About this Research Topic
A considerable amount of psychological research on Learning and Teaching has been hampered by methodological shortcomings.
Studies are usually drawn on small non-representative convenience samples, in addition to this, the majority of the research has been confined to North America and Western Europe, resulting in a knowledge base that has been termed as WEIRD (Western, educated, industrialized, rich democratic societies), by Henrich, Heine, and Norenzayan. Despite the WEIRD knowledge base, emerging international reports indicate that Asian students are leading in education assessment such as Trends in International Mathematics and Science Study (TIMMS) and Program for International Student Assessment (PISA). Western-confined research and the lack of international samples may lead to conclusions that lack cross-cultural generalizability.
Finally, it is important to highlight that a high number of Learning and Teaching studies rely extensively on self-reports, leading to a science qualified by Baumeister, Vohs and Funder in 2007 as “self-reports and finger movements” (referring to the act of responding to a self-report questionnaire). The latter may lead to common method variance and biased parameter estimates.
The aim of this Research Topic is to provide a platform for scholars who harness the power of big data to provide new insights on the Psychology of Learning and Teaching.
The most prominent example of Big Data in education would be the Organization for Economic Cooperation and Development's (OECD), the Program for International Student Assessment (PISA). Other databases include the International Association for the Evaluation of Educational Achievement's (IEA), Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS), among others.
These datasets address the methodological shortcomings of traditional psychological research on Learning and Teaching, the big sample sizes that are nationally representative maximize the generalizability of the results. They also include samples across a wide range of countries that traditionally are not included in mainstream scholarship, thereby providing an especially useful resource for cross-cultural studies. More importantly, they go beyond self-reports by including psychometrically robust achievement data as well as data from teachers and school leaders, addressing the problem of common method variance.
This Research Topic’s goal is to shed light on the Psychology of Teaching and Learning, leveraging on the affordances of such Big Data.
We welcome, but not limit our collection, to the following themes:
· Big Data applied to Learning and Teaching
· Cross-cultural studies in Learning and Teaching
· Culture and motivation
· Student engagement
· Individual differences among students and teachers
· Non-cognitive factors in Learning and Teaching
· Teacher beliefs and attitudes
· Teaching strategies
Studies are usually drawn on small non-representative convenience samples, in addition to this, the majority of the research has been confined to North America and Western Europe, resulting in a knowledge base that has been termed as WEIRD (Western, educated, industrialized, rich democratic societies), by Henrich, Heine, and Norenzayan. Despite the WEIRD knowledge base, emerging international reports indicate that Asian students are leading in education assessment such as Trends in International Mathematics and Science Study (TIMMS) and Program for International Student Assessment (PISA). Western-confined research and the lack of international samples may lead to conclusions that lack cross-cultural generalizability.
Finally, it is important to highlight that a high number of Learning and Teaching studies rely extensively on self-reports, leading to a science qualified by Baumeister, Vohs and Funder in 2007 as “self-reports and finger movements” (referring to the act of responding to a self-report questionnaire). The latter may lead to common method variance and biased parameter estimates.
The aim of this Research Topic is to provide a platform for scholars who harness the power of big data to provide new insights on the Psychology of Learning and Teaching.
The most prominent example of Big Data in education would be the Organization for Economic Cooperation and Development's (OECD), the Program for International Student Assessment (PISA). Other databases include the International Association for the Evaluation of Educational Achievement's (IEA), Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS), among others.
These datasets address the methodological shortcomings of traditional psychological research on Learning and Teaching, the big sample sizes that are nationally representative maximize the generalizability of the results. They also include samples across a wide range of countries that traditionally are not included in mainstream scholarship, thereby providing an especially useful resource for cross-cultural studies. More importantly, they go beyond self-reports by including psychometrically robust achievement data as well as data from teachers and school leaders, addressing the problem of common method variance.
This Research Topic’s goal is to shed light on the Psychology of Teaching and Learning, leveraging on the affordances of such Big Data.
We welcome, but not limit our collection, to the following themes:
· Big Data applied to Learning and Teaching
· Cross-cultural studies in Learning and Teaching
· Culture and motivation
· Student engagement
· Individual differences among students and teachers
· Non-cognitive factors in Learning and Teaching
· Teacher beliefs and attitudes
· Teaching strategies
Keywords: Big Data, Educational Psychology, Teaching and Learning, PISA, TIMSS, PIRLS
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
