Sylvia Chong

Quality teaching and learning: a quality assurance framework for initial teacher preparation programmes

Education and the challenges of preparing quality teachers are important priorities in many countries. Singapore is no different. The success of what Singapore hopes to achieve in education depends on the quality of its teachers. Competent and effective teachers help build a strong educational system. In response to the nations need for quality teachers, the National Institute of Education (NIE), Singapore, reviewed and enhanced their initial teacher preparation programmes in 2005. A Values, Knowledge and Skills (VSK) model listing the attributes of beginning teachers was developed to anchor the review and enhancement. This also provided first steps in developing an overarching Quality Assurance (QA) framework. The QA framework covers key aspects of programme delivery and development, from students entry profiles to beginning teachers competencies. The paper has two parts. Part one details the development and conceptual underpinning of the QA framework. Part two outlines the frameworks structure and components.

Academic quality management in teacher education: a Singapore perspective

Purpose – Quality assurance and management is vital for the continuous improvement of the content, delivery and development of teacher education programmes. This paper seeks to explore theoretical issues and ideas in assessing the quality preparation of teachers in the Singapore context. Design/methodology/approach – An academic quality framework provides an architectural approach to optimizing quality processes, transitioning from a disjointed set of quality processes to an integrated workflow based upon established best practices. Findings – The architectural framework provided a systematic focus to develop and sustain the academic quality of the teacher development programmes. Quality change and developments occur through collaboration and learning. The emphasis is on quality management as a process-oriented strategy. Originality/value – There is a shift in paradigm from traditional models of programme evaluation to a systems approach that incorporate multi-dimensional models to impact administration, ...

Perceptions of student teachers in a Blended Learning environment

Blended learning has been identified as one of the 10 emerging trends in education. This study evaluates students perception of their experience in a blended learning environment. A 35-item survey based on seven factors related to Blended Learning was conducted. Descriptive statistics are used to describe the responses at the factor level. Correlation analysis is used to look at the relationship in each factor. Reliability analysis is used to check the reliabilities of the seven-factor scales. The t-test examined the influence of age and years of teaching experience on the participating teachers view on Blended Learning.

Modelling the Determinants of School Leaders' Perceptions of Beginning Teachers' Efficacy.

The school and especially the school leaders perceptions of a teachers efficacy have influence on aspects of teaching ratings and performance, and this may facilitate or hinder beginning teacher success. This study examines the school leaders perceptions of their beginning teachers efficacy. The studys scope of teacher efficacy is multidimensional to include the beginning teachers Teaching Competencies, sense of Professionalism, and Professional Identity. Of interest is the interconnectedness of these variables that contribute to the school leaders perception. A total of 256 school leaders responded to a survey on beginning teachers efficacy. A hypothesized model was tested. According to the analysis, while Teaching Competencies and Professional Identity did not have a significant direct impact, they each did have a significant indirect effect on perceived efficacy.

Data-mining applications with the admission data of adult learners in higher education: a pilot study

Admission offices are inundated with information from a variety of data sources and applications. This includes admission data such as student profiles and demographics, as well as academic and professional experiences. The paper outlines a pilot study that uses data-mining applications with the admission data of adult learners in a Singapore university. The application methodology has a sequence of four phases that leads to the building of relevant data-mining models. The analysis of the admission data is used to determine the best-fit model to predict applicants academic performance. From the evaluation and validation of the different predictive models, the CHAID decision tree is selected as the predictive model. With this model, the probability of academic performance is computed for incoming and existing students by tracing the decision tree.