of the
activity “Detecting errors”.
Step 7: Elaboration of the description.
The authors built the description of each metacognitive
activity from an iterative study of the patterns found in the
previous step. The patterns have a very important role because
describe how the students had learned to solve problems.
Consequently, the researchers repeatedly analyzed the
semantics of the fragments in order to find data which allowed
the characterization of the student behaviors during PS. For
this reason, the set of patterns found in pilot tests permits to
consolidate the description of a metacognitive activity.
The authors finished the construction of the metacognitive
activities when they achieved the saturation of information,
that is, when there was not new evidence in the pilot tests. It is
important to highlight that the descriptions do not imply
necessarily that a metacognitive activity precedes or succeeds
another; therefore, the descriptions do not suggest a temporary
order for auditors when they analyze the TAP and the Logfiles
reports.
The following paragraphs show an example of the
description associated with the metacognitive activity
“Detecting errors”, which is present within the constructed
metacognitive activities.
Fig. 2. Example of codifying a transcription.
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Example:
The researchers built the description of the activity
“Detecting errors” starting with the patterns of the
metacognitive activities. In this order, the auditors analyzed: i)
the terms that characterized the fragments assigned to that
particular activity; ii) the information from the Logfiles as an
evidence that the student had detected an error; iii) the
fragments that encoders had labeled simultaneously with
diverse metacognitive activities. The incidences associated to
that specific activity were identified because:
Pattern 1
The terms “be wrong”, “screw up” and “but (followed by an
activity)” represent the moment when the student detects an
error in his/her process or in the task execution, fragments that
the students recognized as wrong, or when they forgot to place
certain element and so screwed it up.
There are occasions when the student expresses that he/she
is wrong, without specifying which the error is, and thanks to
the nonverbal information of the TAP transcriptions, the
auditors identified the particularities of the flaw.
Pattern 2
The student does not verbalize associated terms once they
detect an error; instead, he/she employs words which express
an apology for the mistake made (and not verbalized); for
example, an event where the student used the term “sorry”
without having expressed his error.
Pattern 3
Situations where it was possible to evidence that the student
detected an error thanks to the confirmatory information of the
Logfiles. In occasions, the student does not verbalize terms
associated with the error detection (“be wrong,” “mess it up,”,
and “but (preceded by an activity), nor does he express words
representing apologies for a mistake made; instead, those
fragments have data from the Logfiles, which allow to verify
that the student has detected an error.
For example, “I forgot to put the 20 meters to this band (the
student opens a window and adds the missing information)”.
In this situation, the student verbalizes ambiguous terms and
proceeds to correct his/her error (action detected by the
Logfiles).
Likewise, the auditors identify the fragments where the
Logfiles information indicates that the student is wrong and
shows the mistake. In this order, the Logfiles facilitate to
identify the moment when the student detects an error and
proceed to correct it.
Pattern 4
The auditors consider when fragments had been labeled in
two or more metacognitive activities simultaneously. In
specific, the encoders regard that the student usually corrects
his/her mistakes when he/she identifies it. Therefore, most of
the fragments assigned to “Detecting errors” are labeled with
the activity “Take a corrective approach”, which belongs to
the monitoring category.
It is convenient to point out that the simultaneous
assignment of these activities does not depend on the presence
of Logfile information; on the contrary, it is given
transversally to the patterns discussed above.
In this way, the auditors created a description made up of
patterns for each metacognitive activity of the taxonomy, thus
finishing its construction.
IV. CONCLUSIONS AND FURTHER RESEARCH
This paper describes a method to construct a taxonomy of
metacognitive activities. It is a tool that contains an organized
set of activities leading to the analysis of the metacognitive
behavior of university students during their PS learning. The
exposed taxonomy becomes a framework to characterize the
MS that take part in the PS learning.
Likewise, the taxonomy is an essential supporting tool for
the suitable analysis of the TAP declarations and the Logfiles.
The document presented here attempts to be a guide for future
projects to build taxonomies of metacognitive activities.
Future studies could use this taxonomy with a different
population, leading to the tool strengthening.
.
TABLE I
EXAMPLE OF A MEMO OF SEMANTIC ANALYSIS
Category
Monitoring
Metacognitive activity
Detecting errors
Definition of terms of
the metacognitive
activity (RAE)
Detecting (Detection): Action and effect of
detecting. Detect: Finding out the
existence of something which was not
apparent. Error: Wrong concept or false
judgement; misguided or wrong action;
something wrongly made.
Fragment
(The student mistypes a number in the
calculator)* very important, because this
alters the results + 2-9,38 to the 2
nd
power,
I made a mistake.
Definition of terms of
the fragment. (RAE)
Being wrong (To be wrong): Wrongly take
something or someone for true.
Memo of semantic
analysis.
The student points out that she has made a
mistake, the semantic relation with the
activity is immediate, since words are
shared between the fragment and the
activity. This is supported since the term
“error" is explained by the term "being
wrong”. Thanks to the nonverbal
information of the TAP transcription, it
was evidence that the student had wrongly
typed the number and then she recognizes
it and verbalizes "I was wrong"
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REFERENCES
[1] M. V. J. Veenman and D. van Cleef, “Measuring metacognitive
skills for mathematics: students’ self-reports versus on-line
assessment methods,” ZDM - Math. Educ., vol. 51, no. 4, pp. 691–
701, 2019, doi: 10.1007/s11858-018-1006-5.
[2] Z. Ç. Özcan, “The relationship between mathematical problem-
solving skills and self-regulated learning through homework
behaviours, motivation, and metacognition,” Int. J. Math. Educ. Sci.
Technol., vol. 47, no. 3, pp. 408–420, 2016, doi:
10.1080/0020739X.2015.1080313.
[3] M. V. J. Veenman, “Learning to self-monitor and self-regulate,” in
Handbook of research on learning and instruction, 2011, pp. 197–
219.
[4] R. Azevedo, D. C. Moos, A. M. Johnson, and A. D. Chauncey,
“Measuring cognitive and metacognitive regulatory processes
during hypermedia learning: Issues and challenges,” Educ. Psychol.,
vol. 45, no. 4, pp. 210–223, 2010, doi:
10.1080/00461520.2010.515934.
[5] E. Baten, M. Praet, and A. Desoete, “The relevance and efficacy of
metacognition for instructional design in the domain of
mathematics,” ZDM - Math. Educ., vol. 49, no. 4, pp. 613–623,
2017, doi: 10.1007/s11858-017-0851-y.
[6] M. Van Der Stel and M. V. J. Veenman, “Metacognitive skills and
intellectual ability of young adolescents: A longitudinal study from
a developmental perspective,” Eur. J. Psychol. Educ., vol. 29, no. 1,
pp. 117–137, 2014, doi: 10.1007/s10212-013-0190-5.
[7] A. Zohar and S. Barzilai, “A review of research on metacognition in
science education: current and future directions,” Stud. Sci. Educ.,
vol. 49, no. 2, pp. 121–169, 2013, doi:
10.1080/03057267.2013.847261.
[8] J. C. Campione, A. L. Brown, and M. L. Connell, “The Teaching
and Assessing of Mathematical Problem Solving,” in
Metacognition : On the importance of understanding what you are
doing., 3rd ed., no. January 1988, 1988, pp. 93–114.
[9] M. E. Bernal, M. Gómez, and R. Iodice, “Interacción Conceptual
Entre Interacción Conceptual,” Rev. Latinoam. Estud. Educ., vol.
15, no. 1, 2019, doi: 10.17151/rlee.2019.15.1.11.
[10] M. V. J. Veenman, B. H. A. M. Van Hout-Wolters, and P.
Afflerbach, “Metacognition and learning: Conceptual and
methodological considerations,” Metacognition Learn., vol. 1, no. 1,
pp. 3–14, 2006, doi: 10.1007/s11409-006-6893-0.
[11] V. Anderson, R. Jacobs, P. J. Anderson, and M. Spencer Smith,
“Development and assessment of executive function: From
preschool to adolescence,” in Executive Functions and the Frontal
Lobes, vol. 7, no. 9, 2008, pp. 123–154.
[12] A. Diamond, “Executive functions,” Annu. Rev. Psychol., vol. 64,
pp. 135–168, 2013, doi: 10.1146/annurev-psych-113011-143750.
[13] M. Quiroga, “La metacognición como función ejecutiva : su rol en
la compresión de textos,” Rev. del Dep. Let., vol. 5, pp. 516–528,
2016.
[14] A. Peña-ayala and L. Cárdenas, “A Conceptual Model of the
Metacognition Activity,” in Metacognition: Fundaments,
Applications, and Trends A Profile of the Current State-Of-The-Art,
2015, pp. 39–74.
[15] G. Schraw, “Promoting General Metacognitive Awareness,” Instr.
Sci., vol. 26, no. 1, pp. 113–125, 1998, doi: 10.1007/978-94-017-
2243-8_1.
[16] P. Georghiades, “From the general to the situated: Three decades of
metacognition,” Int. J. Sci. Educ., vol. 26, no. 3, pp. 365–383, 2004,
doi: 10.1080/0950069032000119401.
[17] F. Orrego, M., Tamayo, O., & Ruíz, Unidades didácticas para la
enseñanza de las ciencias. 2016.
[18] M. V. J. Veenman, “Assessing Metacognitive Skills in
Computerized Learning Environments,” in International Handbook
of Metacognition and Learning Technologies, 2013, pp. 157–169.
[19] A. Desoete, “Multi-method assessment of metacognitive skills in
elementary school children: How you test is what you get,”
Metacognition Learn., vol. 3, no. 3, pp. 189–206, 2008, doi:
10.1007/s11409-008-9026-0.
[20] C. Y. Wang, “Exploring general versus task-specific assessments of
metacognition in university chemistry students: A multitrait–
multimethod analysis,” Res. Sci. Educ., vol. 45, no. 4, pp. 555–579,
2015, doi: 10.1007/s11165-014-9436-8.
[21] G. Schellings, “Applying learning strategy questionnaires: Problems
and possibilities,” Metacognition Learn., vol. 6, no. 2, pp. 91–109,
2011, doi: 10.1007/s11409-011-9069-5.
[22] M. V. J. Veenman, R. D. Hesselink, S. Sleeuwaegen, S. I. E. Liem,
and M. G. P. Van Haaren, “Assessing developmental differences in
metacognitive skills with computer logfiles: Gender by age
interactions,” Psihol. Teme, vol. 23, no. 1, pp. 99–113, 2014.
[23] J. Meijer, M. V. J. Veenman, and B. H. A. M. Van Hout-Wolters,
“Metacognitive activities in text-studying and problem-solving:
Development of a taxonomy,” Educ. Res. Eval., vol. 12, no. 3, pp.
209–237, 2006, doi: 10.1080/13803610500479991.
[24] R. Azevedo and J. G. Cromley, “Does training on self-regulated
learning facilitate students’ learning with hypermedia?,” J. Educ.
Psychol., vol. 96, no. 3, pp. 523–535, 2004, doi: 10.1037/0022-
0663.96.3.523.
[25] M. E. Bernal Loaiza, M. Gómez Suta, L. M. Ochoa Salinas, M.
Castaño Ramírez, and R. Iodice, “Herramientas para evaluar las
habilidades metacognitivas en estudiantes de ingeniería al resolver
problemas en simulación de eventos discretos,” in EIEI, ago. 2019.
[26] M. Pressley, “Development of Grounded Theories of Complex
Cognitive Processing : Exhaustive Withinand Between-Study
Analyses of Think-Aloud Data,” Issues Meas. Metacognition, pp.
261–296, 2000.
[27] R. J. Cuñat Giménez, “Aplicación De La Teoría Fundamentada (
Grounded Theory ) Al Estudio Del Proceso De Creación De
Empresas,” Decis. Glob., pp. 1–13, 2007.
[28] L. De Backer, H. Van Keer, and M. Valcke, “Exploring the
potential impact of reciprocal peer tutoring on higher education
students’ metacognitive knowledge and regulation,” Instr. Sci., vol.
40, no. 3, pp. 559–588, 2012, doi: 10.1007/s11251-011-9190-5.
[29] B. Carrion, T. Onorati, and P. Diaz, “Designing a semi-Automatic
taxonomy generation tool,” Inf. Vis. - Biomed. Vis. Vis. Built Rural
Environ. Geom. Model. Imaging, IV 2018, pp. 266–271, 2018, doi:
10.1109/iV.2018.00053.
María Elena Bernal Loaiza was born in
Pereira, Risaralda, Colombia, on April 28th
of 1975. She graduated as a Systems
Engineer from UNAD, in 2014. She also
got a Master’s degree in Operations
Research and Statistics form Universidad
Tecnológica de Pereira (Colombia) in
2009, and a Master’s degree in Human and
Organizational Development (2016). She’s a doctoral
candidate in Didactics from Universidad Tecnológica de
Pereira. Professor Bernal has worked as a teacher in the areas
of Operations Research, Production and Logistics. She is a
member of the GEIO and Data Envelopment Analysis
research groups of the Universidad Tecnológica de Pereira.
ORCID: https://orcid.org/0000-0001-8630-3931
Manuela Castaño Ramírez was born in
Guática, Risaralda, Colombia, on
September 24th of 2000. Currently she is
a seventh semester student of the
Industrial Engineering program at the
Universidad Tecnológica de Pereira.
Since 2018, she is the leading student of
the Semillero de Ingeniería Industrial of
UTP and works as a monitor at the UTP,
supporting research works and the activities of the Semillero
de Investigación de Ingeniería Industrial.
ORCID: https://orcid.org/0000-0002-3339-9172
Scientia et Technica Año XXVI, Vol. 26, No. 04, diciembre de 2021. Universidad Tecnológica de Pereira.
473
Manuela del Pilar Gómez Suta was
born in Bogotá, Colombia. She received
the degree in industrial engineering
from the Universidad Tecnológica de
Pereira in 2017 the M.S. degree in
operational and statistical research from
Universidad Tecnológica de Pereira in
2021.
From 2018 to 2020, she was a Research Assistant in the
program Jóvenes Investigadores e Innovadores inside Data
Envelopment Analysis Research Group. Since 2019, she has
been Professor with the Faculty of Business from Universidad
Tecnológica de Pereira. She is the author of four research
articles published in the period 2016 to 2020. His research
interests include engineering education, natural language
processing, statistical and mathematical analysis.
ORCID: https://orcid.org/0000-0001-7108-2689
Rosario Iodice was born in Marcianise
(Italy), on September 25th of 1979. He is
PhD in Neuroscience from University of
Salamanca (Spain). Currently, he is a
professor at the Universidad Católica de
Pereira (Colombia) and his works are in
Cognitive & Behavioral Neuroscience,
specifically human memory.
ORCID: https://orcid.org/0000-0001-7322-3086