Commonly interpreted as knowledge and allied inferential processes ranging from simple forms of perception to complicated forms of reasoning, cognition has long been recognised as one of the three pillars for mental functioning and learning (the other two being emotion and motivation; Moshman, Citation2020). At the same time, subsequent to the Coleman Report (Coleman et al., Citation1966) that affirmed the significant roles of family background and peers in students’ academic achievement, much research has revealed the importance of teachers in both cognitive and non-cognitive outcomes. Indeed, teachers have been widely acknowledged as a primary type of learning environment in students’ learning and performance (Aaronson et al., Citation2007; Zhang, Citationin press).
One might ask: Why is there a need for more research on such a well-established fact that students’ cognitive functions and teacher-relevant factors are crucial in human cognitive and non-cognitive outcomes? Within the context of this issue of Educational Psychology, the answer to this question should be evident: Each of the eight articles has examined the relevant phenomena from a nuanced perspective.
Allen and Giofrè’s (Citation2021) study has succeeded in communicating the importance of looking into the specific components of constructs and/or issues under investigation. The authors tested their hypothesis that different working memory components (i.e., verbal and visuo-spatial) would predict different types of mathematical tasks among school children in the United Kingdom. As expected, some mathematics tasks (i.e., counting and understanding numbers; and using and applying mathematics) were more closely associated with verbal working memory, while others (i.e., handling data and understanding shape) were more significantly linked with visuo-spatial working memory.
Rapid automatised naming (RAN) had been widely recognised as a fundamental predictor for reading processes. Escobar et al. (Citation2021) hypothesised that even after the effects of some of the key cognitive factors (i.e., inhibition, working memory, phonological awareness, and counting) frequently associated with either RAN or arithmetical fluency are taken into consideration, RAN would still statistically predict arithmetical fluency. Among Chilean first-grade school children, the authors found support for their anticipated relationship. The value of this study lies in its discovery of RAN being a general cognitive variable that is essential not only to reading performance but also to mathematics performance.
Hung and Loh (Citation2021) turned their attention to the role of cognitive flexibility, one of the two key components to executive function (the other one being working memory) in metalinguistic skills and reading comprehension. As expected, cognitive flexibility statistically predicted Hong Kong primary school children’s performance on both metalinguistic skills and reading comprehension. More importantly, the study has advanced the literature by demonstrating that cognitive flexibility made unique statistical contributions beyond age and metalinguistic skills – skills that had long been proven to be a major antecedent to reading comprehension.
Collectively, the three aforementioned studies have shown the pivotal functions of cognition in students’ learning and performance. Then, a question that arises is: Can individuals’ cognition be developed? Fan et al. (Citation2021) investigated the malleability of thinking styles, individuals’ preferred ways of processing information and dealing with tasks (Zhang & Sternberg, Citation2005). A large sample of university students in mainland China responded to an inventory assessing their thinking styles – twice over one year. Results from different statistical procedures converged on the conclusion that thinking styles are modifiable. Given that thinking style is a three-dimensional construct (i.e., cognition-, personality-, and activity-oriented), evidence for its modifiability should have heuristic value for developing individuals’ cognitive functions, as it does for developing their personality- and activity-related functions.
As a main type of learning environment, teachers play a vital role in students’ cognitive and non-cognitive outcomes. Each of the next three articles investigated the function of a different teacher factor in students’ cognitive or non-cognitive outcomes. Zhang et al.’s (Citation2021) study investigated the function of cognitive activation, a crucial dimension of instructional quality, in mathematics achievement among a large sample of Chinese eighth graders. The authors looked into the serial mediating effects of two non-cognitive factors: students’ interest in mathematics and their perseverance when confronted with difficulties in solving mathematical problems. As expected, cognitive activation significantly contributed to students’ mathematics achievement (in terms of both content and cognitive complexity), both directly and serially indirectly – through students’ mathematics interest, followed by perseverance.
Adopting a one-academic-year longitudinal design, Valdes et al. (Citation2021) explored the function of teacher expectations and students’ perceived quality of teacher-student relationship in students’ expectations of success in mathematics. Participants were a group of Latino/a school children in the United States. Results showed that higher levels of teacher expectations positively predicted anticipation of success among students with above average maths performance, while better quality of students’ perceived teacher-student relationship was significantly contributory to expectations of mathematics success among all students.
The importance of students’ perceived teacher-student relationship has been manifested not only in students’ expectations of success in learning mathematics but also in their actual achievement in mathematics. In Lee’s (Citation2021) study, teacher-student relationship was conceptualised as classroom climate comprising three dimensions: teachers’ affective quality, classroom management styles, and their cognitive-pedagogical approaches. The author looked into how each of the three aspects of teacher-student relationship was associated with mathematics achievement scores of 2012 PISA (Program for International Student Assessment) participants from seven Confucian Asian jurisdictions. Results confirmed that teacher-student relationship mattered significantly in students’ mathematics achievement among students in all seven jurisdictions, though variations were ascertained for each of the three dimensions of classroom climate.
Finally, the study by Nastasa et al. (Citation2021) examined the statistical effect of Romanian school teachers’ work-home interaction (a process in which individuals’ functioning in one domain influences that in the other; and vice versa) on their life satisfaction. Going beyond the existing work, the authors investigated the moderating role of core self-evaluations in the said relationship. Although core self-evaluations did not moderate the negative relationship between negative work-home interactions and life satisfaction, they did moderate the positive association between positive work-home interactions and life satisfaction. In particular, a significantly positive relationship was found between positive work-home interactions and life satisfaction among teachers with low levels of core self-evaluations, but not among those with high levels of core self-evaluations.
For nearly a century, scholars have been studying the contributions of personological and environmental factors to human cognitive and non-cognitive outcomes (Biggs, Citation1979; Lewin, Citation1936). The authors of the articles in this issue have continued this intellectual journey by investigating the statistical impact of cognition (as a persononlogical factor) and teachers (as an environmental factor) upon cognitive and non-cognitive outcomes among students and teachers.
Without a doubt, each of the articles, like any other publication, has its limitations, as have already been appropriately acknowledged by the respective authors. Paradoxically, it is precisely those limitations that could serve as the impetus for future scientific inquiries. Moreover, irrespective of their limitations, the research findings have surely enriched the literature on the roles of cognition and teachers in human learning and outcomes. Equally, they have practical implications for improving education through stressing the value of cognition and teachers.
Disclosure statement
No potential conflict of interest was reported by the author(s).
References
- Aaronson, D., Barrow, L., & Sander, W. (2007). Teachers and student achievement in the Chicago public high schools. Journal of Labor Economics, 25(1), 95–135. https://doi.org/10.1086/508733
- Allen, K., & Giofrè, D. (2021). A distinction between working memory components as unique predictors of mathematical components in 7–8 year old children. Educational Psychology, 41(6), 678–694.
- Biggs, J. B. (1979). Individual differences in study processes and the quality of learning outcomes. Higher Education, 8(4), 381–394. https://doi.org/10.1007/BF01680526
- Coleman, J. S., Campell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., & York, R. L. (1966). Equality of educational opportunity. U.S. Government Printing Office.
- Escobar, J.-P., Porflitt, F., & Ceric, F. (2021). Evaluating the rapid automatized naming and arithmetical fluency relationship in Chilean first grade students. Educational Psychology, 41(6), 730–747.
- Fan, J., Zhang, L.-F., & Hong, Y. (2021). The malleability of thinking styles over one year. Educational Psychology, 41(6), 748–763.
- Hung, C. O.-Y., & Loh, E. K.-Y. (2021). Examining the contribution of cognitive flexibility to metalinguistic skills and reading comprehension. Educational Psychology, 41(6), 712–729.
- Lee, J. (2021). Teacher-student relationships and academic achievement in Confucian educational countries/systems from PISA 2012 perspectives. Educational Psychology, 41(6), 764–785.
- Lewin, K. (1936). Principles of topological psychology. MaGraw-Hill.
- Moshman, D. (2020). Metacognition and epistemic cognition. In L. F. Zhang (Ed.), The Oxford encyclopedia of educational psychology. Oxford University Press.
- Nastasa, M., Golu, F., Buruiana, D., & Oprea, B. (2021). Teachers' work–home interaction and satisfaction with life: The moderating role of core self-evaluations. Educational Psychology, 41(6), 806–820.
- Valdes, O. M., Denner, J., Dickson, D. J., & Laursen, B. (2021). Teacher expectations and perceived teacher involvement anticipate changes in Latino/a middle school students’ expectations of math success. Educational Psychology, 41(6), 786–805.
- Zhang, D., Wang, C., & Yang, Y. (2021). The association between cognitive activation and mathematics achievement: a multiple mediation model. Educational Psychology, 41(6), 695–711.
- Zhang, L. F. (in press). Preface. In L. F. Zhang (Editor-in-Chief), The Oxford encyclopedia of educational psychology. Oxford University Press.
- Zhang, L. F., & Sternberg, R. J. (2005). A threefold model of intellectual styles. Educational Psychology Review, 17(1), 1–53. https://doi.org/10.1007/s10648-005-1635-4