The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016

Assessing the Metacognitive Awareness among Foundation in
Engineering Students

Betsy Lee Guat Poh, Kasturi Muthoosamy, Chiang Choon Lai,
Ooi Chel Gee48	
  The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	  Abstract

The transition phase is a critical moment to students who have completed their secondary
school education and are proceeding to pre-university education in Malaysia. The long durationof exposure to rote-learning and examination oriented education systems at school has
somehow shaped these students’ perceptions about teaching and learning. Thus, this paper aims
to examine the quality of first year students’ experiences in constructing their knowledge andskills throughout the Foundation in Engineering (FIE) programme. This experience refers tometacognitive awareness, namely students’ learning experience from one mode of thinking tothe other to construct meaningful knowledge and skills. The researchers used the Metacognitive
Awareness Inventory (MAI) (Schraw and Dennison, 1994) as a rating tool to trace the students’baseline in metacognition and access their successive levels of metacognitive awarenessthroughout their first semester in the FIE program. The students showed improvements in a
number of metacognitive sub-processes. The findings provided the details of the quality of the
program’s efficacy and served as a benchmark for future development of effectiveness ofteaching and learning approaches.
Keywords: metacognition; metacognitive awareness; teaching and learning; academic
achievements; MAI.

	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	
  Introduction
The term “metacognition” was coined by John Flavell (1979) to described the state ofconsciousness of one’s own thinking and learning processes (Kayashima et al., 2004). Learners
exhibiting metacognition are acutely aware of the knowledge content in their mental resources
and possess the ability to control and monitor these cognitive activities to perform higher-order
thinking skills (Ozsoya & Ataman, 2009; Pennequin et al., 2010). Thus, two essential
components play a dominant role in the control of metacognition i.e. metacognitive knowledge
and metacognitive skills (Hollingworth & McLoughlin, 2001). Metacognitive knowledge
refers to what one recognizes about his or her own potential in processing information, aboutknowing the features of a task and also allocating appropriate strategies that can be applied tosuccessfully accomplish a task (Flavell, 1987; cited in Hollingworth & McLoughlin, 2001).
Metacognitive skills refer to the ability to use metacognitive knowledge effectively (Ozsoya &Ataman, 2009). Metacognitive activities help to control and monitor one’s own cognitive
system and functioning process. Self-regulation exercises commit one to demonstrate highorder executive skills such as prediction, planning, monitoring and evaluation (Ozsoya &Ataman, 2009; Schneider & Artelt, 2010).
Engineers by definition are real life problem solvers, critical thinkers and innovators. It is
expected from the engineers to develop solutions for various application problems. In otherwords, they are self-regulated learners and possess the ability to think metacognitively. The
path to become an engineer regardless of specialization primarily relies on engineering
education. Thus, the development of engineering students’ thinking abilities highly depends on
the teaching and learning process and the contextual learning environment during theiracademic years. This includes the exposure of students to various engineering concepts andhands-on experience to develop their technical skills. In other word, metacognitive skill is an
integral part of the knowledge development that engineering students should cultivate andmaster as early as possible starting from the Foundation in Engineering (FIE).
Problem Statement
The transition period from school to university is a critical moment to upgrade students’ ability
to university students’ status. Students’ performance at the primary and secondary school levelin Malaysia is constantly assessed by grade levels achieved in their examinations. In theprocess, they fail to develop an inquisitive mind and analytical skills as most of their time isspent attending tuition classes, extra classes, and examination workshops to better prepare themfor the upcoming examinations. As a result, these students retain a rote learning mindset andstudying pattern when they enter the university. These are the common issues observed in firstyear students at other institutions as well (Bowles et. al., 2011; Briggs, Clark & Hall, 2012). In
order to provide the academic preparation of the first year entry in the FIE programme, theFoundation Engineering School has begun to review the performance of its programme toensure that it provides students with top notch engineering education. Thus, this study aims toassess the FIE students’ baseline and follow-up levels of metacognitive awareness throughout
the program.
Literature Review
The importance of metacognitive awareness in teaching and learning has been widely
acknowledged (Hurme & Jarvela, 2001; Ozsoya & Ataman, 2009; Schneider & Artelt, 2010;
Stillman & Mevarech, 2010). Nevertheless, metacognition is an inner awareness rather than anobservable behavior which is crucial to measure such ability. Several explorations have been
carried out by researchers to discover appropriate instruments to measure the metacognitive
	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	
  ability. Schraw and Dennison (1994) developed the 52 item Metacognitive Awareness
Inventory (MAI) to measure adults’ metacognitive awareness. The findings indicated that MAI
provides a reliable initial test of metacognitive awareness among older students. Kazemi and
Ghoraishi (2012) measured university students’ metacognitive awareness in mathematicalproblem solving by using two methods i.e., protocol analysis and self-questionnaire. A total of
64 university students were asked to write their total mental process during problem solving
and subsequently they responded to a metacognitive inventory that rated their metacognitive
abilities. The results showed that both methods were applicable for measuring metacognitiveawareness.
Self-questionnaire is the most extensively used method to measure metacognition, whereby itallows the participants themselves to rate their metacognitive skills without a researcher’sinterference. Young and Fry (2008) assessed Schraw and Dennison’s MAI to ascertain how themetacognitive rating associates to single tests and cumulative GPA as well as end-course
grades for college students within one semester. The findings revealed a significant positive
correlation between the MAI and overall academic performance. However, they were amazedto discover the insignificant correlation between the MAI scores and a single test of a course.
According to their report, single test performance might be influenced by the affective
behaviors of students over a particular course. In another study, Kesici,Erdogan, and Özteke.,(2011) examined differences in metacognitive awareness strategies in prediction of high school
students’ mathematics and geometry course achievements. Schraw and Dennison’s MAI(1994) was also adapted in the study and discovered that declarative knowledge is a significant
predictor of mathematics course achievement while evaluation and procedural knowledge ofmetacognitive awareness strategies are significant predictors of geometry course achievement.
Ciascai and Lavinia (2011) employed the Junior Metacognitive Awareness Inventory to
scrutinize the potential gender differences in metacognitive abilities among a group of eighth
grade pupils. Their statistical analysis indicated that the boys and girls adapted differently intheir metacognitive knowledge and skills in the learning process.
However, subsequent research reports inconclusive findings regarding the differences inmetacognition according to pupils’ gender. Abdolhossini (2012) reported the effects ofcognitive and meta-cognitive methods of teaching mathematics subject for high school
students. The results showed that cognitive and meta-cognitive methods of teaching had
positive effects on educational progress of male and female students. Nevertheless, no positive
relation was observed between the boys’ and girls’ average grades. Ayazgok and Aslan (2014)
examined science and mathematics university students’ reflective thinking skills and level ofmetacognitive awareness according to age, gender and the level of class and found that there
was no significant difference according to gender regarding metacognitive awareness orreflective thinking. Thus, there are a variety of challenges related to metacognition
investigation. For instance, Bersley and Spero (2014) compared three groups of college
students who received different instruction methods of the same course material. They revealed
that the group receiving direct infusion of critical thinking increased the students’ knowledgeof what they knew and did not know. In other word, the students’ metacognitive awarenesswas stimulated through the act of intervening. Hoorfar and Taleb (2015) studied the correlation
between mathematics anxiety and metacognitive knowledge for 323 seventh grade female
students. Results showed that mathematics anxiety was negatively correlated withmetacognitive knowledge. On the other hand, Bayat and Meamar (2016) investigated to what
extend algebra problem solving performance, metacognitive strategies and cognitive strategies
served as predictors of mathematics achievement in a public university in Malaysia. The
findings revealed asignificant contribution of algebra problem solving performance and overall
metacognition in mathematics achievement.
	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	    Thus, the purpose of this study is to trace the students’ baseline in metacognition and accesstheir successive levels of metacognitive awareness throughout their first semester in the FIE
programme. In addition to this,, the researcher would like to measure to what extent the
metacognitive awareness served as a determining factor to students’ overall academic
performance.
Methodology
In this study, a quantitative method was used. The quantitative data helped to tracestudents’baseline in metacognition and access their successive levels of metacognitive awarenessthroughout their second semester in the FIE programme. The researchers also examine to what
extent the MAI scores served as a determining factor to students’ overall academic
performance.
Participants
173 surveys were distributed to the FIE students, out of which 75 were disqualified and 98
valid surveys were analyzed. About 23.5% of the survey participants were female and the rest
were male (Figure 1). This is the usual female to male ratio in any engineering department.
Although gender is sometimes perceived to be a factor in the outcome of the MAI scorea priorreport (Abdolhossini, 2012; Ayazgok & Aslan, 2014) revealed insignificant gender differenceson metacognition abilities, thus in this present study the gender factor has been disregarded.
The programme consists of three semesters and the study was conducted when the participants
were in their second semester. There were six modules offered in Semester 2 i.e., Calculus 1,
Mathematical Techniques, Computer Method, Electricity and Magnetism A, Thermal Science
A and Study Skills. Study Skills was delivered as a project-based subject where the students
worked in groups to organize a charity event such as a marathon, blood donation drive, concert
and others. The aim of this module was to develop report writing skills and soft skills in orderto prepare them for undergraduate studies and for future careers.

Gender

23

75

Male

Figure 1. Gender Composition

	
  

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Female

The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	    Instruments
Schraw and Dennison’s MAI (1994) was used in this study. In the MAI inventory, there are 17
items related to Knowledge of Cognition (declarative knowledge, procedural knowledge,
conditional knowledge) and 35 items related to the Regulation of Cognition (planning,monitoring, evaluation, debugging strategies and information management strategies). The 52
items were measured by a 5-point Likert scale ranging from “strongly disagree” to “strongly
agree.” A list of abbreviations describing the metacognitive components of Knowledge of
Cognition and Regulation of Cognition is exhibited in Table 1 while Figure 2 shows thecomposition of questions in percentage for each metacognitive component.
Table 1. List of abbreviations representing the metacognitive components of MAI
Abbreviation

Meaning

IMS

Information Management Strategies

DK

Declarative Knowledge

M

MonitoringP

Planning

E

Evaluation

PK

Procedural Knowledge

CK

Conditional Knowledge

DS

Debugging Strategies

Composition	
  of	
  questions
9.6%
19.2%
9.6%

9.6%

15.4%

11.5%
13.5%

11.5%
IMS

DK

M

P

E

PK

CK

DS

Figure 2. The percentage of items for each metacognitive component

	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	    Procedure
The participants were given the survey on the 1st, 6th and 10th week of semester two. An
introduction about the study was presented to the students before the first survey was
conducted. The participants were informed about the confidentiality of their responses and their
participation was on a voluntary basis. During the second survey, the results of the first survey
were reported to the participants and were explained briefly about their baseline inmetacognitive skills. At the final survey, the students were given a brief statement about their
metacognitive progression based on the second survey’s results before they filled in thequestionnaire.
Data Analysis
The quantitative data were analyzed using SPSS 15.0 to measure the descriptive status anddistribution of the data set. In order to examine the significance of metacognitive awareness as
an influential factor on students’ academic performances, Spearman’s Rho non-parametriccorrelation analysis was carried out.
Results and Discussions
Overall, there was a gradual increase in positive responses from Survey 1 up to Survey 3(Figure 3), with a significant decrease in the Strongly Disagree sector.

0.6%
Survey  3 6.2%

Responses  for  All  Three  Surveys
28.8%

49.6%

14.8%

2

1.0%
Survey  2

7.7%

29.8%

47.7%

13.9%

10.2%
0%

3
4

1.4%
Survey  1    

1

29.9%
20%

42.6%
40%
60%
Percentage  response  (%)

15.9%
80%

5

100%

Figure 3. The distribution of the agreement and disagreement scales for the three conductedsurveysNote: 1=Strongly Disagree, 2= Disagree, 3= Neutral, 4= Agree and 5= Strongly Agree.
Though no intervention was carried out in this study, the positive response is perceived to be
due to students’ persistent exposure to and awareness of the various skills in learning. The
students were briefed about all the skills involved in metacognition during the three surveys.
For instance, when Survey 2 was conducted, the students were given feedback on the overall
MAI score in Survey 1 before they answered a series of questions reflecting their metacognitiveawareness. Similarly, prior to Survey 3, feedback on Survey 2 was given with extensive
explanation regarding the students’ strengths and weaknesses. This could have initiated thestudents to recognize and reflect on their metacognative abilities and explore unattained
metacognitive skills throughout the whole semester.

	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	    The in-depth study focusing on the Knowledge of Cognition and Regulation of Cognitionfor
both sectors showed a gradual increase in the mean score over the three surveys conducted asshown in Table 2. As aforementioned, with three surveys conducted within a short duration(one semester), the students were constantly reminded of the learning skills available for themto explore and enhance their learning experience. This could have played a role with thepositive outcome on both sectors of Knowledge and Regulation of Cognition. Metacognition
is a self-awareness ability, and students are often not conscious about their knowledge andskillsin the learning process (Kazemi & Ghoraishi, 2012).
Table 2. Mean and standard deviation of the MAI score
Mean and standard deviation
Survey 1

Survey 2

Survey 3

Overall MAI score

3.62 ± 0.350 3.66 ± 0.322 3.72 ± 0.340

Knowledge of
Cognition

3.56 ± 0.827 3.64 ± 0.753 3.68 ± 0.702

Regulation of
Cognition

3.64 ± 0.822 3.67 ± 0.757 3.74 ± 0.713

Responses Difference between the Surveys
Figure 4 compares the score for all eight components categorized in the MAI based on Agree,
Neutral and Disagree divisions. Initially, the students revealed their strong awareness,
especially in their regulation abilities and their strength in debugging skills, which exhibitedthe highest. However, the ten weeks of teaching and learning sessions exposed the students toa variety of activities that added to their metacognitive knowledge and experiences.
InKnowledge of Cognition, the level of agreement on the subdivisions, such as declarativeknowledge and conditional knowledge showed a continuous increase. However, the students’opinion about their procedure knowledge decreased slightly after the second survey.

	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	
  Figure 4. The breakdown of the responses of participants on the eight MAI skills based on three
types of crowds (Agree, Neutral, and Disagree)
Regarding metacognitive experiences (Regulation of Cognition), the students showed greater
abilities in management, evaluation and information management strategies. However,students’ awareness about their debugging skills decreased over the three surveys.
The discouraging response for debugging skills could be due to the fact that initially (during
Survey 1) the students were unfamiliar with the content and depth of knowledge required fromeach module as well as the lecturer’s expectations. However, as the weeks of teaching andlearning passed, the students began to realize the demands and challenges from each moduleand thus the low response in debugging skills. This would be especially felt in modules that
require theoretical knowledge and applications (problem solving skills), such as Calculus 1 andThermal Science A. Anxiety and low confidence has been found to be directly related tonegative metacognition (Hoorfar & Taleb, 2015).
When responses between surveys were compared, more than 5% difference in the evaluation
skills were observed between Survey 1 and 2 (Figure 5). In other word, the students showed
higher positive responses when it comes to items such as “I know how well I did once I finish
a test,” “I summarized what I’ve learned after I finish,” and “I ask myself how well I accomplish
my goals once I’m finished.” On the other hand, the students’ disagreement responses in termsof planning skills exhibited a difference of more than 5% between the two surveys.
Some students presented a lesser negative attitude toward planning when they responded in
Survey 2. They seemed confident when they answered the items such as “I pace myself whilelearning in order to have enough time,, I think about what I really need to learn before I begin
a task,” “I set specific goals before I begin a task,” “I ask myself questions about the material
before I begin,” “I read instruction carefully before I begin a task,” and “I organize my time
to best accomplish my goals.”

	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	    Response  Difference  between  Survey  1  and  2  15.0%Percentage  Change

10.0%
5.0%
0.0%

-­5.0%-­10.0%
Disagree  (1&2)

Neutral  (3)

PK

E
DK
MAI  Skills

Agree  (4&5)-­15.0%
M

P

CK

IMS

DS

Figure 5. Differences in students’ responses between Survey 1 and Survey 2
The students showed a decreased response in the debugging strategy after five weeks ofteaching and learning sessions. The students seemed to be hesitant about their debugging
strength when they responded to items such as “I change strategies when I fail to understand,”“I re-evaluate my assumptions when I get confused,” “I stop and go back over new informationthat is not clear” and “I stop and reread when I get confused.”
After the third survey, the students’ responses in planning showed minimal differences i.e., lessthan 1% between the Survey 2 and 3 (Figure 6). However, many students focused on theirstrengths and weakness in their regulation skills, especially on monitoring, evaluation andinformation management skills. There were some students that felt their strength in evaluation
improved over the ten weeks of teaching and learning sessions.
At the same time, some students were more aware of their information management skills andmonitoring skills when they responded to the items such as “I ask myself periodically if I am
meeting my goals,” ‘I consider several alternatives to a problem before I answer,” “I slow
down when I encounter important information” and “I consciously focus my attention on
important information.”. Nevertheless, there was a tremendous drop in the students’ confidencewith the debugging strategy.

	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	
    Figure 6. Differences in students’ responses between Survey 2 and Survey 3
Obvious positive responses were seen for all the eight MAI components except debugging
strategy (Figure 7) over the ten weeks of teaching and learning sessions. As there are six
modules taught for the semester, there is a wide spectrum of learning skills experienced by thestudents. For instance, the Study Skills module, which is project-based, requires the students
to organize a charity event focusing on management proficiency and related skills such as
monitoring, planning and evaluation.
Response  Difference  between  Survey  1  and  3  20.0%Percentage  Change

15.0%
10.0%
5.0%
0.0%

-­5.0%-­10.0%
Disagree  (1&2)

Neutral  (3)

PK

E
DK
MAI  Skills

Agree  (4&5)-­15.0%
M

P

CK

IMS

DS

Figure 7. Differences in students’ responses between Survey 1 and Survey 3
MAI Score and Overall Academic Achievement
A Spearman’s correlation was conducted to determine the relationship between overall
academic achievement and the MAI subscales. The MAI scores were based on survey 3 where

	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	    the students have completed their Semester 2 teaching and learning sessions. Findings from theanalysis are summarized in Table 3.
Table 3. Correlations between MAI components’ scores and overall academic achievement
Exam   Mean   Mean   Mean   Mean   Mean   Mean   Mean   Mean  Result  Spearman's   Exam   Correlation  rho  Result   Coefficient  Sig.  (2-­tailed)  
N  

M  

PK  

1.000   -­.185   -­.037  

P  

E  
-­
*

.203   

DK  

CK  

IMS  

DS  

-­.054   -­.013  .141   -­.163   -­.066  .  

.068  

.717  

.045  

.595  

.896  

.167  

.109  

.520  

98  

98  

98  

98  

98  

98  

98  

98  

98  

*.  Correlation  is  significant  at  the  0.05  level  (2-­tailed).	
  According to the findings of the study, there was no significant correlation between overall
academic achievement and all MAI components. However, there appears to be a weakcorrelation between planning and overall academic achievement r = -0.203, p < 0.05. This
could be mainly due to the fact that this study serves only as an awareness program rather thanan intervention to the existing teaching and learning delivery system. In addition, the survey
was conducted based on all six modules in the semester, whereas a more focused survey on aparticular module might provide a significant correlation between MAI score and academic
achievement. As previously reported, interventions or direct infusion and continual
reinforcement are necessary to improve the metacognitive skills among students, especially formathematics subjects and subjects that require problem solving or critical thinking (Kesici etal., 2011; Bensley & Spero, 2014). In this case, intervention would be necessary to improve
the students’ debugging skills along with the other seven MAI skills. In addition, a mixed
methodology (protocol analysis and self-questionnaire) would be needed to validate andsubstantiate the measurements of metacognitive awareness (Kazemi & Ghoraishi, 2012).
Conclusion
Based on the findings, the aim of increasing awareness among FIE students on their
metacognitive skills is a useful tool for learning efficiency, critical thinking and problem
solving (Kesici et al., 2011). There was an obvious improvement in the eight tested
metacognitive skills based on preliminary (Survey 1), intermediate (Survey 2) and end of thesemester (Survey 3) surveys, with the exception of debugging skills. Nevertheless, there wasno relation between the MAI score and the overall academic achievement of the students.
Despite this limitation, the current study serves as an awareness program for the students andas preliminary data for the lecturers. As a future study, intervention on a specific module will
be carried out with great emphasis on improving the students’ debugging skills.
Acknowledgement
The work was supported by the Teaching and Learning Funding Scheme from the Universityof Nottingham Malaysia Campus (UNMC). The authors are grateful to the University for theirfinancial support and for providing necessary guidance in the implementation of this project.

	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016

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