Staff development ideas

Jerry Aldridge is an associate professor of Early Childhood Education and Special Education, Department of Curriculum and Instruction, University of Alabama at Birmingham.     

Learning environment and anxiety for learning and teaching mathematics among preservice teachers

Learning Environments Research - The learning environment has been found to be related to mathematics anxiety at a variety of educational levels, including higher education, but to date has not...     

Project on scope,sequence, and coordination: A new synthesis for improving science education

The Project on Scope, Sequence, and Coordination of Secondary School Science (SS&C) is a major national project designed to reform science education, K-12. Based on research on learning science, the project includes provision for hands-on experience, sequencing over time at successively higher levels of abstraction, and taking account of student preconceptions. Associated with SS&C is a performance-based student assessment project which incorporates compact-disc interactive (CD-I) technology. The SS&C project and its assessment component were initiated by the author and have become projects of the National Science Teachers Association (NSTA) funded by the National Science Foundation and the U.S. Department of Education. Projects are underway in California, North Carolina, Iowa, Puerto Rico, Texas, and Alaska.     

Influence of Psychosocial Classroom Environment on Students’ Motivation and Self-Regulation in Science Learning: A Structural Equation Modeling Approach

The primary aim of this study was two-fold: 1) to identify salient psychosocial features of the classroom environment that influence students’ motivation and self-regulation in science learning; and 2) to examine the effect of the motivational constructs of learning goal orientation, science task value and self-efficacy in science learning on students’ self-regulation in science classrooms. Data collected from 1360 science students in grades 8, 9 and 10 in five public schools in Perth, Western Australia were utilized to validate the questionnaires and to investigate the hypothesized relationships. Structural Equation Modeling analysis suggested that student cohesiveness, investigation and task orientation were the most influential predictors of student motivation and self-regulation in science learning. In addition, learning goal orientation, task value and self-efficacy significantly influenced students’ self-regulation in science. The findings offer potential opportunities for educators to plan and implement effective pedagogical strategies aimed at increasing students’ motivation and self-regulation in science learning.     

Learning English as a second language at the university level in Jordan: motivation,self-regulation and learning environment perceptions

The overarching aim of this study was to investigate students’ perceptions of the learning environment and whether these influenced their motivation and self-regulation in learning English as a second language at the university level in Jordan. Our sample involved 994 students, drawn from 13 schools, within three faculties (humanities, health sciences and engineering) of one university. The collection of data involved the administration of two surveys: one to assess students’ perceptions of the learning environment and another to assess students’ motivation and self-regulation in learning English as a second language. The results of analyses strongly supported the reliability and validity of the surveys when used at the university level in Jordan, thereby providing confidence in the results of the present study. Simple correlation and multiple regression analysis were used to examine the influence of students’ perceptions of the learning environment of English language classes on self-reports of motivation and self-regulation. Statistically significant (p < 0.05) simple correlations were found between students’ perceptions of their learning environment and their motivation and self-regulation. The regression weights (β) indicated that scales of the learning environment were positively, statistically significantly (p < 0.05) and independently related to the motivation and self-regulation outcomes.     

Examining a mathematical model of mastery learning in a classroom setting

The purpose of mathematical models in any discipline is to describe accurately the relationships among significant variables of a system. The use of mathematical models is widespread in the sciences, but has rarely found its way into educational research. In developing a mathematical model for mastery learning, empirical research has shown that prior learning, motivation, and time on task are part of the significant variables that work together in some way in a determination of student achievement. A mathematical model that shows the relationship among these variables has been developed. To test the appropriateness of this mathematical model, carefully designed and controlled experiments must be conducted to collect numerical data on the significant variables. Using the model, the accuracy of its predictions can be compared with actual results. This is the technique used in testing mathematical models in the sciences and should be applicable to mathematical models of learning.     

A mathematical model for mastery learning

Prior research on mastery learning has examined relationships between achievement and time on task, motivation variables, as well as the effects of prior learning. One can show that the results of prior research lead to certain requirements on a model for mastery learning. This article uses those requirements to develop a mathematical model for mastery learning. This model includes measures of a motivational factor and of the specific ability of a learner with respect to the content being learned. The model also contains a variable to reflect prior learning. The model has been developed to include a computer program which accepts student data and from which predictions can be made and tested. Assumptions and limitations of the model are discussed, and some research questions are raised. In most respects the model proposed in this article is susceptible to the same tests and refinements as an empirical law in the physical or life sciences.