Math Anxiety – negative impact and potential solutions
Jonatan Finell1, Ellen Sammallahti2, Hanna Eklöf1, Johan Korhonen2, and Bert Jonsson1
1Umeå University, Sweden, 2Åbo Akademi University, Finland
It is well established that math anxiety has a negative relationship with math performance (MP). The Attentional Control Theory (ACT) suggests that anxiety can negatively impact the attentional control system and increase one’s attention to threat-related stimuli. Within the ACT framework, the math anxiety (MA)—working memory (WM) relationship is argued to be critical for MP. The first study is based on meta-analyses that provide insights into the relationship between MA and WM. Through database searches with pre-determined search strings, 1,346 unique articles were identified. After excluding non-relevant studies, data from 57 studies and 150 effect sizes were used for investigating the MA—WM correlation using a random-effects model. This resulted in a mean correlation of r = −0.168. The correlation varied as a function of different factors, such as: age, the use of numerical or alphabetical based cognitive instruments, or anxiety measures that are more cognitively focused rather than affectively. Applying a similar method, a second study synthesised published interventions (50 studies, 75 effect sizes) that attempt to reduce math anxiety. The results show that interventions on math anxiety report a moderate effect size for reducing math anxiety d = -0.46, and improving math performance d = 0.58. These effect sizes varied depending on intervention length and sample age. Categorisations of the intervention into (1) motivation, (2) emotion, and (3) cognitive support, revealed no significant between group differences.
Understanding Globalized Primary Classrooms: A multivariate investigation of the associations between cultural and linguistic diversity, teacher-self-efficacy, classroom emotional climate and reading achievement in the Norwegian context
Jacqueline Peterson1, Maria Therese Jensen2, and Njål Foldnes1
1Norwegian Reading Centre, University of Stavanger, Norway, 2Centre for Learning Environment, University of Stavanger, Norway
Teachers experience numerous demands in the classroom, including the need to recognize the uniqueness of students through differentiated instruction. In teaching reading, such differentiation becomes even greater in classrooms with culturally and linguistically diverse (CLD) students. Yet, research to date is limited on how the CLD in a classroom relates to teachers’ self-efficacy, classroom emotional climate and literacy skills. With the job-demands resource model (Demerouti and Bakker, 2011) as an overarching framework, this study aims to investigate CLD for its relation to teacher self-efficacy and students’ perceptions of classroom emotional climate and its subsequent association to reading achievement. In line with previous research and theory, we define cultural and linguistic diversity as a job demand and teacher self-efficacy as a personal resource. Classroom emotional climate is measured as students’ perception of the emotional climate of the class. Structural equation modeling will be used to investigate associations between the aforementioned variables on data drawn from the Two Teachers project (See Solheim et al., 2017). The sample for the current study consists of 150 classrooms containing 2880 students. Class size will be included as a moderating variable in the analysis, while reported SES and parents’ educational levels will be controlled for in the model. The results of the study will contribute to our understanding of how CLD classrooms are experienced by primary teachers, via self-efficacy, and students, through classroom emotional climate, and the potential implications of these experiences on learners’ reading achievement.
Timeseries analysis extends content analysis to exploring distribution of a topic among data
Jöran Petersson, Faculty of Education and Society, Malmö University, Malmö, Sweden
This poster shows that methods imported from timeseries analysis could benefit the use of content analysis through raising new research questions and allowing enhanced results by showing where, in a sequence of data, the studied phenomenon occurs. For methodological reasons, timeseries analysis in educational research has been effectively absent and, until recently, content analysis in educational research typically meant to look for the mere existence of some explored theme or for comparing their frequency in two data sets. A timeseries requires that the data are an ordered sequence of units of analysis. One example is the analysis of a classroom conversation where the data may follow clock-time. Another example is analysing exercises in a textbook, where the data may not follow clock-time since each exercise can require different amounts of time to solve. A first outcome of a timeseries analysis is a moving average diagram, which is a diagram of the same kind as temperature curves used in climate science for displaying temperature changes over time. For the case of analysing exercises in a mathematics textbook, this diagram shows the changes in the intensity of the explored learning object throughout the textbook. When analysing a classroom conversation, it allows the researcher to compare two moving average diagrams, each displaying where in the conversation two specified topics occur. Specifically, determining the correlation between two timeseries allows the researcher to explore how two learning objects or conversation topics interact with each other. Hence, timeseries analysis provides a new tool for the researcher.