Educational Assessment, Evaluation, and Research Commons^™

Infographic: Collaborative Problem Solving, Dominique Russell

Teacher infographics

The most recent OECD Programme for International Student Assessment (PISA) examines student ability in collaborative problem solving. Today's infographic shows the results from some of the 50+ participating countries.

Pisa 2015 Australia Student And Achievement Data [Sas] [Data Set], Sue Thomson, Lisa De Bortoli, Catherine Underwood

OECD Programme for International Student Assessment (PISA) Australia

This dataset (SAS zipped) is a data source for the report PISA 2015: Reporting Australia’s results.

Pisa 2015 Australia School Questionnaire Data [Spss] [Data Set], Sue Thomson, Lisa De Bortoli, Catherine Underwood

OECD Programme for International Student Assessment (PISA) Australia

This dataset (SPSS zipped) is a data source for the report PISA 2015: Reporting Australia’s results.

Pisa 2015 Australia Student And Achievement Data [Spss] [Data Set], Sue Thomson, Lisa De Bortoli, Catherine Underwood

OECD Programme for International Student Assessment (PISA) Australia

This dataset (SPSS zipped) is a data source for the report PISA 2015: Reporting Australia’s results.

Pisa 2015 Australia School Questionnaire Data [Sas] [Data Set], Sue Thomson, Lisa De Bortoli, Catherine Underwood

OECD Programme for International Student Assessment (PISA) Australia

This dataset (SAS zipped) is a data source for the report PISA 2015: Reporting Australia’s results.

Pisa 2015: Reporting Australia's Results, Sue Thomson, Lisa De Bortoli, Catherine Underwood

OECD Programme for International Student Assessment (PISA) Australia

The Programme for International Student Assessment (PISA) is an international comparative study of student achievement directed by the Organisation for Economic Co-operation and Development (OECD). PISA measures how well 15-year-olds, who are nearing the end of their compulsory schooling in most participating educational systems, are prepared to use the knowledge and skills in particular areas to meet real-life opportunities and challenges. PISA 2015 is the sixth cycle of PISA since it was first conducted in 2000. Seventy-two OECD countries or partner economies participated in PISA 2015. In Australia, PISA is managed by the Australian Council for Educational Research (ACER) and …

Pisa 2015: Reporting Australia's Results Data Tables [6 Zipped Excel Files] [Data Set], Sue Thomson, Lisa De Bortoli, Catherine Underwood

OECD Programme for International Student Assessment (PISA) Australia

This zipped file contains 6 Excel files pertaining to the data tables for the report PISA 2015: Reporting Australia's results (2017). This report provides the Australian results from PISA 2015.

• Chapter 2 - Australian students performance in scientific literacy
• Chapter 3 - Australian students science subscales
• Chapter 4 - Australian students performance in reading literacy
• Chapter 5 - Australian students performance in mathematical literacy
• Chapter 7 - Australian students motivation and beliefs in science
• Chapter 8 - The school learning environment

Reforming Educational Assessment: Imperatives, Principles And Challenges, Geoff N. Masters

Australian Education Review

AER 57 reviews research into assessment, especially in schools; it analyses the pivotal role of assessment in learning and argues for its reconceptualisation by practitioners and policy makers to better support learning. The genesis of this AER was the ACER Research Conference Assessment and Student Learning: Collecting, interpreting and using data to inform teaching, held in Perth in August 2009.

Section 1 outlines some current pressures for assessment reform, introduces the concept of a learning assessment system designed to establish where learners are in their progress within an empirically mapped domain of learning, and sketches a set of design …

The Estimation Of Polytomous Item Response Models With Many Dimensions, Nikolai Volodin, Ray J. Adams

Assessment and Reporting

Identification conditions and an improved estimation method for a D-dimensional mixed coefficients multinomial logit model are discussed. This model is a generalisation of the Adams and Wilson (1997) random coefficients multinomial logit and it can be used to fit multdimensional forms of a wide range of Rasch measurement models. The computational demands of the numerical integration required in fitting such models have limited previous implementations to three and perhaps four-dimensional problems (Glas, 1992; Adams, Wilson and Wang, 1997). This paper illustrates a Monte Carlo integration method that permits the estimation of models with much higher dimensionality. The example in …