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57
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
Original scientic paper
Received: November 09, 2023.
Revised: February 01, 2024.
Accepted: February 23, 2024.
UDC:
37.091.26
37.091.279.7
37.015.3
10.23947/2334-8496-2024-12-1-57-76
© 2024 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*Corresponding author: waskito@ft.unp.ac.id
Waskito Waskito1* , Rizky Ema Wulansari1, Rifelino Rifelino1 , Aprilla Fortuna1, Abel Nyamapfene2, Siti ‘Aat Jalil3
Constructivist Feedback-Based Assessment Method as Key for Effective
Teaching and Learning: The Development and Impact on Mechanical
Engineering Students’ Adaptive Capacity, Decision Making, Problem
Solving and Creativity Skills
1Faculty of Engineering, Universitas Negeri Padang, Indonesia
e-mail: waskito@ft.unp.ac.id, rizkyema@ft.unp.ac.id, rifelino@ft.unp.ac.id, aprillafortuna@student.unp.ac.id
2Institute of Education & Faculty of Engineering, University College London, London, United Kingdom,
e-mail: a.nyamapfene@ucl.ac.uk
3Faculty of Technical and Vocational Education from University Tun Hussein Onn Malaysia, Malaysia
e-mail:
siti.’aat.jalil@moe.edu.my
Abstract: Educators must conduct assessments in their learning; it determines students’ weaknesses in the teaching
material they follow during learning. Unfortunately, the implementation of assessment by educators was not optimal, and the
weakness was that the existing assessment method was only xated on assessing students without providing feedback on the
assessment. At the same time, this feedback was essential for students in learning, which can help learners assess performances
that cannot be seen and felt by themselves, as well as a tool to motivate students, notication or information, and reinforcement.
Therefore, this research aimed to develop a Constructivist Feedback-Based Assessment Method for learning assessment. The
method used in this research was Research and Development (R&D). After development, the Constructivist Feedback-Based
Assessment Method for learning assessment will be implemented to see its effect on students’ adaptive capacity, decision-making,
problem-solving, and creativity skills. Independent sample t-test and linear regression analysis were used as data analysis techniques
describing the impact of the assessment on those skills. The results showed that the Constructivist Feedback-Based Assessment
Method has ve stages: preparing the assessment material, diagnostic assessment, assessment for learning, assessment of
learning, and reection. It effectively affects students’ skills, such as adaptive capacity, decision-making, problem-solving, and
creativity. It can be concluded that the Constructivist Feedback-Based Assessment Method can improve students’ adaptive
capacity, decision-making, problem-solving, and creativity. Novelty in this research was the existence of constructivism integrated
into feedback-based assessment, which the existing assessment has not highlighted the constructivist side of assessment.
Keywords: assessment, feedback, constructivist, teaching and learning, vocational education.
Introduction
Implementing assessment in learning significantly contributes to educators’ professional
development and learners’ learning (Bragg et al., 2021; Hennessy et al., 2022). Assessment serves to
provide feedback to learners. Based on this, educators can analyze the information, comment on it, and
use it to check and regulate learning. Assessment, if implemented correctly, can improve the quality of
learning. The function of assessment is not to give a rating but to see where the learners’ mastery is and
what they have not mastered (Wetzel et al., 2020). Based on the survey results, using assessment as
an evaluation has not been maximally implemented in learning. Too many lecturers/educators still do not
utilize this evaluation function to improve the process and quality of teaching and learning. It is not even an
exaggeration to say that lecturers/educators very rarely develop evaluations to obtain information about
what has been planned for an interaction. It can be seen that assessment is not carried out optimally,
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Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
where tests are usually given only at the time of Mid Test or Final Test (Jalinus et al., 2023). If assessment
is not really implemented properly, then educators cannot know the extent of students’ understanding, so
that learning that occurs is not effective.
Therefore, the solution to this problem is to develop an assessment method. Although the
assessment method has existed before and is applied in schools, the weakness of the existing assessment
method is that the assessment method is fixated on assessing students only, without providing feedback
on the assessment. At the same time, this feedback is essential for students in learning, which can help
learners assess performances that cannot be seen and felt by themselves (Hattie, 2008), as well as a tool
to motivate learners, notification or information, reinforcement, and motivation (Sims et al., 2023). The
advantage of this feedback-based assessment method is that it will obtain information about the pattern of
achieving learning objectives. To meet the learning objectives set, diagnostic information for each learner
can more effectively help learners know which parts of the topic they still have not mastered so that these
learners can quickly learn the lesson topics that have not been mastered (Waskito et al., 2022). The
assessment results from using this feedback-based assessment method provide diagnostic information
from each score obtained by each learner, called individual diagnostic information, and information on
groups of learners or group diagnostic information (Duan et al., 2020).
According to (Fuchs and Fuchs, 1986), many meta-analysis studies on learning quality improvement
factors. Based on the results, assessment methods that include feedback are the most influential factor in
improving learning quality. Based on previous research, the assessment that includes feedback positively
impacts many learner behaviors, especially those related to students’ skills. In the literature, assessment
methods rank at the top of the list of studies, compared to teaching strategies and techniques to improve
learners’ academic achievement. Relevant meta-analysis studies also show that assessments that
include feedback significantly impact learner success (Karaman, 2021; Phelps, 2019; Wisniewski et al.,
2020; Yan et al., 2023). At the same time, constructivism emphasizes students’ active role in constructing
their knowledge through interaction with learning materials and social experiences. This approach also
positively impacts assessment methods (Mohammed et al., 2020).
However, there seems to be an empirical gap in previous research. There is a lack of rigorous
research in the previous literature. Constructivist integrated feedback on assessment in enhancing
students’ ability, which has not been explored, seems essential and worthy of investigation. Empirical
investigation of these issues is necessary because assessment is one of the learning elements that
can provide better learning quality if combined with feedback and constructivism. In addition, previous
research has focused on combining assessment with feedback only, as well as focusing only on improving
learning outcomes (Prasetya, Fajri, et al., 2023; Prasetya, Syahri, et al., 2023; Waskito et al., 2023).
Therefore, this research aims to develop a constructivist feedback-based assessment method as a key
to effective learning in improving the quality of learning in vocational schools and to see the impact of
a constructivist feedback-based assessment method on students’ adaptive capacity, decision-making,
problem-solving, and creativity.
Materials and Methods
Research Design
The method used in this research was Research and Development (R&D). The development model
in this study refers to the Research and Development model based on Borg and Gall (Aka, 2019) (Figure
1). The steps of this research are as follows: 1) identifying problems and analyzing the needs for the
development of constructivist feedback-based assessment methods; this problem identification activity is
carried out using a survey method and analysis questionnaire; 2) designing and developing constructivist
feedback-based assessment methods and its procedures, this development stage was carried out using
the explanatory sequential design method in designing constructivist feedback-based assessment
method, and; 3) expended trials the constructivist feedback-based assessment method to students using
the quasi-experiment method, to see the impact on adaptive capacity, decision making, problem-solving
and student creativity. In the expended trials stage, the group of students who were given the constructivist
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Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
feedback-based assessment method treatment was called the experimental group, where the treatment
was given for 12 weeks. The first week was given a pre-test, weeks 2 to 11 were given treatment, and the
last week was given a post-test.
Figure 1. Research Design
Research Participants
Research respondents in the needs analysis activities amounted to 20 Vocational High School, and
respondents in this trial activity were 121 mechanical engineering students of Vocational High School who
were divided into two groups, namely 61 in the experimental group and 60 in the control group.
Research Instruments
This questionnaire was adapted from the rubric for assessing work-relevant skills developed by
(Sánchez-Ramírez et al., 2022), consisting of a series of closed questions with answers to be rated on a
scale of 1 to 5 to investigate their skills. The pilot study aims to determine the validity and reliability of the
research questionnaire before it is retrieved in an expanded trial, and this pilot study was conducted on
60 vocational high school students. Validity data analysis was carried out using the Intraclass Correlation
Coefficient (ICC); if the rater consistency is 0.500, it is classified as valid (Su et al., 2023). It can be
concluded that the agreement between raters is very strong, and each rater has a pretty good consistency.
The reliability of the questionnaire was tested using Cronbach’s alpha to analyze the suitability of the
research questions (Surucu and Maslakci, 2020). Cronbach’s alpha has been widely used in studies in the
field of science education to see if the questionnaire is reliable (Baidal-Bustamante et al., 2023).
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Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
Table 1. Research Instrument Indicators and Pilot Study Analysis Results
Variables and Indicators Answer Intraclass
Correlation
Coefcient (ICC)
Cronbach's alpha
Coefcient
Adaptive capacity has three
evaluation indicators: adapting and
accepting change, contributing
to change, and encouraging and
handling change.
Rubric
assessment Likert scale - strongly
agree (5), to strongly
disagree (1)
0.890 0.578
Decision Making has two indicators
of evaluation objectives: decision-
making with criteria when alternatives
are proposed and choosing the most
appropriate option to anticipate the
consequences.
Rubric
assessment Likert scale - very
often (5), to never (1) 0.696 0.616
Problem Solving has three
indicators of evaluation objectives:
identifying problems, analyzing
and solving problems, preventing
problems, and overcoming complex
problems.
Rubric
assessment Likert scale - strongly
agree (5), to strongly
disagree (1)
0.821 0.801
Creativity has two evaluation
indicators, namely, generating ideas
and creating original ideas for specic
purposes
Rubric
assessment Likert scale - strongly
agree (5), to strongly
disagree (1)
0.601 0.615
Table 2. Hypothesis Development
Variable Hypothesis Data
Analysis
Technique
Independent Dependent Null Hypothesis (H0) Hypothesis Alternatives (Ha)
Constructivist
Feedback-
Based
Assessment
Method
Adaptive
Capacity There is no difference in the Adaptive
Capacity ability of experimental and
control group students after applying
the Constructivist Feedback-Based
Assessment Method.
There is a difference in the Adaptive
Capacity ability of experimental and
control group students after applying
the Constructivist Feedback-Based
Assessment Method.
Independent
sample t-test
Decision
Making There is no difference in the decision-
making ability of experimental and
control group students after applying
the Constructivist Feedback-Based
Assessment Method.
There is a difference in the decision-
making ability of experimental and
control group students after applying
the Constructivist Feedback-Based
Assessment Method.
Problem-
Solving There is no difference in the problem-
solving ability of experimental and
control group students after applying
the Constructivist Feedback-Based
Assessment Method.
There is a difference in the problem-
solving ability of experimental and
control group students after applying
the Constructivist Feedback-Based
Assessment Method.
Creativity There is no difference in the creativity
ability of experimental and control
group students after applying the
Constructivist Feedback-Based
Assessment Method.
There is a difference in the creativity
ability of experimental and control
group students after applying the
Constructivist Feedback-Based
Assessment Method.
Adaptive
Capacity,
Decision
Making,
Problem
Solving and
Creativity
The constructivist feedback-based
assessment method does not impact
students' adaptive capacity, decision-
making, problem-solving, and
creativity in the experimental group.
The constructivist feedback-based
assessment method impacts
students' adaptive capacity,
decision-making, problem-solving,
and creativity in the experimental
group.
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Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
Data Analysis Technique and Hypothesis Development
This research was analyzed quantitatively using the methods of percentage, average, standard
deviation, and parametric statistics. Hypotheses and data analysis techniques regarding the impact of
the Constructivist Feedback-Based Assessment Method on students’ adaptive capacity, decision-making,
problem-solving, and creativity skills are presented in Table 2.
Results and Discussions
Needs Analysis and Development of Constructivist Feedback-Based
Assessment Method
Need analysis is an analysis conducted to examine a phenomenon of the needs of a program
(Mubai et al., 2020). Respondents used in filling out this needs analysis questionnaire are teachers
of Vocational High Schools majoring in mechanical engineering in Padang City. The needs analysis is
carried out to identify the learning evaluation that has been carried out at this time. This analysis is carried
out as a consideration for developing constructivist feedback-based assessment methods in Vocational
Schools. The results of observations made at several vocational schools in Padang City stated that
teachers had implemented evaluation or assessment in every lesson. However, the assessment teachers
implement is still unstructured; sometimes, teachers give formative tests, and sometimes, they do not.
Thus, teachers have not found the right way to implement assessment in learning. Teachers expect
a structured assessment method that teachers can guide. The assessment plays an essential role in
learning. A well-implemented assessment will improve the quality of education.
The needs analysis data shows a gap between the current assessment and the mean expectation
of assessment implementation. It means that teachers expect an assessment method they can guide in a
structured and easy-to-understand manner. It does not mean that teachers at school have not implemented
the assessment system, but that the assessment has not been implemented optimally due to confusion
in implementing the assessment method. Therefore, teachers expect an innovative assessment method
to improve their education and learning levels. Teachers in vocational high schools are very open-minded
to change, innovation, and the times, which is why they are also open-minded to this new assessment
method that will be developed. Literate teachers always try to adapt to the environment and changing
times. Assessment that is guided and maximally implemented can give students a perfect understanding
of the material because teachers can design learning based on the assessment given, and the feedback
given in this method can result in students being active in learning because there is more discussion about
what has been understood, what will be done to improve understanding.
The implementation of assessment can analyze the extent of learners’ mastery and what they have
not mastered. Even in the literature, this assessment is ranked at the top in improving the quality of learning
(Phelps, 2019). It means that by improving the quality of learning, students’ learning outcomes will also
increase (Hartmann, 2019). So, this research is expected to contribute to the science and related literature
on constructivist feedback-based assessment methods, and the correct implementation of assessment
will be implemented in learning. The assessment method has existed before and is implemented in
schools. However, the weakness of the existing assessment method is that the assessment method
is fixated on assessing students only without providing feedback on the assessment. This feedback is
essential for students in learning, which can help learners assess performances that cannot be seen and
felt by themselves (Jalinus et al., 2023), as well as a tool to motivate learners, notification or information,
reinforcement, and motivation (Hattie, 2008).
This constructivist feedback assessment method was developed by (Middleton et al., 2023), which
uses tutor input and the ‘5 Keys’ indicators of academic value, namely: (i) internal locus of control; (ii)
understanding the class; (iii) forward-thinking; (iv) improvement-focused, and (v) action-oriented. However,
the weakness of the (Middleton et al., 2023) assessment method is that they do not emphasize dialogic
interaction. Thus, the novelty of this research is to develop a constructivist feedback-based assessment
method, which aims to achieve positive outcomes by providing a person with comments or suggestions
that are useful for their learning or future and constructing students’ knowledge. This constructive feedback
focuses on the work rather than being a personally damaging attack on the individual. So, the results can
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Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
be faster processes, improved behavior, identifying weaknesses, or providing new perspectives. The
following constructive feedback-based assessment methods have been developed.
Figure 2. Constructivist Feedback-Based Assessment Method
In this constructivist feedback-based assessment method, there are five steps that the teacher
must take: Preparing the assessment material; at this stage, the teacher prepares the assessment
material before the learning begins. Educators often forget to prepare assessment materials because
they are busy preparing them in the form of syllabuses, lesson plans, and teaching materials before
learning begins, so the preparation of assessment materials is often neglected. When teachers implement
this assessment method, this first stage will remind teachers to prepare assessment materials that will
be implemented in learning. This activity is carried out before learning begins. Diagnostic Assessment:
this second stage is conducted before the learning begins; at this stage, teachers can provide a series of
written questions (multiple choice or short answer) to assess students’ current knowledge base or current
views on the topic/issue to be studied in the subject. Teachers can use this diagnostic assessment to
analyze students’ initial abilities. Thus, the teacher can design the proper learning procedure according to
the student’s initial abilities visible through the diagnostic assessment.
Assessment for Learning This third stage is carried out during the learning process; what is meant
during the learning process is that the teacher can give this test when the subject matter is completed
in one day, for example, daily assignments. The technique used in this stage is formative assessment.
The test given can be multiple choice, essay, or even case. By this stage, students can construct their
knowledge based on the given cases by teachers. After this stage, the teacher provides feedback to
students about their learning understanding and how to improve learning understanding; the technique
used in this feedback is face-to-face/direct comment. Assessment of Learning: an assessment conducted
after all learning has been completed; the technique used in this assessment is a summative test and
can be given at the end of the semester. This assessment is used to determine future learning goals
and pathways for students. After this assessment stage, the teacher also provides feedback written on
notes. The teacher can use the results of this assessment to see if the students’ abilities have reached
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Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
the goals and standards of learning. Reflection: The last stage is reflection; at this stage, students reflect
on themselves about what they have learned, what they understand, and what they have not understood.
This reflection stage can be done at the end of the semester.
Impact of Students’ Skills Toward the Implementation of Constructivist
Feedback-Based Assessment Method
Implementing the constructivist feedback-based assessment method in learning activities can
effectively develop students’ adaptive capacity, decision-making, problem-solving, and creativity. This is in
line with the findings of (Gulikers et al., 2013), which state that Feedback-Based Assessment contributes
to developing students (Furtak et al., 2008) and added that students’ creativity and problem-solving skills
can be improved through Feedback-Based Assessment.
The Impact on Adaptive Capacity
The data analysis results on the impact of the Constructivist Feedback-Based Assessment Method
on the adaptive capacity instrument showed that the “strongly agree” score was the highest percentage
among all items (Table 3). The item “Students can provide several alternative solutions to solve problems”
achieved the highest score (M=67.8, SD=84.9), with 62.3% of students at the very adaptable level and
24.6% at the adaptable level. Furthermore, “Students can control changes and support my friends”
received the lowest score of the five items (M = 64.8, SD = 62.6); 41% of students are competent, and
45.9% can control changes.
Table 3. Data Analysis of Adaptive Capacity Rubric on Control Group
Item Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
M SD
Post-test
Students are adaptable and
work with different groups 20
(33.3%)
0
(0%)
0
(0%)
11
(18,3%)
30
(50%)
30,5 47,5
Students can accept change as
a challenge 15
(25%)
1
(1.6%)
0
(0%)
18
(30%)
27
(45%)
28,8 34,8
Students can provide several
alternative solutions to solve the
problem
16
(26.67%)
1
(1.6%)
1
(1.6%)
18
(30%)
25
(41.67%)
30,8 36,2
Students can control change and
support friends 10
(16.67%)
0
(0%)
0
(0%)
15
(25%)
36
(60%)
20,0 24,5
Students can work towards a
goal 15
(25%)
0
(0%)
1
(1.6%)
10
(16.67%)
35
(58.3%)
24,5 34,8
Pre-test
Students are adaptable and
work with different groups 0
(0%)
0
(0%)
1
(1.6%)
27
(45%)
33
(55%)
18 24,5
Students can accept change as
a challenge 0
(0%)
0
(0%)
0
(0%)
27
(45%)
34
(56.6%)
17,6 25,1
Students can provide several
alternative solutions to solve the
problem
0
(0%)
0
(0%)
1
(1.6%)
24
(40%)
36
(60%)
17,4 22,9
Students can control change and
support friends 0
(0%)
0
(0%)
2
(3.3%)
27
(45%)
32
(53.3%)
18,4 23,9
Students can work towards a
goal 0
(0%)
0
(0%)
1
(1.6%)
29
(48.3%)
31
(51.7%)
18,4 25,7
Based on the data analysis in Table 3, it can be concluded that the control group that did not
implement the Constructivist Feedback-Based Assessment Method of learning has not improved students’
adaptive capacity, which can be seen as the most of highest scores in the ‘never.’ Data analysis of the
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64
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
adaptive capacity rubric on an experimental group can be seen in Table 4.
Table 4. Data Analysis of Adaptive Capacity Rubric on Experimental Group
Item Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
M SD
Post-test
Students are adaptable and
work with different groups 29
(47.5%)
15
(24.6%)
10
(16.4%)
7
(11,5%)
0
(0%) 62.3 58.4
Students can accept change
as a challenge 32
(52.5%)
20
(32.8%)
9
(14.8%)
0
(0%)
0
(0%) 66.8 70.5
Students can provide several
alternative solutions to solve
the problem
38
(62.3%)
15
(24.6%)
5
(8.2%)
3
(4.9%)
0
(0%) 67.8 84.9
Students can control change
and support friends 25
(41%)
28
(45.9%)
6
(9.8%)
2
(3.3%)
0
(0%) 64.8 62.6
Students can work towards
a goal 30
(49.2%)
31
(50.8%)
0
(0%)
0
(0%)
0
(0%) 68.5 79.8
Pre-test
Students are adaptable and
work with different groups 1
(1,6%)
1
(1,6%)
10
(16.4%)
26
(42.6%)
23
(37.7%) 22,8 19,8
Students can accept change
as a challenge 0
(0%)
0
(0%)
11
(18%)
26
(42.6%)
24
(39.3%) 21,8 22,3
Students can provide several
alternative solutions to solve
the problem
0
(0%)
1
(1,6%)
8
(13.1%)
26
(42.6%)
26
(42.6%) 21,2 20,8
Students can control change
and support friends 0
(0%)
0
(0%)
6
(9.8%)
28
(45.9%)
27
(44.3%) 20,2 23,2
Students can work towards
a goal 0
(0%)
0
(0%)
9
(14.7%)
25
(40.9%)
27
(44.3%) 20,8 21,2
Based on the data analysis in Table 3, it can be concluded that implementing the Constructivist
Feedback-Based Assessment Method of learning has improved students’ adaptive capacity, which can be
seen in the highest scores. The significant differences between the pre-test and post-test analysis of the
control and experimental groups can be seen in Table 5 in detail.
Table 5. Analysis of Adaptive Capacity T-test Results
Observations Groups N
Paired Sample T-test
Mean
Differences t df P
Pretest-Posttest analysis of
adaptive capacity instrument
Experimental
Group 61 21.6 6.961 60 0.000
Control Group 60 7.48 0.758 59 0.412
Independent Sample T-test
Post-test comparison
analysis of adaptive capacity
instrument
M t df P
Experimental
Group 61 64.23 5.864 120 0.000
Control Group 60 49.68
Table 5 showed a significant difference between the experimental and control groups on adaptive
capacity (df=120, t=5.864, pvalue=, p<0.05). It indicates that the experimental group had a higher mean
adaptive capacity at the post-test than the control group. Therefore, the impact of the treatment was
already evident in the post-test scores after the implementation of the Constructivist Feedback-Based
Assessment Method in the experimental group. It can be seen in the P-value; if the P-value> 0.05, then
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Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
the null hypothesis is rejected. Based on the data above, it can be interpreted that H0 is rejected, which
means there is a significant difference between the experimental and control groups on the post-test
score. The results of this study indicate that the Constructivist Feedback-Based Assessment Method
can improve adaptive capacity. Alt et al., (2023) found that through the Constructivist Feedback-Based
Assessment Method, students can get feedback from the teacher that will affect logical thinking and
reflective thinking and provide explanations. This increase in logical thinking can improve students’
adaptive capacity (Arsovic and Stefanovic, 2020; Handrayani et al., 2023; Wulansari and Nabawi, 2021).
The Impact on Decision-Making
The results on the impact of the Constructivist Feedback-Based Assessment Method on Decision-
Making showed that the score of “very often” was the highest percentage among all items (Table 5). The
item “Students can make the right decision when trying to choose between various alternative solutions to
a problem” achieved the highest score (M=57.4, SD=105.6), with 80.3% of students very often and 13.1%
of students often discussing with the teacher. Moreover, “Students do not make decisions based on
emotional factors” got the lowest score of the four items (M = 54.4, SD = 77.9), namely 60.7% of students
very often and 27.9% of students often do not make decisions based on emotional factors.
Table 6. Data Analysis of Decision-Making Rubric on Control Group
Item Very often Often Sometimes Rare Never M SD
Post-test
Students discuss with the
teacher to make a decision 20
(33.3%)
0
(0%)
3
(5%)
0
(0%)
37
(61.7%) 29,2 42,4
Students do not make decisions
based on emotional factors 23
(38.3%)
0
(0%)
0
(0%)
0
(0%)
37
(61.7%) 30,4 49,9
Students can discuss the
consequences of various
alternative decisions
15
(25%)
0
(0%)
7
(11.7%)
5
(8.3%)
34
(56.7%) 28 29,2
Students can make informed
decisions when choosing
between various alternative
solutions to a problem.
18
(30%)
0
(0%)
4
(6.7%)
3
(5%)
36
(60%) 28,8 36,8
Pre-test
Students discuss with the
teacher to make a decision 0
(0%)
0
(0%)
7
(11.7%)
12
(20%)
41
(68.3%) 17,2 17,5
Students do not make decisions
based on emotional factors 0
(0%)
0
(0%)
6
(10%)
12
(20%)
42
(42%) 16,8 17,7
Students can discuss the
consequences of various
alternative decisions
0
(0%)
0
(0%)
2
(3.3%)
20
(33.3%)
38
(63.3%) 16,8 20,4
Students can make informed
decisions when choosing
between various alternative
solutions to a problem.
0
(0%)
0
(0%)
6
(10%)
19
(31.7%)
35
(58.3%) 18,2 18,3
Based on the data analysis in Table 6, it can be concluded that the control group that did not
implement the Constructivist Feedback-Based Assessment Method of learning did not improve students’
decision-making, which can be seen as the most of highest scores in the ‘never.’ Data analysis of the
decision-making rubric on the experimental group can be seen in Table 7.
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66
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
Table 7. Data Analysis of Decision-Making Rubric on Experimental Group
Item Very often Often Sometimes Rare Never M SD
Post-test
Students discuss with the
teacher to make a decision
38
(62.3%)
17
(27.9%)
3
(4.9%)
0
(0%)
3
(4.9%) 54 81.0
Students do not make
decisions based on
emotional factors
37
(60.7%)
17
(27.9%)
5
(8.2%)
2
(3.3%)
0
(0%) 54.4 77.9
Students can discuss the
consequences of various
alternative decisions
48
(78.7%)
3
(4.9%)
7
(11.5%)
3
(4.9%)
0
(0%) 55.8 103.3
Students can make informed
decisions when choosing
between various alternative
solutions to a problem.
49
(80.3%)
8
(13.1%)
2
(3.3%)
2
(3.3%)
0
(0%) 57.4 105.6
Pre-test
Students discuss with the
teacher to make a decision
0
(0%)
1
(1.6%)
9
(14.8%)
12
(19.7%)
39
(63.9%) 18,8 16,4
Students do not make
decisions based on
emotional factors
0
(0%)
0
(0%)
4
(6.6%)
22
(36.1%)
36
(59%) 18,4 20,5
Students can discuss the
consequences of various
alternative decisions
0
(0%)
0
(0%)
7
(11.5%)
7
(11.5%)
47
(77%) 16,4 19,4
Students can make informed
decisions when choosing
between various alternative
solutions to a problem.
0
(0%)
0
(0%)
5
(8.2%)
10
(16.4%)
46
(75.4%) 16,2 18,9
Based on the data analysis in Table 7, it can be concluded that implementing the Constructivist
Feedback-Based Assessment Method of learning has improved students’ decision-making skills. The
significant differences between the pre-test and post-test analysis of the control and experimental groups
can be seen in Table 8 in detail.
Table 8. Analysis of Decision-Making T-test Results
Observations Groups N
Paired Sample T-test
Mean
Differences t df P
Pretest-Posttest analysis of
adaptive capacity instrument
Experimental
Group 61 2.11 6.872 60 0.001
Control Group 60 6.16 0.976 59 0.633
Independent Sample T-test
Post-test comparison
analysis of adaptive capacity
instrument
M t df P
Experimental
Group 61 51.05 6.375 120 0.000
Control Group 60 38.29
Table 8 showed a significant difference in decision-making skills between the experimental and
control groups (df=120, t=6.375, p-value=, p<0.05). It shows that the experimental group has a higher
mean decision-making score in the post-test than the control group. Therefore, the impact of the treatment
was already evident in the post-test scores after the implementation of the Constructivist Feedback-Based
Assessment Method in the experimental group. It can be seen that at the P-value > 0.05, the null hypothesis
is rejected. Based on the data above, it can be interpreted that H0 is rejected, which means there is a
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67
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
difference in the decision-making skills of experimental and control group students after implementing the
Constructivist Feedback-Based Assessment Method. This is in line with research conducted by (Fazel
and Ali, 2022), which explains that feedback gives students an overview of their decisions. They can
see the positive and negative aspects of their decisions. By realizing the consequences of the decisions,
students become more cautious and consider their choices better. It also helps students understand
how to solve problems arising from their decisions (Torres et al., 2020). They can see the impact of their
decisions on the situation and learn how to overcome the problems that arise (Teräs et al., 2020).
The Impact on Problem-Solving
The data analysis result of the impact of the Constructivist Feedback-Based Assessment Method
on problem-solving showed that the “strongly agree” score was the highest percentage among all items
(Table 7). The item “Students can make good decisions in solving problems to achieve goals” achieved
the highest score (M=57.8, SD=103.2), with 78.7% of students at the very capable level and 16.4% of
students at the capable level of making good decisions. Furthermore, “Students can consider a variety of
solutions” got the lowest score of the four items (M = 55.4, SD = 79.7); 60.7% of students are capable,
and 32.8% can consider different solutions.
Table 9.Data Analysis of Problem-Solving Rubric on Control Group
Item Strongly
Agree Agree Neutral Disagree Strongly
Disagree M SD
Post-test
Students can identify
problems that arise
28
(46.7%)
1
(1.7%)
5
(8.3%)
10
(16.7%)
15
(25%) 38,8 56,9
Students can make good
decisions in solving problems
to achieve goals
19
(31.7%)
0
(0%)
3
(5%)
13
(21.7%)
25
(41.7%) 31 37,4
Students can solve complex
problems well
16
(26.7%)
1
(1.7%)
3
(5%)
17
(28.3%)
23
(38.3%) 30 30,3
Students can consider a
range of different solutions.
23
(38.3%)
0
(0%)
4
(6.7%)
10
(16.7%)
23
(38.3%) 34 46,1
Pre-test
Students can identify
problems that arise
0
(0%)
0
(0%)
4
(6.7%)
9
(15%)
47
(78.3%) 15,4 19,3
Students can make good
decisions in solving problems
to achieve goals
0
(0%)
0
(0%)
0
(0%)
12
(19.7%)
48
(80%) 14,4 21,5
Students can solve complex
problems well
0
(0%)
0
(0%)
2
(3.3%)
12
(19.7%)
46
(76.7%) 15,2 19,8
Students can consider a
range of different solutions.
0
(0%)
0
(0%)
2
(3.3%)
13
(21.7%)
45
(75%) 15,4 19,7
Based on the data analysis in Table 9, it can be concluded that the control group that did not
implement the Constructivist Feedback-Based Assessment Method of learning did not improve students’
problem-solving, which can be seen as the highest scores in the ‘never.’ Data analysis of the problem-
solving rubric on the experimental group can be seen in Table 10.
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68
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
Table 10. Data Analysis of Problem-Solving Rubric on Experimental Group
Item Strongly
Agree Agree Neutral Disagree Strongly
Disagree M SD
Post-test
Students can identify
problems that arise
39
(63.9%)
20
(32.8%)
0
(0%)
2
(3.3%)
0
(0%) 55.8 85.0
Students can make good
decisions in solving problems
to achieve goals
48
(78.7%)
10
(16.4%)
3
(5%)
0
(0%)
0
(0%) 57.8 103.2
Students can solve complex
problems well
38
(62.3%)
16
(26.2%)
3
(5%)
4
(6.6%)
0
(0%) 54.2 80.1
Students can consider a
range of different solutions.
37
(60.7%)
20
(32.8%)
4
(6.7%)
0
(0%)
0
(0%) 55.4 79.7
Pre-test
Students can identify
problems that arise
0
(0%)
0
(0%)
10
(16.4%)
2
(3.3%)
49
(80.3%) 16,6 22,0
Students can make good
decisions in solving problems
to achieve goals
0
(0%)
0
(0%)
4
(6.7%)
12
(19.7%)
45
(73.8%) 16,2 18,9
Students can solve complex
problems well
0
(0%)
0
(0%)
3
(5%)
10
(16.4%)
48
(78.7%) 15,4 20,0
Students can consider a
range of different solutions.
0
(0%)
0
(0%)
3
(5%)
11
(18%)
47
(77%) 15,6 19,7
Based on the data analysis in Table 10, it can be concluded that implementing the Constructivist
Feedback-Based Assessment Method of learning has improved students’ problem-solving skills. The
significant differences between the pre-test and post-test analysis of the control and experimental groups
can be seen in Table 11 in detail.
Table 11. Analysis of Problem-Solving T-test Results
Observations Groups N
Paired Sample T-test
Mean
Differences t df P
Pretest-Posttest analysis of
adaptive capacity instrument
Experimental
Group 61 2.74 6.286 60 0.001
Control Group 60 4.82 0.885 59 0.736
Independent Sample T-test
Post-test comparison
analysis of adaptive capacity
instrument
M t df P
Experimental
Group 61 57.42 5.582 120 0.001
Control Group 60 36.01
Table 11 showed a significant difference in problem-solving ability between the experimental
and control groups (df=120, t=5.582, p-value=, p<0.05). It showed that the experimental group had a
higher mean problem-solving score in the post-test than the control group. Therefore, the impact of the
treatment was already apparent in the post-test scores after the implementation of the Constructivist
Feedback-Based Assessment Method in the experimental group. It can be seen in the P-value> 0.05 that
the null hypothesis is rejected. Based on the data above, it can be interpreted that H0 is rejected, which
means there are differences in the problem-solving skills of experimental and control group students
after implementing the Constructivist Feedback-Based Assessment Method. The feedback assessment
method has a significant impact on students’ problem-solving skills. By providing excellent and directed
feedback, students can better develop their problem-solving skills (Mubai et al., 2020). Menurut (Kardoyo
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69
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
et al., 2020) also explains that students learn to evaluate their proposed solutions through feedback.
They can see the advantages and disadvantages of their solutions and understand the criteria for good
evaluation (Lacey and Minnis, 2020). These skills are essential in choosing the best solution from various
possible alternatives. Also, feedback gives students an idea of their approach to the problem. They
become more aware of the strategies they choose and realize if there are weaknesses in their approach
(Taherdoost, 2022).
The Impact on Creativity
The analysis of the impact of the Constructivist Feedback-Based Assessment Method on creativity
showed that the score of “strongly agree” was the highest percentage among all items (Table 9). The item
“Students can analyze their ideas to optimize results” achieved the highest score (M=57.2, SD=100.7),
with 77% of students at the very capable level and 16.4% at the capable level of analyzing ideas. Moreover,
“Students are curious and interested in learning new things” received the lowest score of the four items
(M=54.6, SD=76), where 59% were very curious, and 29.5% were curious and interested in learning new
things.
Table 12. Data Analysis of Creativity Rubric on Control Group
Item Strongly
Agree Agree Neutral Disagree Strongly
Disagree M SD
Post-test
Students are interested in
learning new things
28
(46.7%)
1
(1.7%)
6
(10%)
5
(8.3%)
20
(33.3%) 38,4 57,2
Students can analyze their
ideas to optimize results
18
(30%)
1
(1.7%)
4
(6.7%)
18
(30%)
30
(50%) 34,4 33,7
Students can create new
ideas based on limited
information
17
(28.3%)
16
(26.2%)
1
(1.7%)
13
(21.7%)
27
(45%) 29,8 32,6
Students efciently
implement their new ideas
into a project.
17
(28.3%)
1
(1.7%)
17
(28.3%)
6
(10%)
19
(31,7%) 34,2 33,5
Pre-test
Students are interested in
learning new things
0
(0%)
0
(0%) 3 10 47 15,2 19,6
Students can analyze their
ideas to optimize results
0
(0%)
0
(0%) 10 16 34 19,2 17,6
Students can create new
ideas based on limited
information
0
(0%)
0
(0%) 2 15 43 15,8 19,6
Students efciently
implement their new ideas
into a project.
0
(0%)
0
(0%) 4 13 43 16,2 18,4
Based on the data analysis in Table 12, it can be concluded that the control group that did not
implement the Constructivist Feedback-Based Assessment Method of learning has not improved students’
creativity, which can be seen as the most of highest scores being in the ‘never.’ Data analysis of the
creativity rubric on the experimental group can be seen in Table 13.
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70
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
Table 13. Data Analysis of Creativity Rubric on Experimental Group
Item Strongly
Agree Agree Neutral Disagree Strongly
Disagree M SD
Post-test
Students are interested in
learning new things
36
(59.0%)
18
(29.5%)
7
(11.5%)
0
(0%)
0
(0%) 54.6 76.0
Students can analyze their
ideas to optimize results
47
(77.0%)
10
(16.4%)
3
(4.9%)
1
(1.6%)
0
(0%) 57.2 100.7
Students can create new
ideas based on limited
information
39
(63.9%)
20
(32.8%)
0
(0%)
2
(3.3%)
0
(0%) 55.8 85.0
Students efciently
implement their new ideas
into a project.
36
(59.0%)
10
(16.4%)
15
(24.6%)
5
(8.2%)
0
(0%) 55 72.5
Pre-test
Students are interested in
learning new things
0
(0%)
0
(0%)
6
(9.8%)
12
(19.7%)
43
(70.5%) 17 18,1
Students can analyze their
ideas to optimize results
0
(0%)
0
(0%)
1
(1.6%)
15
(24.6%)
45
(73.8%) 15,6 20,7
Students can create new
ideas based on limited
information
0
(0%)
0
(0%)
2
(3.3%)
17
(27.9%)
42
(68.9%) 16,4 20,1
Students efciently
implement their new ideas
into a project.
0
(0%)
0
(0%)
5
(8.2%)
11
(18%)
41
(67.2%) 15,6 17,1
Based on the data analysis in Table 13, it can be concluded that implementing the Constructivist
Feedback-Based Assessment Method of learning has improved students’ creativity ability. The significant
differences between the pre-test and post-test analysis of the control and experimental groups can be
seen in Table 14 in detail.
Table 14. Analysis of Creativity T-test Results
Observations Groups N
Paired Sample T-test
Mean
Differences t df P
Pretest-Posttest analysis of
adaptive capacity instrument
Experimental
Group 61 3.76 7.719 60 0.000
Control Group 60 6.29 0.674 59 0.736
Independent Sample T-test
Post-test comparison
analysis of adaptive capacity
instrument
M t df P
Experimental
Group 61 56.11 6.264 120 0.000
Control Group 60 37.07
Table 14 showed a significant difference in creativity skills between the experimental and control
groups (df=120, t=6.261, p-value=, p<0.05). It showed that the experimental group had a higher
average creativity value in the post-test than the control group. Therefore, the impact of the treatment
was already apparent in the post-test scores after the implementation of the Constructivist Feedback-
Based Assessment Method in the experimental group. It can be seen in the P-value > 0.05 that the null
hypothesis is rejected. Based on the data, it can be interpreted that H0 is rejected, which means there
are differences in the creativity skills of experimental and control group students after implementing the
Constructivist Feedback-Based Assessment Method. This is in line with research conducted by (Jawad
et al., 2021), which states that implementing feedback assessment methods significantly impacts the
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71
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
development of students’ creativity abilities. Feedback given to students can stimulate the exploration of
new ideas. By understanding the positive aspects of their creative ideas, students become more motivated
to try different approaches and expand the boundaries of their creativity (Balakrishnan, 2022); when
students get positive feedback and feel supported in their creative ideas, they feel more ownership of that
creativity. This sense of ownership increases the motivation to continue developing and expressing their
creativity (Cai et al., 2020). Feedback assessment methods can expand and enrich students’ creativity
skills by providing constructive and supportive feedback. This is not only beneficial in the context of formal
education but also prepares students to face creative challenges in daily life and the future (Fortuna et al.,
2023; Sansi et al., 2023).
Linear Regression Analysis
Linear regression analysis is a statistical method to understand a study’s relationship between
two or more variables. In linear regression, the main objective is understanding how the dependent
variable (y) relates to one or more independent variables (x). This relationship can be linear, which means
that constant changes in the independent variables can explain changes in the dependent variable. In
this study, there are four independent variables, namely adaptive capacity (X1), decision-making (X2),
problem-solving (X3), and creativity (X4), and one dependent variable, namely Constructivist Feedback-
Based Assessment Method (Y). So, the linear regression equation is as follows.
y = a + bx1 + bx2 + bx3 + bx4 + ε (1)
Here, y is the dependent variable, x is the independent variable, a is the intercept (the value of
y when x = 0), b is the regression coefficient (shows how much change is expected when increasing
by one unit), and ε is the prediction error. Before conducting a linear regression analysis, the analytical
prerequisite tests of normality (Figure 3a) and linearity (Figure 3b) must be performed first.
Figure 3. Results of QQ-Plots Normality (a) and Scatter Plots Linearity (b) of research variables
Figure 3 above shows that the distribution of research data was normal and linear. Based on the
results of the Saphiro-Wilk analysis, the adaptive capacity variable (Figure 3a1) [p > 0.05, W = 0.76], the
decision-making variable (Figure 3a2) [p > 0.05, W = 0.92], the problem-solving variable (Figure 3a3) [p
> 0.05, W = 0.81], and the creativity variable (Figure 3a4) [p > 0.05, W = 0.79] indicate that the research
data are normal. In addition, the scatter plot illustration on each variable shows that the observation
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72
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
points are evenly distributed, indicating that the linearity assumption has been met. Therefore, it can be
concluded that this research data is eligible for parametric analysis, namely linear regression analysis
(Table 11), because it meets the analysis requirements test.
Table 15. Linear Regression Analysis Results
B t Sig. r2
Constant 16.577
Adaptive Capacity 0.740 9.166 0.000 0.71
Decision-Making 0.766 8.460 0.000 0.75
Problem-Solving 0.806 9.376 0.001 0.82
Creativity 0.788 9.643 0.000 0.77
Sample 121
P-Value 0.000
F Value 18.784
Regression Equation y = 16.577 + 0.740x1 + 0.766x2 + 0.806x3 + 0.788x4 + ε
In this Linear Regression analysis, the pre-test was used to compare adaptive capacity, decision-
making, problem-solving, and creativity abilities between the experimental and control groups after being
given a pre-test before and post-test after treatment. The Linear Regression Analysis presented in Table
15 showed that there is a significant difference between the experimental group and the control group after
implementing the Constructivist Feedback-Based Assessment Method treatment (F (l, 119) = 18.784, p <
0.05). There was a significant difference between the experimental group students and the control group
in the ability of adaptive capacity (p < 0.05), decision-making (p < 0.05), problem-solving (p < 0.05), and
creativity (p < 0.05), where the experimental group students have better abilities than the control group
(Figure 4) after the implementation of Constructivist Feedback-Based Assessment Method. Table 15 also
showed that 71% adaptive capacity, 75% decision-making, 82% problem-solving, and 77% creativity
influence Constructivist Feedback-Based Assessment Method.
Figure 4. Comparison of Experimental and Control Class Skill Percentages
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73
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
This study’s results align with (van der Kleij, 2019), which explains that feedback assessment
provides positive results on student skills. Implementing constructivist feedback-based assessment
methods allows learners to know what and why they will learn to become active participants in the passive
learning process. When introducing a new topic, learners must share the objectives they need to get good
results and notes (Oliveira et al., 2021). From the beginning of learning, learners are responsible for their
learning, allowing each to create their knowledge of the subject, cooperate with their peers and educators,
expand their framework, and move towards better knowledge and understanding of complex subjects
(Falloon, 2020). One of the benefits of sharing learning objectives with learners is that they will be given
tasks that match the learning objectives. According to (Ibarra-sáiz et al., 2020), effective assessment is
applied by providing feedback during learning to regulate the teaching and learning process to improve
learners’ achievement. According to (Tang et al., 2020), assessment can be considered a valid and
essential part of integrating teaching and assessment. Assessments inform educators about whether
learners have learned, and they have qualifying indicators of how educators should plan subsequent
lessons (Firestone and Donaldson, 2019). There are four main components to assessment (Morales,
2022): (i) clarifying learning objectives and success criteria; (ii) improving the quality of inquiry/dialogue;
(iii) improving the quality of marking/feedback/recording; and (iv) using self and peer assessment.
One of the key elements of assessment is asking questions (Guangul et al., 2020). Educators
can use one-third of their teaching time to ask learners questions (Alt et al., (2023)). Asking questions
in the form of a feedback-based assessment is essential for gaining information about learners’ learning
and understanding. This goal can be achieved if questions are active and influential in determining and
constructing the learner’s depth of knowledge (Mohammed et al., 2020). Feedback is at the heart of this
assessment method (Waskito et al., 2023). The impact of this assessment method arises from the power
of feedback given to learners about their learning (Brown, 2019). According to Shute [49], feedback is
information sent to learners that enables or encourages them to regulate thoughts or behaviors to improve
learning. According to (Brown, 2019), feedback provided through assessment significantly benefits
learners’ motivation, helps learners improve the quality of learning, strengthens learners’ memory, and
gives learners a profile of learning.
Conclusions
This study developed the Constructivist Feedback-Based Assessment Method. It looked at the
effectiveness of implementing the Constructivist Feedback-Based Assessment Method on students’
adaptive capacity, decision-making, problem-solving, and creativity. The results show that the Constructivist
Feedback-Based Assessment Method effectively improves students’ adaptive capacity, decision-making,
problem-solving, and creativity. It also proves that a suitable assessment method can affect students’
skills. This research contributes to existing knowledge, especially on assessment in learning, where this
research provides a constructive assessment method that can be used as a consideration for teachers
to apply to learning. The limitation of this research is that the constructivist feedback-based assessment
method that has been developed has only been applied to see the students’ adaptive capacity, decision-
making, problem-solving, and creativity. It is hoped that this constructivist feedback-based assessment
method can be applied for future research to see its effectiveness on other students’ skills and abilities.
Acknowledgments
The author would like to thank Lembaga Penelitian dan Pengabdian Masyarakat Universitas Negeri
Padang for funding this work with contract number 1045/UN35.13/LT/2022.
Conflict of interests
The authors declare that they have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.
Author Contributions
Conceptualization, W.W., R.E.W.; Methodology, W.W., R.R., R.E.W.; Formal analysis, R.E.W., A.F.;
Validation, A.N., writing—original draft preparation: W.W, R.E.W.; writing—review and editing, R.E.W.,
www.ijcrsee.com
74
Waskito, et al. (2024). Constructivist feedback-Based assessment method as key for effective teaching and learning: The
cevelopment and impact on mechanical engineering students’ adaptive capacity, decision making, problem solving and
creativity skills, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 12(1), 57-76.
A.F., S.’A.J.; Visualization: W.W., R.R., A.F., A.N. All authors have read and agreed to the published
version of the manuscript.
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