International Journal of Education and Pedagogy (IJEAP) eISSN: 2682-8464 | Vol. 4 No. 3 [September 2022]
Journal website: http://myjms.mohe.gov.my/index.php/ijeap
THE MEDIATION EFFECT OF KNOWLEDGE COGNITION AND REGULATION COGNITION ON MATHEMATICS CLASSROOM CLIMATE AND STUDENTS ACHIEVEMENT
Priscilla Petrus Dolinting1* and Vincent Pang2
1 2 Faculty of Psychology and Education, University Malaysia Sabah, Kota Kinabalu, MALAYSIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 21 July 2022 Revised date : 25 August 2022 Accepted date : 1 September 2022 Published date : 10 September 2022
To cite this document:
Dolinting, P. P., & Pang, V. (2022).THE MEDIATION EFFECT OF
KNOWLEDGE COGNITION AND REGULATION COGNITION ON MATHEMATICS CLASSROOM CLIMATE AND STUDENTS
ACHIEVEMENT. International Journal of Education and Pedagogy, 4(3), 138- 153.
Abstract: There is a vast number of past studies revealed among the factors that affect the achievement of students in mathematics subject are classroom climate, students’
knowledge of cognition and regulation cognition. Therefore, this study aims to determine the mediation effect of knowledge of cognition and regulation of cognition on relationship between classroom climate (student cohesiveness, teacher support, involvement, investigation, task orientation, cooperation and equity) and students’
mathematics achievement. This study employed survey method involving a total of 326 form four students from five rural secondary schools via multi-stage cluster sampling.
Data were collected using two questionnaires: 1) What is Happening in This Class? (WIHIC) and 2) Metacognitive Awareness Inventory (MAI) as well as mathematics form four achievement test. The data was analysed by using Partial Least Squares-Structural Equation Modelling (PLS- SEM) through Smart PLS version 3.2.8 software. Mediation analysis indicated that knowledge of cognition mediates the relationships between classroom climate (investigation, task orientation and equity) and students’ mathematics achievement, while regulation cognition mediates the relationships between classroom climate (student cohesiveness, investigation and equity) and students’
mathematics achievement. The findings of this study provide valuable insights to the teachers about what they need to prioritise in regard to create a positive classroom climate to develop students’ knowledge of cognition and regulation cognition as well as to improve students’ mathematics
1. Introduction
Improving students’ mathematics achievement has been, and continues to become a major educational goal (Wang & Eccles, 2016). In a school setting, the most common evidence of student acquisition in mathematics skills is their mathematics grade and achievement but other indicators of learning outcomes include active involvement in class activities, improved learning strategies, changes in study habits and increased motivation (Cayubit, 2021).
These learning outcomes are affected by many factors and there have been numerous studies reported the role of classroom climate quality (Reynolds et al., 2017; Riaz & Asad, 2018) and metacogniton awareness (Kaur et al., 2018; Langdon et al., 2019; Mamon et al., 2020) are considered as crucial influencing factors.
2. Literature Review
In the current study, classroom climate is based on Vygotsky’s sociocultural theory (Vygotsky, 1978), which described most of the children’s learning is developed through interactions with others in their social world. Vygotsky (1978) believed that human learning is originated in two levels: first, through individual social interaction with others such as their parents or siblings in terms of students’ home environment meanwhile teachers and students in a classroom setting. Second, the students’ ability to convert the acquisition knowledge from social interaction into their personal understanding. The second level of Vygotsky’s sociocultural theory (Vygotsky, 1978) is the idea that the potential of students’ cognition is limited to a zone of proximal development (ZPD). ZPD is used to describe skills that are difficult to achieve by students can be fully developed with the assistance and social interaction with adults or more capable peers.
The concept of metacognition was initially introduced by Flavell (1979) as thinking about thinking.
After metacognition has been well accepted by most researchers from the field of education and psychology, several researchers regarded metacognition as a two-dimensional concept (Schraw &
Dennison, 1994; Teng, 2017, Kallio et al., 2018, Stephanou & Mpiontini, 2017). Knowledge of cognition and regulation cognition. Knowledge of cognition has three subcomponents namely declarative knowledge, procedural knowledge and conditional knowledge. On the other hand, cognition regulation encompassing a set of activities (planning, monitoring and evaluating) that enable students to control their learning. Thus, knowledge of cognition and regulation cognition play an important role in mathematics learning.
achievement. This study contributes significantly to the body of knowledge by developing and empirically testing the relationship connecting to classroom climate with knowledge of cognition, regulation cognition and students’
mathematics achievement within the rural school context in Malaysia.
Keywords: mathematics, classroom climate, knowledge of cognition, regulation cognition, students’ achievement.
There had been plenty of studies discussing the role of classroom climate quality such as students’
cohesiveness (Ouyang & Chang, 2019; Siti Nazleen & Nor Shahida, 2020), teacher support (Zepeda et al., 2018), involvement (Mccleary et al., 2019), investigation (Muhammad Asy’ari et al., 2019), task orientation (Manwaring et al., 2017), cooperation (Erdogan, 2019; Fischer et al., 2018) and equity (Luria et al., 2017; Tang et al., 2017) in selecting learning strategies especially knowledge of cognition and regulation cognition. These studies highlighted that student cohesiveness, teacher support, involvement, investigation, task orientation, cooperation and equity are indispensable in a classroom setting because it allows students to develop their knowledge of cognition and regulation cognition, and could be helpful to their mathematics achievement.
2.1 Problem Statement
There is no denial of the fact that the disparities between urban and rural schools regarding quality education and performance in public examination are widening at an alarming rate especially in developing countries (Yap & Loke, 2016). In Malaysian context, mathematics has been a critical subject in certain locations, especially in rural schools. Statistical evidence also showed that there is a significant difference of the TIMSS mathematics achievement among urban and rural schools in Malaysia. A summary of the mathematics performance by locality in TIMSS 2011 to TIMSS 2019 is provided in Table 1
Table 1: TIMSS Mathematics Average Score By Locality (Malaysia) from 2011 to 2019
Locality 2011 2015 2019
Urban 448 470 471
Rural 416 442 435
Source: Malaysia Ministry of Education (2020)
The gap in public examination performance of mathematics such as Malaysian Certificate of Education (SPM) between urban and rural students indirectly influences an individual state’s performance in the national ranking. Both urban and rural schools are expected to show the same academic performance as they follow a common curriculum and syllabus. Considerable studies have shown that good quality of classroom climate (Cayubit, 2021) and higher level of knowledge cognition and regulation cognition (Bishara & Kaplan, 2018; Ellah et al., 2018) in mathematics learning are associated with higher achievement. To date, only few studies have reported the interaction effects of the three variables: classroom climate, metacognition awareness (knowledge of cognition and regulation cognition) as well as students’ mathematics achievement especially in Malaysia education but none investigated those variables’ effect simultaneously in the context of rural schools. Therefore, the current study aimed to examine the psychosocial features of the classroom climate (students’ cohesiveness, teacher support, involvement, investigation, task orientation, cooperation and equity) and their relation to students’ knowledge of cognition and regulation cognition toward learning and achievement especially the mediation analysis.
3. Method 3.1 Materials
Two psychometric scales, namely: What is Happening in This Class (WIHIC) (Fraser et al., 1996) and Metacognition Awareness Inventory (MAI) (Schraw & Dennison, 1994) as well as a mathematics achievement test developed by the researcher were used to measure students’ perception of their mathematics classroom climate, knowledge of cognition and regulation cognition as well as their mathematics achievement. The English version of WIHIC and MAI questionnaires were translated into Malay language by the researcher and back translated to English by an English teacher to ensure the validity of the translation. Then, the back translations were checked by a language expert.
3.1.1 Samples
The population of this study were Form four students of all rural secondary schools in Sabah, Malaysia, with a total number of 1319 Form four students (data obtained from Education Planning and Research Division, MOE Malaysia). Based on Krejcie and Morgan (1970) sampling table, it is suggested that the study needs a sample size of 302. The distribution of respondent profiles according to gender and school name are given in Table 2.
Table 2: The Distribution of Respondent Profiles According to Gender and School Name Respondent Profile Information Frequency Percentage
Gender Boy 149 44.1
Girl 189 55.9
School Name A 84 24.9
B 58 17.2
C 82 24.3
D 50 14.8
E 64 18.9
3.1.2 Site
The study was conducted in five Divisions in Sabah, Malaysia: Interior Division, Kudat Division, Sandakan Division, Tawau Division and West Coast Division. Sabah is a state with the largest number of rural secondary schools of all states in Malaysia (data obtained from Education Planning and Research Division, MOE Malaysia).
3.1.3 Procedures
This study employed a survey design to investigate the mediation effects of knowledge of cognition and regulation cognition on relationship between classroom climate and students’ mathematics achievement in the context of rural secondary schools in Sabah, Malaysia. Based on the aim of the current study and literature review in Section 2, this leads to the following hypotheses:
H01 (a): Knowledge of cognition is not a mediator for the relationship between student cohesiveness and the students’ mathematics achievement.
H01 (b): Knowledge of cognition is not a mediator for the relationship between teacher support and the students’ mathematics achievement.
H01 (c) : Knowledge of cognition is not a mediator for the relationship between involvement and the students’ mathematics achievement.
H01 (d): Knowledge of cognition is not a mediator for the relationship between investigation and the students’ mathematics achievement.
H01 (e) : Knowledge of cognition is not a mediator for the relationship between task orientation and the students’ mathematics achievement.
H01 (f): Knowledge of cognition is not a mediator for the relationship between cooperation and the students’ mathematics achievement.
H01 (g) : Knowledge of cognition is not a mediator for the relationship between equity and the students’ mathematics achievement.
H02 (a) : Regulation cognition is not a mediator for the relationship between student cohesiveness and the students’ mathematics achievement.
H02 (b): Regulation cognition is not a mediator for the relationship between teacher support and the students’ mathematics achievement.
H02 (c): Regulation cognition is not a mediator for the relationship between involvement and the students’ mathematics achievement.
H02 (d) : Regulation cognition is not a mediator for the relationship between investigation and the students’ mathematics achievement.
H02 (e) : Regulation cognition is not a mediator for the relationship between task orientation and the students’ mathematics achievement.
H02 (f): Regulation cognition is not a mediator for the relationship between cooperation and the students’ mathematics achievement.
H02 (g) : Regulation cognition is not a mediator for the relationship between equity and the students’ mathematics achievement.
Figure 1 shows the conceptual framework of the current study. This framework comprises of independent, mediating and dependent variables under secondary schools setting in rural area of Sabah, Malaysia. The independent variables include students’ cohesiveness, teacher support, involvement, investigation, task orientation, cooperation and equity which are adapted from psychosocial aspects of a classroom climate proposed by Fraser et al. (1996). The mediating variables include knowledge of cognition and regulation cognition which are adapted from the concept of metacognition awareness by Schraw and Dennison (1994).
Figure 1: Conceptual Framework of the Current Study
H01
Student Cohesiveness Teacher Support
Involvement Investigation Task Orientation
Cooperation Equity
Knowledge of Cognition
Regulation Cognition
Students’
Mathematics Achievement
H01
H02
H02
By using multi-stage cluster sampling, one district was selected representing each Division with the district functioning as the first cluster at the first stage. Then, one school was selected from the selected district (first cluster) as the second cluster at the second stage. All Form Four students from each cluster were selected to finally determine the final sample. The total number of 338 Form four students from five selected rural secondary schools in Sabah, Malaysia, were selected for the final sample. The sample size was sufficient for the purposes of the current study.
3.2 Measurement
Students’ perceptions of their mathematics learning experiences in classroom were measured using WIHIC, a multidimensional scale which consisted of seven sub scales: student cohesiveness (KP), teacher support (SG), involvement (PL), investigation (PS), task orientation (OT), cooperation (KJ) and equity (KS), each with eight items. Each item in WIHIC is rated on a Likert-type scale where students answer the items according to five statements which are ‘Never’ (1), ‘Seldom’ (2),
“Sometimes’ (3), ‘Often’ (4) and ‘Always’ (5). The questions in WIHIC have been modified specifically on mathematic learning experiences due to the original questions in WIHIC encompassing a more general view on classroom climate. For example, the item “I discuss ideas in the class” is changed to “I discuss ideas in mathematics class”.
Students’ knowledge of cognition and regulation of cognition were measured using the MAI. The MAI consists of 52 items:17 items addressing knowledge of cognition includes eight items on declarative knowledge, four items on procedural knowledge and five items on conditional knowledgea. Meanwhile, 35 items addressing the regulation cognition includes seven items on planning, 10 items on information management strategies, seven items relating to comprehension monitoring, five items on debugging strategies and six items on evaluation. Items included in MAI were rated on a five-point Likert-type scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’
(5).
The students’ mathematics achievement was evaluated based on the total scores obtained from a mathematics achievement test developed by the researcher. Six topics: standard form, quadratic expression and equation, set, logical mathematical, straight lines and statistics were tested via eight items multiple choice questions and five items subjective questions.
3.3 Data Analysis
Data analysis was based on the SEM approach to test the proposed hypotheses. The Smart PLS program version 3.2.8 was utilized to run the data analysis. As mentioned earlier, the current study hypothesizes two mediators in a model study, the researcher estimated specific indirect effect instead of total indirect effects as suggested by Memon et al. (2018). A statistically significant indirect effect (t-value > 1.96, two -tailed, p <0.05) are evidence for mediation (Preacher & Hayes, 2004; Zhao et al., 2010). In addition, an assessment of the confidence interval is also an important criterion to confirm the mediation effect. If a confidence interval for the specific indirect effect does not straddle a zero in between, this supports the presence of mediation effect and vice versa (Memon et al., 2018).
Bootstrapping is a nonparametric procedure that can be used to test the statistical significance of various PLS-SEM results. However, the bias-corrected and accelerated (BCa) bootstrap confidence
intervals method is the most consistent effective technique (Puth et al., 2015) and it adjusts the confidence intervals for skewness (Sarstedt et al., 2019).
3.3.1 Validity and Reliability
The reliability and convergent validity of the constructs is evaluated by analyzing the Cronbach’s alpha and composite reliability of the indicator Hair et al. (2019) recommends a value of greater than 0.708 as a threshold value for this indicator. Loadings above 0.708 indicate that the construct explains more than 50% of the indicator’s variance, demonstrating that the indicator exhibits a satisfactory degree of reliability. The Cronbach’s alpha scores ranged between 0.902 and 0.947 while the composite reliability scores ranged between 0.902 and 0.947, indicating adequate convergence or internal consistency. Overall, these results provide clear support for the construct’s internal consistency reliability as all criteria (i.e. CR, rho_A (𝝆𝑨), and Cronbach’s Alpha) are well above the commonly recommended threshold of 0.708 as indicated in Table 3 and Figure 2.
This study assesses the discriminant validity via cross-loadings analysis and Heterotrait–Monotrait (HTMT) ratio. The correlation matrix in Table 4 shows that for each pair of constructs, the AVE square root of each construct is higher than the absolute value of their correlation. The results of cross-loadings show that all items loaded higher on their respective constructs than on the other constructs and the cross-loadings differences are much higher than the suggested threshold of 0.10.
Finally, the results of the HTMT ratio as shown in Table 4 confirm that values of the HTMT are below the threshold of 0.85 or 0.90 (Henseler et al., 2015).
Table 3: Validity and Reliability of Measurement Scales
Construct Item Outer
Loading
Rho_A(𝝆𝑨) CR Cronbach’s Alpha
AVE Student
Cohesiveness
KP1 0.817 0.948 0.947 0.947 0.690
KP2 0.896
KP3 0.782
KP4 0.892
KP5 0.751
KP6 0.863
KP7 0.794
KP8 0.837
Teacher Support
SG10 0.814 0.928 0.924 0.925 0.636
SG11 0.703 SG12 0.726 SG13 0.802 SG14 0.929 SG15 0.820 SG16 0.769
Involvement PL19 0.744 0.921 0.917 0.917 0.648
PL20 0.859 PL21 0.749 PL22 0.721 PL23 0.870 PL24 0.872
Construct Item Outer Loading
Rho_A(𝝆𝑨) CR Cronbach’s Alpha
AVE Investigation PS26
PS27 PS28 PS29 PS30 PS31 PS32
0.740 0.828 0.839 0.909 0.824 0.731 0.931
0.944 0.940 0.938 0.692
Task Orientation
OT33 OT34 OT35 OT36 OT37
0.832 0.850 0.814 0.860 0.746
0.914 0.912 0.911 0.674
Cooperation KJ39 0.808 0.928 0.926 0.925 0.642
KJ40 0.827 KJ42 0.754 KJ43 0.748 KJ44 0.871 KJ45 0.858 KJ46 0.730
Equity KS47 0.805 0.933 0.930 0.929 0.627
KS48 0.890 KS49 0.771 KS50 0.714 KS51 0.769 KS52 0.858 KS53 0.796 KS54 0.715 Declarative
Knowledge
PK1a 0.840 0.930 0.929 0.929 0.653
PK1b 0.749 PK1c 0.768 PK1d 0.808 PK1e 0.824 PK1f 0.831 PK1g 0.832 Procedural
Knowledge
PK2a 0.801 0.903 0.902 0.902 0.698
PK2b 0.827 PK2c 0.860 PK2d 0.853 Conditional
Knowledge
PK3a 0.814 0.924 0.924 0.923 0.708
PK3b 0.880 PK3c 0.855 PK3d 0.806 PK3e 0.850
Planning RK1a 0.885 0.938 0.937 0.936 0.748
RK1d 0.872 RK1e 0.828 RK1f 0.830 RK1g 0.908
Information Management
RK2a 0.841 0.945 0.944 0.944 0.707
RK2b 0.896 RK2c 0.819 RK2d 0.799 RK2h 0.837 RK2i 0.860 RK2j 0.829 Comprehention
Management
RK3a RK3b RK3c RK3d RK3e RK3f
0.842 0.808 0.802 0.755 0.859 0.789
0.921 0.919 0.919 0.656
Debugging Strategy
RK4a 0.908 0.822 0.896 0.848 0.904
0.944 0.943 0.942 0.768
RK4b RK4c RK4d RK4e
Evaluation RK5a 0.826 0.945 0.944 0.944 0.738
RK5b 0.836 RK5c
RK5d RK5e RK5f
0.859 0.871 0.863 0.898
Achievement Score 1.000 1.00 1.000 1.000 1.000
Table 4: The Result of Discriminant Validity for HTMT Ratio
KJ KP KS OT PC PK PL PS RK SG
KJ
KP 0.590
KS 0.719 0.552
OT 0.691 0.578 0.578
PC 0.536 0.502 0.523 0.522
PK 0.537 0.482 0.548 0.545 0.724
PL 0.532 0.752 0.552 0.527 0.401 0.472 PS 0.528 0.394 0.495 0.585 0.467 0.497 0.443
RK 0.523 0.510 0.541 0.504 0.756 0.789 0.432 0.491
SG 0.610 0.881 0.566 0.551 0.434 0.430 0.790 0.379 0.420 0.352
Figure 2: Measurement Model Result
4. Results and Discussion
The mediation analysis was conducted using 5000 bootstrapping samples. To test the mediating role of knowledge of cognition (H01a, H01b, H01c, H01d, H01e, H01f, H01g) and regulation cognition (H02a, H02b, H02c, H02d, H02e, H02f, H02g), researcher applied bootstrapping technique using Bca to test the specific indirect effects. Table 1 shows the results of the indirect effects of the independent (exogenous) variables (KP, SG, PL, PS, OT, KJ, and KS) on the dependent (endogenous) variable (students’ mathematics achievement) through their mediators (PK and RK).
The indirect effect of investigation (β =0.058, p = 0.026, 95% CI; LB:0.013, UB: 0.114), task orientation (β =0.059, p = 0.069, 95% CI; LB:0.002, UB: 0.131) and equity (β =0.073, p = 0.022, 95% CI; LB:0.013, UB: 0.136) on students’ mathematics achievement through knowledge of cognition were significant. There is no mediation effect for knowledge of cognition on student cohesiveness (β =0.048, p = 0.316, 95% CI; LB:-0.029, UB: 0.149), teacher support (β =-0.051, p = 0.330, 95% CI; LB:-0.156, UB: 0.044), involvement (β =0.048, p = 0.182, 95% CI; LB:-0.013, UB:
0.130) and cooperation (β =0.036, p = 0.332, 95% CI; LB:-0.028, UB: 0.119).
In line with previous findings, through knowledge of cognition, classroom climate that encourages investigation (Cheng & Wan, 2017; Nurulhuda & Saemah, 2017), task orientation (Neuenhaus et al., 2018; Hayat et al., 2020) and equity enhanced students’ mathematics achievement significantly (see Table 1). The other four factors (Student Cohesiveness, Teacher Support, Involvement, and Cooperation) of classroom climate did not show significant mediation level by knowledge of cognition. This shows that only certain components in classroom climate are influenced by knowledge of cognition. Thus, the findings of the current study regarding student cohesiveness contradict the result of Sadipour et al. (2017) and Wentzel et al. (2018). More specifically, student with positive emotion tends to participate and interact with other students in the classroom, affect the use of learning strategies such as metacognitive strategies and lead to the improvement of students' academic performance. Moreover, the inconsistent findings of teacher support as reported in Chong et al. (2018) show that more future studies are needed to investigate types of teacher support other than emotional support as measured in the current study to explain teacher support on knowledge of cognition and students’ achievement. Furthermore, these findings contradict the work by Law et al.
(2019), when students are self-initiated to participate in learning and thinking activities, interact with others will positively and directly affect their learning performance. These apparently contradictory results can be explained by the passive interaction of teacher-student classroom climates in the current study due to the fact that lecture-based teaching is still common in Asian education context (Tran et al., 2019).
The indirect effect of student cohesiveness (β =0.174, p = 0.011, 95% CI; LB:0.0070, UB: 0.338), investigation (β =0.099, p = 0.007, 95% CI; LB:0.033, UB: 0.177) and equity (β =0.114, p = 0.012, 95% CI; LB:0.024, UB: 0.205) on students’ mathematics achievement through regulation cognition were significant. There is no mediation effect for regulation cognition on teacher support (β =-0.123, p = 0.094, 95% CI; LB:-0.290, UB: 0.001), involvement (β =0.019, p = 0.700, 95% CI; LB:-0.079, UB: 0.116), task orientation (β =0.037, p = 0.404, 95% CI; LB:-0.052, UB: 0.126) and cooperation (β =0.061, p = 0.235, 95% CI; LB:-0.029, UB: 0.174).
Regulation cognition mediated the effects of classroom climate component: student cohesiveness, investigation and equity on students’ mathematics achievement. This suggests that students with high perception of close relationships with classmates, do more inquiry, have equal learning opportunities with other students in the classroom, are likely to plan, manage information, monitor their comprehension, use debugging strategies and evaluate their understandings which in turn leads to improved mathematics achievement. These results echo our expectations as well as previous findings from Zhou et al. (2021) and Nurulhuda and Saemah (2017) which indicated that students’ regulation cognition, is positively linked to their reports of closeness with their classmates and the emphasis of inquiry activities in the classroom. This result implied metacognitive aspects (knowledge of cognition and regulation cognition) to be a prerequisite for effective learning and is believed to directly contribute to an increase in student achievement.
To sum up, the bootstrapping results presented in Table 4 indicated that the mediation effect of classroom climate (Investigation, Task orientation and Equity) on students’ mathematics achievement through knowledge of cognition and the mediation effect of classroom climate (Student Cohesiveness, Investigation and Equity) on students’ mathematics achievement through regulation cognition were significant.
Table 4: The Result of Mediation Tests Independent
Variable
Dependent Variable
Hypotheses Path Coefficient
T value P value BCa CI [LB, UB) Knowledge of Cognition (PK) as Mediator
KP
Students’
Mathematics Achievement
H01a 0.048 1.003 0.316 [-0.029, 0.149]
SG H01b -0.051 0.974 0.330 [-0.156, 0.044]
PL H01c 0.048 1.333 0.182 [-0.013, 0.130]
PS H01d 0.058 2.230 0.026 [0.013, 0.114]
OT H01e 0.059 1.820 0.069 [0.002, 0.131]
KJ H01f 0.036 0.969 0.332 [-0.028, 0.119]
KS H01g 0.073 2.283 0.022 [0.013, 0.136]
Regulation Cognition (RK) as Mediator KP
Students’
Mathematics Achievement
H02a 0.174 2.546 0.011 [0.070, 0.338]
SG H02b -0.123 1.673 0.094 [-0.290, 0.001]
PL H02c 0.019 0.385 0.700 [-0.079, 0.116]
PS H02d 0.099 2.699 0.007 [0.033, 0.177]
OT H02e 0.037 0.834 0.404 [-0.052, 0.126]
KJ H02f 0.061 1.188 0.235 [-0.029, 0.174]
KS H02g 0.114 2.515 0.012 [0.024, 0.205]
Note: KP: Student cohesiveness, SG: Teacher Suport, PL: Involvement, PS: Investigation, OT: Task Orientation, KJ:
Cooperation, KS: Equity
5. Conclusion
This study investigated the mediation effects of knowledge of cognition and regulation cognition on the relationships between classroom climate and students’ mathematics achievement. The findings indicated a significant indirect effect between component of classroom climate (i.e., student cohesiveness, task orientation, investigation and equity) and students’ mathematics achievement through knowledge of cognition and regulation cognition. Based on the findings, when students make good friendship with other students, help each other, do more investigation, task oriented and their mathematics teacher behaves equally towards all students significantly influence students’ knowledge of cognition and regulation cognition and mathematics achievement. The result of this study will add value to existing literature on the relationship between perceived classroom climate on students’
achievement particularly on issue related to learning outcomes such as knowledge of cognition and regulation cognition. The significant mediating role played by knowledge of cognition and regulation cognition have drawn attention to the need for a well-organized workshop for teachers to provide the support and learning tools necessary to further develop metacognitive strategies. It is also suggested that schools should endeavour to create a positive classroom climate which emphasize student cohesiveness, task orientation, investigation and equity to support students’ knowledge of cognition and regulation cognition in order to improve academic performance. Further investigations might be needed to understand the nature of this relationship.
6. Acknowledgement
Part of this article was extracted from a doctoral thesis submitted to University Malaysia Sabah, Kota Kinabalu, Sabah.
References
Bishara, S., & Kaplan, S. (2018). The Relationship of Locus of Control and Metacognitive Knowledge of Math with Math Achievements. International Journal of Disability, Development and Education, 65(6), 631–648. https://doi.org/10.1080/1034912X. 2018.1432033
Cayubit, R. F. O. (2021). Why learning environment matters? An analysis on how the learning environment influences the academic motivation, learning strategies and engagement of college students. Learning Environments Research, 1-19. https://doi.org/10.1007/s10984-021-09382-x Cheng, M. H. M., & Wan, Z. H. (2017). Exploring the effects of classroom learning environment on
critical thinking skills and disposition: A study of Hong Kong 12th graders in Liberal Studies.
Thinking Skills and Creativity, 24, 152–163. https://doi.org/10.1016/j.tsc.2017.03.001
Chong, W. H., Liem, G. A. D., Huan, V. S., Kit, P. L., & Ang, R. P. (2018). Student perceptions of self-efficacy and teacher support for learning in fostering youth competencies: Roles of affective and cognitive engagement. Journal of Adolescence, 68, 1–11.
https://doi.org/10.1016/j.adolescence.2018.07.002
Ellah, B. O., Nworgu, L. N., & Achor, E. E. (2018). Meta-Cognitive Awareness and Study Process of Senior Secondary School Science Students of Low Cognitive Ability Leve. Journal of Research in Curriculum and Teaching, 10(1), 161–180.
Erdogan, F. (2019). Effect of cooperative learning supported by reflective thinking activities on students’ critical thinking skills. Eurasian Journal of Educational Research, 19(80), 89–112.
https://doi.org/10.14689/ejer.2019.80.5
Fischer, P., Camba, L., Ooi, S. H., & Chevalier, N. (2018). Supporting cognitive control through competition and cooperation in childhood. Journal of Experimental Child Psychology, 173, 28–
40. https://doi.org/10.1016/j.jecp.2018.03.011
Flavell, J. H. (1979). Metacognition and Cognitive Monitoring A New Area of Cognitive — Developmental Inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003- 066X.34.10.906
Fraser, B., McRobbie, C., & Fisher D. (1996). Development, validation and use of personal and class forms of a new classroom environment questionnaire. WAIER (1996). Proceedings Western Australian Institute for Educational Research Forum 1996.
http://www.waier.org.au/forums/1996/contents.html
Hayat, A. A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academic self- efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: A structural equation model. BMC Medical Education, 20(1), 1–11. https://doi.org/10.1186/s12909-020-01995-9
Kallio, H., Virta, K., & Kallio, M. (2018). Modelling the Components of Metacognitive Awareness.
International Journal of Educational Psychology, 7(2), 94–122.
https://doi.org/10.17583/ijep.2018.2789
Kaur, P., Saini, S., & Vig, D. (2018). Metacognition, Self-regulation and Learning environment as determinant of academic achievement. Indian Journal of Health and Well-Being, 9(5), 735–739.
Langdon, J., Botnaru, D. T., Wittenberg, M., Riggs, A. J., Mutchler, J., Syno, M., & Caciula, M. C.
(2019). Examining the effects of different teaching strategies on metacognition and academic performance. Advances in Physiology Education, 43(3), 414–422.
https://doi.org/10.1152/advan.00013.2018
Law, K. M. Y., Geng, S., & Li, T. (2019). Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers and Education, 136, 1–12. https://doi.org/10.1016/j.compedu.2019.02.021 Lee, J., & Stankov, L. (2018). Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA. Learning and Individual Differences, 65, 50–64.
https://doi.org/10.1016/j.lindif.2018.05.009
Luria, S. R., & Kaufman, J. C. (2017). Examining the relationship between creativity and equitable thinking in schools. Psychology in the Schools, 54(10), 1279-1284.
https://doi.org/10.1002/pits.22076
Mamon, M. A. C., Esperanza, A. G., & Vital, J. A. T. (2020). Research-based learning: effects on the metacognitive awareness of senior high school students. International Journal of Indonesian Education and Teaching, 4(2), 277–285. https://doi.org/10.24071/ijiet.2020.040210
Manwaring, K. C., Larsen, R., Graham, C. R., Henrie, C. R., & Halverson, L. R. (2017). Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. Internet and Higher Education, 35, 21–33.
https://doi.org/10.1016/j.iheduc.2017.06.002
Mccleary, D. F., Mccreary, B., & Coles, J. (2019). Cognitive Variables, Classroom Behaviors, and a Participation Intervention on Students’ Classroom Participation and Exam Performance.
International Journal of Teaching and Learning in Higher Education, 31(2), 184–194.
http://www.isetl.org/ijtlhe/
Memon, M. A., Cheah Jun Hwa, T. Ramayah, Hiram Ting & Chin Wei Francis. 2018. Mediation Analysis Issues and Recommendations. Journal of Applied Structural Equation Modeling Editorial, 2(1), i-ix. http://repo.uum.edu.my/
Muhammad Asy’ari, Muhamad Ikhsan & Muhali. (2019). The Effectiveness of Inquiry Learning Model in Improving Prospective Teachers’ Metacognition Knowledge and Metacognition Awareness. International Journal of Instruction, 12(2), 455–470.
https://doi.org/10.29333/iji.2019.12229a
Neuenhaus, N., Artelt, C., Schneider, W., & Lingel, K. (2018). Does metacognitive knowledge mediate the relation between goal orientations and educational achievement in secondary school students? Electronic Journal of Research in Educational Psychology, 16(44), 5–33.
https://doi.org/10.25115/ejrep.v16i44.1935
Nurulhuda Md Hassan & Saemah Rahman. (2017). Improving students’ achievement in a holistic learning environment through metacognitive awareness. Advanced Science Letters, 23(3), 2019–
2022. https://doi.org/10.1166/asl.2017.8570
Ouyang, F., & Chang, Y. H. (2019), The relationships between social participatory roles and cognitive engagement levels in online discussions. British Journal of Educational Technology, 50(3).
1396-1414. https://doi.org/10.1111/bjet.12647
Pratiwi, B., & Primana, L. (2018). The Relationship between Teachers’ Fostering Relevance Behavior and Middle School Students’ Cognitive Engagement in Mathematics. Advances in Social Science, Education and Humanities Research, 139, 1–6. https://doi.org/10.2991/uipsur- 17.2018.1
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior research methods, instruments, & computers, 36(4), 717- 731.
Puth, M. T., Neuhäuser, M., & Ruxton, G. D. (2015). On the variety of methods for calculating confidence intervals by bootstrapping. Journal of Animal Ecology, 84(4), 892–897.
https://doi.org/10.1111/1365-2656.12382
Reynolds, K. J., Lee, E., Turner, I., Bromhead, D., & Subasic, E. (2017). How does school climate impact academic achievement? An examination of social identity processes. School Psychology International, 38(1), 78–97. https://doi.org/10.1177/0143034316682295
Riaz, H. M., & Asad, A. R. (2018). Effect of Classroom Learning Environment on Students’
Academic Achievement in Mathematics at Secondary Level. International Journal of Academic Research, 40(2), 207–218.
Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197-211. https://doi.org/10.1016/j.ausmj.2019.05.003
Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology. 19. 460-475 https://doi.org/10.1006/ceps.1994.1033
Siti Nazleen Abdul Rabu & Nor Shahida Badlishah. (2020). Peer Interaction Patterns in a Collaborative Learning Environment Using Google Docs. ASM Science Journal, 13(3), 50-55 Tang, G., Cilli-turner, E., Savic, M., Karakok, G., & Plaxco, D. (2017). Inquiry as an entry point to
equity in the classroom. International Journal of Mathematical Education in Science and Technology, 48(S1), S4–S15. https://doi.org/10.1080/0020739X.2017.1352045
Tran, V. D., Nguyen, T. M. L., De, N. Van, Soryaly, C., & Doan, M. N. (2019). Does cooperative learning may enhance the use of students’ learning strategies? International Journal of Higher Education, 8(4), 79–88. https://doi.org/10.5430/ijhe.v8n4p79
Vygotsky, L. (1978). Interaction between learning and development. Readings on the development of children, 23(3), 34-41.
Wang, M. Te, & Eccles, J. S. (2016). Multilevel predictors of math classroom climate: A comparison study of student and teacher perceptions. Journal of Research on Adolescence, 26(3), 617–634.
https://doi.org/10.1111/jora.12153
Wentzel, K. R., Jablansky, S., & Scalise, N. R. (2018). Do friendships afford academic benefits? A meta-analytic study. Educational Psychology Review, 30(4), 1241-1267.
https://doi.org/10.1007/s10648-018-9447-5
Zepeda, C. D., Hlutkowsky, C. O., Partika, A. C., & Nokes-malach, T. J. (2018). Identifying Teachers’ Supports of Metacognition Through Classroom Talk and Its Relation to Growth in Conceptual Learning. Journal of Educational Psychology, 111(3), 522-541.
https://doi.org/10.1037/edu0000300
Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of consumer research, 37(2), 197-206.
https://doi.org/10.1086/651257
Zhou, A., Guan, X., Ahmed, M. Z., Ahmed, O., Jobe, M. C., & Hiramoni, F. A. (2021). An analysis of the influencing factors of study engagement and its enlightenment to education: Role of perceptions of school climate and self-perception. Sustainability, 13(10), 1-11.
https://doi.org/10.3390/su13105475
