e-ISSN: 2550-1461 https://ijeisr.net
THE STUDENT TEACHERS’ INTELLECTUAL QUALITY IN MALAYSIA PUBLIC UNIVERSITIES
Nor Asniza Ishak1 Siti Mastura Baharudin2
Nurul Ashikin Izhar3
1School of Educational Studies, Universiti Sains Malaysia (USM), Malaysia (E-mail: [email protected])
2School of Educational Studies, Universiti Sains Malaysia (USM), Malaysia (E-mail: [email protected])
3School of Educational Studies, Universiti Sains Malaysia (USM), Malaysia (E-mail: [email protected])
Abstract: As student teachers in the higher education field are the future frontline in nurturing intellectual quality among future generations at schools, they must first be equipped with intellectual skills. According to the latest studies, intellectual quality includes abilities such as higher-order thinking, extensive knowledge and profound comprehension, constructive debate, problem-solving abilities, and metalanguage. Therefore, this study measures the intellectual quality among 601 student teachers from six public universities in Malaysia. Consequently, the findings show that deep understanding and deep knowledge have a strong positive and significant relationship with constructive discussion dimensions (r > .05). The findings also stated that only gender, the field of study, and year of the study show significant relationships in developing intellectual quality among student-teachers via learning experiences.
Accordingly, the current research serves as an updated scenario of intellectual quality among student-teachers, in accordance with all the initiatives stipulated by the university, educators, and programmes that are aimed at upgrading 21st century skills.
Keywords: Intellectual Quality, Quality Education, Higher-Order Thinking, Deep Understanding And Deep Knowledge, Metalanguage, Constructive Discussion, Problem- Solving Skills, Higher Education
1. INTRODUCTION
The vast majority of educational institutions throughout the world have taken the lead in promoting activities that focus on improving intellectual quality among university students through a variety of curriculum, extracurricular activities, and coursework (Nygren et al., 2019). Intellectual integrity is important because learners, especially ‘student-teachers,’ will act as future agents in cooperation with their peers, community groups, regional, and international bodies to optimise their critical and creative potential (Battaglini & Schenkat, 1987; Katung et al., 1999). Furthermore, in the context of university student-teachers, it is reasonable for students to be more competitive by nature. In Canada, for example, multidisciplinary collaborative projects are aimed at improving the academic and intellectual quality of life. (Huot et al., 2020). Furthermore, Deep et al (2019) and Shively et al (2018)
noted that learning at the university level demands students to understand and question complicated ideas.
Notwithstanding, Tindowen et al. (2017) noted that establishing the crucial capabilities that form intellectual quality is a massive obstacle since this requires learners to acquire knowledge, exercise it, and apply it to concerns and challenges that result in everyday life through rational and effective problem solving. At the tertiary level, the quality of students with key characteristics, such as creativity and critical thinking, contributes directly to Malaysia being a developed country by 2020 (Nor Asniza Ishak, 2015; Yusliza Mohd Yusoff, 2011). Likewise, Qadir and Al-Fuqaha (2020) mentioned that the primary aim of tertiary education is to promote learners’ ability to think independently, creatively, and imaginatively.
At the university level, all student-teachers must apply and share their intellectual abilities, as all students’ intellectual elements must be demonstrated at the tertiary level (Tindowen et al., 2017).
When learners are provided with difficult tasks and engage in meaningful conversations with classmates and instructors while completing assignments, the research established by Nor Asniza Ishak (2020) revealed that learners possess cognitive talents, such as higher order thinking abilities and problem solving tendencies. While communicating with classmates through discussion activities and lecturers in drafting assignment solutions and presenting their assignment products, students also require in-depth knowledge and understanding of themes covered. During the debate and thinking processes, students have the opportunity to voice their thoughts and opinions (Nor Asniza Ishak, Hazri Jamil & Nordin Abd Razak, 2016).
The emphasis of developing intellectual abilities, specifically critical and creative thinking abilities and problem-solving tendencies, is effectively organised throughout Malaysia’s school curricula and higher education subjects. As specified in the Malaysian National Blueprint (2013–2025), the approaches are intended to provide probabilities for and inspire learners to engage and seek answers to any queries they may have regarding the subject’s material (Ministry of Education, 2013). Furthermore, the Ministry of Education emphasised the university’s potential to produce critical thinkers, with creative, critical, and analytical minds, as well as a high level of mind exploration and invention. Higher-order thinking skills (HOTS) have been highlighted in the academic curricula and the Education Development Plan 2013–2025, which expect learners to exhibit and implement the knowledge of testing hypotheses, gathering data, drawing conclusions, and making assertions in the class environment in order to meet the required necessities for 21st century learning strategies (Ministry of Education, 2013). To summarise, Malaysia’s educational system places a strong emphasis on intellectual development as one of the learning outcomes in order to help pupils reach their full intellectual and spiritual potential (Ministry of Education, 2013).
Deep grasp of the topic, issue, or discipline of study associated with the topic, issue, or other disciplines is one of the intellectual quality components emphasised in pupils (Yadav, 1985). Six constructs based on the productive pedagogy framework’s intellectual quality dimensions will be used in the research to assess the extent of intellectual quality among university learners (QSRLS, 2001; Lingard et al., 2001). The intellectual quality component of the productive pedagogical framework has six sub-dimensions: (i) higher order thinking abilities; (ii) extensive knowledge; (iii) extensive comprehension; (iv) constructive debate; (v) problem solving; and (vi) metalanguage. Figure 1 shows that according to research, the six
stated sub-dimensions are subsequently changed by elements of generic abilities (Kember, 2009; Rodiah Idris et al., 2009; Halizah Awang, 2010; Nor Asniza Ishak, 2015)
Figure 1: The Intellectual Quality Dimensions
Higher-Order Thinking
This is the degree to which learners are involved in the alteration of data and ideas. These changes occur when learners synthesise, generalise, explain, hypothesise, infer, and translate facts and ideas.
Deep Knowledge and Deep Understanding
The degree to which learners concentrate on a certain idea or discipline is deemed important.
On a topic or field, learners might develop complicated links between fundamental concepts.
Additionally, learners may create new knowledge via the construction of connections, the resolution of issues, the creation of descriptions, and the drawing of conclusions.
Constructive Discussion
This is the degree whereby educators and learners have positive interactions on the topic at hand. There is bilateral interaction that leads to mutual opinion.
Problem-Solving Skills
This is the degree whereby learners can resolve classroom conflicts relating to a certain subject, problem, or topic imposed by the instructor.
Metalanguage
This metric measures the proportion of learners who use correct grammar (vocabulary, specific technical words) in classroom discussion and writing about a given topic.
As a consequence, the aim of this research is to gather data on the intellectual integrity of student teachers at Malaysian public higher education institutions to help students build their intellectual assets to pace with the swift progress of the nation in the 21st century. Furthermore,
the findings of this study will aid lecturers and university administrators in gaining a better understanding of how to build students’ intellectual quality through activities in the courses they take in preparation for entering the real world of education.
2. METHODOLOGY
The intent of this study is to utilise quantitative techniques, namely the survey method, to ascertain the extent of intellectual quality among the university students utilising a large sample size. The sample provided information through questionnaires designed to gather research data.
According to Creswell (2018), surveys may elicit a larger number of responses and provide more thorough coverage. Additionally, when a questionnaire is properly designed, it is easier to administer and the data is readily processed for analysis.
A survey was performed using the Students’ Intellectual Quality Survey instrument, which was created by adapting the questionnaire from Nor Asniza Ishak (2015). Each questionnaire item is classified into six categories in accordance with productive pedagogy; i) Higher Order Thinking Skills (7 items); ii) Deep Knowledge and Deep Understanding (8 items); iii) Constructive Discussion (7 items); iv) Problem Solving (13 items); v) and Metalanguage (5 items). The questionnaire comprises 51 questions on a 5-point Likert-type scale. A pilot study was conducted on 100 student-teachers studying at one of the local public universities. The actual research did not include all 100 students who participated in the pilot study. The pilot test data was processed by means of Statistical Package for Social Science (SPSS) version 24. A Cronbach’s Alpha reliability test was applied to determine the internal consistency of the items created as indicated in Table 1.
Table 1: Summary Of Each Construct Reliability In The Survey Of Students’ Intellectual Quality
Construct Reliability Conclusion
Higher Order Thinking Skills Cronbach’s Alpha = 0.74 This instrument has good reliability Deep Knowledge and Deep
Understanding
Cronbach’s Alpha = 0.81 This instrument has very good reliability
Constructive Discussion Cronbach’s Alpha = 0.84 This instrument has very good reliability
Problem Solving Cronbach’s Alpha = 0.93 This instrument has the best reliability
Metalanguage Cronbach’s Alpha = 0.79 This instrument has good reliability
The actual study involved 601 student-teachers in the Malaysian public universities, involving five universities in the North zone, the Central zone, the South zone, and the West Coast zone of Malaysia. The samples were chosen using random sampling techniques.
Quantitative data gained from the fieldwork was analysed using descriptive and inferential statistics. Statistical analyses, including independent t-test, ANOVA, and regression via SPSS 24 were used in this research to answer the research questions. Ethical approval was obtained from the Human Research Ethics Committee of USM (JEPeM) (USM/JEPeM/19030176) prior to the distribution of the survey to the respondents.
3. RESULT
In the current study, females contributed to the larger proportion with 456 respondents, while student teachers from USM answered most of the questionnaire with a corresponding number of 271 students. Malay student teachers dominated the current study with 486 students, and most of the respondents are among the age of 19-22 with the result amounting to 495. The most chosen field of study in the questionnaire among the student-teacher is the literature field, with 267 respondents, while first-year student teachers make up the largest respondent contribution, with 384. Table 2 tabulates all the demographic data of the current study
Table 2: Demographic Data
Demographic Categories N
Gender Male 145
Female 456
University USM 271
UUM 119
UPSI 92
UTHM 90
UMS 29
Ethnicity Malays 486
Chinese 25
India 24
Others 66
Age 19-22 495
23-26 99
27-30 7
Field of Study Literature 267
Special Education 2
Science 20
Biology 43
Chemistry 25
Physics 3
Business Administration 49
Accounting 41
Vocational 88
Modern Language 3
TESOL 26
Moral 12
IT 3
Mathematics 2
Counselling 4
Art 1
Early Child Education 5 Psychology and Education 3
Sports Science 2
History 1
Communication 1
Year of Study First Year 384
Second Year 109
Third Year 65
Fourth Year 43
Level 3 Level 2
Level 1 100
80 60 40 20 0
Percentage
The Level Of Intellectual Quality Among Student Teachers
The sum score was computed to measure the level of intellectual quality among student teachers. According to the following Table 3, the sum score is 96,497.00.
Table 3: The Sum Of Score Obtained In The Current Study
N Minimum Maximum Sum Mean Std. Deviation
(SD)
Sum Score 601 40.00 200.00 96497.00 160.56 23.21
Valid N (listwise) 601
To identify the level of intellectual quality, the following formula was used:
Mean – 1 SD < mean < mean + 1 SD 137.35 <160.56 < 183.77
Hence, the current study obtained three levels of intellectual quality among student teachers, as in Table 4.
Table 4: Level Of Intellectual Quality Among Student Teachers
Level Description Value
(Mean – 1 SD < mean < mean + 1 SD)
N Percentage (%)
Level 1 Weak 137.50 45 7.50
Level 2 Moderate 160.56 472 78.60
Level 3 High 183.77 84 14.00
TOTAL 601 100.0%
With the tabulated data of Table 3, Figure 2 of the level of intellectual quality among student teachers was illustrated.
Figure 2: The Level Of Intellectual Quality Among Student Teachers
From the result, the current study found that the level of intellectual quality among student teachers is moderate.
The Intellectual Quality Level Among Student Teachers For Each Dimension of Intellectual Quality
According to Table 5 below, the highest intellectual quality level among student teachers is Problem-Solving with the sum score of 31,037.00, followed by Deep Knowledge and Deep Understanding with the sum score of 19,396.00. Meanwhile, the lowest intellectual quality level among student-teachers is Metalanguage with the sum score of 12,358.00.
Table 5: The Level of Intellectual Quality Among Student Teachers Based on Each Dimension N Minimum Maximum Sum Mean Std. Deviation
Higher Order Thinking 601 7.00 35.00 16922.00 28.16 4.29
Deep Knowledge and Deep Understanding
601 8.00 40.00 19396.00 32.27 5.14
Constructive Discussion 601 7.00 35.00 16784.00 27.93 4.55
Problem Solving 601 13.00 65.00 31037.00 51.64 8.83
Metalanguage 601 5.00 25.00 12358.00 20.56 3.68
Valid N (listwise) 601
The Intellectual Quality Levels Among Student Teachers According To Gender And Educational Field
The intellectual quality levels of student teachers on the basis of gender were determined by means of an independent sample t-test. Before proceeding with the independent t-test, the mean between male and female student-teachers was obtained and tabulated in Table 6.
Table 6: Mean Between Gender
Gender N Mean Std. Deviation Std. Error Mean
Sum Score Male 145 166.5655 19.84872 1.64843
Female 546 158.6513 23.89002 1.11875
Table 7 shows Levene’s test for equality of variances. The variance is assumed to be not equal with F = .10 and p = .75. Additionally, the intellectual quality levels among student teachers according to gender are different with the p = <.05. Thus, the null hypothesis is rejected.
Table 7: Levene’s Test Between Intellectual Quality Among Student-Teachers And Gender Levene’s Test for Equality
of Variances
t-test for Equality of Means 95% Confidence Interval of the
Difference
F p t df p (2-
tailed)
Mean Difference
Std. Error Difference
Lower Upper
Sum Score
Equal variances assumed
.10 .75 3.61 599 .000 7.91 2.19 3.61 12.22
Equal variances not assumed
3.97 287.88 .000 7.91 1.99 3.99 11.84
Next, ANOVA was used to measure the intellectual quality levels among student teachers according to educational levels. One of the assumptions of ANOVA is that data must be followed by a normal distribution. Hence, in accordance with the Kolmogorov-Smirnov and
Shapiro-Wilk tests of normality, the sum score data is normally distributed with a statistic of
= .14 (Sig. = <.05) and a statistic of = .84 (Sig. = <.05) respectively, as tabulated in Table 8.
Table 8: Normality Test
Kolmogorov-Smirnov Shapiro-Wilk Statistic df p Statistic df p Sum Score .14 601 <.001 .84 601 <.001
*Lilliefors Significance Correction
In addition, referring to ANOVA in Table 9, the intellectual quality levels among student teachers according to educational fields, e.g., science, mathematics, etc., are different with the F = 1.85 and p = .01. Thus, the null hypothesis is rejected.
Table 9: ANOVA Analysis Between Intellectual Quality And Education Level Sum Score Sum of Squares df Mean Square F p
Between Groups 19426.001 20 971.30 1.85 .01
Within Groups 303886.032 580 523.94
Total 323312.033 600
Correlation Between Each Dimension of Intellectual Quality Among Student Teachers Based on the Pearson correlations, all factors influenced intellectual quality development among student-teachers with all p = <.001. All the correlations show that the relationship between each combination of intellectual quality among student teachers is significantly and positively correlated to the Pearson correlation values above 0.60. The highest positive correlations were seen between Deep Knowledge and Deep Understanding with Constructive Discussion with .77. Table 10 shows the tabulated data.
Table 10: Correlation Between Each Dimension Among Student Teachers
1 2 3 4 5
1. Sum Higher Order Thinking
Pearson Correlation 1 .75** .76** .66** .66**
p (2-tailed) <.001 <.001 <.001 <.001
N 601 601 601 601 601
2. Sum Deep Knowledge & Deep Understanding
Pearson Correlation .75** 1 .77** .71** .70**
p (2-tailed) <.001 <.001 <.001 <.001
N 601 601 601 601 601
3. Sum Constructive Discussion
Pearson Correlation .76** .77** 1 .72** .66**
p (2-tailed) <.001 <.001 <.001 <.001
N 601 601 601 601 601
4. Sum Problem Solving
Pearson Correlation .66** .71 .72** 1 .64**
p (2-tailed) <.001 <.001 <.001 <.001
N 601 601 601 601 601
5. Sum Metalanguage Pearson Correlation .65** .70** .66** .64** 1 p (2-tailed) <.001 <.001 <.001 <.001
N 601 601 601 601 601
**. Correlation is significant at the 0.001 level (2-tailed)
Factors That Influence The Development of Intellectual Quality Among Student Teachers Multiple linear regression utilised to ascertain the variables influencing the intellectual growth of student teachers. The predictors were adjusted to Study Year, Gender, Ethnicity, and Field of Study as in Table 11.
Table 11: Multiple Regression
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .201a .041 0.35 22.80
Predictors: (Constant), Study Year, Gender, Ethnicity, Field of Study Dependent variable: Sum Score
Based on the ANOVA Table 12, the multiple linear regression model is significant with F = 6.43 with p =<.001.
Table 12: ANOVA Table Of Multiple Regressions
Model Sum of Squares df Mean Square F p
Regression 13370.975 4 3342.74 6.3 .000b
Residual 309941.06 596 520.04 Total 323312.03 600
Dependent variable: Sum Score
Predictors: (Constant), Study Year, Gender, Ethnicity, Field of Study
According to the Sig. all the factors gender (t= -3.72, p = .001, ethnicity (t= .14, p =.89), field of study (t=-2.48, p =.01) and year of study (t=-1.49, p = .14) significantly influence the intellectual quality with Sig. less than .05. Table 13 and Table 14 shows all the tabulated data.
Table 13: The Factors That Influence The Intellectual Quality Model Unstandardised
Coefficients
Standardised Coefficients
t p 95% Confidence
Interval for B
B Std. Error Beta Lower Upper
1 (Constant) 180.32 4.46 40.46 .001 171.57 189.07
Gender -8.11 2.18 -.150 -3.72 .001 -12.39 -3.83
Ethnicity -.14 .96 -.006 -.14 .89 -2.02 1.75
Field of Study -.57 .23 -.107 -2.48 .01 -1.02 -.12
Year of Study -1.56 1.04 -.063 -1.49 0.14 -3.61 .49
Table 14: Residual Statistics
Minimum Maximum Mean Std. Deviation N
Predicted Value 148.65 169.95 160.56 4.72 601
Residual -129.95 44.53 .00000 22.73 601
Std. Predicted Value -2.52 1.98 .000 1.00 601
Std. Residual 15.70 1.95 .000 .99 601
a. Dependent variable: Sum Score
Figure 3: Scatter Plot
Development Of Intellectual Quality Via Learning Experience
Multiple linear regression was used to examine the intellectual quality via learning experience.
Based on ANOVA in Table 15, the multiple linear regression model is significant with F = 4.91 with p = <.001.
Table 15. ANOVA For Intellectual Quality Via Learning Experiences
Model Sum of Squares df Mean Square F p
1 Regression 15263.28 6 2543.88 4.91 <.000b
Residual 308048.75 594 518.60
Total 323312.03 600
Dependent variable: Sum Score
Predictors: (Constant), Study Year, Gender, Ethnicity, Field of Study, Age, University
In addition, the multiple linear regression model is shown below:
𝑆𝑢𝑚𝑠𝑐𝑜𝑟𝑒 = 178.06 + (−7.54) 𝐺𝑒𝑛𝑑𝑒𝑟 + (−.29) 𝐸𝑡ℎ𝑛𝑖𝑐 + (1.90) 𝑈𝑛𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 + (. 44)𝐴𝑔𝑒 + (−.91) 𝐹𝑖𝑒𝑙𝑑 𝑜𝑓 𝑠𝑡𝑢𝑑𝑦 + (−2.97) 𝑌𝑒𝑎𝑟 𝑜𝑓 𝑠𝑡𝑢𝑑𝑦
According to the Sig as in Table 16, only gender (t = -3.40, p = <.001), field of study (t
= -3.01, p = <.003), and year of study (t = -2.08, p = <.04) significantly influenced the intellectual quality with a p of less than .05. Consequently, the final multiple linear regression model is as follows:
𝑆𝑢𝑚𝑠𝑐𝑜𝑟𝑒 = 178.06 + (−7.54) 𝐺𝑒𝑛𝑑𝑒𝑟 + (−.91) 𝐹𝑖𝑒𝑙𝑑 𝑜𝑓 𝑠𝑡𝑢𝑑𝑦 + (−2.97) 𝑌𝑒𝑎𝑟 𝑜𝑓 𝑠𝑡𝑢𝑑𝑦
in which the model consists of significant factors and constant only.
Table 16: The Factors That Influence Intellectual Quality Via Learning Experiences
4. DISCUSSIONS
Students in this study showed strong patterns of intellectual quality based on their field of study, age, ethnicity, and year of study. The majority of them are first-year students between the ages of 19 and 22, Malay, and USM students who majored in language. According to the findings, the highest mean intellectual quality score is problem solving.
On the basis of these results, it can be stated that students majoring in languages are much more inclined to have high intellectual quality problem-solving attributes than in other intellectual qualities. This is because most of the structured language field of study courses no longer use traditional methods, but instead use problem solving methods utilising ill-structured problems that mirror real-world problems. In fact, language field of study courses emphasise the active involvement of students during learning. Therefore, the result also reflects the ability of students to independently overcome the language problems arising in the process of communication. According to Jeong and Hmelo-Silver (2010), active learning during the learning process, especially in groups, can regulate problem-solving skills, thus, having a great effect on students’ achievement.
Additionally, findings show that deep knowledge and deep understanding have a very high correlation with constructive discussion. This suggests that the function of discussion or interaction during learning is very important for building problem-solving skills. According to Joksimovic et al. (2014), active involvement via conversation enables these learners to develop at a more profound level. Interactions between students, peers, and teachers facilitate the development of a better or common knowledge of concepts or subjects (Kamin et al., 2001).
The authors emphasise that such specific academic discussions transcend the reporting of facts, processes, or definitions, and instead concentrate on drawing differences, applying concepts, developing generalisations, and asking questions. Similarly, Newmann and Wehlage (1995) identified one of the most effective strategies as “substantive conversation,” in which students engage in extensive interactional conversations regarding a particular subject with the teacher and/or peers in order to develop an enhanced or equivalent understanding of concepts or subjects.
5. CONCLUSION
Increased intellectual capacity is necessary to develop critical, creative, analytical, and inventive human capital. Thus, student-teachers must possess the intellectual capacity to
Model Unstandardised
Coefficient
Standardised coefficient
t p
B Std Error Beta
1 (Constant) 178.06 4.72 37.73 <.001
Gender -7.54 2.22 -.14 -3.40 <.001
Ethnicity -.29 .97 -.01 -.30 .77
University 1.91 1.23 .10 1.55 .12
Age .44 .83 .03 .53 .60 Field of Study -.91 .30 -.17 -3.01 .003 Year Study -2.97 1.43 -.12 -2.08 .04
generate innovative and high-quality ideas and innovations that would eventually constitute routine trend and tradition in the daily life of Malaysians in years to come. The current study concludes that the intellectual quality among student-teachers in Malaysia is still moderate.
This shows that the programme moulded by the Higher Education and university curriculum does shape the intellectual quality of students. However, there is still a loop of improvement that can be made to enhance intellectual quality, such as emphasising the activities that trigger 21st century skills. Nevertheless, Ghazali et al. (2012) discovered that the delivery quality of lectures was positively associated with the lectures’ characteristics, as good understanding was derived when the students understood the content. Therefore, educators’ contributions and active participation from the students are very important. They should be able to demonstrate confidently the ability to choose efficient pedagogy that is congruent with the teaching and learning process and geared toward the development of the students’ intellectual level. The content is then enriched with various active learning coursework such as case studies, problem- based learning, assignments, individual and group presentations, and tutorials, which are platforms that will help in shaping the intellectual quality of the students. It shows that good design coursework can develop the intellectual quality among student-teachers. Thus, universities are seen as a great medium in developing the intellectual quality among student- teachers to implement once they become in-service teachers. Lecturers may also develop ICT proficiency by incorporating suitable pedagogy into efforts to improve students’ intellectual quality. Perhaps, instructors can use blended learning, flipped classrooms, or other forms of digital learning via the use of mobile devices, online education, and social networking. Apart from that, including cooperative learning via the use of ICT will assist in improving the intellectual quality of students in the class environment.
The current study also found that factors such as age, gender, ethnicity, year of study, and field of study influence the development of intellectual quality. While the results of factors that influence the development of intellectual quality via learning experiences supported the first part of the results, as experiences contribute to their ability with gender, the field of study and year of study influence the development of intellectual quality. Nonetheless, developing an intellectual quality is not a one-night effort. It takes time as it involves human cognitive development. Thus, the current study shows that as students are exposed to higher education levels more than others, e.g., senior students compared to junior students, it can be seen that there is progress in terms of intellectual quality possessed by the students. The intellectual quality could also be based on the nature of the subject itself. The data obtained from this study shows that most of the respondents were from literature backgrounds. Therefore, it is suggested that future research be conducted specifically according to the respective educational field. The updated data on the level of intellectual quality among student-teachers could then be obtained.
Thus, proper planning, specifically designed according to educational fields, will be able to show results in the context that better students with better intellectual quality could be produced. Eventually, it is sincerely hoped that the documentation acquired from this research will help researchers gain a deeper understanding and new insight about intellectual views and pedagogical approaches that are able to increase the intellectual quality of interactions among student-teachers, thereby raising students’ intellectual capital and putting them on par with rapidly progressing nations of the twenty-first century.
ACKNOWLEDGEMENT
This paper is under the funding of a Short-Term Grant from Universiti Sains Malaysia (PGURU.6315267).
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